Yitao Wang1, Zhian Liu1, Xiaoxuan Cai2
1The School of Arts and Media, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China
2School of Architecture and Arts and Design, Hebei University of Technology, Tianjin, 300401, China
Abstract:

As the birthplace of national culture, traditional villages can convey cultural and social natures through spatial configuration. Based on the theory of spatial syntax, this paper combines the genetic algorithm to design the fitness function for optimization, and selects the streets and lanes of Wengji Village as the research sample, focusing on the analysis of its morphological evolution mechanism from 1975 to 2020. Through quantitative analysis, it is found that although the streets and alleys of Wengji Village show spatial scale expansion due to social and economic development, the village streets and alleys can still maintain the original spatial texture and style. The integration degree, selectability, synergy (0.4273~0.6395) and comprehensibility (0.3744~0.5761) of the streets and alleys in Wengji Village are all characterized by increasing, indicating that the spatial accessibility, spatial openness and spatial wholeness of the streets and alleys in Wengji Village have been improved. However, the degree of synergy and comprehensibility are still lower than 0.7, and there is some room for optimization of the wholeness and cognizability of the streets and lanes of Wengji Village. It is necessary to protect and continue the overall structure of the village, optimize and integrate the key spaces of the village, and rationally control the development process of the village, so as to promote the protection of the spatial form of the streets and lanes of Wengji Village and the continuation of the cultural lineage.

Yi Xu1, Lingxiu Wang1, Zhaolu Wang2, Meng Li2
1Beijing Building Construction Research Institute C o., Ltd ., Beijing, 100039 , China
2Beijing Construction Engineering Quality First Testing Institute C o., Ltd., Beijing, 100039 , China
Abstract:

With the development of renewable energy technology and the pursuit of sustainable development in the construction industry, the design of direct-soft photovoltaic systems integrated with buildings has become an important research direction. In this paper, a variety of photovoltaic power generation modules are selected and combined with building roof functions to design a solar photovoltaic building integration system. In addition, this paper constructs a multi-objective optimization configuration model, improves the multi-objective particle swarm algorithm, and analyzes the optimization effect of the improved particle swarm algorithm on the photovoltaic building integration system by using multiple sets of test functions and evaluation indexes, combined with a number of experiments. The improved particle swarm algorithm in this paper converges to the optimal value of 0.21 when iterating to 25 rounds. And with the increase of the number of nodes, the optimized particle swarm algorithm, the distribution of node voltages in the vicinity of the standard voltage. The PV building integrated system designed in this paper still has a generation output efficiency higher than 85% after 20 years, which shows good stability of power generation. And the power generation in its whole life cycle is about 1645710kwh, which greatly reduces the consumption of conventional energy. In conclusion, the PV building integrated system in this paper not only has significant advantages in terms of capacity efficiency, but also shows strong potential for environmental protection.

Jingdong Shan1,2, Chen Wang3, Huan Zhang1,2, Runhe Qiu1,2
1College of Information Sciences and Technology, Donghua University, Shanghai, 261620 , China
2Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Shanghai, 261620 , China
3Shanghai Institute of Special Equipment Inspection and Technology Research, Shanghai, 200062 , China
Abstract:

Elevator is a convenient building transportation for people to travel, and more and more elevators are being registered and put into use, and the ACCOMPANYING problems of elevator failure and maintenance are becoming more and more prominent. In this study, the Kalman filter algorithm is used to optimize the feature extraction performance and prediction accuracy of the deformable convolutional TimesNet model for elevator operation time series data, and the improved TimesNet model is fused with the DLinear model to construct the TimesNet DLinear model for predicting elevator operation accidents. Finally, the TimesNet DLinear model is used as the main analysis modu le to design the elevator operation accident prediction system. After testing, it is found that the TimesNet DLinear model can maintain a low error in the prediction of elevator operation data, with an average absolute error of 0 167 , and the prediction ac curacy is better than other prediction models. It is also found that the elevator operation accident prediction system is able to predict the accidents in the elevator operation in a certain district and make a warning according to the current error thresh old situation. The elevator operation accident prediction system proposed in this study is able to realize real time monitoring and early warning of elevator failures, providing an effective solution for real time decision making and scheduling of elevator maintenance.

Yun Zhang1
1Guangzhou College of Commerce, Guangzhou, Guangdong, 511363, China
Abstract:

Smart contract technology based on artificial intelligence background is gradually becoming a brand-new path to improve the efficiency of economic transactions due to its unique advantages. This paper initially explores the impact of smart contract technology on economic transaction efficiency through empirical analysis of models and data. The credit mechanism is introduced as an intermediate variable to analyze its mediating effect in the process of improving economic transaction efficiency by smart contract technology. The optimization of Fabric transaction mechanism is realized by using the improved credit model, which further exerts the role of smart contract technology in enhancing economic transaction efficiency. The principal component analysis is used to calculate the comprehensive score of economic transaction efficiency before and after the optimization of smart contract trading mechanism to show the effect of the development of smart contract technology on the improvement of economic transaction efficiency. This paper concludes that the development of smart contract technology will significantly and positively promote the improvement of economic transaction efficiency through benchmark regression analysis, mediation effect test and other methods. After the optimization of smart contract transaction mechanism, the comprehensive score of economic transaction efficiency produces significant improvement compared with the pre-optimization period, in which the average value of the comprehensive score of transaction efficiency in Guangdong, Jiangsu, Shanghai, and Beijing is improved by 20.18%, 24.52%, 33.77%, and 35.54%, respectively. It further indicates that smart contract technology is an effective path to improve economic transaction efficiency.

Xiangjun Yu1, Qi Pan2
1School of Management, Guangdong University of Science and Technology, Dongguan, Guangdong, 523083, China
2School of Management, Wuhan Donghu University, Wuhan, Hubei, 430212, China
Abstract:

Mobile communication technology is a universal technology. It is one of the most advanced technologies in human history due to its rapid development speed, strong penetration and wide application. The rapid development and wide application of information and communication technology has brought profound impact on economic growth and social transformation. In order to test the relationship between population change and economic development in the process of urban development by mobile communication technology, cluster analysis and similarity coefficient are used to analyze the indicators of economic development and to predict the law of economic development. In order to understand the changes brought by mobile communication technology to economic development in detail, through the analysis of the usage of mobile communication users in China and the economic development of the region from 2017 to 2019, the results showed that Beijing was in the best development situation. Compared with other regions, Tibet was the lowest. It grew over time, with a 5% increase from 2017 to 2019. It could be seen from this point that vigorous development in the field of mobile communications would provide new opportunities for major cities to break through development bottlenecks and solve development dilemmas as well as promote urban transformation and innovative development.

Ting Qian1, Rubing Xie1, Qingshan Xu1
1Fuzhou Power Supply Branch Power Supply Service Center, State Grid Jiangxi Electric Power Co., Ltd., Fuzhou, Jiangxi, 344099 , China
Abstract:

This paper proposes a user electricity data mining method based on deep learning and improved locust optimization algorithm, and at the same time adopts the Pearson correlation coefficient method to reduce its dimension to improve the data mining effect of linear weighted KFCM algorithm. In order to deal with the electricity demand of massive electricity customers, the user electricity demand forecasting model is constructed based on the Extreme Learning Machine ELM algorithm by combining the relationship between short-term loads and factors of electricity customers. Construct the service optimization model with the maximization of benefit index as the objective function, and use the BAS algorithm to solve the optimal solution in order to achieve the effect of user service optimization. Determine the experimental platform and model parameters, and carry out an example analysis of demand forecasting and service optimization for electricity users.C class users have a small electricity load except for breakfast and dinner, and the maximum time period of the electricity load is from 18:00 to 20:00 hours. Combined with MAPE, the ELM model improves 4.57% than SVR, 21.9% than LSTM, and 34.37% than ARIMA, which indicates that the ELM model is more effective and higher in demand forecasting for electricity users. In addition, the optimal solution of the effect of the BAS algorithm is 69 yuan, 102 yuan and 49 yuan higher than that of the GA algorithm in terms of dividend transmission benefit, energy saving and emission reduction benefit, and electricity right trading benefit, respectively, and the optimal solution based on the BAS algorithm is closer to the actual benefit value, which fully proves the effectiveness of the service optimization model based on the BAS algorithm.

Junxiao Han1, Shumin Wang2, Xiaochan Xu1
1School of Foreign Languages, Handan University, Handan, Hebei, 056000 , China
2School of Mathematics and Physics, Handan University, Handan, Hebei, 056000 , China
Abstract:

Digital teaching strategies can significantly stimulate students’ interest in learning and provide personalized learning pathways. This paper proposes a multimodal action recognition method that integrates the word vector method, and designs a teaching decision optimization strategy based on this idea. Firstly, we compare the information of different modalities, complete the construction of multimodal action recognition network through the processing of image information and optical flow information, and combine the word vector method to guide the semantic learning of students’ actions. Then the design and realization process of the teaching decision aid system is introduced. Based on the above proposed action recognition method to collect students’ classroom behavior data for model training to be used in the system, the system consists of four modules: model training, classroom data collection, behavior recognition and data presentation. After the data collection, the action recognition of student behavior is carried out to provide teachers with feedback on student behavior information and assist them in making teaching decisions. In this paper, the above algorithms and systems have been verified by relevant experiments. After comparison with other algorithms, it is verified that the multimodal action recognition method designed in this paper, which incorporates the word vector method, has a high accuracy rate. In the comparison of the overall quality of instructional design decisions, the average value of the instructional decision aid system in this paper is 17.35, which is higher than the average score of excellent human teachers in the overall quality of instructional design decisions, indicating that the instructional decision aid system designed in this paper achieves the optimization of instructional decisions and reaches the level of excellent decisions.

Zhaofang Lv1
1School of Foreign Languages, Hunan University of Humanities, Science and Technology, Loudi, Hunan, 417000 , China
Abstract:

The integration of Civics elements into the EFL classroom is an organic supplement and deepening of the teaching content and materials, while EFL Civics classroom teaching is a powerful means to strengthen the deep and longitudinal development of students’ critical thinking. This paper discusses the relationship between EFL Civics classroom teaching and the development of critical thinking ability from the theoretical and practical levels respectively. On the basis of existing research, the evaluation index of students’ critical thinking ability is proposed. The CVM coefficient of variation method is improved, and the ICVM and BP neural network algorithm are combined to constitute the evaluation model of students’ critical thinking ability based on ICVM and BP neural network. According to the evaluation process, the level of students’ critical thinking ability after EFL-based Civics classroom teaching is derived. It also integrates teachers’ and students’ evaluation of the effect of English Civics elements integrated into the EFL classroom, and finally obtains the practical teaching effect of the EFL Civics classroom. The overall mean value in the teacher’s side is greater than 3.5 points, which indicates that teachers are basically positive about the effect of integrating Civics in EFL courses, and basically agree with the positive impact of English Civics elements on EFL classroom teaching. Based on the evaluation results of ICVM and BP model, the evaluation scores of students’ critical thinking skills and critical thinking monitoring are higher than the evaluation scores of critical thinking tendency, i.e., the elements of English Thinking can be effectively integrated into the EFL classroom and promote the development of students’ critical thinking skills.

Yanni Shen 1, Jianjun Meng1,2,3,4
1School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070, China
2Institute of Mechanical and Electrical Technology, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070, China
3Gansu Engineering Technology Research Center of Logistics and Transportation Equipment Informatization, Lanzhou, Gansu, 730070, China
4Gansu Logistics and Transportation Equipment Industry Technology Center, Lanzhou, Gansu, 730070, China
Abstract:

In order to improve the efficiency of rail bolt automation operation, this study proposes a non-dominated sorting genetic algorithm II (NSGA-II) based on the improvement of elite strategy for the multi-robot task allocation problem. First, a multi-objective optimization model is established by combining the actual demands of rail bolt operations. Then, the classical NSGA-II algorithm is improved by introducing an elite strategy to enhance its global search capability and convergence performance. Finally, the effectiveness and superiority of the improved algorithm in task assignment are verified by simulation experiments. The experimental results show that the improved NSGA-II algorithm has significant advantages in optimizing the efficiency of rail bolting operations and task balancing, which provides a strong support for task allocation in multi-robot systems.

Chen Dong1, Bo Zhang2, Siqi Wang2
1School of Finance at, Anhui University of Finance and Economics, Bengbu, Anhui, 233000, China
21 School of Finance at, Anhui University of Finance and Economics, Bengbu, Anhui, 233000, China
Abstract:

The study uses a multilevel nonlinear optimization algorithm to optimize the low-carbon development path of agriculture with the dual constraints of government regulation and agricultural insurance. The algorithm solves the development path optimally through convergence analysis, parameter setting and constraint problem modeling. In addition, the study establishes an index system for evaluating agricultural low-carbon development, and assesses the effectiveness of low-carbon development through field application. The algorithmic path optimization in this paper has better performance in terms of solution quality, iteration number and solution time. At iterations 17, 43, 62 and 82, the algorithm of this paper found feasible solutions for path optimization. By 2023, the annual increase in pollutant emissions from agricultural production, total carbon emissions, carbon emission intensity of 10,000 yuan output value, and comprehensive energy consumption of 10,000 yuan output value are projected to be reduced to 42696.39(tons), 21141.5(10,000 tons), 1017.9(tons), and 6422.6(tons), respectively. The evaluation indicators Agricultural activity average carbon intensity, Reduction of carbon intensity and other indicators have relatively high weights, which is the main reason for the differences in low carbon development.The correlation between the effectiveness of agricultural low carbon development and the optimal sequence in 2023 is 0.9981, which demonstrates that the role of government regulation and agricultural insurance in promoting agricultural low carbon development.

Lili Wu1, Qiming Gao2
1College of Education, Handan University, Handan, Hebei, 056005, China
2Handan Key Laboratory, Intelligent Perception and Application, Handan University, Handan, Hebei, 056005, China
Abstract:

Wind energy is a widespread natural phenomenon, which receives more and more attention because of its renewable and non-polluting nature, but the unpredictable and unstable wind speed makes the wind power control technology become a hot spot of concern. Firstly, the working principle of the wind turbine system is introduced, and the wind turbine speed model of the wind turbine drive system is established according to the system stability characteristics. Then on the basis of the traditional PID control algorithm, a wind turbine rotation speed regulation optimization algorithm based on PID optimization control is proposed-PID neural network control. The algorithm designs a three-layer forward PID neural network, and through the PID variable structure control, the low-speed axis of the fan connects the rotor axis with the gear box, which excites the operation of the aerodynamic gate for speed regulation, and compared with the traditional PID control, the method can regulate the airflow of the coal mine fan more quickly, and the overshooting amount is reduced by about 22%. Then, the BP neural network control is used to predict the air demand, and the deviation of the predicted air demand from the current air demand and its chemical rate are input into the controller. Finally, through the comparison of the control system and simulation experiments, it is proved that the BP neural network control has stronger robustness and adaptability, and can achieve better control effect.

Bodong Wang1, Hong Zhang1
1Sino European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin, 300300, China
Abstract:

The temperature gradient formed by cooling and heat dissipation after shutdown of aero-engine will lead to thermal bending of rotor, which is also the main reason for bending vibration of rotor after engine secondary start-up. In this paper, the rotor system model of a GTF motor considering the gearbox structure is established, and the thermal bending deformation of the fan-gearbox-low pressure rotor caused by temperature gradient is analyzed. The critical speed of the rotor system considering the temperature field is calculated and the vibration characteristics of the engine after the second start are analyzed, which provides a reference for the design of the rotor system of GTF engine. The results show that the rotor mainly appears hot bending deformation in the direction of vertical axis, especially in the joint of disc axis. The large bearing stiffness of the gearbox has obvious inhibition on the hot bending deformation of the rotor, and the effect is obvious when the bearing stiffness is above 1E6. The vibration characteristics of rotor are greatly affected by temperature field. The amplitude of rotor system is larger and the sensitivity of gearbox structure is higher under the influence of temperature field. The amplitude is also the largest when the thermal bending amount is maximum about 10min, and the amplitude decreases by 50% after 40min. The bearing stiffness of the gearbox has a great influence on the vibration characteristics of the rotor system with hot bending deformation, and the vibration suppression effect is best when the bearing stiffness is between 1E6 and 1E8, and the peak point above 1E8 is close to the operating speed of the fan, which is bad for the safe operation of the engine.

Jia Bian1
1School of Teacher Education, Qilu Normal University, Jinan, Shandong, 250200, China
Abstract:

The family, as a socially balanced system, possesses the functions of social information communication and social resource conversion. From the perspective of the family system as a provider of educational resources, the input of various forms of resources into the family environment either helps parents to teach their children, helps them to learn, or is detrimental to the healthy growth of their children. In this paper, based on the nonlinear model of the static resource-opportunity allocation problem study, the objective constraints are added to establish a linear programming model. The column enumeration method is used to solve the linear programming, while the sensitivity of the linear programming is analyzed by pairwise test. On the basis of the random initial solution, a multilayer transportation algorithm is designed as the initial solution to further reduce the time of enumerating columns and complete the construction of the solution framework for the resource-opportunity allocation problem. The model is used to solve the problem of the distribution of socio-economic resources to educational opportunities between “two-child” and “one-child” families. The results show that the socio-economic resources of different families have different opportunities for children’s education, and there are significant differences between different types of “two-child” families in the three aspects of parent-child relationship satisfaction, feelings of parenting, and interpersonal evaluation of the child, with the F-values of 5.265, 4.859, and 5.136, respectively, with a p<0.01. The “last-child advantage” in children's education is related to the number of years of education of the fathers. In the 1949-1969 generation, the average number of years of education of the fathers was only 6.763, while in the 1970-1990 generation, the average number of years of education of the fathers increased to 8.685, and the cultural level of the family as a whole improved significantly, and the mechanism of resource constraints on the cultural level of the family began to take effect. The resource constraint mechanism at the cultural level is beginning to take effect.

Weiping Wang1, Tingting Yang2, Wenlong Liu2
1Zhejiang Vocational and Technical College of Economics, Hangzhou, Zhejiang, 310018, China
2Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China
Abstract:

Sustainable agricultural development is a key component of the rural revitalization strategy, and strengthening the guidance and support for sustainable agricultural development is an inevitable choice for improving agricultural production capacity and realizing rural revitalization. The study constructs an evaluation system of sustainable agricultural development based on four dimensions: economic opportunity, social well-being, environmental quality, and climate action, selects relevant index data of each province from 2004 to 2022, and adopts a multi-level factor analysis method to comprehensively evaluate the sustainable agricultural development as well as the dynamic distribution of 31 provinces in China. The results show that Henan leads other provinces in economic opportunities with a score of 1.21, and Hebei ranks first in social well-being with abundant human resources and policy support. In the level of regional sustainable agricultural development, there is an uneven distribution pattern of “North > Central > South”. From the dynamic distribution of agricultural sustainable development in 31 provinces from 2004 to 2022, it is concluded that the development trend of agricultural sustainable development in China is better, and the gap between provinces has been narrowed. Finally, policy recommendations are put forward based on the situation of agricultural sustainable development to provide reference for the subsequent work on agricultural sustainable development.

Bangke Wang1
1Art Design College, Henan University of Urban Construction, Pingdingshan, Henan, 467000, China
Abstract:

This paper analyzes the development trend of Adobe After Effects-based movie special effects technology, including accelerated GPU research, integration and enhancement of 3D special effects production functions, cross-platform and cross-software collaboration, as well as the impact on the way special effects artists create. In terms of GPU research, the acceleration performance of different GPUs in movie simulation in China and abroad is compared, and the 3D effects production function involves the creation of 3D models, movie settings, rendering standardization, as well as the rendering output and the addition of special effects. Cross-platform and cross-software collaboration focuses on the cross-platform nature of AE, designing a tagged text file format and a movie playback engine based on the Cocos2d-JS game engine, and dividing the file system module. The analysis shows that the waiting time for movie rendering under this paper’s model is 1s and 2s, and the end-of-task ratio is 0.02, which are the lowest in both sets of experiments. The highest mean values for the 10 simulations of GPU utilization are 72.42% and 72.83%, respectively. It can be seen that the CPU acceleration model based on Adobe After Effect in this paper can effectively reduce the waiting time for movie rendering and improve the processing speed and stability of movie special effects.

Yufang Huang1
1 School of Education, Xi’an FanYi University, Xi’an, Shaanxi, 710105, China
Abstract:

This paper used wireless network selection algorithm to apply it to preschool education collaborative management. Through the analysis of its necessity, the final conclusion was drawn. In the survey of teaching resources, it was found that after the cooperative management of preschool education using wireless network, the number of teachers has increased more than three times, and the number of textbooks has also increased significantly. In the survey of teaching courses, it was found that the cooperative management of preschool education by using wireless network can promote the rationalization of courses offered by schools, thus promoting the improvement of students’ learning ability. In the survey of children’s learning ability, it was found that after the cooperative management of preschool education through wireless network, the average learning ability of large class students was 88 points, which was increased by 27 points, and the speed of improvement was the fastest. In the survey of children’s living ability, it was found that children’s living ability has been greatly improved after the cooperative management of preschool education by wireless network. In the investigation of the teaching environment, it was found that the teaching environment of the school has been greatly improved after the cooperative management of preschool education with wireless network. By applying it to the collaborative management of preschool education, it brings convenience and advantages to the collaborative management of preschool education, which promotes the development of preschool education in the direction expected by people, and the development of children is more healthy and lively.

Yangyulong Wu1
1School of Business and Management, The Hong Kong University of Science and Technology, 999077 , Hong Kong
Abstract:

In the current period, green finance has become an inevitable trend in the development of the financial industry. The study collects the audience demand of green financial products through Octopus collector, uses micro-word cloud analysis system for data de-weighting and Chinese word segmentation, and calculates the keyword weights in the words using TF-IDF algorithm, and realizes the identification of green financial product innovation and economic benefits by combining with multi-dimensional innovation map. Subsequently, the indicators on green financial product innovation and environmental economy from 2007 to 2022 are combed, and a VAR-based econometric model is established to analyze the impact relationship between green financial product innovation and environmental economic benefits. The results show that when the lag period is 10 periods, the contribution of environmental economic benefit itself to the change of environmental economic benefit tends to be 23.79%, while the contribution of green investment products, green bond products and green insurance products to environmental economic benefit tends to be 9.72%, 20.23% and 21.83%, respectively. Green product innovation has a certain influence on the fluctuation of environmental economic benefits, and green bond products and green insurance products have a greater impact on environmental economic benefits.

Tianmeng Yuan1, Liang Ning2
1Tangshan Power Supply Company State Grid Jibei Electric Power Co., Ltd., Tangshan, Hebei, 063000, China
2 Tangshan Power Supply Company State Grid Jibei Electric Power Co., Ltd., Tangshan, Hebei, 063000, China
Abstract:

In an energy plan with a high rate of renewable energy acquisition, the comprehensive development of wind and solar energy and flexible power sources such as energy storage will play a key role in this process. The power supply structure in some areas is dominated by coal power, and there is a serious shortage of flexible power supply, which hinders the development of renewable energy. Therefore, this paper proposed a new energy operation and consumption planning method considering the flexible adjustment of the power supply ratio. This paper established a two-layer power planning model with the lowest cost to the whole society and the largest consumption of renewable energy. Then, based on the copula theory, a wind-solar combined consumption probability model is established, and the new energy output curve of the planning year is predicted. Finally, the power supply optimization was solved by the Hooke-Jeeves iterative method. The experimental part took a certain region as the research object, setting the proportion of flexible power supply to 24%. It found out the newly installed capacity of various power supplies, and compared the actual data in the region. The research results have shown that increasing the proportion of flexibly regulated power supply can effectively improve the operation and absorption capacity of new energy, the wind abandonment rate is reduced by 6.21%, and the light abandonment rate is reduced by 5.38%.

Cheng Wang1
1Hubei Institute of Fine Arts, Public Course Department, Wuhan, Hubei, 430205 , China
Abstract:

The development of time and technology has made the traditional basic computer teaching unable to meet the needs of current students, and the cultivation of computer thinking ability has become a hotspot of computer education concern. The article combines the flipped classroom model with the MOOC platform of network education, and establishes a MOOC flipped classroom teaching model applicable to computer basic education courses in art colleges. In this model, students’ assessment results data are collected, and the Apriori algorithm optimised by matrix optimisation and prior pruning strategy is used to mine the association relationship of the assessment results, which helps teachers to understand students’ computer knowledge mastery. T College of Fine Arts is used as a research example to illustrate the effectiveness of MOOC flipped classroom through the changes in students’ performance, competence, and satisfaction. The improved Apriori algorithm has an execution time of only 2.93s when its minimum support is 100%, which can be used to understand students’ computer application ability for different question types, majors and skill performances. The mean score of the final exam of the students in the experimental class was 82.79, which reached 111.79% of the score of the control class, and more than 80% of them were satisfied with the MOOC flipped classroom. The use of flipped classroom and network education model can achieve the innovative development of computer basic education courses in art colleges and help to enhance the teaching quality of computer basic education courses in art colleges.

Lu Zhong1
1Yantai Nanshan University, Yantai, Shandong, 265713 , China
Abstract:

Whether tourism culture and economy develop in a coordinated manner is the key to realize the transformation and interaction of industrial structure. This paper takes the related data of 11 prefecture-level cities in Shanxi Province from 2013 to 2022 as the research object, and after demonstrating the intrinsic mechanism (the relationship of mutual influence) of the development of tourism culture and local economy, it applies the econometric panel Granger causality test to quantitatively test the interactive relationship between the development of tourism culture and local economy. After that, we constructed the index system of tourism culture and local economy, used entropy value method and coupling coordination model to analyze the comprehensive development level and coupling coordination degree of tourism culture system and regional economic system, and used Robust regression analysis to study the influencing factors of coupling coordination degree. The results of the study show that at the 5% significance level, with a lag of 5 and 6 periods, the local economic development is the Granger cause of tourism culture, and the local economic development has an obvious driving effect on tourism culture. In the 10 years of the examination period, the coupling coordination between tourism culture and local economy keeps growing, and the coupling coordination is improved, but there is still a certain gap with the high-quality coordination, meanwhile, the regression results show that focusing on the holistic and balanced development of the influencing factors is conducive to further coordination and interaction between the two systems.

Zhigang Wang1, Yang Song1
1Business School, Zhuhai College of Science and Technology, Zhuhai, Guangdong, 519041 , China
Abstract:

Supply chain optimization configuration contributes to the improvement and development of enterprise business application system. This paper takes the supply chain of manufacturing enterprises as the research object and analyzes the economic benefits of supply chain optimization of manufacturing enterprises. Aiming at the current development environment of enterprises, it puts forward the necessity of the development of enterprise supply chain flexibility, and establishes the overall supply chain flexibility model that contains the supply flexibility of the supply chain, the manufacturing flexibility and the distribution flexibility of the distributors. Simplify the total cost model of supply chain and establish the demand-driven supply chain optimization model. Analyze and validate the parameter settings of the improved particle swarm algorithm, and obtain the operating efficiency of the improved particle swarm algorithm with the changes of ordering cycle and inventory capacity. Combined with the sample enterprises, analyze the financial savings of each link after supply chain optimization. Further measurements show that after supply chain optimization of Company R, the saving percentage is 10.24%, and the annual saving amount is 562,807 yuan, with obvious economic benefits.

Yan Li1, Wenshi Wang1, Jie Wang1, Xu Dong1, Lihong He2
1Changsha General Survey of Natural Resources Center, China Geological Survey, Ningxiang, Hunan, 410600 , China
2Geophysical and Geochemical Survey Institute of Hunan Province, Hunan Green Intelligent Exploration Engineering Technology Research Center, Chan gsha, Hunan, 410014 , China
Abstract:

Most areas in Hunan Province are rich in shale gas blocks, and their shale gas reservoir physical properties, geological characteristics, and enrichment rules need to be further studied. The article chooses the five # logging data of the Xiaoyanxi Formation in Anhua, Hunan Province, as the research object, preprocesses the logging data by curve environmental influence correction, curve reconstruction, and normalization, calculates the total organic carbon content and mineral composition change of shale gas by multiple linear regression, and uses the multi-mineral content calculated by optimization algorithm combined with the volumetric model to realize the matrix porosity of the variable skeleton. Then, the differential equivalent medium, self-compatible approximation, and K-T models were used to calculate the shale rock skeleton modulus. Then the shale gas reservoir petrophysical model was constructed. The adsorbed gas and free gas of the shale gas reservoir were solved separately to obtain the total gas content of the shale gas reservoir. The average TOC content solved by the model is 1.79%, which is only 2.23% higher than the absolute error of the actual data. When the volume fraction of the organic matter mixture increased from 0 to 0.25, the relative change of the longitudinal and transverse wave velocity ratio was only 0.87%. The shale gas content in Anhua Xiaoyanxi Formation 5# in Hunan Province ranges from 0.87 to 8.41 cm³/g, significantly higher than the lower limit value for shale gas industrial development. Recorded well data can clarify the reservoir’s physical characteristics of shale gas in Hunan Province and provide data support for exploring shale gas.

Xiao Wang1
1School of Economic and Trade, Henan Polytechnic Institute, Nanyang, Henan, 473000 , China
Abstract:

Accompanied by the increasing consumer quality and the exploration of enterprises centered on user experience, the new retail model has emerged, and the emerging retail model also plays an important role in enhancing customer loyalty. This paper establishes an experiential sensory marketing model by combining perceptual theory and emerging technology from the basic features of the new retail model. Multiple linear regression model is used to study the influence of experiential sensory marketing mode on customer loyalty, and the correlation coefficient is used to analyze the correlation between the two. The correlation coefficients between the experiential sensory marketing model and customer loyalty range from 0.457 to 0.669, which is a moderate correlation. For every 1 percentage point increase in experiential sensory marketing mode, there is a significant increase of 0.647 percentage points in customer loyalty, and the average score value of customer attention under experiential sensory marketing strategy is 4.31 points. The sensory marketing strategy in experiential retail environment needs to improve the marketing standardization system, relying on professional service platform to improve the customer’s emotional experience, and then enhance customer loyalty.

Xianjun Meng1, Yanqin Liu2
1Department of Planning and Development, Xi’an Fanyi University, Xi’an, Shaanxi, 710105, China
2College of Physical Education, Xi’an Fanyi University, Xi’an, Shaanxi, 710105, China
Abstract:

More and more problems are revealed in the process of popularization of higher education, especially the imperfection of the quality assurance system of higher education, which restricts and hinders the development of colleges and universities to a certain extent. This paper uses structural equation modeling to analyze the influencing factors of higher education quality. And consequently, it combines digital technology to build a higher education quality assurance system. Take a university as an example to practice, through the higher education quality assurance system evaluation index selection and empowerment, combined with the fuzzy comprehensive evaluation method to establish an evaluation model, to assess the effectiveness of the practice of the educational quality assurance system of the sample university. The management level (0.4380) and faculty (0.1472) of the university have the most significant influence on the quality of higher education. Under the constructed higher education quality assurance system, the comprehensive scoring result of the sample colleges and universities is at a good level (8.227), with the highest quality level in the dimensions of teaching effectiveness (8.7341) and student development (8.7000), which indicates that the digitization-based higher education assurance system is able to effectively ensure and promote the quality of education in colleges and universities.

Ang Li1
1Office of Educational Administration, Guangdong University of Finance & Economics, Guangzhou, Guangdong, 510320, China
Abstract:

Teaching evaluation is the feedback on the teaching effect of teachers and the learning effect of students. It has become a critical link in colleges and universities teaching management and teaching inspection. This paper proposes and applies an improved BT-SVM multi-classification algorithm to the education evaluation model. By calculating the relative distance between classes, the error accumulation phenomenon existing in the traditional SVM when dealing with multi-classification problems is solved. A classifier structure based on an incomplete binary tree is constructed to automatically classify teaching data by gradually dividing the data set and training the SVM classifier. By calculating the decision function value of the test sample in the binary tree, the category to which it belongs can be quickly determined. The education evaluation model follows the principle of legal compliance to improve the quality and efficiency of model evaluation and ensure the rule of law construction in colleges and universities. The research results show that the error rate of the BT-SVM algorithm in machine learning is below 0.1%, the fairness index is between 0.1-2, and the prediction accuracy is 96%. It shows that the machine learning algorithm can effectively improve the efficiency of education evaluation work and has the principle of fair legal compliance.

Shuai Yang1, Qiong Cao1, Wei Zhang1, Hao Guo1
1State Grid Shanxi Marketing Service Center, Taiyuan, Shanxi, 030000, China
Abstract:

Electricity theft management is closely related to the economy of electric power enterprises. This paper proposes a power theft estimation method based on semi-supervised learning and time series analysis prediction. The electricity consumption data of power theft users are extracted as time series data, and in order to achieve multi-step prediction, MMD is utilized to improve the LSSVR semi-supervised learning algorithm. In addition, a perturbation term is introduced to optimize the convergence effect of the artificial bee colony algorithm, and a time series prediction algorithm based on improved artificial bee colony is established. Bringing in the power theft monitoring process to identify whether the user has power theft behavior, using the real power consumption dataset as the experimental validation data, comparing the identification accuracy of the prediction model. Predict the potential power theft of each user, solve the optimization model with the goal of optimal economic efficiency, and determine the actual ranking order of power theft users. The improved time series prediction algorithm proposed in this paper has a global error of 0.0003 and 0.0027 in dataset 1 and dataset 2, respectively, with the lowest global error and the highest overall accuracy of PSE prediction. And the algorithm predicts the list of users to be scheduled is basically the same as the list of users determined by the real PSE, which can achieve the maximum economic benefits.

Xiaoqiang Tian1, Xiaosheng Ding1
1Dunhuang Academy, Lanzhou, Gansu, 736200, China
Abstract:

This paper is based on the digital image processing technology, using the undamaged image information to restore and protect the frescoes. The discrete binary wavelet change is used to decompose and denoise the image signal. And decompose and filter the high-frequency component and low-frequency component of the image, choose different components, respectively, carry out coefficient transformation, and solve the OMP least-paradigm for different random matrices. The color space is selected, and the mural color space is channel decomposed according to the grayscale mode and restored separately. Establish an assumed datum for each independent face of the mural, establish a spatial coordinate system for it, realize the transformation of spatial coordinates, and realize the super-resolution three-dimensional reconstruction of the mural based on the generative adversarial network and the self-attention mechanism. Objective evaluation indexes and subjective evaluation indexes are established to compare the protection effect of different algorithms on murals. Compared with the traditional algorithm CDD, this paper’s algorithm improves the restoration time by 9.545~15.625 s, and the peak signal-to-noise ratio index improves by 1.35~4.769 db. In the results of the image extraction and processing, the calculated values of discrete curvature of the mural segments AB, CD, and EF ranges from -0.00945 to -0.00478, and the difference of standard deviation of the curvature from the target curvature is 6.477%. The approximate target curvature is obtained, and the algorithm has strong adaptive ability.

Miao Jin1
1Dance Academy of Nanjing University of the Arts, Nanjing, Jiangsu, 210000, China
Abstract:

Dance drama is a comprehensive art with dramatic conflicts and plots based on the use of dance’s own language system, which plays an important role in cultural dissemination and aesthetic experience. The article designs a resource library of classic dance drama works in the way of WEB site, establishes a data dynamic distribution strategy to deal with structured data, and combines the consistent hash algorithm to optimize the load balancing of structured data in the resource library. Then, a graph convolutional neural network model and a sample-weighted aesthetic classification model are combined to establish an aesthetic assessment model for images of classical dance drama works, and a regularized matching module is designed. For the application effectiveness of the structured data processing strategy, the structured data processing of the classic dance and drama works resource library is verified, and the hyperparameters of the model, evaluation results and ablation experiments are also analyzed. Combined with the data in the resource library of classic dance drama works, the aesthetic experience of the audience was analyzed using a questionnaire. After using the dynamic distribution strategy to process the structured data, its write and query times were shortened by 40.05% and 17.89% compared to before use, and the response time under different index query load balance degrees did not exceed 55ms.The accuracy of the aesthetic assessment model for classical dance and drama works was 48.85%, and the accuracy improvement of the G-AANet model compared to BoTNet ranged from 0.93% ~ between 6.12%. The resource base of classical dance drama works established through structured data processing helps to enhance the audience’s aesthetic experience of dance drama works and helps them to appreciate the spiritual connotation of dance drama works.

Yiyu Chen1, Lin Ma1
1College of Humanities and Social Sciences, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
Abstract:

The development of media technology profoundly affects the presentation mode, dissemination rate and scale of news information, which in turn reshapes the business chain and business landscape of the entire news media industry. Based on the analysis of the shortcomings of the LDA model, this paper proposes an improved LDA model with binomial distribution, and applies it to the analysis of the evolution of news topics. The model introduces binomial distribution to enhance the discriminative ability of lexical items, and parallelises it to improve the classification effect of news topics. In order to effectively obtain the relevant features of cultural communication in news documents, this paper introduces BERT to obtain word embedding and word vector matrix, and then realises the generation of theme word structure and theme words. The performance of the improved LDA model is verified through the THUCNews dataset, and the news topic morphology is visualised and analysed with the example data, and its morphological evolution, as well as the degree of contribution to cultural communication, is studied. The theme consistency score of the improved LDA model is -13.39 when the word generation probability is 1, which is 19.14% higher than that of the traditional LDA model. The intensity of the ‘cultural policy’ news format theme increases 14.44 times from 2010 to 2023, and the mean value of the ‘cultural governance’ news format theme’s contribution to cultural dissemination reaches 0.091. Based on the innovation and evolution of news forms, we can empower more communication channels for culture and spirit, so as to enhance people’s cultural self-confidence and national cultural soft power.

Yixuan Zhou1
1School of Foreign Languages ,Wuhan University of Bioengineering, Wuhan, Hubei, 430415, China
Abstract:

Machine learning-based learning analytics can fully use the learner learning behavior interaction data recorded by online English teaching systems, providing support for observing students’ learning process from the perspective of learning behavior. In this paper, we construct a framework for recognizing college students’ English learning behavior patterns, propose an SGT-based feature extraction algorithm for learning sequences, and use Gaussian mixture models to identify the extracted learning characteristic sequences. Subsequently, a K-means clustering algorithm is used for sequence clustering and lag sequence analysis. At the same time, the English personalized teaching method is designed by combining the proposed personalized knowledge point recommendation method of multi-knowledge fusion in-depth knowledge tracking and group feature collaborative filtering. The results show that college students’ English learning behaviors are classified as active, passive, and passive, and the behavioral sequences of students in different modes are differentiated, in which the sequence residual value of active learners is greater than 1.96. There is a significant difference between the personalized teaching mode and the ordinary teaching mode in terms of the learning mode and the learning effect (P<0.05), and it can achieve a better English teaching effect.

Fei Huang1
1Pingdingshan Polytechnic College, Pingdingshan, Henan, 467000, China
Abstract:

With the development of the informationization era, it has become the norm for teachers of Civics and Political Science courses in colleges and universities to assist classroom teaching through network resources. In order to further utilize network resources to make them better serve the classroom teaching of Civics and Politics courses in colleges and universities, this paper optimizes the teaching resources recommendation technology based on deep neural network. Defining the network teaching resources data as a ternary group , we put forward the research hypothesis and LSTM model, and establish the G-LSTM recommendation model for recommending the teaching resources of ideological network. The overall framework of G-LSTM model is described, and the recommendation based on G-LSTM is applied to the ideological network teaching resources recommendation. Adopt AUC, MRR and NDCG as evaluation indexes to check the performance indexes of G-LSTM model. Combined with the actual teaching of ideologic theory class, the practical effect of G-LSTM recommendation model is analyzed. 67.81% of students and 39.71% of teachers recognize each recommended online teaching resources. It shows that the improved LSTM model in this paper can further screen the ideological and political network teaching resources, and the teaching resources recommended by the model are more suitable for the teaching of ideological and political theory.

Zhiwei He1
1Basic Teaching Department, Shangqiu Institute of Technology, Shangqiu, Henan, 476000, China
Abstract:

When a laser beam passes through a solid physical material with a nonlinear refractive index, it can produce an optical nonlinear effect, which depends on the refractive index that changes with the light intensity. Based on an analysis of the linear principle of nonlinear optics, the article describes the coupled wave equations under the nonlinear optical phenomenon. It introduces the phase-matching method of frequency conversion and the theoretical basis of optical waveguide. Starting from the classical Maxwell’s equations, the nonlinear optical transmission equations and the optical effect model are established, and then the finite element method (FEM) simulation model is constructed based on the FEM model to analyze the nonlinear optical phenomena of solid-state physical silicon materials. To verify the validity of the FEM model, the optical bistability effect and four-wave mixing spectrum of the nonlinear optical phenomena are simulated and analyzed, and the homochiral spinning effect and transmission spectrum are investigated. When the solid-state physical silicon material is rotating, the laser power required to observe the optical bistability is up to 9.51 W when the rotation rate is increased from 12 kHz to 24 kHz, and the four-wave mixing intensity decreases from 0.115 to about 0.028 when the oscillator frequency of the solid-state physical silicon material is increased from 15 MHz to 30 MHz. The plasma resonance absorption wavelength of the solid physical silicon material is at 791 nm, and the effective refractive index obtained from the simulation is 0.61 in the real part, which is only 1.64% lower than the actual refractive index. The trend of nonlinear optical phenomena in solid-state physics can be effectively obtained by using the FEM model, which provides a new idea for the application expansion of the optical force system.

Liuying Zhou1, Yuanyuan Wang2
1School of Foreign Language, Yancheng Institute of Technology, Yancheng, Jiangsu, 224051, China
2School of Information Technology, Yancheng Institute of Technology, Yancheng, Jiangsu, 224051, China
Abstract:

The flipped classroom relies on a smart platform to assist the implementation of English translation teaching, combining the smart platform with the students as the core to realize the efficient interaction of English translation teaching and enhance the students’ interest in English translation learning. This paper develops an easy-to-use interactive system for English translation teaching in flipped classroom based on Fine Report, and utilizes MySQL database to store the relevant data generated in the process of use. In this system, the BERT model trained by matrix masking strategy is used as the basis, and the neural machine translation model that assists teachers in English translation homework correction is established by combining the NMT model. Then the K-Means clustering algorithm is optimized by the adaptive K-value selection method, and the students’ learning data on the system is clustered by using the improved K-Means, and the student performance evaluation model is established by combining the CART decision tree. A pedagogical comparison experiment was carried out for the feasibility of the interactive system for teaching English translation in the flipped classroom. The BLUE value of machine translation using the BERT-NMT model was always above 30, and the average accuracy of student performance prediction of the K-Means-CART model could reach 84.85%. The English translation performance of the students in the experimental class was significantly improved after the teaching experiment, and the overall satisfaction of the students with the interactive system for teaching English translation was 4.038 points, which was between the satisfied~very satisfied level. Fully combining intelligent technology to assist teachers in teaching English translation under the flipped classroom can help to enhance the quality of cultivating English translation talents in colleges and universities.

Junfen Han1
1Social Science Department, Jiangsu University of Technology, Changzhou, Jiangsu, 213001, China
Abstract:

Under the background of the current information age, the electronic and intelligent transformation of the bidding industry has become an inevitable trend, and the e-bidding model stands out and greatly improves people’s understanding of bidding. Aiming at the traditional e-bidding system, in order to solve the problem of the lack of the traditional e-bidding system that provides the bidding body with referable opinions, this study firstly constructs the e-bidding risk assessment indexes and realizes the optimization of the evaluation module of the system. Then the recommendation algorithm based on deep learning implements the optimization design of the e-header bidding system. This study constructs an optimized recommendation model by fusing knowledge graphs on the basis of deep learning. Then the e-tendering optimization system is designed according to the actual needs of e-tendering, combined with the recommendation model of this paper. The accuracy index ACC of this paper’s recommendation model is improved by about 3% on average compared with other best-performing recommendation models on each dataset, which verifies the excellent performance of this paper’s recommendation algorithm. This study constructs an optimized e-tendering system and proposes suggestions for the development and operation strategy of corporate e-tendering, contributing to the development of e-tendering transactions and the participation of social capital.

Mu Mu1
1School of Digital Arts, Suzhou Industrial Park Institute of Services Outsourcing College, Suzhou, Jiangsu, 215123, China
Abstract:

Colleges and universities are an important part of higher education, providing a large number of talents for social development. The study optimizes the way of student management in colleges and universities based on artificial intelligence technology. Firstly, the K-means algorithm in cluster analysis is used to classify students’ campus behavioral characteristics. Then use Apriori algorithm to correlate students’ behavioral characteristics with academic performance. Finally, colleges and universities can take differentiated management measures for different categories of students. The clustering analysis of 12,885 students’ consumption behavior, work and rest behavior, and study behavior in college Z, followed by the correlation analysis between the clustering results and academic performance, and a total of 10 correlation rules were found. Colleges and universities can formulate management rules based on the analysis results to improve management efficiency. In addition, the student management work of colleges and universities can be optimized and upgraded in several directions, including the awareness of student management work in colleges and universities, the information platform, the archive management work, the student management team, and the information security work.

Tingliang Yan1,2
1Zhuhai College of Science and Technology, Zhuhai, Guangdong, 519041, China
2City University of Macau, Macau, 999078, China
Abstract:

In the current business environment, artificial intelligence is becoming a key force driving performance management and organizational change. In this paper, finance, customer, internal process, learning and growth are selected as the indicators of performance evaluation of Enterprise A through the balanced scorecard model, and the fuzzy algorithm is used to provide comprehensive scores and grades for the above indicators. In addition, this paper sets up an organizational structure change evaluation model and analyzes the effect of the system on the organizational change of Enterprise A through the measurement of key indicators. The recognition rating of the questionnaire set up in this paper by the employees of Enterprise A is 4.15, and the recognition degree of the Intelligent Performance Management System is “basically recognized”. The intelligent performance management system improves the design and execution of internal processes in Enterprise A, and promotes the organizational change of Enterprise A in terms of total resource utilization. In conclusion, this study provides reliable technical support for enterprise performance management and organizational change.

Peng Li1
1School of Civil and Transportation Engineering of Henan University of Urban Construction Pingdingshan, Henan, 467002, China
Abstract:

Groundwater seepage has a greater impact on the stability of foundation engineering, and it is also an important factor that restricts the development of geotechnical engineering projects and the quality of engineering surveys. In this paper, tunnel engineering is selected as the foundation engineering project under study to investigate the specific influence of groundwater seepage in the process of tunnel excavation. The flow-solid coupling model is constructed, and the safety coefficients of the tunnel project in different situations are calculated based on the strength discount method and the ultimate strain method. Numerical analysis software is used to establish the calculation model of the influence of groundwater seepage on the stability of tunnel excavation. The displacement of surrounding rock around the hole is selected as the evaluation index of tunnel stability, and the effect of groundwater seepage on each index is calculated by the analysis software. The study shows that groundwater seepage will make the rock body around the palm face after tunnel excavation change significantly from the pre-excavation bending. The seepage increases the displacement of the surrounding rock, and the coefficient of increase of vertical displacement is larger than the coefficient of increase of horizontal displacement. At the same time, the flow-solid coupling effect of groundwater seepage increases the surrounding rock stress, and the increase coefficients of each key point of the tunnel excavation are between 1.11-1.50, resulting in significant deformation of the bottom of the arch, the top of the arch, and the arch girdle. In addition, the groundwater seepage makes the tunnel safety coefficient decrease from 9.03 to 5.18, which significantly causes the decrease of tunnel stability.

Fan Wang1
1Art College, Zhengzhou Shengda University, Zhengzhou, Henan, 451191, China
Abstract:

Artificial Intelligence has been applied in many aspects of life, however, AI algorithms have been less used in the field of music. In this paper, a multi-track based pop music generation model MuseGAN is proposed, due to its poor contextualization and excessive tempo jumps in generating pop music samples. In this paper, a new multi-track pop music generation model-Recurrent Feature Generation Adversarial Network RFGAN is proposed. the model addresses the temporal relevance of the music structure and the repetitive nature of the musical section, and proposes a temporal model that enhances the contextual relevance of the music samples in terms of the time series, and improves the generative model according to this temporal model by converting the unidirectional structure in the original model to a recurrent structure, adding the feature extractor to the previous level of training information, which is combined with arbitrary noise and passed to the next training. An average pooling layer is added at the end of the generative model as a solution to the situation where the model generates too much noise for pop music samples. The improved model is superior to the pre-improvement model in terms of stability, convergence speed, and overfitting in pop music generation. In the audience scoring experiment, 60% of the top 5 pop music scores were generated using the RFGAN model proposed in this paper, indicating that the pop music generated using the RFGAN model has reached a high level comparable to the level of artificial pop music composition.

Rui Li1, Feng Zhao1, Boyu Zhao2,3
1School of Digital Commerce, Zhejiang Yuexiu University, Shaoxing, Zhejiang, 312000, China
2Business School, Lishui University, Lishui, Zhejiang, 323000, China
3School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 212000, China
Abstract:

Based on the research of digital economy and digital transformation of manufacturing industry, the article constructs the evaluation index system of digital economy development level and digital transformation of manufacturing industry in Yangtze River Delta respectively. The entropy weight TOPSIS method is used to measure and analyze the level of digital economy development and the level of digital transformation of manufacturing industry in the Yangtze River Delta respectively. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta in general and in each province and city is calculated and analyzed. On the basis of the relevant conclusions, recommendations for the digital transformation of the manufacturing industry and the elimination of the “digital divide” in the Yangtze River Delta are proposed. The overall trend of digital economy in the Yangtze River Delta (YRD) is on the rise, but there is a significant “digital divide”. The increase in the level of digital economy development in the Yangtze River Delta over the past 10 years is 10.76%. The average value of digital economy development water in Shanghai, Jiangsu, Zhejiang and Anhui is 0.358, 0.549, 0.491, 0.185 respectively, and the development of digital economy in Anhui is insufficient. Shanghai’s manufacturing digitalization water average is 0.750, the highest level in the Yangtze River Delta. Zhejiang and Anhui are slightly behind in manufacturing digital transformation. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta grew from 0.592 to 0.879, and the type of coupling coordination development shifted from barely coordinated to well coordinated. Shanghai and Jiangsu have reached the good coordination level. Zhejiang is at the intermediate coordination level. And Anhui is at the barely coordinated stage, which is the lowest level in the Yangtze River Delta.

Ge Zhang1
1Network Information Service Center, Henan University of Economics and Law, Zhengzhou, Henan, 450000, China A
Abstract:

Graph neural networks are an effective method for action recognition using human skeletal data, but previous recognition methods lack attention to spatial features. In order to improve this research deficiency, this paper conducts action recognition research based on ST-GCN. An action recognition network based on two-way skeletal joint information is proposed, where the human body is divided into various parts to calculate the representation vectors, and a graph convolutional neural network is trained to obtain the classification results. Attention mechanism is designed to minimize the effect of background noise, and data enhancement by means of flipping and shifting is performed to improve the model performance. Ablation experiments verify high accuracy when using both the attention mask matrix and the global self-attention mechanism, as well as when using both the joints network branching and the parts network branching. The model in this paper recognizes all 12 actions of the NW-UCLA dataset with accuracies higher than 92%, and the data enhancement effect is also verified.

Feiran Yang1, Guozhong Wang1, Haiwu Zhao1
1Shanghai University Of Engineering Science, School of Electronic and Electrical Engineering, Shanghai, 201600, China
Abstract:

The popularity of network video service, which leads to the specification of the network video service quality is becoming more and more urgent, and human is the ultimate watch users of network video, Evaluation of human observers on the video’s perception of the situation is becoming more important, depending on the source video network video know nothing, you will need to refer to the participation of video quality evaluation algorithm. However, when the quality evaluation is done by human rating, it is time-consuming and laborious, so the computer is required to make an objective evaluation of the video. In the objective evaluation, the excellent performance of convolutional neural network based on deep learning in feature extraction contributes to the rapid development of the research field of video quality evaluation. However, the development of deep learning algorithms requires appropriate data sets for training and testing. The existing data sets are relatively small in scale and not comprehensive in terms of video content types and distortion types. Therefore, it is necessary to provide a new data set to evaluate the quality of video without reference, expand the scale of the data set, expand the content and distortion type of the video. At the same time, considering the new development of network video services, the video resolution is positioned as high definition, and the original video sampling ratio is 4:2:2. The dataset is freely available to relevant researchers for scientific research.

Ziwei Wang1, Chenguang Yuan1, Yanhua Song1, Jianbo Yang1, Yan Tian1, Lei Wang1,2, Xihui Yang1,2
1Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou, Henan, 450052, China
2Research and Development Co., Ltd. of Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou, Henan, 450052, China
Abstract:

Food security is an important foundation of national security, and the fundamental of guaranteeing national food security lies in arable land protection. In order to realize the protection of arable land in the context of ecological civilization, this paper designs a governance framework for arable land protection based on supply, empowerment, and control on the basis of the trinity of arable land protection policy of quantity, quality, and ecology, and constructs a multi-objective land use structure optimization model, and obtains a scenario prognosis for the optimal allocation of the land use structure by using a hybrid genetic algorithm. Taking County A as the specific research object, it can be seen by predicting the land use structure under the natural development scenario of County A that, relative to the status quo in 2023, the predicted share of arable land in 2050 has the largest decrease of 0.49%, and the shares of garden land, forest land, grassland and water area have all decreased, which is mainly converted into construction land (increased by 0.78%). From the Pareto frontier solution of land ecological benefit objective and economic benefit objective, three typical schemes of land use structure optimization were obtained, among which, the optimization scheme of balanced development of economy and ecology balanced economic and ecological development balanced economic development and ecological protection, and was selected as the optimization scheme of this paper. The increase of arable land area in this scheme is 0.38%, much higher than the -2.46% in the unoptimized case, which is in line with the requirement of arable land retention and can be used as a reference for further optimization of arable land protection framework.

Lida Wang1, Yuqiong He1, Yongqi Wang1, Yunxi Zhao1
1Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650500, China
Abstract:

This study focuses on the Dingjiafen slope in Chuxiong City, China, with the aim of improving the accuracy of slope landslide risk prediction. Formulas for calculating the critical soil layer thickness at the onset of slope instability are derived based on the physical model of the slope. Using the Digital Elevation Model (DEM) and ArcGIS, the critical and maximum soil layer thickness of each slope unit are calculated to predict potential landslide areas. FLAC-3D is employed to simulate and analyze the slope’s stability under natural conditions, and the numerical simulation results are compared with the predictions in ArcGIS. The findings reveal variations in the critical and maximum soil layer thickness among different slope units due to diverse topography. The slope units on both sides of the Chumeng Highway slopes, with a critical soil layer thickness ( ) between 1 and 3 meters, are connected, aligning with the results of FLAC-3D three-dimensional numerical simulation and the actual sliding positions on-site. Applying this method to simulate the soil layer thickness at the critical state for each slope unit enables slope stability prediction, offering a new perspective for the analysis and prediction of slope stability.

Fei Li1, Lin Lu2
1School of Physical Education, North University of China, Taiyuan, Shanxi, 030051, China
2School of Mechatronics Engineering, North University of China, Taiyuan, Shanxi, 030051, China
Abstract:

This paper designs a military intelligent wearable device, aiming at realizing human-computer interaction and monitoring military status signs and data characteristics through this device. First of all, the overall system of the product contains temperature and humidity module, blood pressure detection module, heart rate measurement module and display module, and the extracted feature data are subjected to data intelligence preprocessing. Then the pre-processed feature data is functionally compressed and an artificial intelligence feature classification model is constructed, through which the compressed feature data is analyzed and displayed. Finally, the interactive performance of the wearable device is completed through data intelligent processing and device relevance calculation. After application analysis, it is found that the actual monitoring error is below 0.1 under different fatigue levels, and the specificity and positive prediction value of the wearable system can reach up to 100%. The highest accuracy of monitoring the physical state of military personnel is 99.31%, in addition to monitoring the heart rate in sedentary state and exercise state, with an average error value of 1.24 and 1.29. Therefore, the smart wearable device designed in this paper can well realize human-computer interaction, and the performance of product design is superior.

Shunping Ji1
1School of Mechanical and Electrical Engineering, Sanjiang University, Nanjing Jiangsu, 210012, China
Abstract:

Industrial processes are constantly developing towards large-scale and automation, and the smooth, safe, high-quality, and efficient operation of industrial processes has become a hot spot of concern, and higher requirements have been put forward for the control of production processes. This study analyzes the high-dimensional data in the closed-loop system of industrial network based on the HOPLS-SVM algorithm with higher-order singular value decomposition method. A BP neural network model is constructed with the processed data as the input set to realize the real-time prediction of the main data in the project, and the error of the prediction model is corrected by using linear regression method, and then the project prediction control system is constructed by piggybacking on the model. The results show that the prediction performance of the model in this paper is better than that of the comparison model, and the average absolute error is only 0.0347. At the same time, it is found that the control system in the decomposition project of CHP is able to regulate and optimize the temperature and flow rate of the crude product, which ensures the balance between the product temperature and yield, and the safety of the project operation. The engineering control method designed in this study has strong adaptability and effectiveness, and can provide solutions to engineering control problems in complex industrial processes.

Leqian Ouyang1, Mengying Lei1, Lining Zeng1
1Business Administration College, Hunan University of Finance and Economics, Changsha, Hunan, 410002, China
Abstract:

Social network structural characteristics of top management (TMT) are important variables that affect the outcome of team functioning, and variability in network structural characteristics leads to variability in TMT performance. This paper analyzes TMT social network structure characteristics based on TMT’s social relationship network using machine learning techniques. The top management interlocking network and technological innovation (machine learning technology) are divided into dimensions respectively, and the machine learning technology is used as a mediating variable to establish a model of the mediating effect of machine learning technology between top management interlocking network and green innovation. Statistical analysis of sample data and structural characterization of TMT social relationship networks by machine learning technology are conducted, and regression equations are used to verify the research hypotheses. The test results of the mediating effect of utilized innovation and exploratory innovation covered by the machine learning technology show that the overall regression effect of the model is good ( =0.537, =0.579, F-statistical test is significant), i.e., the mediating variables, utilized innovation and exploratory innovation, positively affect the green innovation performance and are significant. Meanwhile, the heterogeneity and size of TMT’s social relationship network, as well as relationship strength and relationship quality all have a significant and positive effect on green innovation.

Junzi Liu1, Yonghong Gu1, Lingling Xiao1, Xiaomin Wen1, Zhi Sun1, Lu Liu1
1School of Mechanical and Electrical Engineering, Hubei Three Gorges Polytechnic, Yichang, Hubei, 443000, China
Abstract:

In this paper, the structures of three phosphorus-containing organosilicon compounds, including N,N-di-methylenephosphoric acid n-propylamine (DPPA), N,N-di-methylenephosphoric acid aminopropyldimethylsilanol (DPDS), and N, N-di-methylenephosphoric acid aminopropyldimethylsilylene glycol (DPMS), have been designed by using a molecular dynamics simulation method. And the preparation of three phosphorus-containing organosilicon compounds was accomplished experimentally by using raw materials such as bisphenol A-type epoxy organosilicon, n-propylamine and phosphite. The structures of the above several substances were proved by means of characterization such as Fourier infrared spectroscopy, hydrogen NMR , and epoxy value. Molecular dynamics simulation analysis revealed that the bond lengths of N atoms to Si atoms, N atoms to O atoms, and N atoms to were 3.03 Å, 3.05 Å, and 2.85 Å, respectively. Si did not participate in the addition reaction, but the intermolecular interactions caused a change in the chemical environment of Si, which reduced intermolecular distances and made it easier for the phosphorus groups to aggregate. This study is very important for the development of new preparation strategies of phosphorus-containing organosilicon and the promotion of phosphorus-containing organosilicon industry.

Huijiao Chen1, Yan Wang1
1Institute of Art and Design, Wuhan University of Technology, Wuhan, Hubei, 430070, China
Abstract:

In today’s society, ancient cities, as important components of historical and cultural heritage and urban development, are receiving increasing attention for their protection, utilization, and management. This research mainly focuses on the construction of an evaluation system for the spatial historical evolution of ancient city streets and the corresponding management strategies. Through a comprehensive evaluation of the spatial issues and characteristics of the ancient city streets, a multi-dimensional evaluation system for the historical evolution of the ancient city space with a total of 13 indicator factors, including historicity, is constructed. Taking Suzhou Ancient City as an example for empirical analysis, five typical types of ancient city streets are identified. Finally, corresponding update strategies are proposed for different types, especially the utilization of biomaterials and the design of plant landscapes, providing more innovative and sustainable management suggestions for the revitalization planning of the ancient city.

Xingyu Hu1, Dengwei He1
1Orthopedics, Lishui Hospital Affiliated to Zhejiang University School of Medicine, Lishui, Zhejiang, 323000, China
Abstract:

Objective, to investigate the correlation between abdominal aortic calcification and paravertebral muscle degeneration, and to explore possible common risk factors for both. Methods, all patients with lumbar spinal stenosis admitted to Hospital X for MC and CT examination from 2016 to 2024 were selected, and through screening and exclusion, a total of 352 patients with LSS were included in the study, which consisted of 202 males and 150 females aged 40-80 years, with a mean of 63.24 years. The degree of paraspinal muscle degeneration in lumbar MRI, the degree of abdominal aortic calcification in lumbar CT scanning, as well as the patient’s age, duration of LSS, glomerular filtration rate and other indicators were counted, and the distribution characteristics of abdominal aortic calcification and its correlation with paraspinal muscle degeneration were analyzed by the method of multiple regression. Results, of the 352 patients with LSS who were included to meet the criteria, the calcification group (151, 42.90%) and the non-calcification group (201, 57.10%). Mild, moderate and severe paravertebral muscle degeneration accounted for 56.53%, 28.69% and 14.77%, respectively. The AACS in patients with mild PD degeneration stage, moderate PD degeneration stage and severe PD degeneration, all showed a gradual increasing trend with age (P<0.001). Regression results showed that age, paravertebral muscle degeneration and eGFR were risk factors for AAC in patients with LSS. Conclusion, there was a significant correlation between abdominal aortic atherosclerotic calcification and paravertebral muscle degeneration (P<0.001), and the degree of PD degeneration can be used as an effective indicator for early warning of the occurrence of AAC in patients with LSS.

Tong Ye1, Chenchen Liu1, Daru Zhang1
1School of Economics and Management, Anhui Polytechnic University, Wuhu, Anhui, 241000, China
Abstract:

This paper constructs a two-party evolutionary game model based on the perspectives of sharing platforms and consumers, exploring the dynamics of platforms’ decisions to actively operate with blockchain technology and the evolution of consumer rights protection behaviors. It is discovered from analysis that certain variables exert considerable influence on the stability of the strategies of both parties. From the consumer perspective, the improvements in the performance of the blockchain technology significantly increase the consumers’ willingness to protect their rights: the consumers with initially high levels of rights protection activation intensified their actions when their rights were violated. Thus, with the effective reduction of the cost of safeguarding rights, this trend has been additionally strengthened. As for the platform side, the performance of the blockchain technology exerts positive incentives on the operation of the platforms, although the marginal impact gradually declines with the developing blockchain technology, which in return reveals that platforms need to pay attention to a range of aspects including technology maturity. Measures of dual constraints including heavy fines from government and negative impacts of passive operations help to rein in passive operation among the platforms. Significantly, higher values of fines or negative effects lead to higher tendencies of having proactive operation strategy among the platforms.

Bo Zhang1, Dongmei Yuan2
1Institute of Advanced Technology for Carbon Neutrality, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, 210023, China
2College of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, Jiangsu, 211171, China
Abstract:

The research combines PBL teaching method, CDIO theory and school-enterprise collaborative education mechanism to construct a school-enterprise collaborative teaching model based on PBL-CDIO. And then, the empirical research of PBL-CDIO school-enterprise collaborative teaching mode is realized through the teaching experiment method. The independent sample t-test is used to test the changes in the professional knowledge level and basic working ability of the experimental group and the control group before and after the experiment, and to judge the teaching effect of the school-enterprise collaborative teaching mode based on PBL-CDIO in this paper. The pre-test sig values of professional knowledge and basic work ability of the experimental and control groups are greater than 0.05, and there is no significant difference between the two groups. The posttest sig values of the dimensions of professional knowledge in the experimental group increased by 10.20, 10.46, 10.49 and 9.47 respectively, and the sig values of the dimensions of basic work competence increased by 9.89, 9.72, 8.66 and 10.10 respectively. The overall change in the level of professional knowledge and basic work competence in the control group was less than 1 point. The posttest expertise and basic work ability sig of both groups were less than 0.05. After the experiment, the expertise and basic work ability of the experimental group were much better than that of the control group. The school-enterprise cooperative teaching mode based on PBL-CDIO proposed in this paper has good teaching effect.

Yuchen Wang1
1Business School, Monash University, Melbourne, VIC 3145, Australia
Abstract:

As the trend of economic globalization continues to develop, air transport, as a fast and convenient mode of transportation, is playing an increasingly important role in economic development. This study analyzes the driving force of airside economic construction from four levels: primary influence, secondary influence, derivative influence and permanent influence. It also analyz es the dynamic relationship between the aviation industry and the construction of airside economy. In order to further research on the development of airside economic construction, this paper uses the entropy weight method to optimize the gray situation de cision making theory, and conducts research on the development and countermeasures of airside economic construction in Henan Province. According to the gray decision making effect measurement calculation, it is known that the key construction area of airsi de economy in Henan Province should be selected as H2 area, with the effect measurement score of 0.9789, the highest value. The economic effect achieved by prioritizing the development of tertiary industry or the joint development of secondary and tertiary industries in the construction of airside economy in the H2 area is the highest, with the effect measurement scores of 0.755 and 0.749, respectively.

Bo Chen1,2, Hongyu Zhang1,2, Runxi Yang1,2, Xiao Fang1,2, Yi Ding3
1State Grid Beijing Electric Power Company, Beijing, 100031, China
2Beijing Electric Power Economic Research Institute Co., Ltd., Beijing, 100055, China
3Nanjing Artificial Intelligence Research of IA, Nanjing, Jiangsu, 211100, China
Abstract:

With the deepening of the exploration of informationization in the construction industry, the smart construction site comes into being under the support of technological development and policy. The article combines artificial intelligence technology with electric power smart site, and deeply researches the application of artificial intelligence technology in electric power smart site. For the security monitoring in the smart construction site, a SSD7-FFAM lightweight target detection method is proposed based on the SSD7 algorithm, using feature fusion and attention mechanism methods. Then, based on the fast acquisition of temporal information of surveillance video scenes, an adaptive compression technique with wavelet sparse measurement is designed. Through the model comparison analysis, the SSD7-FFAM algorithm achieves better detection effect and detection speed of 84.97% and 83.45FPS in real application scenarios, and has a smaller number of parameters and computation.The AVCS method can be effectively adaptively adopted, and most of the reconstructed image PSNR values of this method are greater than 40dB under different sampling rates, and the quality of the reconstructed image is better than the Contrast compression technique, which can be used for the high rate compression of intelligent construction site monitoring video. The research results will provide informative ideas for construction companies to introduce AI technology in power smart construction sites.

Qinglei Zhang1, Xueying Niu1, Jianguo Duan1, Jiyun Qin1
1School of Logistics Engineering, Shanghai Maritime University, Shanghai, 200135, China
Abstract:

When a steam turbine blade has cracks, fractures, or other flaws, the steam turbine’s operating circumstances will change the vibration characteristics of the blades, complicating the problem identification process. The important defect features are difficult to automatically and effectively extract from the recorded vibration signals. In this study, the input signal characteristics for a particular operating situation are used as labels to reconstruct a trained autoencoder utilizing a reverse error. The supervised autoencoder receives the fault features for various speed circumstances, which it then protectively maps to a series of reference condition features. The goal is to eliminate the disruption brought on by variations in fault feature values brought on by alterations in operating circumstances. The experimental findings demonstrate that this approach can more effectively convert feature sequences under various working situations and address the issue of fault feature distortion brought on by changes in working conditions. In addition, comparison of clustering visualization and accuracy of classification methods on data before and after commutation demonstrates that the proposed supervised autoencoder model can extract accurate classifiable features for fault classification.

Lili Song1
1Henan Institute of Economics and Trade, Zhengzhou, Henan, 450000, China
Abstract:

Deep learning, as a multilayer neural network structure for deep learning of data features, can describe the nonlinear mapping relationship for the assessment of college civic education. Aiming at the current education quality assessment model based on deep learning, this paper proposes an optimized convolutional neural network (HOA-CNN) based on Hummingbird Optimization Algorithm to assess the quality of Civic and Political Education in colleges and universities. According to the correlation coefficient between the objective assessment results and the subjective assessment results, the objective assessment results of the quality of Civic and Political Education in colleges and universities are obtained. The test results show that the linear correlation coefficient and the rank correlation coefficient between the assessment results of this method and the subjective assessment results are closer to 1. The goodness-of-fit of the assessment of the quality of college civic education under the model of this paper is significantly higher than that of the two control models. The simulation test results show that the assessment results of the university civic education quality assessment model constructed by the optimized convolutional neural network based on the hummingbird optimization algorithm are more accurate.

Dandan Wang1
1Zhengzhou Railway Vocational & Technical College, Zhengzhou, Henan, 451460, China
Abstract:

This paper evaluates the quality of university English teaching based on the hierarchical analysis algorithm (AHP) and fuzzy comprehensive evaluation algorithm (FCEA) in order to grasp the teaching situation more objectively. Based on the principle of evaluation index system construction, the evaluation indexes of university English teaching quality are determined. Using hierarchical analysis algorithm to calculate the weights of its indicators, and constructing a fuzzy comprehensive evaluation matrix based on expert ratings to finalize the assessment of university English teaching quality. Taking a university as the research subject, the English teaching quality assessment result of the university is 3.7351, and its corresponding fuzzy comprehensive evaluation A=(0.2893,0.3981,0.1359,0.1120,0.0648), which summarizes the teaching quality of university English as good according to the principle of maximum affiliation degree. In order to improve the teaching of college English in this university, corresponding teaching strategies of college English are proposed.

Kaiyi Zheng1
1Economics and Management School of Yantai Nanshan University, Yantai, Shandong, 264000, China
Abstract:

Garden is an important support for regional economic development, but also an important support for regional ecological environmental protection, the rational allocation of water resources in the garden is one of the effective ways to solve the problem of water shortage. This paper takes the Internet of things, digital twin as the technical basis, uses the multi-objective optimisation algorithm to construct the water resource management model of the garden area, and uses the artificial fish swarm algorithm to solve the model. By constructing a digital twin irrigation district water resources scheduling management platform, the water resources elements of the garden area are comprehensively monitored and sensed, and the intelligent simulation of the water resources allocation management process and decision-making scheme evaluation and optimisation are achieved, so as to enhance the intelligent and refined management level of water resources scheduling of the garden area, and comprehensively realise the saving and intensive use of water resources. Taking X garden area as a research case, the water resources management model finally derives the optimal water resources allocation scheme under 50%, 75% and 90% in each planning year, which provides support for the efficient use of water resources in X garden area.

Xiangchen Wu1, Youfang Yu2
1Applied Engineering College, Zhejiang Business College, Hangzhou, Zhejiang, 310053, China
2College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang, 832003, China
Abstract:

Flexibility control and vision of robots are important acquisition and feedback links in robot control, and the study of multi-sensor data fusion is becoming more and more important as the complexity of robot tasks increases. This paper describes the robot kinematics and inverse kinematics process by studying the knowledge of D-H model theory and parameter definitions in the machine kinematics model, reveals the changing relationship between the robot joint control and end pose, and establishes a kinematics-based vision servo control model. On this basis, the coupling error compensation algorithm is used to combine the visual position control quantity as well as the force sensing position correction quantity to form the final visual and force sensing supple control strategy. Meanwhile, for the lack of adaptability of classical impedance force control on unknown constraint environments, a two-fuzzy adaptive sliding mode controller is designed according to the Lyapunov stability theorem to drive the robot end in order to achieve the actual position tracking expectation. The results of simulation experiments and motion contour tracking experiments show that the control algorithm proposed in this paper has better control accuracy and is more robust to noise and uncertainty, and the controller is also able to reduce the effect of torque saturation on the robot system.

Hongquan Wang1, Jun Yang1
1Physical Education Department, Weifang University of Science and Technology, Weifang, Shandong, 262700, China
Abstract:

The development of urbanization is rapidly changing, and various undertakings are flourishing, while the sports industry, as an important segment of urban regional economic development, plays an inestimable role in the development of the entire city construction. The study takes the sports industry and economic development of 27 provincial capital cities in China from 2018 to 2022 as the research object, establishes the evaluation indexes for the high-quality development of the sports industry based on the principle of index construction, and establishes the weights of the indexes. Taking Harbin as a case study, the effect between urban sports industry and economic growth is analyzed with the help of impulse response analysis, Granger causality test, and variance decomposition of VAR model. The results show that the development of urban sports industry and economic growth can promote each other, with a long-term cointegration relationship, and the positive effects between the two are slowly reduced over time when they are impacted in the long term. Granger test shows. It indicates that there is a unidirectional causal relationship between urban sports industry and economic growth.

Yukun Wang1, Xinpo Zhu1, Yu Yu1, Jian Wu1, Hua Liu1, Jianbo Wang1
1Beijing China Power Information Technology Co., Ltd., Beijing, 100192, China
Abstract:

This paper first introduces the regional power marketing management platform, after which the 3 major functional modules of this power marketing management platform are designed. Then MobileFaceNet is used as the basic network for face recognition feature extraction in the context of deep learning, and the SE module is used to optimize the network performance and network expressiveness. Afterwards, the Taylor expansion of the negative log-likelihood function is used as an optimization criterion to optimize the face detection model (MTCNN) and the face recognition model (SE-MobileFaceNet). Finally, the running effect and performance of SE-MobileFaceNet model are measured. The main conclusions are as follows: in 1:1 mode, the accuracy of SE-MobileFaceNet model for the three datasets DRDS, DE and DPDS is 95.99%, 96.98% and 98.83%, respectively. In addition, the SE-MobileFaceNet model can avoid excessive redundant calculations, so that its recognition rate reaches 95%.The accuracy of the SE-MobileFaceNet model for monitoring and recognizing the information of the management platform ranges from 97.43% to 100%, and it has a good operating effect in the identification of the regional electric power marketing management information platform, and the overall satisfaction rate of the testers for the model is also >85%. The overall satisfaction of the testers to the model is also >85%. Obviously, the SE-MobileFaceNet model proposed in this paper has a very broad application in regional power marketing management information platform identity recognition.

Liang He1, Yanlong Wang1, Wensong Huang1, Xiaoyu Liu1
1China Construction Sixth Bureau Civil Engineering Co., LTD, China State Construction Sixth Engineering Bureau Co., LTD, Tianjin, 300450, China
Abstract:

Planted roofs have good heat preservation and insulation properties, which can effectively alleviate the urban heat island effect and reduce the energy consumption of buildings and the carbon dioxide content in the atmosphere. The study describes the heat transfer process of planted roofs into three parts, derives the heat transfer equations of the leaf layer, soil layer, and roof layer of planted roofs, and clarifies the calculation of relevant parameters in the model of planted roofs. Taking integrated design as the technical standard, the stereotypical design of planted roof buildings and their building parts, components, fittings, engineering equipment, etc. The insulation exterior wall panel enclosure system is standardized to realize industrialized production of wall panel components, integrated design of connection nodes, and assembly construction. The analysis results show that during the test time, the average convective heat transfer heat flow of Module H containing vegetation is a maximum of 119.21W/m2, and the total convective heat transfer heat flow of the whole day is 2835.99w/m2, which has the best thermal insulation performance. Among all the roof modules, only Module H has the heat transfer direction from outdoor to indoor throughout the day. Finally, based on the above conclusions, the self-insulated exterior wall system’s specific construction method and technology are given to provide the basis and reference for the specific construction in practice.

Yunyue Xiu1
1Yantai Library, Yantai, Shandong, 264000 , China
Abstract:

In today’s big data environment, the demand for digital transformation of traditional libraries is becoming more and more urgent. The article adopts BERT-BiLSTM-CRF model to extract digital library resources and retrograde entities, and constructs digital library resources knowledge graph. On the basis of digital library resources integration, it combines the collaborative filtering algorithm based on users and items to construct and improve the intelligent recommendation mechanism of digital book resources. The integration results of digital library resources and intelligent recommendation results are analyzed separately, and a survey on reader satisfaction is conducted. The recognition accuracy of this paper’s method is significantly higher than that of the traditional text-like processing data model. The collaborative filtering algorithm in this paper provides statistical analysis of the types of book resources read by each reader, and recommends the top 5 book types in terms of similarity to him/her. This paper’s method has better results in book resource division and book resource recommendation accuracy compared to other recommendation methods. The average value of readers’ satisfaction with the resource recommendation mechanism of the digital library in S city for each dimension and each index is more than 4 points.

Juanhui Ren1, Qin Liu2
1Chengdu Aeronautic Polytechnic, Chengdu, Sichuan, 610100, China
2Chengdu Guangxunda Technology Co., LTD., Chengdu, Sichuan, 610100, China
Abstract:

With the continuous development of high-power laser equipment and the continuous expansion of the scope of the application platform, the demand and application of high-power laser equipment in various fields are becoming more and more extensive, and its output power has also put forward higher requirements. In order to promote the development of high power laser equipment toward higher energy conversion efficiency, research and design temperature control device to manage the waste heat generated in the energy conversion process of high power laser equipment. On the basis of PID control algorithm using LADRC algorithm, rapid realization of temperature precision control, so as to enhance the energy conversion efficiency of high-power laser equipment. When the temperature control device in the temperature control range of 10 ℃ ~ 40 ℃, the temperature control accuracy is better than ± 0.03 ℃, and in 144s to reach the set temperature, the temperature control overshoot is less than 2.33%, to meet the requirements of the laser working temperature control in the working process of high-power laser equipment, and to lay the foundation for the realization of high energy conversion efficiency. Compared with the modified PID controller, the energy conversion efficiency is relatively improved by 1.57%. The temperature control device designed based on the improved PID control algorithm in this paper can significantly improve the energy conversion efficiency.

Zhengqiong Wang1
1Yunnan Yuntong Shulian Technology Co., Ltd., Kunming, Yunnan, 650100, China
Abstract:

ETC gantry data and other monitoring data provide data support for highway traffic flow prediction, for this reason, this paper proposes an attention mechanism-driven traffic flow prediction model to scientifically coordinate and schedule highway traffic conditions. Based on the fusion of multivariate monitoring data, the model utilizes ConvLSTM to generate global location coding, learns the data characteristics through the jump expansion attention structure, and completes the traffic flow prediction using the mask attention structure. The example analysis verifies that the predicted values of traffic flow and speed of this paper’s model are closer to the real values, and compared with the models such as ARIMA, LSTM and BiLSTM, this paper’s model has lower values of RMSE and MAE indexes in the prediction of traffic flow and speed, and the prediction error is smaller. The article also validates the model’s prediction under 5min, 15min and 30min prediction lengths, showing that the model has excellent performance and good prediction stability.

Yao Lu1
1School of Education, Xi’an FanYi University, Xi’an, Shaanxi, 710105, China
Abstract:

The development of society and the change of the times have brought some degree of change to the development of preschool music classroom. This paper introduces the OBE concept into the education of preschool music course, designs the teaching objectives of the music course according to the guidance of the concept in order to realize systematic teaching, and analyzes the students’ cognition of various dimensions in the preschool music course by using the cognitive level diagnosis method. Based on this method and the Hadoop system, a big data platform for integrated teaching of preschool music course is constructed, and teachers are assisted to intervene in teaching through the platform’s teaching data query, statistics and analysis functions, so as to realize the integrated teaching mode of preschool music course and mathematical statistical analysis. The results of the teaching practice show that after the implementation of the integrated teaching mode, students’ learning attitude towards the preschool music course and their knowledge of music-related knowledge increased significantly (P<0.05), and the level of independent and inquiry learning was also improved. This study can make the teaching of preschool music course more meaningful, more adaptable to the needs of talent training in today's society, and create an integrated teaching curriculum that is more conducive to the cultivation of students' musical literacy and interest.

Heng Zhang1, Fa Wang1
1College of Electronic Information and Engineering, Huaibei Institute of Technology, Huaibei, Anhui, 235000, China
Abstract:

Deep learning-based methods can be combined with skeleton data, but they only consider the feature vectors formed by joint coordinates and do not extract the spatio-temporal dependencies between skeletons. In order to provide a more comprehensive detection and recognition of spatio-temporal relationships in human action sequences, this paper proposes a graph neural network-based human action detection and recognition method by combining YOLOv5, AlphaPose, and spatio-temporal graph convolutional network (ST-GCN) algorithms under the interpretable artificial intelligence (XAI) perspective. Firstly, the improved YOLOv5s target detection algorithm is used to get the human body detection frame and obtain the human body position information, then the AlphaPose pose estimation algorithm is used to obtain the coordinate information of the joint points of the human skeleton, and finally the improved ST-GCN algorithm is used to construct the spatio-temporal graph and extract the spatio-temporal dependencies between the joints to complete the human body action recognition. Through experimental verification, the method can accurately recognize human fall, running, kicking, and squatting actions on the dataset, with a recognition accuracy of 92.04%, and compared with the five baseline models, the method has higher recognition accuracy, with the values of each index greater than 91%, which can provide technical support for human behavior recognition.

Kaifeng Lin1, Bo Zhang1, Qing Zheng1, Weiyan Zheng1, Yan He2, Di Huang2
1State Grid Zhejiang Electric Power Co., Ltd. Jinhua Power Supply Company, Jinhua, Zhejiang, 321000, China
2Zhejiang Dayou Industrial Co., Ltd. Hangzhou Science and Technology Development Branch, Hangzhou Zhejiang, 310000, China
Abstract:

By optimizing the automation configuration of medium-voltage distribution lines, capturing the initial signals of cable insulation hidden danger, combining the real case data of 6 years of distribution network insulation faults and hidden danger in a city of Zhejiang, summarizing the waveform law and progressive signal characteristics in the process of insulation hidden danger deterioration, a set of real-time monitoring method based on the analysis of big data of the medium-voltage distribution line cable insulation deterioration of the corona hidden danger has been developed. The method is based on the master station to realize localization, instead of periodic on-site equipment charged detection, has been verified on-site and found discharge traces cable head in advance. This method utilizes distribution automation and dispatch automation configurations to capture the instantaneous zero-sequence overcurrent signals corresponding to insulation degradation discharges, waveform characteristics, acoustic mutations, and environmental information as input. A quantitative risk algorithm consisting of eight analysis dimensions such as zero-sequence spike characteristics, number of spikes, and synchronization of acoustic ripple and spike timing is used. Three optional computational media, including master station, enhanced DTU, and DTU external component, are used to give hidden risk localization. The two methods, local discharge detection robot and manual detection, are used to confirm the site and then carry out out outage maintenance to prevent the further expansion of hidden dangers. The method relies on the distribution automation of existing protection devices and master station configuration to assist a small number of sensors and edge computing devices to realize, through the protection device uninterrupted monitoring instead of manual periodic local discharge detection. It solves the problems of high cost of periodic testing, unavoidable accidents caused by continuous insulation degradation in the interval of testing cycle, hidden location of some cables and blind area of testing, and effectively improves the reliability of power supply.

Yinuo Guo1
1Faculty of Arts, Modern College of Northwest University, Xi’an, Shaanxi, 710000, China
Abstract:

MOOC as a new teaching mode is developing in full swing, however, MOOC courses face the thorny problems of high dropout rate and low completion rate. Therefore, this paper selects 12 learning behaviors and uses logistic regression model, decision tree and other methods to predict the withdrawal behavior according to the MOOC data on 365 University platform. The logistic regression prediction is analyzed for prediction accuracy, and its AUC value is 0.83 and 0.75, which proves that the logistic regression analysis can achieve the prediction of MOOC withdrawal behavior more stably and accurately, and helps to provide scientific guidelines for improving MOOC learning mode and learning efficiency. From the case study, it is obtained that among all the learning behaviors, the weight of online rate is 0.7582, which has the highest weight, indicating that the online rate of college students is an important index for judging whether they will produce withdrawal behaviors, which deserves the attention of MOOC platforms and educators.

Hao Yuan1, Jianping Fu2, Ziyun Guo3, Kai Ren2, Rui Yang1, Zhigang Chen2, Kuiwu Li2
1School of Mechanical and Electrical Engineering, North University of China, Taiyuan, Shanxi, 030051, China
2Institute of Intelligent Weapons, North University of China, Taiyuan, Shanxi, 030051, China
3JINXI Industries Group CO., LTD., Taiyuan, Shanxi, 030051, China
Abstract:

The numerical simulation of the velocity decay characteristics of multilayer spherical fragments under bombardment loading is carried out by using LS-DYNA, and the distribution law of the velocity decay characteristics of multilayer spherical fragments is obtained. The ballistic limit (V50) of the multilayer spherical fragment on a 4mm 2024 aluminum target at 90° angle of attack is also obtained by ballistic test. Based on the consistency between the numerical simulation and the test results, the influence of the quality of the multilayer spherical fragment on V50 is analyzed. The air resistance coefficient is calculated with the numerical simulation results by constructing a rag flight distance calculation model. The maximum error between the calculated results and the test results is about 2%, and the theoretical calculated values are in good agreement with the numerical simulation and test results. Under the condition of the same initial velocity, the attenuation coefficient of the spherical fragment in long-distance flight is constant. The aerodynamic drag coefficient is related to the initial velocity of the fragment, which is linearly related to the initial velocity in the range of the design concern of the combat unit (1.2-2.2km/s).

Yan Zhuang1, Xiaodong Mao2, Yanling Yu1
1University of Sanya, Sanya, Hainan, 572011, China
2Sanya Institute of Technology, Sanya, Hainan, 572011, China
Abstract:

China’s tourism industry has become a strategic pillar industry in China, playing an important role in developing the economy and providing employment. Therefore, how can we avoid or reduce the hazards of tourism emergencies and give full play to the development advantages that tourism brings to the city has become the focus of this paper. In this paper, the objective function is used to construct a two-stage stochastic optimization model without opportunity constraints to minimize the partial cost of the first stage and the expected total cost of the second stage. Considering the problem of maximizing the utilization rate of emergency shelters in tourist attractions, the opportunity constraint model is introduced to help decision makers allocate resources reasonably. Based on the center siting cost and vehicle distribution cost, a mixed integer nonlinear objective function model is constructed and the model is solved using the improved ant colony algorithm. Seven emergency management simulation scenarios are set up to analyze the effect of emergency management by combining simulation and empirical research. The experimental results show that among the emergencies at all levels of the sites in Y scenic area in the past 5 years, the number of level 2 emergencies is the highest, and the average number of emergencies occurred in each site in the past 5 years is 7.48. According to the model’s solution of the site selection results, the emergency center A covers 5 distribution warehouses, and the emergency center B covers 10 distribution warehouses.

Jingze Liu1
1Faculty of Arts and Humanities, University of Macau, Macau, 999078
Abstract:

The countries in East Asia are neighbors in one country, and their cultures are cross-fertilized with each other, so it is of practical significance to enhance the sense of regional community on this basis. In order to explore the effect of cultural exchange and cooperation on the enhancement of regional community consciousness, this paper constructs a semantic graph for the relevant comments on social platforms, combines GNN and LSTM, and constructs a GNN-LSTM sentiment recognition model to identify and quantitatively represent regional community consciousness. Regression analysis is used to test the enhancement effect of cultural exchange and cooperation on regional community consciousness. The experimental results show that the GNN-LSTM model has a better emotion recognition effect and can provide help for the extraction and quantitative representation of regional community consciousness. The regression coefficients of cultural exchange status on the two models are 0.423 and 0.439 (p<0.01), indicating that cultural exchange has an enhancing effect on regional community consciousness. Cultural distance acts as a mediating variable, the more frequent the cultural exchange and cooperation, the smaller the cultural distance, the more the regional community consciousness can be enhanced.

Jingze Liu1
1Faculty of Arts and Humanities, University of Macau, Macau, 999078
Abstract:

The nation-state is regarded as the basic form of the modern state, but whether the modern state is really a “one nation, one country” type of political community as depicted by the nation-state narrative. This paper explores the influence of national narratives on national identity by constructing an evaluation index system, using a questionnaire survey method, and taking adolescents as the research object. The independent variables of this study are national narrative, including national language, national spirit and national memory, and the dependent variable is national identity, including cognitive tendency, emotional tendency and behavioral tendency. The weights of the indicators and regression results were calculated by AHP-entropy weighting method. The analysis results show that national vocabulary has a great influence on national language, while sense of belonging is the biggest factor affecting emotional tendency, and most of the dimensions of national narrative are positively correlated to national identity with a significant effect. The correlation coefficient between national etiquette and national identity is 0.203, and the correlation coefficient between national history and national identity is 0.254. National history and national etiquette have a significant effect on national identity.

Haili Yu1
1Mengxi Honors College, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
Abstract:

Local colleges and universities are an important part of China’s higher education reform and development, and the quality of cultivation of top-notch innovative talents has a direct impact on the development speed and level of local economy. In this study, the decision tree algorithm is used to establish a prediction and early warning model for students’ performance in the process of cultivating new engineering top-notch innovative talents in colleges and universities, and the K cross-validation method is used to optimize the model parameters and improve the prediction accuracy. Then, based on the intelligent prediction model and the cyclic structure intervention theory, we constructed a dynamic adjustment model for the cultivation system of new engineering top-notch innovative talents. The results of the empirical application of the model show that the hardware and facility conditions of talent cultivation in college D have significant improvement under the application of the dynamic adjustment model. In addition, both graduates (>4.00 points) and employers (>3.67 points) gave a high degree of achievement to the training quality of the university’s top innovative talents in new engineering disciplines. This study helps to meet the demand for high-quality engineering talents for regional economic and social development, enhance the adaptability of higher education and improve the quality of talent cultivation.

Ping Zheng 1, Qinghua Xiao 1, Wei Xiong 1, Ying Yang 1
1Department of Natural Resources, Hunan Vocational College of Engineering, Changsha, Hunan, 410151, China
Abstract:

The current gemstone jewelry design is not specialized and systematic enough, and most of the gemstone jewelry styles are common and single, which makes the value of gemstone not reasonably reflected. Starting from the evolution of gemstone facets, facet texture and cut classification, the article takes the three-dimensional features of gemstone facets as the basis and optimizes the cut parameters of gemstone facets by combining with geometric transformation theory. The optimized cutting parameters were used as the basis for 3D modeling, and the 3D model of the round faceted gemstone facets was established with the simulation software, and the quantitative analysis of the data was carried out through the brightness, uniformity, and scintillation values. When the table width ratio is 50-60%, the difference between the crown angle and the inclination angle of the star facet corresponding to the circular faceted gemstone facets varies from 9.172° to 20.673°. When the length ratio of the lower girdle facets is 65% to 95%, respectively, the range of the difference between the lower girdle facet inclination and the pavilion angle is between 0.781° and 1.967°. The scintillation value obtained after designing the gem facets using the method of this paper is 4.67 times higher than that of the traditional method. The optimized design model of round faceted faceted gemstones constructed on the basis of geometric transformation theory can provide new ideas for the jewelry design of round faceted faceted gemstones.

Jia Wang1
1Science and Technology Division, Open University of Yunnan, Kunming, Yunnan, 650500, China
Abstract:

In the context of continuous innovation in science and technology, consumer demand is becoming increasingly diversified, especially in product packaging design, personalization and uniqueness have become a new pursuit. The article proposes a computer image fusion DPformer-GAN model based on Transformer model and GAN, which is used to realize personalized packaging image fusion and generation. The digital image is then converted into a personalized packaging object through digital printing technology, which then realizes the innovative practice of personalized packaging.The DPformer-GAN model reduces the overlap and occlusion by 12.81% and 2.98%, respectively, compared with the better-performing CGL-GAN model when fusing and generating personalized packaging images. When personalized packaging images fused with computer images are used for digital printing proofing, keeping the percentage of dots lower than 50% can achieve the maximum degree of color reproduction and better retain the visual effect of digital images. Consumers are more favorable to the aesthetics and user experience of personalized packaging, with a favorability rating of 9.16 points and 9.29 points, respectively. It is feasible to use digital printing technology to print the packaging images generated by computer image fusion into personalized packaging, which can also further enrich the image practice options of personalized packaging.

Qiaolan Yuan 1
1Zhengzhou Academy of Fine Arts, Zhengzhou, Henan, 450000, China
Abstract:

In the field of modern architectural design, the application of artificial intelligence technology and pictorial interaction design is gradually causing revolutionary changes. This paper explores how to integrate these advanced technologies into architectural space design with a view to improving design efficiency and enhancing user experience. Artificial Intelligence Generated Content (AIGC) technology and BIM+VR technology are applied to architectural space design in general, where BIM+VR technology is used for visual modeling of architectural space design solutions and realizing 3D image interaction with users. Specifically, this paper proposes an intelligent assisted design method for architectural space based on Pointnet++ deep learning neural network and a simulation design method for 3D virtual architectural space to realize intelligent and personalized design of architectural space.The average class accuracy and overall assessment accuracy of Pointnet++ trained assessment points reached 83.47% and 76.63% respectively The design scheme given by this model has intelligence, objectivity and authenticity, which can better realize the intelligent assistance for architectural space design. In addition, the 3D virtual architectural space experience system constructed in this paper scores more than 90 points in all experience indicators, with good user experience performance, to meet the user’s image interaction needs, so as to provide a basis for the optimization of architectural space design.

Peiling Quan 1, Tianyue He 2, Yinzhi Yu 3
1School of Accounting, Anhui University of Finance and Economics, Bengbu, Anhui, 233030, China
2School of Economics, Anhui University of Finance and Economics, Bengbu, Anhui, 233030, China
3School of Business Administration, Anhui University of Finance a nd Economics, Bengbu, Anhui, 233030, China
Abstract:

This paper proposes an accounting statement evaluation model based on hierarchical analysis algorithm (AHP)-fuzzy comprehensive evaluation (FCE) under the theory of combinatorial mathematics. The initial evaluation index system is determined based on the principles of evaluation index system construction, and after the Delphi method screening, the final accounting statement evaluation index system is composed of 14 secondary indicators and 5 primary indicators. Using hierarchical analysis algorithm to calculate the weights of the indicators, and substituting the calculated weights into the comprehensive fuzzy matrix to finalize the task of evaluation and analysis of accounting statements. The first-level indicators are Solvency A2 (0.1680) < Profitability A1 (0.1797) < Operating Capacity A4 (0.1971) < Cash Capacity A5 (0.2093) < Development Capacity A3 (0.2459), while the weights of the second-level indicators are distributed in the range of [0.0174, 0.2079]. The comprehensive evaluation score of the accounting statement of X Breweries Group Company is 73.31, indicating that the overall condition of the company's accounting statements is good.

Qian Qiao 1, Yu Wang 1
1Department of Electrical Engineering, Shanxi Engineering Vocational College, Taiyuan, Shanxi, 030009, China
Abstract:

In this paper, the CD4511 chip is selected as the focus of this research to build the LK8820 platform, which mainly consists of the power supply, interface and reference voltage board (IV), power supply and measurement board (PM), digital function pin board (PE), and analog function board (WM). The input and output pins of the CD4511 IC chip are connected to the PIN pins on the PE board of the LK8820 test platform for testing, and the test functions are written in the C language environment. After the test program is written, the LK8820 test platform is used to test the CD4511 integrated circuit chip for the environmental adaptability of electrical parameters. The use of highly integrated chip CD4511 makes the small range of measurement accuracy is very high, but the large current range error is relatively large, due to the external large current range using precision resistors with an accuracy of 0.5% in parallel, after calibration, the error is controlled within the allowable range. 6 input pins of the input high level test results and the input low level test results are in the range of RMS, the number of anomalies is 0, which meets the IC electrical parameters environmental adaptability test. The test results of input high level and input low level of 6 input pins are all within the RMS value range, and the number of abnormalities is 0, which meets the requirement of environmental adaptability test of integrated circuits.

Junliang Hou 1
1Geely College, Chengdu, Sichuan, 610000, China
Abstract:

Based on the overall demand analysis of intelligent class scheduling system, this paper determines the overall structural design scheme of intelligent class scheduling system, and realizes the intelligent class scheduling system using software development language. Aiming at the problems of overfitting and easy to fall into the local optimum of the benchmark genetic algorithm, the adaptive genetic algorithm optimization in the intelligent scheduling system is realized through the nonlinearization of the fitness function, the crossover operator, and the variational operator. Determine the experimental environment and set up groups (experimental group and control group) to evaluate the optimization performance of the algorithm and the application effect of the system. The program based on Improved Adaptive Genetic Algorithm (IAGA) (class time distribution balance: 0.79) is 0.23 higher than the program based on Adaptive Genetic Algorithm (AGA) (class time distribution balance: 0.56) in terms of class time distribution balance, and IAGA algorithm is more effective and superior in solving the problem of class scheduling in colleges and universities as compared to AGA algorithm. This system can reduce the heavy workload of teaching affairs, and also solve the scheduling difficulties of colleges and universities in the case of teacher shortage.

Yingying Sun 1, Zhimin Li 2, Yanyan Liu 1
1Dongfang Electronics Co., Ltd., Yantai, Shandong, 264000, China
2Ibatterycloud Co., Ltd., Yantai, Shandong, 264000, China
Abstract:

In order to realize the strategic goal of environmental protection and low carbon, designing a set of resource clustering and regulation strategies that take into account energy saving and operating costs has become a research challenge for virtual power plants. In this study, the ICEEMDAN-CNN-SSDAE hybrid model is used to realize high-precision prediction of electricity price and load data in virtual power plants. The objective function and constraints of resource clustering and cooperative regulation of virtual power plants are established under the condition of demand response, and solved by Markov process. Finally, the virtual power plant resource clustering and co-regulation model is constructed on the basis of the deep reinforcement learning model framework by combining the prediction model and objective function. The results show that the ICEEMDAN-CNN-SSDAE model proposed in this paper can guarantee high prediction speed (0.062s and 0.059s) while having high prediction accuracy. It is also found that the average capacity of the output power of each component in the virtual power plant system after the model clusters and optimizes the regulation of a virtual power plant resources increases by 0.535-0.686 MW/h compared with the pre-optimization period, and the economic efficiency and energy utilization are also improved to different degrees. The research in this paper verifies the rationality and effectiveness of the proposed model, and provides certain theoretical basis and guidance method for virtual power plant resource clustering and cooperative scheduling.

Cheng Tang1
1School of Humanities, Hunan City University, Yiyang, Hunan, 413000, China
Abstract:

Under the background of mediatized society, the fusion of reality and reality between the real world and cyberspace has made the role of social network public opinion more and more significant, and the occurrence of any major emergencies will trigger network public opinion. In this paper, the TF-IDF algorithm is used to extract the feature items of social media opinion data, synthesize them into text vectors and input them into the LDA topic model to mine the opinion topic words, and then combine the co-occurrence of the key topic words to draw the semantic maps of the opinion topic words on the web, so as to explore the dynamic evolution of the opinion topic words. The opinion text vectors are then used as inputs to extract the local features of the opinion text through CNN model, combine with BiLSTM model to obtain the global features and temporal information of the opinion text, and realize the dynamic prediction of opinion sentiment through SoftMax classifier. Taking the Xin Guan epidemic event as an example, and divided into three phases: latent period, outbreak period and recession period, the number of public opinion comments on microblog platform during the outbreak period can reach 1942.59 comments/day, and the evolution of public opinion topic words in different public opinion phases are dominated by themes such as “epidemic”, “pneumonia” and so on. When the CNN-BiLSTM model is used to predict the public opinion sentiment dynamics, the prediction accuracy is between 95.84% and 97.56%. Through the effective use of deep learning technology, it can clarify the orientation of public opinion development driven by social media data and provide reliable data support for social media public opinion guidance.

Siyu Li1, Wendi Duan1, Lifeng Yang2, Zhenyi Li3, Huan Liao4
1Education School, University of Glasgow, Glasgow, G128QQ, United Kingdom
2Hongyun Honghe Group Kunming Cigarette Factory , Kunming, 650231, Yunnan, China
3Education school, University of Nakhon Phanom, Nakhon Phanom , 48000, Thailand
4School of Foreign Languages, Wuyi University, Jiangmen, 529020, Guangdong, China
Abstract:

With the wide application of deep learning in the field of education, student emotion perception has become one of the research hotspots. The study recognizes learners’ facial expressions by face detection algorithm after collecting learners’ data and preprocessing. Algorithms such as convolutional neural network and ConvLSTM are used to recognize learners’ emotions, and learners’ emotions are constructed to be modeled. Evaluate the learner emotion performance of this paper’s model and compare it with other emotion recognition models. The model of this paper is used for practical research to collect students’ emotions in six classes, and statistics and analysis are performed. Finally, by studying the relationship between students’ emotions and behaviors, targeted suggestions for improving students’ behaviors are proposed. The accuracy of this paper’s model in recognizing student emotions on the RAF-DB dataset and classroom dataset is 90.32% and 97.65%, respectively, which is much higher than that of other pre-trained models. The recognition accuracy of this paper’s model for eight types of student emotions is between [0.93, 0.98]. In the statistics of classroom students’ emotions, the main emotions of students in session 1 were concentration, in session 2 were surprise and concentration, in sessions 3, 4, and 6 were surprise and delight, and in session 5 were concentration and disappointment. Focus was significantly positively correlated with “serious attendance”, “thinking”, “answering questions”, “discussing” and “doing tests”, tiredness was significantly positively correlated with “answering questions”, “reviewing” and “deserting”, boredom was significantly positively correlated with “answering questions”, “doing quizzes”, “reviewing” and “desertion”, doubts were significantly positively correlated with “discussing”, “doing quizzes” and “reviewing”, distraction was significantly positively correlated with “reviewing” and “desertion”, happiness was significantly positively correlated with “discussion”, and disappointment was significantly positively correlated with “desertion”.

Fan Wang1
1Shijiazhuang College of Applied Technology, Shijiazhuang, Hebei, 050080, China
Abstract:

Based on the realization path of new quality productivity on industrial transformation and upgrading as well as talent supply, this paper carries out theoretical analysis in three directions, namely, direct effect, indirect effect and non-linear characteristics, and puts forward relevant research hypotheses. The panel data benchmark regression model, mediation effect model and threshold regression model are constructed respectively to verify the proposed hypotheses. The panel entropy method is used to measure the level of new quality productivity and carry out related research on the driving of new quality productivity. The partial differential decomposition of the spatial Durbin model shows that the direct and indirect effects of the new quality productivity are significantly positive at the 5% and 1% levels, respectively, indicating that the new quality productivity can promote the upgrading of industrial structure in the theoretical provinces. Introducing the variable of technological innovation for the mediation effect test, by observing columns (1) and (2), the regression coefficients of new quality productivity on technological innovation and new quality productivity on industrial structure upgrading are both positive at 0.2484 and 0.2048, respectively, which indicates that regional technology plays a mediating effect in the influence of new quality productivity on industrial structure upgrading.

Wenliang Ji1,2, Ming Jin3, Yixin Chen4
1Institute of Journalism, Communication University of China, Beijing, 100024, China
2Institute of Military Management, National Defense University, PLA, Beijing, 100091, China
3Institute of Humanities and Law, Yanshan University of China, Qinhuangdao, Hebei, 066004, China
4Institute of Journalism and Communication, Henan Academy of Social Sciences, Zhengzhou, Henan, 450000, China
Abstract:

The combination of deep learning and digital media technology provides great scope for content creation. The article uses Generative Adversarial Network (GAN) in deep learning for content generation. Based on the three major forms of digital media content (image, audio, and video), image, audio, and video are generated by U-Net_GAN model, MAS-GAN model, and SSFLVGAN model, respectively, to construct a digital media content generation model based on generative adversarial networks. Subsequently, the model is validated for performance and the generated images, audio and video are evaluated for effectiveness. By studying the shortcomings of digital media content generation, we propose suggestions to improve its dissemination effect. The U-Net_GAN model outperforms other image generation models in all the indexes of generating images. The performance of speech generation and enhancement of MAS-GAN is much better than other audio generation and enhancement models. The average score of HDR video generated by SSFLVGAN is 4.20, and the average DMOS score is 5.97. The average DMOS score of SSFLVGAN is 5.97. DMOS score is 5.97, which are both 0.16 points higher than the traditional scheme. SSFLVGAN and the traditional scheme are comparable in terms of the picture impact of the generated video. The picture detail effect of the SSFLVGAN generated video is much better than the traditional scheme.

Peixian Sui 1, Xiangyang Bian 1, Jinying Mou 1
1College of Fashion and Design, Donghua University, Shanghai, 200051, China
Abstract:

Tang Dynasty costumes are regarded as a brilliant brushstroke in the history and culture of Chinese costumes, and the fate of the whole Tang Dynasty can be analyzed through the evolution of Tang Dynasty costumes. In this paper, we have constructed a dress semiotics system from the social level, psychological level and cultural trait level, through the transfer of the imagery dress structure to the real dress, to express its symbolic meaning, and applied the constructed system to the Tang dress symbols, to interpret the meaning of the Tang dress symbols from the two levels of society and culture. Using CiteSpace information visualization software, combined with the literature of “Tang Dynasty Costume”, the study explores the dynamic evolution law of Tang Dynasty costume culture. The results of the study show that the earliest year for the keyword “Tang Dynasty costumes” is 1985, and the frequency is as high as 68 times. The keywords with the highest degree of centrality are dress and Tang Dynasty culture, both of which are 0.38. A number of new keywords with strong salience emerged in 2013-2023, among which the ones with a sudden increase of intensity greater than 5 are artistic features, clothing styles, clothing colors, clothing shapes, sweater, structural design, cheongsam, and knitted fabrics, and therefore the future hotspot of the Tang Dynasty dress research shifts to these keywords.

Yiming Li1, Chenxi Ye2, Peiyi Wang3, Lingjian Yang4, Shengdong Zhou5,6
1School of Liberal Arts, Zhuhai College of Science and Technology, Zhuhai, Guangdong, 519000, China
2Faculty of Science, Hongkong Baptist University, Hongkong, 999077, China
3School of Culture and Creativity, Hongkong Baptist University, Hongkong, 999077, China
4Film and Television Media College, Nanchong Film Industry Vocational College, Nanchong, Sichuan, 637000, China
5Fujian Digital Media Economy Research Center, Fujian Social Science Research Base, Minjiang University, Fuzhou, Fujian, 350108, China
6Faculty of Decorative Arts, Silpakorn University, Bangkok, 10700, Thailand
Abstract:

In the era of digital imaging, the art of photography has undergone profound changes, in which the calculation method of temporal art and symbolic space has become the key to understanding this art form. This paper analyzes the temporal form of photographic art and designs the symbolic space in photographic art, using digital photography technology based on drone remote sensing combined with collage photography technology. And through a variety of calculation methods, the time consciousness and symbolic space of the creative works are quantitatively embodied. The resolution of the photographic works obtained by scanning drone photography and surround photography is 9.448 and 9.966mm respectively, and the error in plane and height is low. The use of collage technology to express different emotions is demonstrated by the audience’s recognition score of more than 4. Digital technology embodies the time consciousness and symbolic space of photography, “storytelling”, “composition and perspective”, “light and color”, with an average increase of 16.9%, 20.36%, and 13.06% respectively. The regression results show that “image capture and processing”, “post-processing”, “high resolution and color reproduction”, “autofocus”, “Digital Signal Processing” can all contribute to the time-conscious and symbolic spatial embodiment of photographic art at the 0.001 level.

Zhuo Li1
1School of Tourism Sciences, Beijing International Studies University, Beijing, 100024, China
Abstract:

Common wealth is the essential requirement of socialism, and it is also the goal that countless people have been relentlessly pursuing for thousands of years, and people have never ceased to earnestly aspire to and relentlessly pursue for equal enjoyment and common wealth. This paper studies the impact of coastal wetland tourism resource protection on the realization of common wealth. It analyzes the tourism resources of coastal wetland from the aspects of economic value and resource protection and utilization strategy. On this basis, a multilevel regression model is used to analyze the impact between the two.The tourism economy of each coastal wetland developed rapidly in 2023, which increased by 65%~97% compared with 2015, implying that the conservation and utilization of tourism resources can lead to economic growth and promote economic development. In the multilevel model, resource protection strategy expenditure (0.070), ecological condition (0.265), strategy realization channel (0.053) and institutional trust (0.166) all show significant effects on the level of common wealth. While the regional level variables GDP per capita, provincial ecological level, and tourism resource utilization have unstable effects on the common wealth of urban residents. Based on the multilevel regression model, this study investigates the influence mechanism of tourism resources protection and utilization strategy on common wealth, which provides a basis for the full development of the positive effect of coastal wetland tourism resources protection and utilization strategy on the realization of common wealth.

Dongling Zhou1
1School of Preschool and Art Education in Zhoukou Vocational and Technical College, Zhoukou, Henan, 466001, China
Abstract:

Higher vocational colleges and universities should realize the optimal allocation of teaching resources to provide the necessary guarantee for the improvement of talent cultivation quality. The study puts forward the evaluation index system of teaching resource allocation for teaching resource allocation in higher vocational education, constructs the multi-objective allocation optimization model of teaching resources on this basis, determines the index weights by using the objective combination assignment method combining the principal component analysis method and entropy weight method, and applies NSGA-II algorithm to solve the model. Simulation analysis is carried out with several higher vocational colleges and universities in a city as an example, and the allocation optimization results of multiple teaching resources in higher vocational colleges and universities are obtained. After the optimization of resource allocation, the utilization efficiency and allocation efficiency of teaching resources in each college and university as a whole have been improved by 16.6% and 3.4%, respectively, and all of them tend to be in the state of equilibrium of allocation. The constructed teaching resource allocation optimization model can realize the optimization of teaching resource allocation and promote the reasonable allocation and utilization efficiency of teaching resources in higher vocational education.

Fengfei Su1, Zhen Xu1, Yiqing Shi2, Gang Chen1, Lei Lei3,4, Qingyun Cheng3,4
1State Grid Weinan Power Supply Company, Weinan, Shaanxi, 714000, China
2State Grid Shaanxi Electric Power Co., Ltd., Xi’an, Shaanxi, 710000, China
3State Grid (Xi’an) Environmental Technology Center Co., Ltd., Xi’an, Shaanxi, 710000, China
4Electric Power Research Institute of State Grid Shaanxi Electric Power Co., Ltd., Xi’an, Shaanxi, 710000, China
Abstract:

Cables are widely used in power transmission, and the measurement of key dimensions of cables is an indispensable part of the cable preparation process to help ensure their quality. In this article, a handheld laser 3D scanner is used to collect 3D point cloud data of cable dimensions, and the point cloud is denoised by bilateral filtering algorithm and combined with the direct method of coarse alignment and the ICP method of fine alignment to realize the alignment of 3D point cloud data of cables. Then, the cable diameter coordinates are obtained by fitting the cylindrical surface of the cable size to realize the calculation of the cable diameter, and a residual network-based edge detection model of the cable insulation layer is proposed to improve the feature extraction capability of the cable 3D point cloud data through the hollow convolutional residuals and the spatial attention mechanism. For the effectiveness of the above method, the cable 3D point cloud data is quantitatively verified. The average accuracy of the cable diameter calculation based on cylindrical surface fitting is 0.025 m. The AP value of the cable insulation layer edge detection model constructed based on residual network is 0.816, and the error range of the calculation results is between -1.46% and 1.44% when the cable insulation layer thickness is calculated based on the cable insulation layer edge detection results. Learning and analyzing dimensional features of cable 3D point cloud data by deep learning training model can significantly improve the measurement accuracy and measurement efficiency of cable dimensional features, which can provide a guarantee for improving the safety of cable operation.

Zihan Dong1, Wenchao Ding1, Hong Wang2, Wangqiang Wu1, Lei Lei2,3, Liang Wang2,3
1State Grid Weinan Power Supply Company, Weinan, Shaanxi, 714000, China
2State Grid (Xi’an) Environmental Technology Center Co., Ltd., Xi’an, Shaanxi, 710000, China
3Electric Power Research Institute of State Grid Shaanxi Electric Power Co., Ltd., Xi’an, Shaanxi, 710000, China
Abstract:

In the process of airborne LiDAR point cloud cable line extraction, there are problems such as complex shape of the pole tower and high noise influence, which lead to low accuracy of cable line point cloud extraction. This paper proposes a cable line point cloud extraction and reconstruction method based on point cloud chunking processing, improved multidimensional filtering, and density clustering algorithm. Firstly, the point cloud filtering data processing technology, and its three key techniques of streamlining, filtering, and alignment in point cloud data preprocessing are introduced. Secondly, the overall point cloud is processed in chunks according to the direction of power lines. Then, on the basis of surface fitting algorithm, the idea of grid division is introduced to propose an improved multidimensional filtering algorithm with point cloud filtering. Finally, the cable line point cloud is accurately extracted by the given adaptive density clustering solution, and the method of this paper is tested and evaluated for accuracy based on the measured point cloud data. The results show that: using the algorithm to extract the cable line points of the integrated integrity rate of 95.9796% or more, a time can be realized in the successful extraction of the power line, in order to ensure the accuracy of the extraction at the same time to improve the extraction efficiency, the research in this paper can be for the intelligent inspection of the cable line to provide a good value of engineering applications.

Dandan Wang1
1Guangxi Transport Vocational And Technical College, Nanning, Guangxi, 530000, China
Abstract:

Cross-border e-commerce refers to a kind of international commercial activity in which trading entities belonging to different customs borders reach transactions, make payments and settlements through e-commerce platforms, and deliver commodities through cross-border logistics to complete the transactions. Based on the relevant components of cross-border logistics cost and risk, this paper applies Markowitz, Capital Asset Pricing Model (CAPM), Value at Risk Model (VaRM), and Creditmetrics model to measure the risk of cross-border logistics, respectively. Through the cost measurement of cross-border logistics losses, a simplified logistics risk cost minimization model is derived. The model is applied to Guangxi’s cross-border logistics company M. Monte Carlo simulation is used to estimate the risk and cost of its cross-border logistics, respectively, and the probability of IRR>13.246% is simulated to be 32.963%, which indicates that the probability of cross-border logistics results exceeding the IRR of 13.246% given in the economic analysis is 32.963%. It can be seen in the logistics cost estimation that the mean value of the monthly logistics cost estimation of Company M is 2764000.564 yuan, and the standard deviation is 15126.36321 yuan, and after 3000 simulation operations, the logistics cost estimation has a 95% probability of falling on the interval [2572000 yuan, 2964000 yuan]. In response to the results of the simulation operations, a logistics risk and cost control strategy is proposed that is consistent with the long-term development of M Company.

Man Liu1
1Hunan Vocational College of Science and Technology Art and Design College (Xiangci College), Changsha, Hunan, 410000, China
Abstract:

Starting from the concept of digital media art, virtual reality technology is used to complete the design of the virtual simulation environment, and the interaction function of the virtual simulation environment is perfected through relevant development software. With the support of artificial intelligence technology, we propose a strategy to improve the visual expression of digital media art based on support vector machines, and we also design a detailed implementation process. The subjects of this study were selected to evaluate the design of the virtual simulation environment and the visual expressiveness enhancement strategy using the scale test method. The experimental group was significant in the dimensions of integration (P=0.005, T=1.553), immersion (P=0.007, T=2.693), interactivity (P=0.001, T=0.867), and virtuality (P=0.002, T=3.581) before and after the intervention, and it was concluded that Support Vector Machines have an enhancement effect on the visual expressiveness of the creation of digital media art.

Sumei Shi1, Zhenye Xing2
1School of Intelligent Science and Engineering, Xi’an Peihua University, Xi’an, Shaanxi, 710100, China
2Design Department, Shanxi Huarui Investigation and Design Limited Company, Xi’an, Shaanxi, 710100, China
Abstract:

Under the rapid development of China’s infrastructure, concrete materials are widely used. Traditional concrete materials have defects such as poor compressive performance, and steel fibre concrete has a broad engineering application prospect. In this study, the compressive performance of steel fibre recycled concrete was analysed and tested using experiments such as cubic compression test and split tensile compression test. Subsequently, the compressive strength prediction model of steel fibre recycled concrete is constructed by combining the experimental test results and the uniaxial compression constitutive model, and the prediction effect of the model is analysed. The results show that when the volume rate of steel fibre admixture in recycled concrete is 1.2%, the compressive strength of recycled concrete is the highest under different loading conditions, indicating that the admixture of steel fibre can improve the compressive performance of recycled concrete. It was also found that the prediction error of the prediction model for the compressive strength of concrete under standard curing conditions and low-pressure conditions averaged 1.49% and 1.19%, which has good prediction effect. The compressive strength prediction model proposed in this paper can achieve reliable prediction of the compressive properties of steel fibre recycled concrete, which lays a foundation for the reasonable use of recycled concrete materials under different conditions in infrastructure projects.

Ze Wang1
1School of Inner Mongolia University, Economics and Management, Hohhot, Inner Mongolia Autonomous Region, 010000, China
Abstract:

Accelerating the formation of new-quality productivity is a must for transforming the mode of economic growth and realizing high-quality development. Taking the new quality productivity as the research perspective, this study selects the panel data of 30 provincial-level administrative regions in China from 2013 to 2023 as the research object, combines the regression analysis method to empirically analyze the influence paths between high-performance computing, the new quality productivity, and the economic policy innovation, and explores the role mechanism of the new quality productivity through the mediation effect analysis. The results show that the regression coefficients of high-performance computing on new quality productivity and economic policy innovation, as well as on new quality productivity and economic policy innovation, are all greater than 0 (p < 0.01), i.e., high-performance computing accelerates the formation of new quality productivity and further promotes the development of economic policy innovation. In the economically developed eastern region, this path of action is significant at the 1% level, and its driving effect of HPC accelerating new quality productivity on economic policy innovation is stronger, compared to the central, western and northeastern regions, which are significant at the 5% level. The article's analysis advances the understanding of the drivers of new quality productivity development and the effects, mechanisms, and regional differences of high-performance computing-enabled new quality productivity and economic policy innovation.

Yangtian Yan1, Jianguo Huang1, Shiguang Xu2, Ran Tao1
1College of Land and Resources Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650000, China
2Yunnan Geology and Mineral Engineering Survey Group Company, Kunming, Yunnan, 650000, China
Abstract:

The stability of slopes is related to many factors, among which rainfall and water table fluctuation are the most common natural factors leading to slope damage. Based on a non-homogeneous slope, numerical simulation analysis using FLAC3D software and intensity discounting method was conducted in the article to discuss the stability of the slope under different morphologies, to explore the influence of diving surface height and pit water level line on the slope stability, to put forward the support scheme and to carry out the effect test. The analysis shows that the increase of slope gradient, step length and slope height negatively affects the slope stability, among which the effect of slope height is the most significant, and the stability coefficient decreases by 49.61% when the slope height increases by 60m under the action of groundwater. The height of diving surface and pit water level line are both inversely proportional to the slope stability, and the decrease of slope stability produced by the increase of both is 10.53% and 42.06%, respectively, and the latter’s influence on the slope safety coefficient is much larger than the former. In addition, the comprehensive landslide prevention and control program, which adopts the construction of drainage facilities, the selection of drainage scheme and the strengthening of support at the rock stratum interface area, effectively improves the safety coefficient of the slope.

Qian Lian1
1Department of Dance, Taiyuan Normal University, Taiyuan, Shanxi, 030000, China
Abstract:

Dance drama performance is an important part of human civilization, which runs through the long history of human development. With the continuous improvement of people’s spiritual and cultural needs, the pursuit of dance theater performance has become more and more intense. In this paper, we start from the basic knowledge of fractal geometry, such as the theory, concept, dimension, and generation mechanism of fractal, to launch the research on the optimization of the spatial layout of dance drama performance. The main work of this paper involves the following aspects: (1) It focuses on the definition and characteristics of fractal geometry, and deeply studies the application of fractal geometry in the optimization of the spatial layout of dance drama performances. (2) The demand for dance drama performance space optimization is analyzed in terms of developmental changes and audience demand. It can be seen that the footprint of the dance performance continues to increase, but the use of space is gradually decreasing, and the audience has higher expectations for the stage performance, the demand for the development of the dance performance and the audience demand to promote the process of optimization of the space layout of the dance performance. (3) This paper adopts the left-right symmetrical presentation, fully considers the visual effect of the viewers, and constructs the optimized layout of dance drama performance space based on fractal geometry. (4) The optimized layout method of this paper is applied to the actual evaluation of the effect, in which the two groups A and B, located in the main audience area, have higher overall ratings, respectively 5.78 and 6.04, and the main audience area has a better perspective experience, so the overall experience is relatively better. The score of the viewer experience after optimization of the dance drama performance space layout in this paper is 5.715 points, which indicates that the viewers are satisfied with the effect of optimization of the layout in this paper, and the better results of the optimization of the dance drama performance space layout in this paper provide some reference for the dance drama performance space layout.

Qian Lian1
1Department of Dance, Taiyuan Normal University, Taiyuan, Shanxi, 030000, China
Abstract:

Dance is interpreted through human body movements, dance movements can express the thoughts and emotions of dancers, and whether the dance movements are standardized or not in the creation of dance drama determines the quality of the creation of dance drama. In this paper, with the support of artificial intelligence and information technology, based on image recognition technology, we carry out the optimization research of dance movement recognition for dance drama creation. In this study, the principle and process of image recognition technology are first studied in depth, and then the motion detection method for dance movements is analyzed considering the static state of the background of the dance drama. On this basis, the recognition optimization of dance movements is completed based on the deep convolutional embedding attention mechanism, and the evaluation method based on recognition optimization is proposed for the creation of dance drama. The embedding method in this paper improves 12% over the baseline method, with an OA of 98.65%, while the amount of participation and FLOPs increase slightly. And the score1 and score2 of this paper’s method are the highest, which indicates that this method obtains a high model accuracy while sacrificing less number of model parameters and computational complexity. In addition, the network model structure of this paper is more efficient compared to other network model results. In the recognition effect analysis, the correct recognition rate of six standard dance movements such as center of gravity transition, time step, square step, lock step, fixed step and others are above 80%, with high recognition accuracy and excellent model performance.

Di Wu1
1Public Foreign Language Teaching and Research Department, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, 150040, China
Abstract:

The article evaluates and predicts the effectiveness of medical English teaching through stepwise regression analysis in multivariate analysis method. It constructs an analytical prediction model of medical English teaching evaluation based on multivariate regression analysis. After initially establishing the evaluation index system of medical English teaching, effective evaluation indexes are screened out through stepwise regression, an effective evaluation index system of medical English teaching is constructed, and multiple regression equations for the quality of medical English teaching (students’ performance in medical English) are established. The prediction model of medical English teaching quality is constructed by eliminating the influential factors and abnormal data that do not have significance through multiple linear regression analysis. Teaching quality prediction equations were constructed by choosing teaching content, teaching method, teaching organization, teaching expression, teaching attitude, and overall effect of teaching. Among them, teaching content and teaching expression were significant, and the final prediction model of medical English teaching quality was Y=0.1958+0.1142*teaching content+0.7232* teaching expression. The 79.26% of students’ medical English performance can be explained by the multivariate linear regression analysis model.

Yuhua Yan1, Yongkang Shi1, Ning Wang1
1College of Mechanical Engineering, Xinjian University, Urumqi, Xinjian, 830000, China
Abstract:

VTOL uavs combine the advantages of VTOL capability of multi-rotor uavs and efficient fixed-wing cruise, but they also face challenges in performance, including weak wind resistance when hovering, low flight mode conversion efficiency and high-altitude fluctuation during conversion. In view of this, this paper introduces a new type of composite wing UAV, namely lifting wing quadrotor. Compared with quadrotor UAV, it is unique in that it is equipped with a lifting wing installed at a special Angle, which effectively improves the range and load, and solves the problem of weak wind resistance in the hovering stage of tail-seat UAV, and can realize efficient transition flight. A longitudinal position controller based on TECS total energy control algorithm is designed according to the flight characteristics of the transition stage. The effectiveness of the dynamic model and controller design is verified by experiments. The results show that the control algorithm can effectively improve the flight stability of the lift-wing quadrotor during the transition stage.

Qian Liu1
1Department of Music, Sichuan University of Science and Engineering, Zigong Sichuan, 643000, China
Abstract:

In this paper, the entire chord progression is added to the generation process through a bidirectional LSTM model, and the Skip-connection method is used to accelerate the convergence speed in all recursive layers except the first one. Different musical emotions are classified based on the Hevner emotion model, and features such as pitch, duration, and tempo of musical emotions are parameterized. The forward neural network is used to construct the music emotion classification model, and the gradient descent learning algorithm is used to algorithmically control the forward neural network model. At the same time, this paper explains the significant enhancement of college students’ self-identity by music aesthetic education based on neural network model from two perspectives: theoretical research and empirical analysis. The results show that the music generation and music emotion classification models constructed based on the neural network algorithm in this paper show good performance in the experiments. After applying the neural network model containing music generation and music emotion classification to music aesthetic education and counseling college students on self-identity, the mean score of self-identity scale of students in the experimental group increased from 50.83 to 88.56, with an improvement of 75.78%, and the results were significant at the 1% level. The effectiveness of this paper’s method in enhancing college students’ self-identity is fully demonstrated.

Yanyan Wang1, Chao Fan1, Wenzheng Zhang1
1Information and Communication Branch of State Grid Zhejiang Electric Power Company, Hangzhou, Zhejiang, 310000, China
Abstract:

Robotic process automation (RPA) technology along with the rapid development of information technology is increasingly widely used in various industries. This paper mainly explores its application in the field of electric power, and utilizes RPA technology to improve the quality and efficiency of power marketing audit. In order to solve the data anomaly problem in the process of power marketing audit, this paper adopts K-means algorithm to cluster the anomalous data, and combines with the correlation calculation to realize the identification and monitoring of the anomalous data of power marketing audit. Applying RPA technology to intelligent power marketing audit, we learn the normal pattern of data by training the self-encoder network, and correct or reject the abnormal data monitored. Reinforcement learning is used to optimize the audit strategy of RPA technology, and the efficiency of the audit is improved by maximizing the cumulative rewards. The application of RPA technology significantly improves the efficiency and accuracy of the overdue prediction and the work order generation and dispatching in the electric power marketing audit, in which the average working time of the overdue prediction work is reduced by 94.92% after the application of RPA technology, the average accuracy is improved by 21.80%, and the average working time of the work order generation and dispatching process is reduced by 21.80%, and the average working time of the work order generation and dispatching process is improved by 21.80%. The average working time of work order generation and dispatching process is reduced by 97.99% and the average accuracy rate is increased by 14.54%. The application of RPA technology effectively improves the efficiency and quality of power marketing audit.

Ping Zheng1, Qinghua Xiao1, Wei Xiong1, Ying Yang1
1Department of Natural Resources, Hunan Vocational College of Engineering, Changsha, Hunan, 410151, China
Abstract:

This paper describes the teaching problems and methods that need to be solved for the innovation of education mode from the needs of teaching conditions, teacher strength, school-enterprise cooperation, etc., and puts forward the “three lines and four passes” education mode based on situational teaching workshop for geology majors. The evaluation index system is constructed for the quality of “three lines and four passes” education model, and various colleges and universities in a certain province are selected for empirical analysis to analyze the quality of education for students in the province from three aspects. In order to determine the contribution size of the 10 secondary indicators in the education quality evaluation index system, the method of determining the weights of the indicators by using principal component analysis was used to calculate the education quality indexes of the public higher vocational colleges and universities and private higher vocational colleges and universities. The K-means cluster analysis method was performed on the basis of the hierarchical cluster analysis method, and seven tiers were divided. The analysis results show that professional education, facilities and equipment, teacher-student cooperation, group competitions, practical exercises, talent cultivation and vocational training have a greater weight of 5% in the evaluation index system of public and private institutions. In conclusion, it is concluded that the “three lines and four passes” education model based on master craftsmen workshop proposed in this paper has a better teaching effect on the quality of education.

Yuan Xue1, Lei Zhang1, Kun Gao1
1Zhangye Power Supply Company State Grid Gansu Power Supply Company, Zhangye, Gansu, 734000, China
Abstract:

The purpose of this paper is to explore the effectiveness of integrated energy electric energy substitution in agriculture in the environment of energy saving and emission reduction. The fuzzy clustering algorithm is used to divide different family clusters according to the energy saving and emission reduction ability based on the use of comprehensive energy in agricultural enterprises. Based on the exponential smoothing method and equivalent calorific value method, a prediction model of electric energy substitution efficiency was constructed. Combined with the ANP method, the evaluation indexes of agricultural electric energy substitution efficiency were weighted and graded. In this paper, 100 agricultural enterprises are divided into 5 clusters according to their energy saving and emission reduction ability, which are optimization level, managed level, development level, pressure-based level, and initial level. The model of this paper predicts that the consumption of terminal coal, oil, and electric energy of agricultural projects in 2027 is between 31663.68 and 7447.991.67 million tons of standard coal, and that the electric energy substitution in the same year can be up to 693,755.69 million tons of standard coal. The comprehensive scores of the first-level indicators of economic benefit, environmental benefit, and social benefit are 91.06, 91.26, and 92.01 in order, and the comprehensive efficiency grade is “Excellence”. To summarize the results, this study suggests increasing the investment of electric energy substitution equipment in agricultural production to promote the synergistic development of integrated energy system and electric energy substitution strategy.

Xinyan Chen1
1South China University of Technology Architectural Design & Research Institute CO., LTD., Guangzhou, Guangdong, 510640, China
Abstract:

The steady development of economy makes the number of high-rise buildings increasing, and the water supply and drainage system becomes an important part of high-rise building construction. The purpose of this paper is to explore the drainage efficiency and fire fighting efficiency of BIM and green new energy in water supply and drainage system. Revit Mep software is mainly used to design the fire fighting module in the drainage system of high-rise buildings by combining BIM technology and new energy. A simulation mathematical model of water supply and drainage efficiency (including fluid control equations, etc.) was designed, and simulation experiments were conducted on the fire treatment efficiency of green new energy fire protection technology. The investigators’ ratings of the evaluation indexes of the water supply and drainage system design of BIM synergistic green new energy fire protection technology ranged from 4.06 to 4.54, indicating the feasibility of the approach. When the building floor is 25 floors, the water supply and drainage efficiency of the system in this paper is 80.45% and 84.52%, respectively. The fire fighting simulation experiment shows that the green new energy fire fighting technology integrating BIM can reduce the temperature of the room to 200~500℃ in 5 minutes. In the experiment, the method can extinguish the fire quickly and the residual concentration of smoke after extinguishing can be reduced to the normal range.

Yalei Shang1
1School of Electronic Information Engineering, Hebi Polytechnic, Hebi, Henan, 458030, China
Abstract:

Deep reinforcement learning, as an advanced machine learning method, is capable of automatically learning optimal decision-making strategies in complex environments. The core objective of this paper is to apply deep reinforcement learning algorithms to SolidLab’s microcontroller programming in order to realize the intelligent control of the linear one-stage inverted pendulum system. The study takes the linear one-stage inverted pendulum produced by A Technology Company as the control object, and adopts the model-free control structure of the deep reinforcement learning algorithm to build the controller and conduct virtual simulation experiments. Comparing the experimental effects of LQR and DQN algorithms, the LQR algorithm is better than the DQN algorithm in stabilizing pendulum control of inverted pendulum. Accordingly, a balance controller based on the offline Q learning algorithm is further designed to realize the inverted pendulum stabilization in kind. After optimizing the design strategy, the inverted pendulum system can be rapidly stabilized within 0.9s when it is perturbed by a small angle of about 12°. It shows that the method in this paper can realize the intelligent control of the inverted pendulum system at the linear level.

Yujia Nie1
1Faculty of Economics and Management, College of Arts and Science, Hubei Normal University, Huangshi, Hubei, 435109, China
Abstract:

Fintech has not only greatly improved the operational efficiency of banks by introducing cutting-edge technologies such as big data, artificial intelligence, and blockchain, but also posed new challenges to banks’ risk management. This paper uses Monte Carlo simulation to explore the impact of fintech on banks’ operational efficiency and risk management. A VaR data model is used to analyze the impact of fintech on the operational efficiency of three types of commercial banks: big five banks, joint-stock banks, and city commercial banks. The non-performing loan ratio of China MS Bank is used as the empirical object for quantitative analysis of bank risk management. Monte Carlo simulation is used to realize the VaR calculation of banks’ NPL ratio. The empirical analysis finds that the impact of fintech on the operational efficiency of all three types of commercial banks is relatively significant, but there are differences in direction and lag period. Meanwhile, FinTech increases banks’ NPL ratio. It shows that fintech has a negative impact on bank risk management, for this reason, this paper develops relevant strategies to deal with the risk challenges brought by fintech according to bank types.

Wenjing Jiaof1
1English Department, HeGang Normal College, Hegang , Heilongjiang, 154107, China
Abstract:

The big data environment is dynamically changing, so the multi-objective optimization algorithm for the integration of English translation information technology needs to have dynamic adaptability. In this paper, we first construct a multi-objective learning parameter model for English translation information technology. Then a reference point-based environment unpredictable dynamic multi-objective optimization algorithm (UDERP) is proposed to realize the dynamic adaptability of the multi-objective optimization algorithm. Finally, the designed English translation information technology incorporating the UDERP algorithm is simulated and tested. The performance of UDERP algorithm, DNSGA-II algorithm and DSS algorithm are compared with each other using three test functions of FDA series. When the environment changes the optimal solution derived from the algorithm proposed in this paper is closer to the real Pareto solution. Comparing the neural machine translation based on cross-language pre-trained language model and the neural machine translation based on multi-coverage model, the English translation information technology designed in this paper has a better convergence effect and can realize more accurate parameter estimation.

Li Ma1
1Yunnan Yihui Architectural Design Co., Ltd., Kunming, Yunnan, 650200, China
Abstract:

The concept of urban green development promotes the development of intelligent technology as a new energy power, so that digital intelligent technology continues to enter into people’s vision, but also gradually accepted by the people. However, there is a lack of research on intelligent perception that focuses on residents’ attitudes, so this paper takes the theory of perceived value as the basis to analyze the influence path of intelligent perception of urban green space. Based on structural equation modeling, this paper explores the relationship between intelligent perception and residents’ attitudes in terms of perceived functional value, perceived emotional value, perceived social value, cognitive value and perceived risk. Then the intelligent perception prediction model for urban green space is constructed by using variational modal decomposition (VMD) combined with support vector regression (SVR), and the actual performance of this paper’s model is examined through experiments. This paper takes City Y as an example for prediction, and the results show that the intelligent perception of green space in City Y from 2023 to 2026 continues to show an upward trend. In addition, in order to prove the superiority of this paper’s model, its MAE, MAPE, RMSE and IA are compared with the prediction models of ARMA, BP, SVR and RF, respectively, and this paper’s model achieves the best results with the values of 4.2495, 15.8082, 3.5247 and 0.5225 for each index. In conclusion, the prediction model proposed in this paper has high accuracy in intelligent perception prediction.

Qiwei He 1
1College of Marxism, Ganzhou Polytechnic, Ganzhou, Jiangxi, 341000, China
Abstract:

The further deepening of education informatization has led to a significant shift in teaching methods as well as learning tools, and it is of research significance to explore how to use online learning platforms more effectively in non-traditional teaching environments. In this study, after pre-processing the online teaching data of Marxist theory in the Civics course, the Squeeze method is used to extract the relevant features of teaching interaction behavior in the data. Convolutional neural network is used to realize the prediction of teaching interaction behavior based on the input features, so as to realize the real-time intervention and effect enhancement strategy of teaching interaction. It is verified that the ICAM-ResNet neural network prediction model proposed in this paper has a good effect in making online teaching interactive behavior prediction, and the prediction accuracy can reach 0.816. After implementing the intervention strategy according to the prediction results, the average online learning time of students increased from 30.61 min (1 class period) to 44.54 min (16 class periods), and most of the students would actively answer the questions in the classroom, and the rate of answering correctly increased, so that the effect of teacher-student interactions was substantially improved. On the one hand, this study provides a new way of thinking for the teaching research of Marxist theory course, on the other hand, the results of the study are conducive to optimizing the teaching practice of the course and promoting the teaching interaction, so as to promote the development of the teaching of the course.

Weiwei Su1,2
1School of Nursing, Hebi Vocational and Technical College, Hebi, Henan, 458000, China
2Department of Fine Arts, International College, Krirk University, Bangkok, 10220, Thailand
Abstract:

As people’s demand for high-quality development of education becomes stronger and stronger, the field of education is paying more and more attention to the fundamental task of education by establishing moral values. In this paper, the improved genetic algorithm based on predation strategy is applied to the learning path recommendation system to realize the auxiliary teaching of Civics education. The study first proposes a Bayesian knowledge tracking model based on multiple interactions for knowledge tracking of students’ Civics and Political Science competence, and carries out model comparison and knowledge tracking visualization and analysis on three datasets and the real dataset of practice questions. Then according to the constructed learner model and knowledge connectivity state model, the personalized learning path construction model is designed by using learner features, knowledge point features, and generic learning paths as inputs, combined with the improved genetic algorithm based on predation strategy. The intelligent assisted teaching system designed in this paper is put into practice for Civics teaching and scored by questionnaire and paired t-test method. The results of the study said that the knowledge tracking model proposed in this paper compared with other models, the model in this paper improves the accuracy rate by 1%~2%. Using the non-elite individual set to enrich the population diversity to participate in genetic operation and iteration, the experiment shows that PSGA performs well in multiple comparisons with PSO and SGA methods, and can construct personalized learning paths more accurately, stably and effectively. The results of teaching practice show that the teaching system proposed in this paper can effectively improve students’ learning ability in Civics.

Qian Wang1, Zhaoqi Fang1, Xinchun Ye1
1Transportation Management School, Zhejiang Institute of Communications, Hangzhou, Zhejiang, 311112, China
Abstract:

Optimization problems usually involve multiple objectives, while fuzzy cognitive maps can effectively show the causal relationship between concepts, and the combination of the two can greatly advance the development of the education field. In this paper, we design a fuzzy cognition-based knowledge map for labor education courses and a multi-objective optimization model for labor education courses to optimize learners’ learning paths and recommend personalized exercises from multiple stages. Through teaching experiments and regression analysis, the teaching effect of the multi-objective optimization algorithm in labor education courses is evaluated. This paper borrows the k-means algorithm to classify learners into four clusters, and the algorithm provides learning path optimization for different clusters of learners in labor education courses. The exercise recommendation accuracy of this paper’s algorithm ranges from 0.91 to 0.97 and has better novelty and diversity recommendation performance. In the experimental class in the fuzzy cognitively oriented multi-objective optimization labor course, the learners’ labor scores improve faster and are about 3.8 points higher than those of the traditional teaching, and the regression results show that this paper’s model has a positive and positive effect on the teaching effect. The average satisfaction scores of this paper’s model in labor education courses for the friendliness of teaching aid, effectiveness of cognitive diagnosis method, usefulness of path optimization, and reasonableness of personalized recommendation of exercises are above 4.3, indicating that the model has practical application value in labor education courses.

Mengshan Lin 1, Xiangyuan Zeng2, Cheng Wang3
1School of Creative Design, Quanzhou University of Information Engineering, Quanzhou, Fujian, 362000, China
2School of Design, Fujian University of Technology, Fuzhou, Fujian, 350118, China
3School of Art and Design, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
Abstract:

Intangible cultural heritage is an important part of national culture, carrying rich historical information and cultural value. This paper mainly generates digital media art based on the characteristics of intangible cultural symbols through fractal geometry, and builds a digital media communication mode of intangible cultural art on the basis of dynamic texture, so as to realize the digital protection and inheritance of intangible cultural heritage. On the basis of Transformer network model, further combined with fractal geometry technology, inductive bias lacking in Transformer is introduced from the perspective of translation invariance and locality, and the dynamic texture generation method of digital media art combined with fractal geometry and Transformer model is formed in this paper. Experimental results show that the application of translation invariance and locality can increase the ODS, OIS and AP indexes by 17.17%, 27.72% and 25.38%, respectively. In the self-built Miao pattern data set, this method can generate dynamic texture features of Miao culture and art more completely and clearly. At the same time, this paper can create digital media art for Miao embroidery patterns through the generated results of this method, and improve the audience influence and satisfaction of intangible cultural heritage.

Na Yuan1
1College of Marxism, Xi’an Peihua University, Xi’an, Shaanxi, 710100, China A
Abstract:

This topic is based on the perspective of diagnostic evaluation, formative new evaluation, summative evaluation, in-depth analysis of deep learning to help the intelligent development of ideological and political education. The initially formulated questionnaire was modified several times, and the questionnaire design task was finally completed, and the formal distribution of questionnaires began to obtain the data for this study. At this level, the empirical research method combining quantitative research and qualitative research is used to deeply analyze the current situation of the intelligent development of ideological and political education in colleges and universities under the perspective of deep learning, with a view to contributing to the intelligent development of ideological and political education in colleges and universities. The mean value of the five dimensions of ideological and political education to stimulate the effectiveness of active learning (A1), promote the degree of in-depth understanding (A2), deepen the effect of interactive participation (A3), enhance the ability of higher-order thinking (A4), and expand the quality of the transfer of the use of the quality of the five dimensions (A5) is higher than 3.55 points, in addition to the significant difference in the characteristics of the samples (gender, academic qualifications, major, political profile), and puts forward five paths for the development of the five development paths, which are aimed at helping to promote the new era of the intelligent development of Civic and Political Education.

Xiaocen Kou1, Yange Zheng2, Baomin Wang1
1Law School, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China
2Marxism School, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China
Abstract:

The convergence of constitutional fundamental rights and administrative enforcement power should pursue multiple legal values, which requires that the operation of the power therein should be more division of labor than cooperation, and that constraints and synergies should be given equal importance. The article will construct the basic constitutional rights and administrative power into the constitutional construction of the subject and the subject of the executive branch, the introduction of the evolution of the game theory to construct the constitutional construction of the subject and the subject of the executive branch of the evolution of the game model, and the design of the game model of the gain function, the replication of dynamic equations and ESS equilibrium point. The initial value of each parameter in the evolutionary game model is set, and the evolutionary stable point of administrative power is simulated by MATLAB software, and the influence of the reward and punishment allocation coefficients on the evolutionary results of the system is explored. When the system evolution stable point strategy is (0,0) and (1,1), the two sides of the game tend to the stable equilibrium state of active cooperation, strengthened regulation and strict supervision. When the reward distribution coefficient and the punishment distribution coefficient gradually increase, the two sides in the evolutionary game system tends to stabilize the point (1,1) the faster the rate will be. In the process of constructing the fundamental rights of the constitution, combining internal and external with the administrative rights list monitoring mechanism can realize the optimal restriction on the application of administrative rights and promote the orderly and stable operation of administrative power.

Wenrui Xu 1
1Guangdong Polytechnic of Science and Technology, Dongguan, Guangdong, 523000, China
Abstract:

Driven by big data, e-commerce platforms have accumulated massive user behavior data, which can be transformed into valuable information after cleaning and feature selection. This study analyzes users’ historical behavioral data on e-commerce platforms, constructs a gradient boosting decision tree prediction model based on user, product, category, and two-by-two interaction behavioral features, and extracts designed feature data from raw CSV data based on Hiv as the prediction basis of the model. At the same time, clustering analysis is performed based on the user’s purchasing behavior (dwell time, browsing frequency) to generate user profiles. The experimental results show that after 7 days, the purchase conversion rate of browsing, collecting, adding to cart and purchasing tends to 0. Therefore, the time window for purchase behavior prediction is chosen to be 7 days. In this paper, the prediction model is only trained to 20 epochs, and the Loss value converges to about 0.14, which shows a good training effect. The model has the best classification performance for user purchase behavior prediction, with precision, recall, and F1 values between 0.91 and 0.97. The clustering algorithm divides the user purchase behavior into four clusters, where cluster class 4 has the best user value. In summary, using the gradient boosting decision tree model, e-commerce platforms can more accurately predict user purchasing behavior, thus improving user experience and platform economic benefits.

Linghui Kong1
1Discipline & Inspection Office, Tianjin Vocational University, Tianjin, 300200, China
Abstract:

The continuous development of data analysis technology in the era of big data provides new methods for the analysis of college students’ ideological dynamic data, and also provides new ideas for the scientific construction of ideological and political education disciplines in colleges and universities. In this paper, based on the word frequency analysis, set up the keyword context, collect the keywords of the ideological dynamics of party members and students, extract and examine the feature vectors in them, put all the keyword feature vectors, form the keyword feature vector set, use the method of keyword vector research, carry out the descriptive and differential analysis of the survey data of the ideological dynamics of the party members’ development in the management of colleges and universities, and construct the party members’ ideological dynamics management mechanism based on the existing problems , analyzing the effect of management decisions in colleges and universities. The kurtosis values of the four indicators of party members’ political thoughts, learning thoughts, innovative thoughts and consumption thoughts are -1.4685, -0.4496, -0.9871 and -1.5614 respectively, which are on the low side, indicating that the performance of the respondents is more unified and concentrated in these four indicators, and laying a feasible foundation for the subsequent relevant analysis. In campus adaptation, self-efficacy, and life satisfaction, the number of party students who performed agreeably, correctly, and positively were 116, 202, and 142, respectively, and after the reform of college management decisions, the students’ performance in these three aspects changed.

Na Zhang1
1College of Computer and Artificial Intelligence, Henan Finance University, Zhengzhou, Henan, 450046, China
Abstract:

Existing natural language generation models often face the problems of context loss and incoherent responses when dealing with multi-round dialogs. In this paper, a multi-round dialog system based on Transformer architecture is constructed, and an intention recognition algorithm is used to form a technical support for the construction of multi-round dialog system. And the attention adapter is introduced into the natural language generation module in the system, which utilizes contextual features to improve the performance of the natural language generation model. Semantic slot extraction experiments are carried out on the ATIS dataset, and the F1 values of the semantic slot extraction task and intention recognition task of the BERT multi-task natural language generation model with the addition of the attention mechanism are improved by 0.64% and 0.15%, respectively. The multi-round dialog system designed in this paper has a perplexity of 18.33, and the BLEU metrics are higher on orders 1-4 compared to other models. Manually evaluated in terms of syntactic semantic coherence, relevance, and information content, the system performs better. It shows that the natural language generation model incorporating the attention mechanism can effectively improve the application effect of the multi-round dialog system.

Fangfang Yu1, Leilei Chen2, Jiqin Wu1
1School of International Trade, Jiangxi Tourism and Commerce Vocational College, Nanchang, Jiangxi, 330100, China
2School of International Trade, Jiangxi Tourism and Commerce Vocational College, Nanchang, Jiangxi, 330100, China
Abstract:

As the material foundation of language, speech is the basis for mastering language skills and capturing language information, and English learning must begin with the correct mastery of spoken language. Therefore, spoken language teaching occupies a rather important position in English teaching. In this study, we extract various features such as time-domain features and frequency-domain features from English spoken audio signals, use fuzzy logic inference model to represent each audio feature mapping as an affiliation function, and then optimize the parameters of the affiliation function by using adaptive neuro-fuzzy inference system, and solve the affiliation function to get the result of speech matching by the center of gravity method. Subsequently, a speech evaluation system is designed based on the speech matching model to assist intelligent spoken language teaching. The results of teaching practice show that students in the experimental class using the voice assessment system as a learning aid are significantly better than the control class in terms of speaking skills and learning attitudes (P<0.05). Through real-time feedback and personalized practice, the voice assessment system enables students to correct pronunciation errors immediately and gradually improve their speaking fluency and accuracy. It can also improve students' self-efficacy and learning motivation. This study confirms the effectiveness of the fuzzy logic-based audio classification and speech matching model in improving students' spoken English proficiency and reveals its potential for wide application in future spoken English education.

Chenyue Hui1
1Shaanxi Police College, Xi’an, Shaanxi, 710021, China
Abstract:

The legal positioning of blockchain technology applied to evidence and its attributes are the basis for its evidence review and rule design. This paper starts from analyzing the evidence attributes of blockchain electronic data, combines relevant regulations and judicial interpretations, and clarifies the legal effect of blockchain electronic data. Combined with the judicial application of blockchain evidence at home and abroad, it points out the specialized review rules of blockchain evidence. Obtain the blockchain access evidence process, and propose the block file storage method based on RS code as well as the decryption outsourcing attribute-based encryption scheme with the same sub-policy to improve the CP-ABE encryption scheme. Explore the rules for blockchain deposits and clarify the rules and institutional value of blockchain deposits for admissibility. Analyze the theoretical and practical operational performance of the improved attribute-based encryption algorithm. Optimize the evidence storage capacity of blockchain, and analyze the performance of the blockchain technology scheme designed in this paper in the intelligent review of access evidence. In the forensic scenario run by the algorithm in this paper, the stored evidence data is reduced by 1417 characters, the transaction response time is shortened by 175.361ms on average, and the block size is reduced by about 4 times. It proves that the blockchain algorithm scheme proposed in this paper can shrink the cost of depositing evidence, reduce the time of depositing evidence, and improve the efficiency of depositing evidence in the public security forensic system.

Guannan Yang1
1Zhengzhou Academy of Fine Arts, Zhengzhou, Henan, 451450, China
Abstract:

The pattern design of clothing appearance is one of the important links in clothing design, which makes an important contribution to the overall aesthetics and sales of clothing. As a product of computer technology, the development and application of graphic processing technology has been extended to various industries and fields of society, especially in the field of design with more extensive use. However, the current clothing pattern design is still too dependent on the designer, so this paper is based on pattern processing, combined with fractal algorithm and genetic algorithm to build a pattern generation algorithm for clothing pattern. And the quality of the generated pattern is optimized based on the anti-alignment algorithm, so as to improve the overall quality of the generated pattern. After testing, the real-time generation speed of the pattern generation algorithms for clothing patterns in this paper is greater than 15FPS, and from the subjective and objective points of view, the generated patterns have good quality to meet the needs of use. After the anti-alignment optimization of this paper’s algorithm in different error intervals in the number of pixels accounted for the percentage of screen pixels are the highest, are more than 99%, to further validate the optimization effect of this paper’s method. Finally, in the evaluation of the use of the algorithm, the testers have a high degree of satisfaction with the dimensions of this paper’s algorithm, respectively, 4.04, 3.98, 4.21 and 4.11, which shows that this paper’s algorithm can satisfy the practical needs and can realize the intelligent generation of clothing pattern design.

Yueyue Song1
1School of Economics and Management, Urban and Rural Cultural Development Research Center, Guangzhou College of Applied Science and Technology, Guangzhou, Guangdong, 510000, China
Abstract:

In today’s rapid development of information technology and big data technology, consumer behavior is undergoing a profound transformation. This study focuses on the decision-making stage of consumer journey, selects indicators based on webpage click stream data, improves the K-means algorithm, and realizes the identification of consumer journey nodes using the binary K-means algorithm. Based on the review recommendation scenario, from the perspective of consumer decision-making journey, we introduce the “attention-attitude-understanding-purchase intention” stage-based decision-making model, apply it to the model design of deep learning, and combine the attention mechanism and co-attention mechanism to propose a product recommendation method based on online reviews. The results show that consumers in clusters 1-4 are in the consumer journey nodes of attention, understanding, attitude, and purchase intention, respectively. The product recommendation model exhibits better recommendation accuracy and time efficiency, with accuracy improved by 18.72%~67.12% and time reduced by 8.39%~62.03% over the comparison method. This paper realizes the innovation of deep learning method with the support of consumer behavior theory, and improves the methodological technical support for accurate online marketing strategy.

Yangyang Li1, You Yang2
1Wuhan Technical College of Communications, Wuhan, Hubei, 430065, China
2Wuhan Qingchuan University, Wuhan, Hubei, 430204, China
Abstract:

The article builds a simulation system based on human physiological parameters, collects human physiological data through the human physiological model, and simulates human physiological signals. The load adaptability of trainers to aerobics training was explored by studying the changes in the SI values of T-lymphocytes of the subjects’ bodies during aerobics training. SPSS and independent samples t-test were used to analyze the exercise data of the experimental group and the control group, so as to verify whether the aerobics training has a good exercise effect. At Week 0, the SI values of T lymphocytes in the immediate post-exercise group and the 3-hour recovery group after exercise were 0.88, 0.61 and 0.70, respectively. In Week 2, it dropped to 0.34 and 0.49, respectively. At Week 6, the SI values of lymphocytes in the two groups were 0.60 and 0.30, respectively. The SI values of T lymphocytes in Week 0, Week 2, Week 4 and Week 6 in the quiet group were 0.88, 0.48, 0.80 and 0.50, respectively. Before the experiment, there was no significant difference between the experimental group and the control group in terms of exercise effect, and after the experiment, a significant difference was produced, and the exercise effect of the experimental group far exceeded that of the control group. The experimental group’s exercise effect improved by 6.43, 5.13, 6.91, 6.38, and 5.80 points on each of the five dimensions, a significant difference. The control group, on the other hand, remained essentially unchanged.

Qiong Liu1,2, Xianfeng Liu3, Wenbo Fu4
1School of Accounting, Tongling University, Tongling, Anhui, 244000, China
2Woosong University, Deajeon, 365100, South Korea
3School of Finance and Economics, Jiangxi Institute of Applied Science and Technology, Nanchang, Jiangxi, 330100, China
4Jiangsu Public Engineering Construction Center C o., Ltd., Nanjing, Jiangsu, 210000, China
Abstract:

The assessment of economic quality is of great significance in grasping the state of national economic development at a macro level. This paper focuses on exploring the assessment methods of economic quality and introducing deep learning models to improve the shortcomings of the traditional economic quality assessment in the assessment process. The economic quality assessment system is constructed from five dimensions, including economic vitality, and the MIV indicator values are improved by combining set-pair analysis and generalized regression neural network, so as to realize the automatic screening of economic quality evaluation indicators. According to the screening results of the indicators, the hierarchical analysis method is used to assign weights to the indicators, and the comprehensive index of economic quality is measured based on the results of the assignment.From 2012 to 2022, the economic quality of the 30 provinces in China shows an upward trend as a whole, and the comprehensive index of economic quality in 2022 is 0.90, which is an increase of 52.54% compared with that in 2012. The assessment results are consistent with the actual results, indicating that the method of this paper can effectively complete the measurement and assessment of the economic quality index, which is important for the study of economic quality.

Shaoping Li1
1Department of Intelligent Manufacturing, Shandong Vocational College of Science and Technology, Weifang, Shandong, 261053, China
Abstract:

Generative artificial intelligence, as a new technology paradigm, has received more and more attention for its powerful generative ability and wide application prospects. Especially in automated control systems, the application of technology based on generative artificial intelligence is gradually becoming a hot spot of research. In this paper, the generative AI automation control system is divided into four levels: input layer, processing layer, instruction generation and control execution layer, and combined with dual encoders, the attention model of multilingual to semantic expression is constructed. Two-dimensional variables are selected to construct a fuzzy PID control system to realize automation control for generative AI system. Comparing the control effects of fuzzy control PID and classical PID, the average errors of the two systems are 1= , 2= respectively. The maximum overshoot and rise time are 9% and 0.08 s, 5% and 0.04 s. The fuzzy PID control effect is more accurate, and at the same time improves the dynamic performance of the system. Analyze the implementation effect on the innovative service application of generative artificial intelligence. Comparing the overall recognition effect of the control system B proposed in this paper, and the two systems with reference to A, their overall recognition effect indexes are 0.94755 and 0.87211, respectively, and the fuzzy PID control system plays an auxiliary enhancement role in the contextual feature recognition of translation services in the intelligent library.

Lihua Dong1
1The School of Culture and Media, Guangdong Cadre College of Science and Technology, Zhuhai, Guangdong, 519090 , China
Abstract:

From the perspective of artificial intelligence (AI), this paper explores the application and impact of cluster analysis in the criticism of narrative ethics in Chinese new century literature. Utilizing AI paper processing technology, a large amount of literary text data is quickly obtained and processed, and a knowledge map of narrative literary works is constructed. Meanwhile, a clustering algorithm is used to divide the keywords of literary works into cluster classes to improve the efficiency of rapid literary analysis. The regression model is used to evaluate the effect of the cluster analysis method in the AI perspective on the ethical criticism of literary narratives. The accuracy, recall, and F1 value of the two AI techniques selected in this paper in the classification of literary text themes, keywords, and emotions are 85% to 90%, which is higher than the comparison methods, and combined with the clustering algorithm, the keyword categories of the literary text can be obtained quickly and precisely. In addition, by constructing a knowledge graph, this paper can help users grasp the character relationships in literary texts more clearly and assist in ethical criticism. The investigators are highly satisfied with the method of this paper, the average rating of each dimension is between 4.09 and 4.7, and the method has a significant contribution to the effect of ethical criticism of literary narratives.

Zhaoyan Shang1
1Accounting Department, Shandong University of Finance and Economics, Jinan, Shandong, 250014 , China
Abstract:

This paper follows the principle of construction of evaluation index system to formulate the evaluation index system of teaching quality of college courses, which is mainly composed of 5 first-level indexes and 25 second-level indexes, and in addition, the real assessment data of ten students of a 985, 211 college on teachers’ teaching quality assessment is taken as the main source of data for this study. The combined algorithm of hierarchical analysis and fuzzy comprehensive evaluation is used to construct a university course teaching quality assessment model, and the model is analyzed by example verification. The comprehensive evaluation scores of the secondary indicators of the university’s course teaching quality are (2.1781, 2.879, 2.1934, 1.7756, 0.9739), and based on the principle of maximum affiliation degree, it is concluded that the students’ grade of the university’s course teaching quality is good (2.879), and the results are in line with the university’s actual course teaching, and at the same time, it is proved that the model of this paper has an excellent application effect.

Hong Li1
1School of Physical Education, Guizhou University of Engineering Science, Bijie, Guizhou, 551700 , China
Abstract:

Knowledge mapping, as an emerging knowledge management tool, provides a new perspective of knowledge learning for physical education teaching. In this study, knowledge mapping is introduced into physical education teaching, and a comprehensive physical education knowledge map is constructed by integrating the teaching resources and contents of physical education teaching and utilizing related techniques such as knowledge extraction and knowledge fusion. The method of fusion of sports knowledge graph is also proposed, including three parts: graph approximation, similarity calculation, and subgraph fusion. Finally, the constructed knowledge graph is practically applied, and a recommendation model based on sports knowledge graph and neural network is constructed to realize the sports teaching application of intelligent educational knowledge graph. The entity recognition module optimized the recognition accuracy rate on the objective existence entities of sports by 1.45%, and the relationship extraction module outperformed AGGCN in all three indicators. The training method of this paper is better than the MICT sports training method in improving students’ cardiorespiratory capacity and flexibility quality. The improvement of students’ 800m running performance under this paper’s training program is 0.13min more than that of MICT.It is proved that the sports course recommendation model based on knowledge graph and neural network provides a reference for the management and application of knowledge data in physical education, with a view to promoting the progress in the field of intelligent education.

Xuebo Hu1
1Mental Health Education Center, Zhengzhou Shengda University, Zhengzhou, Henan, 451191 , China
Abstract:

In recent years, with the increasing psychological pressure on students, psycho-pedagogical methods have been highly emphasized. This article takes students’ multimodal emotion recognition as a research perspective. The article firstly studies the unimodal emotion recognition methods of expression, text and speech respectively. Then it proposes a multimodal emotion recognition algorithm based on dual-attention mechanism and gated memory network, and then conducts emotion recognition experiments to validate this paper’s method. The article further proposes an intervention pathway to further assist in solving students’ mental health problems by designing a virtual reality mental health intervention system. Using the method of this paper in Multimodal database unimodal emotion recognition experiments, found that the network of the model used in this paper has better experimental results, which verifies the effectiveness of the method of this paper, and the accuracy rate of emotion recognition is 60.65%. After testing the mental health level of 8000 students in a school, it was found that the number of hypermodality and the screening rate were low except for the high score of compulsion, from which it can be concluded that the students in our school are in good mental health as a whole after applying the method of this paper.

Jiangrui Niu1,2, Yingqiang Su1,2, Lu Sun1,2, Liangliang Chen2,3
1School of Architectural Engineering, Huzhou Vocational and Technical College, Huzhou, Zhejiang, 313000 , China
2Huzhou Key Laboratory of Green Building Technology, Huzhou Vocational and Technical College, Huzhou, Zhejiang, 313000 , China
3School of Architectural Engineering, Huzhou Vocational and Technical College, Huzhou, Zhejiang, 313000 , China
Abstract:

The physical and mechanical properties of the rock body at the foot of the slope are prone to deterioration under water-rock action, which affects the stability of the slope body. Accurate understanding of the damage mechanism of anticlinal rocky slopes in reservoir area under the condition of deterioration of the rock body at the foot of the slope is the key to the reasonable evaluation of stability. In this paper, the main lithological characteristics of the anti-dipping rocky slopes in the reservoir area and the distribution characteristics of slope height, slope angle and inclination angle of the rock layer are investigated as the research object, and the deformation and damage characteristics and laws of the rock body are obtained. Numerical simulation of anticline slopes was carried out using GDEM mechanical analysis software based on the discrete element method of continuous medium mechanics. It is found that the upper and middle parts of the slope where the invert body is located in the studied engineering example have deep tensile cracks and shallow surface block tipping damage, while the middle and lower parts show deep bending deformation, and there is a gradual transition zone in the contact between the deformed rock layer and the bedrock. As the distance between the cave and the basement increases, the rock layers gradually tilt towards the Yellow River from the north-east to the north-west, with the rock layers at the base of the cave tilting between 340° and 350°. The inclination of the rock layer in the example slope is 76°, and the main rupture surface of the slope, i.e. the location of the largest bending moment of the rock layer, has a small inclination angle with the horizontal plane. The slope angle is 48°, and the sum of the angles of the slope angle and the inclination angle of the rock layer is obviously larger than 117°, and the slope will be deformed and damaged, which is consistent with the value of the conditions for the slope to be deformed and damaged.

Xi Zhuang1
1Faculty of Military Law, Officers College of PAP, Chengdu, Sichuan, 610213, China
Abstract:

Teachers’ educational decision-making behavior is a deep factor affecting the quality of teaching and has a guiding role in the whole process of teaching activities. In this paper, lagged sequence analysis is used to focus on comparing the differences in multi-objective educational decision-making behaviors between backbone teachers and novice teachers. At the same time, a collaborative filtering recommendation algorithm based on improved cosine similarity combining teacher users and teaching resources is designed to achieve personalized teaching resources recommendation for teachers. And the personalized teaching path for teachers was designed by combining the characteristics of teachers’ educational decision-making behaviors. In terms of static decision-making behavior, backbone teachers pay more attention to cognitive decision-making, while novice teachers pay more attention to procedural decision-making. In terms of dynamic decision-making behavior, backbone teachers’ decision-making strategies are more balanced and diverse and goal-focused than novice teachers. The personalized teaching path of this paper is much better than traditional teaching methods in actual teaching experiments, and there is a highly significant difference between the pre and post-test scores of students in the experimental group using the path (p=0.000<0.01), and teachers are more satisfied with the accuracy of the resource recommendation and the teaching effect of the path. The personalized teaching path designed in this paper helps teachers' educational decision-making in teaching and provides a feasible implementation path for personalized teaching.

Yingchuan Liu1
1Public Education Department, Tangshan Preschool Teachers College, Tangshan, Hebei, 063000 , China A
Abstract:

Apriori algorithm is a classic frequent itemset mining algorithm, but it has the problems of more time consumed by the self-connection process and high overhead of conversion between memories. In order to improve the frequent itemset mining effect of Apriori algorithm, this paper improves the existing adaptive genetic algorithm by using the average population fitness and fitness value discretization, and improves the Apriori algorithm by using the optimized genetic algorithm, so as to solve the strong association rules. Compared with the traditional Apriori algorithm, the algorithm in this paper has less time overhead and improves 2.4%, 2.4%, and 2.7% on average in recall, accuracy, and F1 value. On the Accidents and Retail datasets, the improved Apriori algorithm is faster than the NSFI algorithm by 6.12% and 13.52% on average, reducing the computational complexity. Using the improved algorithm to analyze the characteristics of cross-provincial migrants, it is found that the migrant population is younger in age, with lower education level, mostly of agricultural household registration, and mostly of Han nationality, which verifies the practical application value of the improved algorithm.

Yani Liu1
1School of Foreign Languages, Liaodong University, Dandong, Liaoning, 118001 , China A
Abstract:

This paper introduces a multilayer Bayesian model based on probabilistic and Bayesian inference models to infer discourse hierarchical features in the English corpus at both intra-individual and inter-individual behavior levels. Based on the existing English corpus observation data, the Bayesian method is used to organically combine the prior knowledge and the observed English corpus discourse hierarchical features data, derive and incorporate the posterior probability distribution of many uncertain information target variables, and obtain the discourse hierarchical features of excellent English teachers in the English corpus, which provides more valuable information for educational management. For the emotional features of different teachers’ classroom discourse, the level of “emotionally full” is higher than that of “emotionally depressed”, which indicates that the classroom emotions of excellent teachers are more full, demonstrating the discourse hierarchy features of excellent English teachers. The Bayesian estimation has excellent estimation accuracy and explains the discourse hierarchy of teachers in the English corpus well.

Hong Jiang1, Enqian Tao2
1School of Marxism, Center for Innovative Development of Ideological and Political Work in Colleges and Universities, Ministry of Education, Zhejiang Shuren University, Hangzhou, Zhejiang, 310015 , China
2School of Marxism, Zhejiang Shuren University, Hang zhou, Zhejiang, 310015 , China
Abstract:

With the rapid development of social networks, the powerful interactive function of social networks and the high degree of user participation make the information generated in large quantities and spread rapidly, which also provides a dissemination path for the mass dissemination of Marxism. The article analyzes the overall structure of complex social networks and node centrality indexes for the overall topological characteristics of the networks, in order to further analyze the information dissemination characteristics of social networks for the mass dissemination of Marxism. Subsequently, a social network information dissemination model based on SIR is established according to the specific structural characteristics and dissemination modes of the social network, and the experimental test of the information dissemination effect is carried out. Finally, a Marxist mass communication strategy is proposed based on the experimental results. In the experiments on the effect spreading of node information, the nodes numbered 4, 12, and 20 have the strongest information spreading effect, with the number of nodes infected exceeding 30, and the corresponding number of interaction activities are 575, 511, and 663.This suggests that individuals within the aggregated group can build trust with the group members by participating in frequent interactions to improve the effect of information spreading. The development and dissemination of Marxist mass social networks cannot be separated from a series of measures such as a sound social network regulatory system.

Jiawei Chen1
1Discipline Inspection Commission and Office of Resident Supervisory Commissioner, Jiangsu Vocational Institute of Commerce, N anjing, Jiangsu, 210000 , China
Abstract:

Quality management is one of the factors determining the running level of a university, so it is necessary to evaluate university management scientifically and comprehensively. In this paper, a university management evaluation index system is constructed and optimized with reference to the multi-factor decision tree technology, and the cause degree and centrality degree are calculated through the DEMATEL method model, the causality diagram is established, and the ISM hierarchical structure analysis is subsequently carried out. The management influences with high centrality degree are service ability, student quality, employment, school size and presidential leadership. Through the results of the reachable matrix, four levels of college management influencing factors are divided, and it is found that the fundamental factors affecting the management level of colleges and universities are concentrated in the socio-economic and cultural level, the deeper factors are mainly the school’s own scale, funds and teachers and students, and the leadership quality and service ability are the superficial factors. Therefore, the improvement of university management evaluation system can be carried out with reference to the above levels and indicators.

Meijie Zhao1, Guozhu Liu1
1College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong, 266061 , China
Abstract:

In this paper, the embedding vectors are obtained by Bert coding, and then the obtained embedding vectors are adaptively fused with features to realize legal text classification by a classifier, on the basis of which a multi-label text classification model (AFDAM) is proposed to capture the target words in a sentence. At the same time, the pre-trained continuous bag-of-words representation (CBOW) is used to initialize the vector representation of the label information, and then these label information is adaptively fused with the feature information of the text, which effectively promotes the multi-label legal text classification, and accelerates the development of informationization and intelligence in the legal field. The results show that the text feature enhancement module has the most prominent impact on the text classification effect, and its accuracy on the three datasets is improved by 0.46%-1.19%. In addition, the introduction of target vectors and text expansion also gained 0.54%-1.7% and 0.59%-1.53% and 1.08% increases in model accuracy, respectively. In addition, the addition of offense and statute information can significantly improve the prediction of sentence length, and the statute information improves the results more significantly than the offense information. And the classification effect of the AFDAM model proposed in this paper increased by 0.1453-0.257 than the other five models.

Wuxiao Chen1, Zhijun Jiang1, Xuan Deng1, Han Lin1, Zhexin Lin1, Yihao Zou1, Bingjin Zhang2
1State Grid Fujian Marketing Service Center, Fuzhou, Fujian, 350001 , China
2Beijing Tsintergy Technology Co., Ltd., Beijing, 100084 , China
Abstract:

As a small-scale power generation and distribution system, microgrid, by virtue of its high efficiency and clean power generation, has been taken by scholars around the world as a key research object for the sustainable development of national energy. Taking microgrid as the main research object, this paper explores the construction of power load identification model and optimization of scheduling capacity of microgrid. The improved Least Squares Support Vector Machine (LS-SVM) algorithm is used to construct the power load identification model, which realizes the accurate prediction of power load data. The optimal scheduling model of the microgrid is constructed based on the nonlinear planning method, and the co-evolutionary genetic algorithm (DCGA) with the improved difference strategy is used to solve and find the optimal model.The curve of the predicted value of the power load of the LS-SVM is basically fitted to the curve of the real value, and its prediction of the power load is more accurate than that of the BP neural network model. The daily running costs of the genetic algorithm, CCGA algorithm and DCGA algorithm are 1750.34 yuan, 1730.59 yuan and 1709.83 yuan, respectively. The daily running cost of the improved DCGA algorithm in this paper is 1763.59 yuan, which is reduced by 2.31% and 1.20% compared with the genetic algorithm and co-evolutionary genetic algorithm, respectively, and the DCGA algorithm has the fastest convergence speed, which indicates that it has the strongest ability to search for optimization, and it can effectively reduce the operating cost of microgrids, and it has a high practical value.

Jing Qu1, Pingping Bao1
1Information Engineering College, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310000 , China
Abstract:

Cross-language text categorization techniques can achieve more efficient localization and use of text data in multilingual languages by overcoming the differences between different languages. In this paper, firstly, by combining cross-language word vectors and adversarial training, support vector machines are utilized to improve the alignment effect of English-Chinese cross-language words and sentences in the feature space, and to achieve higher quality English-Chinese cross-language text classification. Then the variational mechanism is combined with multi-task learning to align the potential semantic space of multimodal data, maintain the domain invariance of different modal data representations, improve the generalization ability of the model, and ensure the consistency of the variational machine translation training process and the prediction process. The two are combined to construct a hybrid variational multimodal machine translation model based on semantic alignment, experimentally validate the effect of the text categorization algorithm on datasets such as Multi30k, and examine the quality of English-Chinese and Chinese-English translations. In the experiments, it is found that on the MSCOCO dataset, the BLEU of English to Chinese and Chinese to English of this paper’s model is 61.26 and 60.15 respectively, and the translation quality is significantly better than the baseline model. The model achieved the best results in all 3 actual translation tasks. And compared with the complete model, the translation performance of different removal cases in the ablation experiments are decreased, which verifies the effectiveness of the model of this paper as a whole and different components. The method in this paper can effectively reduce the feature differences between different languages, and has important practical application value for solving cross-language text categorization and machine translation problems.

Dong Wang1
1School of Fine Arts and Calligraphy, Nanjing University of the Arts, Nanjing, Jiangsu, 210013 , China
Abstract:

As an important part of Chinese traditional culture and art, how to efficiently realize the recognition, retrieval and style appreciation of calligraphy is of great significance. Aiming at the shortcomings of the traditional geometric feature recognition model with low recognition efficiency, this paper applies morphological neural network to the geometric feature recognition of calligraphy to design a geometric feature recognition model for calligraphy. Image enhancement is performed on the calligraphic graphics, the expansion pooling subnet is designed to replace the maximum pooling layer, and the calligraphic geometric feature recognition network is constructed by combining the residual block structure. The average recognition accuracy of this model in the geometric feature refinement recognition task is as high as 97.23%, which is higher than that of the comparative models such as CNN, LeNet-5, and the recognition accuracies are not less than 96% for the Euclidean, Liu, Zhao, and Yan styles. Using the model of this paper to explore the influence of calligraphic line fluidity and structural changes on the geometric features, it is analyzed that the “line” has a more significant influence on the geometric features of calligraphy than the “structure”. In the six types of traditional calligraphy, such as large seal, small seal, official script, regular script, line script, and cursive script, cursive script is only similar to the geometric characteristics of line script, and the geometric characteristics are very unique.

Sukai Liu1
1College of Art and Design, Pingdingshan University, Pingdingshan, Henan, 467000 , China
Abstract:

Mural paintings in tombs are always facing protection problems in the process of display. In order to achieve the necessary balance between fresco protection and display, this paper discusses the application scenario and implementation steps of utilizing digital twin technology to protect frescoes, and builds a fresco protection display system. Focusing on gesture interaction, this paper uses Kinect interactive device to realize the recognition of human gestures. The average recognition speed of this paper’s method is about 0.02s, and it has high recognition accuracy under different angles and depths, and different gesture movement trajectories. The designed gesture virtual interaction system can improve the satisfaction of visiting the tomb murals and realize the balance between the protection and display of murals.

Qi Ding1, Yunjia Li1
1Department of International Trade, Hainan College of Economics and Business, Haikou, Hainan, 571127 , China A
Abstract:

Aiming at some configuration and scheduling problems of automated guided vehicles (AGV), shore bridges and yard bridges in the loading and unloading operation process of container terminals in the port logistics system, the flow characteristics of containers between ships and yards are analyzed in detail in the light of operational characteristics. Considering the intersection of AGVs with shore bridges at the quay front and the intersection of AGVs with yard bridges in the yard area, a container truck scheduling optimization model based on the objective of minimizing the operation cost is designed. And adaptive particle swarm algorithm (APSO-C) is used to solve the three-dimensional scheduling model of container in port logistics system. The results show that the fastest arrival scheduling rule is basically better than the shortest distance scheduling rule, and with the increase of the container task volume, the gap between the two scheduling rule optimization objectives in the same situation is getting bigger and bigger. Compared with the shortest distance, the fastest arrival has a shorter total completion time, which is more in line with the actual terminal operation scheduling. In addition, as the number of shore bridges increases, the operation time gap between single-load AGV mode and multi-load AGV mode is proportional to the number of shore bridges. Obviously the APSO-C algorithm has better performance in the container scheduling optimization process, which is more in line with the actual operation requirements of the terminal.

Junlin Li1
1College of Art and Design, Liaoning Petrochemical University, Fushun, Liaoning, 113001 , China
Abstract:

Visual communication design, as an indispensable part of product design, plays an important role in enhancing the cultural connotation and aesthetic value of products. Based on fractal theory and supported by Iterative Function System (IFS), this paper studies the visual communication style design of patterns. Taking the flower pattern as an example, a method of automatic generation of flower pattern based on fractal geometry is proposed, and the effective value ranges of each parameter are derived through experiments and analyses to realize the digital visual communication design of the traditional handmade pattern. Then the generated fractal graphic is used as the content graphic, the style graphic is determined, the style migration technology is introduced, and the convolutional neural network model is constructed to build the style migration model of the product graphic, and experimental analyses are carried out to further improve the visual communication design of the product graphic. The average scores of this paper’s product graphic style migration method on aesthetics and style similarity are 3.95 and 3.81, respectively, and the p-values of the Mann-Whitney U-test are all less than 0.0001, which are significantly better than the baseline method. The average overall style similarity of this method on the real dataset is 86.27%, and the accuracy and mean square error on local style features are better than the VividGraph method, which has higher efficacy in performing product pattern style migration to realize visual communication style design.

Xiaodan Li1
1School of Foreign Languages, Liaodong University, Dandong, Liaoning, 118001 , China
Abstract:

Multidimensional vector space is the basis of lexical semantic correlation computation, which is able to assess the similarity between lexical semantics. In this paper, we implement a Japanese lexical named entity recognition and semantic relation calculation method based on this method. Dependency relations are fitted using N-Gram and knowledge expansion, contextual relations are corrected using collocation frequency, and semantic interactions are determined by semantic linking methods. The accuracy and recall of the identification of this method are higher than that of the spatial semantic role method by 0.78% and 4.93%, respectively, and the quantized values of the calculated correlations accurately reflect the strong and weak lexical semantic relationships. The results of the disambiguation experiments show that the maximum correlations computed using the method of this paper are consistent with the corresponding semantic items. Therefore, the method designed in this paper for recognizing named entities and calculating semantic relations of Japanese words has a relatively accurate recognition rate of semantic relations and has the ability of disambiguation.

Pei Wang1
1School of Foreign Languages, University of Sanya, Sanya, Hainan, 572000 , China
Abstract:

Semantic accuracy plays an important role in improving the quality of English translation teaching. This paper proposes a semantic translation model based on convolutional neural network. It is based on the semantic correlation expression and the statistical machine translation model of hierarchical phrases, and combines the convolutional neural network to propose a translation model optimization method that integrates sentence and document information. The method evaluates the semantic match between source language phrases and candidate target phrases by utilizing the sentence context of the source language phrases and the topic information of the documents in which they are located. The optimization method for evaluating the accuracy of English semantic translation is also given. In the simulated translation experiments, the accuracy of the translation correctness evaluation of this method is maintained at 92.5% and above, with high semantic accuracy. The research constructs a high and stable English semantic translation model, which provides informative aids for English translation teaching.

Guangyi Zhu1, Fa Wang2
1BYD Auto Industry Co., Ltd., Shenzhen, Guangdong, 518000 , China
2META, Newark, CA, 94560 , USA
Abstract:

Based on the full active suspension and road input model, this paper introduces the fuzzy control theory and genetic algorithm design theory, adopts the fuzzy control method to control the actuator’s actuation force, creates the fuzzy control system of the automobile active suspension system, and optimizes the fuzzy control rules by using the improved genetic algorithm to ultimately realize the vibration damping effect enhancement in the process of driving the automobile vehicle. Simulation experiments and sample vehicle road experiments are used to verify the performance and utility of the fuzzy controller based on the improved genetic algorithm proposed in this paper. In the simulation experiments carried out with the help of Matlab/Simulink software, the control active suspension body controlled by the fuzzy controller based on the improved genetic algorithm reduces the root mean square value of angular acceleration of pendulum vibration, pitching rotation and lateral tilting rotation by 58.93%, 52.31% and 57.74%, respectively, compared with that of the conventional controller, the root mean square value of the dynamic deflection of the suspension is reduced, and the vehicle driving performance shows good stability and stability. The vehicle traveling shows good smoothness and stability. In the prototype road test, the root mean square value of the corresponding acceleration of the fuzzy-controlled active suspension optimized based on the improved genetic algorithm in this paper is reduced by 42.67%, 39.45% and 37.23%, respectively, compared with that of the passive suspension. Overall, the optimized design of fuzzy controller based on genetic algorithm proposed in this paper greatly improves the vibration damping effect of the active suspension system.

Pinjie Liu1
1Music School, Shandong Normal University, Jinan, Shandong, 250000 , China
Abstract:

In order to solve the shortcomings of the sound source separation method, this paper proposes a melody extraction method based on saliency and improved joint neural network, constructs the pitch saliency feature function according to the idea of harmonic energy superposition, pre-processes the audio, and then builds the joint neural network based on Res-CBAM according to the idea of joint neural network of music detection and pitch estimation classification to realize the melody pitch contour tracking. In addition, the calculation of the significance function is introduced to highlight the pitch significance features, so that the graphs input to the neural network have clearer melodic features. The results show that before and after the suppression of the accompaniment, the difference in the time-domain waveforms is not significant in the treble range, but there is a significant difference in the low-frequency range. In addition, the OA accuracy of the Res-CBAM algorithm proposed in this paper is up to 41.14% higher than other algorithms (P < 0.05), and the accuracy of the model is good. Applying this recognition model to teaching found that teaching with this model can significantly improve the subjects' perception of music (t=.197, p=0.002<0.05). It can be seen that the application of the Res-CBAM algorithm to actual music teaching is of great practical importance.

Xueyuan Cao1
1Basic Teaching Department, Luohe Food Engineering Vocational University, Luohe, Henan, 462000 , China
Abstract:

Semantics in public English texts are more challenging to understand accurately because they are influenced by specific contextual contexts. Traditional English text semantic understanding methods do not design their semantic understanding methods based on the conceptual semantic features of the text, and they have the problem of poor accuracy in understanding the deep semantics of English texts. For this reason, the article takes the public English text semantic algorithm as the research perspective, firstly conducts relevant theoretical research on English text semantic feature representation, then explores the text semantic extraction method based on the Dependency Tree-CRF, and deepens the understanding of English text semantics through the conceptualization and attention embedding methods. In the experiment of comparing the semantic coherence model with manual scoring, the experiment shows that by applying the semantic analysis model designed in this paper to the task of correcting the English writing of domestic college students and comparing it with the experimental results of manual scoring, it is found that the average absolute error between the scoring of the English compositions by this paper’s model and the scores of the compositions corrected by the teachers is 3.2051, i.e., the difference between the results of the manual correcting and the results of the correction by this paper’s model is It is not big, from which we can get that the model of this paper has good practical value.

Chaodong Yang1, Bangsheng Liu2, Changsong Wu2, Xi Qin3
1Guilin University of Electronic Technology, Guilin, Guangxi, 541000, China
2Guangxi Xinfazhan Communication Group Co., Ltd., Nanning, Guangxi, 530000, China
3Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, Guangxi, 530000, China
Abstract:

The extreme high temperature and erosive environment service environment in bridge construction puts forward higher requirements for high performance concrete and other aspects of performance. In this paper, compound mineral admixture is selected as a research breakthrough, and X-ray diffraction analysis (XRD) and Raman spectroscopy are used to explore the micromechanical behavior of compound mineral admixture in high-performance concrete. In the Raman spectral analysis, the stress distribution of the fitted curve of the compound mineral admixture is more flat and uniform, and the offset of the G’ peak position is higher than that of the reference concrete and the single-mineral-admixture concrete, and the stress can reach 2.5 MPa under 1% strain, showing good interfacial bond, stress transfer efficiency, etc. The physical phase data of the XRD also shows the frost resistance of compound mineral admixture, with the ability to mitigate carbon dioxide, and the ability to reduce the carbon footprint of the concrete, with the ability to reduce the carbon dioxide. The XRD data also show the frost resistance of the compound mineral admixture, which has the performance of slowing down carbonization. The NSGA-II algorithm is introduced and improved to propose a concrete proportion optimization model. The final evaluation function converges from 35 generations and the final value is 0.4558, which achieves the adaptive optimization of compound mineral admixture.

Jianhua Bu1, Fuqiang Wang2, Xingmin Yang3
1School of Physical Education, Qiqihar University, Qiqihar, Heilongjiang, 161001 , China
2Central School of Bathing Pool Town, Tailai County, Qiqihar, Qiqihar, Heilongjiang, 161001 , China
3Central School of Xihe Town, Keshan County, Qiqihar, Heilongjiang, 161001 , China
Abstract:

The concept of “Internet+Sports” has promoted the application of artificial intelligence and other emerging technologies in the field of sports. This paper mainly focuses on the special physical training, and explores the application and realization path of artificial intelligence technology in physical training test. In this paper, PSO-BP model is constructed based on BP neural network optimized by PSO intelligent algorithm and applied in physical training test. In addition, for the classification of physical training, this paper follows the basic principles of physical training system construction, establishes the physical training measurement index system through the results of expert solicitation, and determines the weights of each index by using the hierarchical analysis method. Through the empirical analysis of the PSO-BP model in this paper, it can be seen that the fitting results of the training samples of male and female students show that the corresponding correlation coefficients of male and female students are 0.99908 and 0.99898, respectively.The errors of the evaluation output values of the physical training measurements and the expected values are within ±3.5, and the prediction error of the BP neural network model optimized by the PSO algorithm is significantly reduced, and the relative errors of the evaluation of male and female students are reduced by 0.988% and 0.833%, respectively. The results show that the results of physical training measurement and evaluation using PSO-BP neural network model are more accurate, which proves that the performance of PSO-BP neural network in this paper has been effectively improved and optimized, and at the same time, it can meet the application requirements of physical training measurement and evaluation.

Yuxuan Wu1
1School of Economics and Management, Weifang University, Weifang, Shandong, 261061 , China
Abstract:

As an important part of economic activities, logistics industry ushers in new development opportunities and challenges in the wave of digital transformation. The study explores the path of integration and development of digital economy industry and logistics industry, designs the path of building intelligent logistics ecosystem, and constructs the logistics distribution path optimization model based on time window. When analyzing and solving the logistics distribution path optimization problem, the ant colony algorithm (ACO) is improved by introducing the hierarchical idea of the artificial bee colony algorithm (ABC) and limiting the pheromone concentration on each path, controlling it within a known range, to make up for the shortcomings of the ant colony algorithm of precocious maturity and search stagnation. Using MATLAB software to simulate the logistics and distribution of M fresh food e-commerce enterprises, the comprehensive cost solved based on ABC-ACO algorithm is 75.64 yuan and 33.45 yuan less than the results of ACO and GA solving, respectively, and the optimal route traveling mileage is 21.35 km and 6.03 km shorter than the mileage solved by ACO and GA solving, respectively. It shows that the performance of the improved ant colony algorithm is better than that of the basic ant colony algorithm and the genetic algorithm, and it points out the direction for the future logistics and distribution of the distribution center. The empirical analysis found that the digital economy industry and logistics industry show a synergistic trend, and there is a large space for integration and development.

Keyuan Ding1, Ruoyu Yang2
1College of National Security, People’s Public Security University of China, Beijing, 100038, China
2College of Economics and Management, Civil Aviation University of China, Tianjin, 300300, China A
Abstract:

Urban safety development is one of the guarantees for the overall development of the city, and the study uses Delphi method, entropy weight method and TOPSIS method in the assessment of urban safety development. An improved Delphi-entropy weight-TOPSIS combination assessment model is constructed to evaluate the urban safety development. The evaluation index system of urban safety development is constructed, and the evaluation indexes of urban safety development are calculated by Delphi method and entropy weight method respectively, and the subjective and objective weights of the evaluation indexes of urban safety development are derived, and finally, the comprehensive weights are calculated by the method of combined weight assignment. The comprehensive weights of the guideline layer of the urban safety development evaluation index system are 0.1874, 0.2080, 0.2005, 0.2187, and 0.1854, respectively.The evaluation index system is used for empirical research, and City A is taken as the object of the research to assess its urban safety development status during the 10-year period from 2014 to 2023. From the evaluation results, it is known that the overall urban safety development of City A during the 10-year period shows an upward trend, with slight fluctuations in the process, but the overall development is good, and the evaluation score of urban safety development improves from 0.4657 points in 2014 to 0.6479 points in 2023.

Shuai Liu1,2, Meng Huang1,2, Song Zhang1,2
1Institute of Disaster Prevention, Sanhe, Hebei, 065201, China
2Hebei Province University Smart Emergency Application Technology Research and Development Center, Sanhe, Hebei, 065201, China
Abstract:

Entity-relationship extraction task is one of the very important research directions in the field of natural language processing, aiming at identifying and determining the existence of specific relationships between entity pairs from unstructured text. The study firstly introduces the related theories of graph neural networks in terms of graph representation learning and graph neural networks, and then makes full use of the information of dependent syntactic trees to propose a relationship extraction model based on dependency graph convolution (DGGCN). The validity of the model and the entity extraction effect are verified through relevant experiments.The DGGCN model is fully experimented on the public datasets NYT and WebNLG, and the F1 value is effectively improved.According to the results of the ablation experiments, it is shown that the DGGCN model improves the entity and ternary extraction results by 0.5% and 4.3%, respectively. In the long and short distance entity extraction results, the DGGCN model outperforms the benchmark model in both long and short distance entity relations, but the extraction performance gap between short and long distance entity relations is still large and needs further improvement.

Wanfen Wang1
1School of Architecture and Civil Engineering, West Anhui University, Lu’an, Anhui, 237000, China
Abstract:

In today’s increasingly stringent sewage discharge standards, the construction of a new generation of wastewater treatment plants more and more urgent. This paper adopts MBBR as the main process to treat wastewater, the pretreatment process of wastewater treatment plant adopts coarse and fine grating + cyclone sand sedimentation tank, and the secondary treatment process selects AAO process. Through the reasonable calculation of water volume and hydraulics, and then calculate the size of each structure. Based on the ASM2 model, combined with the conversion rate equation of the AOO reaction tank, the kinetic model of the wastewater treatment system was constructed. Analyzing the inlet and outlet water quality monitoring data of the high-efficiency wastewater treatment plant for one year of operation, it was found that the average values of inlet and outlet water COD concentration in one year of operation were 255.437 and 10.556 mg/L, respectively, and the annual average removal rate was 94.37%. The average values of ammonia nitrogen in and out of the water for the whole year were 32.085 and 1.107mg/L, and the average ammonia nitrogen removal rate was 96.98%. All the effluent indicators have reached the “urban sewage treatment plant pollutant discharge standards” level A discharge standards and environmental protection departments on the effluent indicators, indicating that the overall operational efficiency of the research-designed high-efficiency wastewater treatment plant is good, and has reached the expected goals, with significant environmental and social benefits.

Zhimu Gong1
1Design Department, Taiyuan Normal University, Taiyuan, Shanxi, 030600, China
Abstract:

According to the connotation of traditional and modern design elements in rural landscape beautification, multi-dimensional data cube mining method is adopted to construct the research data set of this paper. According to the ratio of 2:8, the data set is divided into test set and training set. The data of traditional and modern design elements are used as inputs, substituted into the decision tree model for training and classification, and the CART algorithm is used to construct a decision tree model for traditional and modern design elements in rural landscape beautification. Combining the dataset and the model in this paper, the simulation analysis of traditional and modern design elements in rural landscape beautification is carried out. The data show that based on the Gini index calculation formula of CART algorithm, it is concluded that the Gini index of X9 (0.9581) is the largest, so X9 is chosen as the root node for decision making, and the decision tree is derived downward until the leaf node, and the decision tree oriented to the countryside landscaping is obtained, and the rural landscape beautification scheme is induced based on the results of the analysis and the effect of the rural landscaping is found to have the difference between the before and after mean values of 3.36 ( 20.11-16.75=3.36), while there is a significant difference between the two, similarly, there is also a significant difference in the building living comfort above. This study enhances the effect of rural landscape beautification, which is of great significance in promoting rural revitalisation and architectural design development.

Guanhong Zhan1
1College of Art and Design, Mudanjiang Normal University, Mudanjiang, Heilongjiang, 157000, China
Abstract:

The inheritance and protection of urban cultural heritage faces the dilemma of narrow coverage and lack of change in form, and to solve this dilemma, we need to find a breakthrough in cultural creation and animation design, and carry out creative activities and popularisation among all people. The article proposes a feature extraction model that integrates multi-scale features and housing element information mining, and applies it to the feature extraction of housing elements in urban cultural heritage. A hybrid attention module is embedded in the ResNet-18 backbone network to enhance housing element features and suppress redundant information, and a CEB module and learnable parameters are combined to filter out the background information of the low-level features, so as to obtain finer architectural housing element features. The extracted housing elements are used as the basis for the design of creative products and animation scenes, and the feasibility of the programme is investigated through questionnaires. The overall evaluation mean value of the research respondents on the design of cultural and creative products for the housing was 7.64 points, and more than 95% of the evaluation respondents indicated that the housing elements were more suitable for the animation scene design. Relying on modern technology to extract housing elements from urban cultural heritage and realising the innovative application of cultural heritage in the form of cultural creation and animation provides a new path for the revitalisation and inheritance of urban cultural heritage.

Yuming Zhang1, Shiyang Lin1
1School of Architecture and Engineering, Weifang University of Science and Technology, Weifang, Shandong, 262700, China
Abstract:

Shallow loess landslides, as one of the widely distributed and high-frequency geologic hazards, have brought great economic losses and ecological damage to human society. In this study, Qinzhou District, Tianshui City, Gansu Province, is taken as the study area, and the Scoops3D model is used to predict the occurrence of loess landslides in the area based on the DEM data of the area. Bishop’s simplified method and box search method were used to calculate and analyze the landslide stability in the study area. The landslide prediction results of the Scoops3D model of this paper are compared and analyzed under different DEM data resolutions. Subsequently, local environmental data are collected to study the correlation between environmental impact factors and shallow loess landslides. Finally, the prediction accuracy of the shallow loess landslide prediction model based on Scoops3D in this paper is tested by comparing the difference between the prediction results of the Scoops3D model of this paper and other prediction models with the actual results. The resolution of the DEM data has an important influence on the prediction results of the Scoops3D model, and the accuracy of the high-resolution DEM prediction results is higher than that of the low-resolution prediction results. There is a significant correlation between landslide displacement and humidity and cumulative precipitation, and the difference between the predicted and measured values of the GA-BP and GA-Elman models is within 8 mm, and the difference gradually increases. The difference between the predicted and measured values of the Scoops3D model in this paper is between 0.00 and 2.30 mm, and the prediction effect is optimal.

Qingqing Huo1
1College of Digital Technology and Design, Shanghai Sipo Polytechnic, Shanghai, 201300, China
Abstract:

With the deep development of digital transformation, the field of environmental art design is experiencing unprecedented changes. In this study, under the 3D scene reconstruction algorithm, the feature points of environmental art design images are collected and extracted using the camera selfcalibration algorithm, and the shape and topology of the point cloud dataset interpolated surfaces are explored using the triangular meshing algorithm. The rotation matrix is obtained by optimising the internal and external parameters of the camera using the essential matrix, basis matrix and Kruppa’s equation to clarify its effect on the efficiency of digital feature extraction of images in the process of environmental art design. The results show that the mesh surfaces constructed by the algorithm proposed in this paper make better use of the point cloud data when the number of cloud points input for environmental art design is the same. The rotation matrix algorithm used in this paper can increase the correct matching point pairs of the data, reduce the false matching point pairs, reduce the false matching rate, reduce the matching time, and eliminate more false matching points. And the triangular grid formed by this method is more uniform, and the quality of the grid is improved. In addition, the average satisfaction ratings of the subjects on the nine secondary test indicators are 4.45, 4.95, 4.75, 4.18, 4.70, 4.60, 4.44, 4.50 and 4.40, respectively. It can be seen that the effect of the application of the digital transformation of the 3D model proposed in this paper has been affirmed.

Daming Xu1
1Hunan College for Preschool Education, Changde, Hunan, 415000, China
Abstract:

In this paper, the development of blast and shock engineering technology problems using linear algebra’s measure analysis is used to make expected judgements through the performance of the data. The problem can be simplified and the frequency stability of the communication transmission system can be optimised by using the data as a benchmark through linear transformations, eigenvectors, matrices and other arithmetic methods. Regularisation and quantisation process the image to improve the science and accuracy of large-scale image restoration algorithm operation. It has been shown that the optimised prediction formula is very consistent with the experimental results in blasting experiments with a building as the object of study. The frequency drift of the optimised laser is reduced from 850 MHz to 160 MHz. the acquired noise intensity is optimal at different communication transmission moments, and the highest noise intensity acquired at frequency is 0.097 dB. The stability is optimal at different times of communication signal switching. The regularisation optimised ship navigation images have the largest values of structural similarity and information entropy metrics.

Xinglong Sheng1, Qing Yan1, Kaiqiang Hu1, Chaoju Li1, Yanghua Xie1
1Guiyang Power Supply Bureau, Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, 550001, China
Abstract:

Underground cable tunnels are important infrastructures to maintain the normal operation of cities, and problems such as cable insulation aging and discharge can easily cause fires or even explosions, so the requirements for maintenance are high. In this study, the DGPS positioning method is used to optimise the positioning system of the intelligent inspection robot for underground cable tunnels, and the LQR controller is used to realise the deviation correction of angle and position in the motion path of the intelligent inspection robot. Then the inspection robot and UHF sensor are used to detect and accurately locate the defects in the cable tunnel, and finally the deviation correction and defect detection methods are integrated to design an intelligent management system for underground cable tunnels. The results of simulation experiments and field surveys show that the proposed method can correct the deviation of the robot in the inspection process in a timely manner, avoiding the problems of hitting the obstacles and the path around the long distance, and the average time consumed in the simulation map scenario is only 6.89 s. The communication scheme of the intelligent management system is practicable, and it can effectively detect and identify the defects and the specific location of the defects in the underground cable tunnels. The system proposed in this paper is able to detect defects and faults in time in practical applications, providing a new solution for the inspection of underground cable tunnels.

Ling Xu1, Han Zhang1, Xinhao Zhu1
1Nanjing Lishui People’s Hospital, Nanjing, Jiangsu, 211200, China
Abstract:

With the development of big data, cloud computing and 5G digital technology, smart finance has emerged. The use of modern information technology to create a smart financial management system to transform and upgrade the original financial management system of the hospital has become an indispensable part of the effective operation and management of public hospitals. The article focuses on the current problems in the development of smart finance in public hospitals, plans the smart finance space from the front, middle and back office, and proposes a financial resource allocation mechanism from the perspective of smart finance. In the performance evaluation analysis of smart financial construction, the weights of the professional level of accounting personnel, financial accounting, comprehensive budget management situation, medical revenue management, outpatient satisfaction, and the standardisation of data sets are 0.1067, 0.0857, 0.0670, 0.0630, 0.0512, and 0.0476 in that order. The weights of cultivating human resources, consolidating the hospital’s financial foundation work, strengthening comprehensive budget management, promoting data standardisation and enhancing patient satisfaction are important ways to promote the development of smart financial construction in hospitals. The purpose of this paper is to provide reference and reference for the financial revenue management of public hospitals, to help hospitals optimise the management process, to improve the quality of service and to ensure financial security.

Yongren Ma1, Jiao Wang1, Meiling Zhu1
1College of Economics and Management, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China
Abstract:

In order to improve the efficiency of agricultural irrigation industry and ensure the economy and environmental protection in the production process. The study proposes an agricultural water-saving irrigation path optimisation method based on the NGSA-III algorithm, and establishes a multiobjective water-saving optimal allocation model for the agricultural water source irrigation system. The NGSA-III algorithm is used to obtain the optimal solution of the model and achieve the path optimisation of agricultural water-saving irrigation resources. The results show that the running time of the article method to get the optimal path result is 0.31s, which can improve the economic and environmental benefits of the agricultural irrigation industry, the model in this paper can achieve the effect of smaller environmental objectives when the economic objectives are larger, and three solutions are selected to trade-off the analysis of economic and environmental objectives. Among the three different optimal solutions, the decision maker can choose the decision scheme according to the actual situation, which provides reference for agricultural water saving path planning.

Yu Wang1
1ZHENGZHOU SHUQING MEDICAL COLLEGE, Zhengzhou, Henan, 450000, China
Abstract:

Due to the continuous increase of housing prices in recent years, many special groups of low and middle income do not have enough financial ability to pay for the high housing prices, and the problem of living environment is becoming more and more prominent. Based on the utility function in economic theory, this paper constructs a utility function model under the constraints of household budget income and price, and determines the income line of housing security households. The distributional efficiency of the implementation of the guaranteed housing policy is estimated through both in-kind rent allocation and rent subsidy. Based on the empirical distribution characteristics and public opinion surveys, a rational distribution model for the current stage of sheltered housing is proposed. Taking Singapore’s guaranteed housing policy as a case study, combining empirical evidence and simulation experiments, the effect of improving the living environment of special needs groups under the framework of social security is explored. The results show that: using a 10% allocation ratio of subsidised housing (5% each for affordable housing and public rental housing), the vacancy rate of public rental housing shows an oscillating state in the period of 7~16. In the period from 16 to 20, it shows a gradual increase. Therefore, this guaranteed housing policy should be gradually adjusted or cancelled around period 16.

Wang Luo1, Xiju Yang2, Zhenkai Shi1
1School of Civil Engineering, Tangshan University, Tangshan, Hebei, 063000, China
2Teacher Training Center, Tangshan University, Tangshan, Hebei, 063000, China
Abstract:

Anaerobic biological treatment of wastewater is an important technology in environmental engineering and energy engineering, and it is one of the methods for powerful treatment of highly concentrated organic wastewater. The study was conducted to design an optimal control strategy based on the anaerobic digestion model ADM1. Taking the maximisation of total gas production as the control objective, the Composite Intelligent Optimised Extreme Value Control Algorithm (CIOEC) was designed by combining the extreme value search control method with the model-free optimisation algorithm. The effectiveness of the proposed algorithm is verified by a combination of simulation tests and empirical analyses, and the CIOEC algorithm can maintain fast convergence and relative stability under both stable and changing input materials, and obtain the highest real-time gas production. Among them, the average daily gas production of the ADM1 system with the addition of the CIOEC algorithm can reach 873.9 mL, which is an increase of 124.3% compared with the original system. It shows that the algorithm proposed in this paper can enhance the total gas production and optimise the treatment effect in performing anaerobic digestion of high concentration organic wastewater.

Meng Shi1, Jing Wu1, Yue Peng 2
1Department of Economic Management, Changzhi Vocational and Technical College, Changzhi, Shanxi, 046000, China
2Financial Management, University of Hong Kong, Hong Kong, 999077, China
Abstract:

In this paper, the weights of different risks in the management process of e-commerce platforms are calculated on the basis of hierarchical analysis. After that, with the help of fuzzy comprehensive assessment algorithm, the risk level is divided. Finally, with the assistance of decision tree, simulation is carried out to simulate the risk of the first-level indicators affecting the risk control of e-commerce platform. According to the survey results, reasonable countermeasures are given to the management of e-commerce platform risks. Among the first-level indicators of the five major risk categories, the business model risk belongs to the high-risk category of Class I, with a fuzzy comprehensive evaluation score of >4.5. The rest belong to the risk category of Class II, with fuzzy comprehensive evaluation scores ranging from 3.5 to 4.5. Among the Level II indicators, there are 6, 6 and 3 Level II indicators rated as high risk category, medium risk and low risk respectively, with their fuzzy composite scores ranging from 4.7495-5.6370, 3.6807-4.4988 and 3.1356-3.2435 respectively Between. In the comprehensive risk simulation prediction of the case-based e-commerce platform, only the logistics model risk belongs to the medium risk control strategy with a risk value of 4.8614 (day 60). The simulation results for the remaining four risk types were all low risk, and their risk values decreased (3.5 points) when the simulation time was day 60. The experimental results provide a prediction for the change of risk and provide reasonable countermeasures and suggestions for the risk control of ecommerce platforms.

Yan Hu1, Gefei Shi 1
1Hunan Modern Logistics College, Changsha, Hunan, 410131, China
Abstract:

Container and cargo matching is a key issue to realize the construction of container and cargo supply and demand matching platform, through the intelligent matching of cargo and container information, improve the efficiency of container and cargo matching, which is conducive to the integration of resources, and improve the platform professional services. In this paper, we analyze the process of container cargo matching and transportation distribution center operation, put forward the two-stage container cargo model assumption in accordance with the basic principle of distribution optimization, and complete the establishment of container cargo matching model under the demand of cargo owners. Optimize the container and cargo matching and vehicle path model respectively, derive the optimized combination mathematical model, and solve the combination optimization model through genetic algorithm. Simulation experiments are designed to analyze the effectiveness of the model. The results of the analyses of the algorithms show that when the crossover probability is increased from 0.6 to 0.8, the average value of the RV value decreases from 1078.76 to 915.76, and the recommended value of the crossover probability is obtained as 0.8. After optimization, the average vehicle load and average loading volume of the recommended scheme of the combined model reach 98.436% and 87.963%, respectively, with a total mileage of 23.456km for distribution, and the total cost of distribution in the region is 1246.489 yuan, which achieves the optimal container-cargo matching and path scheduling scheme.

Hao Li 1
1Baoding Open University, Baoding, Hebei, 071000, China
Abstract:

High-fidelity modeling of complex surfaces is the basis for accurate characterization of surface quality and realistic analysis of performance in the fields of digital process design of products and digital twin. This paper proposes to improve the new polynomial interpolation algorithm to improve the effect of the polynomial interpolation algorithm fitting in complex surface modeling through the center variable, and combines the moving least squares approximation function with the new polynomial interpolation algorithm to further optimize the effect of the complex surface modeling through the regular moving construction of the fitting surface by the local approximation method. It is found that the overall average error and standard deviation between the turbine blade surface roughness modeled based on the new polynomial interpolation algorithm and the roughness meter measurements are within 1.7 μm (0.7580-1.6715 μm), and the error is within the acceptable range. It is also found that using the method of this paper can save a lot of time and realize the rapid modeling of complex surfaces of the body. It also has good smoothness, which provides convenience for the subsequent processing of complex surface modeling. The new polynomial interpolation algorithm proposed in this paper provides a new idea for the research in the field of complex surface modeling, and can be applied to the actual production to assist the design and production of related products.

Weijian Li1, Jigui Liang1, Chunyi Yu1, Jianfei Wang 2
1Qujing Xuanfu Highway Investment and Construction Development Co., Ltd., Qujing, Yunnan, 655000, China
2Yunnan Construction and Investment Holding Group Co., Ltd., Kunming, Yunnan, 650500, China
Abstract:

In this study, we construct an unmanned vehicle path optimization model based on fast extended random tree, and after kinematic modeling of unmanned vehicles, we introduce the artificial potential field method to improve the fast extended random tree algorithm, and apply it to the path optimization of unmanned vehicles. According to the swarm intelligence perception decision-making algorithm, the end-to-end unmanned vehicle decision-making model based on vehicle-circuit collaboration is constructed. The effectiveness of this paper’s driverless path optimization and decision-making model based on vehicle-circuit collaboration is examined. The waiting time for red light of this paper’s model is shorter than other path planning schemes, and the vehicle passing benefit at intersections is the highest. The passing benefit values of this paper’s model are 70.3% and 46.8% higher than Maxband scheme and Synchro scheme, respectively. In the right-turn simulation experiments, the main vehicle speed change shows a tendency to accelerate and the path is basically overlapped with the edge of the lane without offsetting the center of the lane. In the normal driving speeds of [14,38], the fuel consumption of the driverless vehicle shows an up and down trend, and the carbon dioxide emission varies with the fuel consumption. The total cost of traveling decreases with increasing speed.

Xin Liu1, Ya’nan Zhang2, Huifeng Zhao 1
1College of Economics and Management, Hebei Agricultural University, Baoding, Hebei, 071000, China
2School of Economics and Management, North China University of Science and Technology, Tangshan, Hebei, 063000, China
Abstract:

Through the examination and calculation of each link of the dairy industry chain, we analyze the benefit distribution pattern of the dairy industry chain and highlight the necessity of optimizing the benefit distribution strategy of the dairy industry chain. The Shapley value method of the equilibrium of interests in game theory is chosen to study the benefit distribution strategy of each subject in the dairy industry chain under the cooperative game, and the model is revised by using the input factor, the risk factor and the correction factor, so as to further improve the rationality of the benefit distribution strategy. The research data were obtained by visiting the dairy industry chain in Xilingol League through field investigation, and the modified Shapley values of the herdsmen, middlemen, milk processors and retailers were finally obtained as 3976.43 yuan, 3839.31 yuan, 4175.53 yuan, and 3977.47 yuan after the modeling calculation, respectively. The comprehensive cost profit margin of each subject after correction is 2.17%, 1.82%, 7.43%, 7.68%, respectively, and herdsmen and milk processors are compensated in the benefit distribution strategy of this paper, and the amount of benefit distribution and the comprehensive profit margin of all the subjects in the dairy industry chain have been improved compared with that before the cooperation.

Yukun Lu1, Zexi Sun 1
1College of Physical Education, Xinjiang Hetian Normal College, Hetian, Xinjiang, 848000, China
Abstract:

Aiming at the demand for scientific training of athletes in college sports education, this paper integrates data mining technology to propose athlete training and optimisation methods, and constructs an athlete training quality monitoring system and intelligent recovery assessment system. The traditional Apriori algorithm is improved by using multidimensional association rules, and multidimensional attribute mining is carried out on the collected data of athletes’ training data to search for frequent item sets and output strong association rules, so as to achieve the monitoring of training quality and adjustment of training programmes. Using the improved fuzzy decision-making method to filter out the optimal feature subset, and integrating the improved whale algorithm and random forest to achieve intelligent recovery effect evaluation. By carrying out the practice of training and recovery optimisation, it can be seen that the total score of physical fitness test of track and field athletes increased from 18.19 to 19.8 before the experiment, and the training quality was significantly improved. Various health indicators such as heart rate, blood lactate, serum creatine kinase, etc. gained significant improvement in adopting the recovery optimisation method of athletes in this paper. The mean values of training status, coaching factors, and personal situation satisfaction evaluation dimensions were 4.35, 4.425, and 4.38, respectively, and the training and recovery plan of this experiment was well received by the subject athletes.

Rina Su1, Yunfeng Yan1, Fei Hao1, Hao Sun1, Peng Chen1, Tianlong Zhang1
1Inner Mongolia Power Group Wuhai Extra High Voltage Power Supply Company, Wuhai, Inner Mongolia, 016000, China
Abstract:

The load of power supply has been increasing in recent years, and the scale of the power grid has been expanding. The impact of electromagnetic radiation on the lives of residents is also increasingly visible, and the electromagnetic environment around high-voltage AC transmission equipment has attracted great attention. Based on the principle of electromagnetic induction and Gauss theorem, this paper proposes the calculation method of electromagnetic radiation to evaluate the distribution law of spatial electromagnetic field around high-voltage AC transmission lines. Then the risk analysis of the electromagnetic environment around the high-voltage AC transmission line is carried out from the height from the ground and the presence of woods according to the measured data. Finally, according to the electromagnetic law of high-voltage transmission lines, the safety control technology to reduce the environmental impact of electromagnetic fields is proposed, mainly by raising the vertical height of the arc of the transmission line from the ground and reasonably designing the distribution of forest planting in the vicinity of the transmission line. When the vertical height of the conductor’s arc height from the ground was increased from 10m to 40m, the electric field strength and magnetic induction strength were reduced by 2.9kV/m and 2.35µT correspondingly, and at the same time, the electric field strength in the vicinity of the building was reduced by 71% at the most. The study proposes measures to effectively mitigate the electromagnetic impact by reasonably analysing the electromagnetic environment in the area where the UHV transmission line is located.

Shaofang Sun1, Juan Wan 2
1College of Marxism, Wuchang Shouyi University, Wuhan, Hubei, 430064, China
2School of Economics, Wuhan Donghu University, Wuhan, Hubei, 430064, China
Abstract:

Supply chain finance innovation has a significant impact on regional economy. In this paper, blockchain technology is applied to supply chain finance business to improve the technology and security of traditional supply chain finance business. Drawing on relevant research results, we construct a blockchain-based supply chain financial innovation efficiency evaluation index system and measure the supply chain financial innovation efficiency using Malmquist index. A spatial econometric model is used to test the spillover effect and spatial synergy between supply chain financial innovation and regional economic growth, and to demonstrate the promotional effect of blockchain-based supply chain financial innovation on regional economic growth.The centres of the distribution curves of the kernel density function of the logarithmic value of GDP and supply chain financial innovation of the 30 provinces and regions are all shifted to the right, and the height of the main peak rises gradually.The 2013-2023 regional Moran’s index of economic growth and supply chain financial innovation are both significantly positive. The regression coefficients of supply chain financial innovation under the two spatial weights are significant at the 1% level, which provides strong data support for the view that supply chain financial innovation can promote regional economic growth in this paper.

Azhen Ye1, Jiannan Yang 1
1Fujian Polytechnic of Information Technology, Fuzhou, Fujian, 350007, China
Abstract:

With the rise of major e-commerce, how to make more customer groups choose to buy items in their own websites is the goal that major e-commerce platforms have been relying on. Therefore, a set of personalised recommendation system that can intelligently explore customers’ needs comes into being. In this paper, a graph neural network model is used to sort out the multi-path fusion neighbourhood relationship among three objects: user, product and query. The utility matrix is established and the collaborative filtering algorithm is used to derive the user’s preference situation for commodities. Subtractive clustering is combined with fuzzy C-means to obtain the clustering centre of gravity and cluster e-commerce users. Graph neural network is introduced to ensure that the data sparsity of the user dataset is within a reasonable range. The practical application effect of the model is evaluated through simulation experiments and empirical analysis, respectively. In this paper, according to the age of the users, the users are clustered and analysed, and three clustering centres of gravity are obtained, which are (3.16, 32.73), (45.35, 40.25), and (14.03, 52.89), so the users are classified into three clusters, and the analysis of simulation experiments is carried out. The training effect of this paper’s model is fitted, and the adjusted R² = 0.8292, which shows that the accuracy of personalised recommendation is high. Meanwhile, comparing with other algorithms, this paper’s method reaches a recommendation satisfaction level of 100% when the number of learning times is 60, which is significantly better than other algorithms.

Yan Zhao1, Meng Yu1, Zhenyu Zhao1, Xuewei Guo1, Jun Wang 1
1State Grid Jiangxi Power Supply Service Management Center, Nanchang, Jiangxi, 330000, China
Abstract:

Measurement and verification play a crucial role in flexible production, and with the development of technology, advanced measurement systems in flexible production systems gradually integrate fault diagnosis and prediction techniques to improve production efficiency. In this paper, a deep confidence neural network model, combined with the ISSA-VMD feature fusion model, is used to model fault diagnosis and prediction in flexible production of power systems. The training effect, prediction performance, feature extraction and fault diagnosis of this paper’s model in flexible production are evaluated and analysed through simulation experiments. The Loss value of this paper’s model converges to about 0.05 after 15 rounds of training, and has a good fitting effect on the training and test sets. The RMSE, MAE and R² of the model in this paper are 0.613, 0.371 and 0.988, respectively, which show good prediction performance. And the prediction results in the measurement system of power generation in flexible production are also more close to the real results. In addition, the DBN model incorporating ISSA-VMD feature fusion can completely separate the five fault signals, and the overall fault identification accuracy reaches 98.53% for the fault test set selected in this paper, which has strong diagnostic effect. This study provides more scientific and effective technical support for metrological verification in flexible production.

Fuping Zhou 1
1College of Accounting, Guangzhou College of Technology and Business, Guangzhou, Guangdong, 510850, China
Abstract:

The digital era requires enterprises to pay attention to technological innovation and optimise ESG performance in the development process, so as to achieve high-quality development. Based on this, this paper proposes the hypotheses related to enterprise ESG, technological innovation and enterprise high-quality development. And construct the regression model of enterprise ESG performance and high-quality development. Basic statistics and correlation analysis are used to provide a preliminary description of enterprise ESG performance and high-quality development. Through the total effect test, the role of enterprise ESG performance on high-quality development is clarified. Through the mediation effect test, the role played by technological innovation between corporate ESG and highquality development is clarified, and the proposed hypotheses are verified, and the property rights, geographic and industry differences in the impact of corporate ESG performance on high-quality development are further explored by using robustness test and heterogeneity analysis. Finally, corresponding recommendations are made. Most of the enterprises selected in this paper have low levels of high-quality development, unsatisfactory ESG performance, and large overall gaps in technological innovation.The correlation coefficients of ESG performance (ESG) with corporate highquality development (LnTFP) and technological innovation are 0.402 and 0.335, respectively, and all of them are significantly and positively correlated at the 1 per cent level. Hypotheses H1, H2, and H3 are all valid.ESG performance and technological innovation have more significant effects on the highquality development of state-owned enterprises, eastern regions, and high-pollution enterprises.

Lanlan Zhou 1
1Sichuan University of Science & Engineering, Yibin, Sichuan, 644002, China
Abstract:

Consumer data is an important support for analysing and observing consumer behaviours in the era of digital marketing, and constructing models to predict consumer purchasing behaviours. In this paper, we select the Retailrocket consumer behaviour dataset based on real shopping websites, analyse the distribution of various types of consumer behaviour over time and other data characteristics, and gain insights into the behavioural habits of consumers when shopping. Based on the XGBoost algorithm in machine learning, a prediction model of consumer behaviour is constructed, and the genetic algorithm is used to optimize and improve the XGBoost algorithm.The XGBoost prediction model has a significantly better prediction performance than the LSTM prediction model and the LR prediction model when facing the data under the under-sampling data balancing method and the improved random under-sampling method based on the K-means algorithm. . The performance of the GA-XGBoost prediction model optimised by the genetic algorithm is significantly improved compared to the XGBoost prediction model, and substantially better than the LSTM prediction model and the LR prediction model. The accuracy and F1 value of the GA-XGBoost prediction model in the data under the improved stochastic undersampling method are 0.90865 and 0.92435, respectively, which are improved by 14.69% and 17.26% relative to the XGBoost prediction model. Meanwhile, the stability of GA-XGBoost prediction model is also significantly improved compared to XGBoost prediction model.

Wei Gao 1
1State Grid Shanxi Electric Power Company, Taiyuan, Shanxi, 030000, China
Abstract:

In response to cybersecurity threats such as security breaches, data leakage, supply chain attacks, and ransomware viruses in digital network environments, more reliable cybersecurity architectures are needed to address these challenges. The article builds a zero-trust firewall applied to network security protection based on zero-trust architecture by integrating SPA single-packet authorisation technology and authentication scheme. Then SPA single packet authorisation technology with SM3 hash algorithm and SM4 algorithm for fully nominal encryption processing is constructed as a network security protection scheme, and the authentication protocol and trust evaluation algorithm are established by using hash and different-or function. In the simulation verification results, the communication volume of SDP client to complete one authentication is 981B, which reduces 27.17% compared to WaverleySDP overhead. The server in the SDP+SPA scenario still retains a certain amount of legitimate data after DDOS attacks and Web attacks, and receives only 53.47% of the traffic of the SDP scenario. The CPU usage of the client deployed with SPA is only 11.47 percentage points higher than that without SPA mechanism. The combination of SPA single-packet knocking technology and zero-trust architecture can achieve network security protection, and can also effectively deal with DDoS and Web attacks, and improve the performance of network security protection.

Shui Cao1, Chunjun Cheng2, Guangyan Tang3, Fang Ma3, Yu Sun4, Di Cui4, SAGGELLA MADHUMITHA2
1College of Medical Humanities, Jinzhou Medical University, Jinzhou, Liaoning, 121000, China
2College of International Education, Jinzhou Medical University, Jinzhou, Liaoning, 121000, China
3School of Computer Science, Jinzhou Normal College, Jinzhou, Liaoning, 121000, China
4Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, 121000, China
Abstract:

This paper defines doctor-patient interaction from the perspectives of interaction form and maintenance of patients’ health respectively, and also constructs a doctor-patient interaction discourse model. Based on the data mining technology to obtain the research data, the acquired data are preprocessed and stored in the form of dataset. Bi-LSTM is used to extract topic sentence features from the dataset, and the unsupervised pattern is transformed into a self-supervised pattern through the training and learning of auxiliary tasks to complete the construction of the discourse model of doctor-patient interaction based on topic structure. Combined with the processing flow of natural language processing and semantic technology, the communication strategy generation system for doctor-patient interaction discourse is designed, and finally the communication strategy based on natural language technology is researched and analyzed. There are significant differences between the experimental group and the control group in terms of expression ability and cognitive level (P<0.05), which concludes that compared with the traditional discourse model, the doctor-patient interactive discourse model has a higher priority, and it can effectively improve the expression ability and cognitive level of the patients' medical terminology. On the CMedQA2.0 dataset, the average performance of this paper's model is improved by 46.34% compared with the baseline model GPT-2, indicating that this paper's model has excellent performance. Under the condition of Chinese participle and topic extraction fusion, the average accuracy of this paper's system is as high as 85.02%, which indicates that the system can provide doctors with precise communication strategies based on patients' medical-related information, thereby effectively enhancing the discourse communication skills in doctor-patient interactions.

Shan Cong 1
1School of Law, Dongguan City University, Dongguan, Guangdong, 523000, China
Abstract:

Research on event extraction and constraint encoding of legal cases, using Lawformer as a pre-trained language model for legal sentence prediction model, constructing MJP-Law model to predict the sentence of legal cases. The HAN encoder in the model is utilized to extract the inter-sentence relations in the legal case and construct the relations among the law, the charge, and the sentence period. Compare the performance of this paper’s MJP-Law model with other prediction models on law, charge, and sentence period, and explore the effects of the three subtasks of law, charge, and sentence period on the model through ablation experiments, and compare the prediction effects of a single MJP model and the MJP-Law model on low-frequency charges. In this paper, the MJP-Law model outperforms other prediction models in terms of prediction performance on statute, offense, and sentence. The four models of “MJP-Law”, “MJP-Law_law”, “MJP-Law_SG” and “MJP” had the same prediction performance, which were 95.54%, 89.86%, 89.73% and 89.81%, respectively. “MJP-Law” and “MJP-Law_law”, “MJPLaw_SG” and “MJP” have the same performance in law prediction. After removing the sentencing guidelines and legal sentences, the macro F1 values of the MJP-Law model all showed a decrease.The predictive performance of the MJP-Law model on low-frequency offenses was better than that of the single MJP model.

Yi Huang 1
1School of Foreign Studies Yiwu Industrial and Commercial College, Yiwu, Zhejiang, 322000, China
Abstract:

As the main link of international trade, logistics plays a pivotal role in the entire international trade transactions, and choosing the appropriate logistics path is conducive to cost savings for enterprises. This study combines the traditional logistics model with the actual situation of international trade to select the headway transportation, overseas warehouses and tail distribution as the main elements of enterprise logistics cost optimization in international trade. Based on the cost calculation of the main elements, we design the objective function and constraints of enterprise logistics cost optimization, build the optimization model, and obtain the optimal solution by iterative analysis using the fitness function and genetic operator in genetic algorithm. The empirical analysis shows that after applying the optimization model, the total logistics cost of enterprise D is reduced from US$99,373,500 to US$72,653,400, indicating that the model is effective in optimizing the logistics cost of enterprise D in international trade. This study provides an effective method for the optimization of cross-border enterprise logistics costs, which has a positive role in promoting the development of international trade.

Xiongwen Wang 1
1School of Law, Dongguan City University, Dongguan, Guangdong, 523419, China
Abstract:

Prediction of legal decisions using machine learning and artificial intelligence techniques has gradually become an important part of smart court technology. In addition the crime prediction and law recommendation also face the problem of easily confusing crimes. In order to solve these problems, this paper unites multi-task learning models and proposes a model fusion legal verdict prediction model. An attention neural network fusing Transformer Encoder and DPCNN encodes the key semantic information in the case description. The TF-IDF algorithm and TextRank algorithm are applied to extract the keywords of the charge, and the forward propagation network is used as a classifier to constitute a multi-task learning legal verdict prediction model. Using 9 CAIL2018 legal datasets as experimental data, the metrics performance of the multi-task learning legal judgment prediction model proposed in this paper is measured on three subtasks (offense prediction, legal provision prediction, and punishment duration prediction) in LJP. Combining real case information for legal verdict prediction as well as charge differentiation. The verdict prediction results on the CAILBig-Multi dataset show that the mean MP value of the comparison algorithms is 82.925% in the charge prediction. And the MP index of the charge prediction of the multitask learning legal verdict prediction model proposed in this paper is 89.13%, which is significantly higher than the mean value of the comparison algorithms. And the multitask learning model incorporating the keyword information of charges in case analysis can effectively solve the problem of confusing charges.

Yusong Liu1, Jianjun Song 2
1Graduate School, Sehan University, Mokpo, 57447, South Korea
2School of Electronic Technology and Engineering, Shanghai Technical Institute of Electronics & Information, Shanghai, 201411, China
Abstract:

The era of big data in education has come, data-driven intelligent decision-making has become the development trend in the era of big data, and precise teaching has become the keyword in the era of big data. This paper establishes a real-time dynamic teaching strategy adjustment decision-making model based on the learning characteristics in the process of industry-teaching integration practical training in higher vocational education, and uses Markov decision-making and Q-learning algorithms to solve the optimal teaching strategy in each stage of practical training and learning, which assists the teachers in decision-making and precise intervention. The results of the practical training teaching experiment found that the students in the experimental group, after the dynamic adjustment and intervention strategy implementation of the industry-teaching integration practical teaching, the scores of the practical training theory and application knowledge test were significantly improved (P<0.05), and the students' self-efficacy control sense, sense of effort, and sense of competence were all improved to different degrees. In addition, the scores of depth of understanding (P=0.000) and strategic approach (P=0.000) in practical training learning competencies also increased significantly. The strategy proposed in this study is able to capture the dynamic characteristics of educational data and use the multi-stage dynamic decision-making method to study the development of teaching strategies, which can provide stronger support for accurate teaching decisions and industry-teaching integration of practical training learning.

Huihui Sun 1
1Foreign Language Department, Lyuliang University, Lyuliang, Shanxi, 033000, China
Abstract:

Writing skills not only promote the learning of other English skills such as listening, speaking and reading, but also effectively promote the internalization of language knowledge, laying the foundation for further improving the development of students’ comprehensive language skills. In this paper, with reference to the application path of information technology in English literacy teaching, we design a SCN-LSTM-based language model, and on this basis, we adopt a bidirectional recurrent network as the language model, and propose an improved SCN-BiLSTM network, which can effectively obtain the contextual relationship of the input sequence. Through the linear interpolation of the language model, the cached language model adaptation is obtained, and the teaching scene corpus is utilized to train the model, and the teaching context-oriented language model adaptation is obtained. Construct ANFIS model to improve the evaluation of English literacy teaching. After the empirical research experiment, the average English reading score of the students in the experimental class after the experiment is 53.631, which is 11.942 points higher than that before the experiment. The writing score is 8.45, which is 0.97 points higher than before the experiment. The application of the adaptive model of English reading and writing based on SCN-LSTM network is very effective.

Qinhai Wang 1
1College of Physical Education, Huaqiao University, Quanzhou, Fujian, 362021, China
Abstract:

The application of modern information technology in track and field training has become an important means to improve the training effect. The study analyses the application of smart wearable devices in track and field training, takes the real-time feedback data of smart wearable devices as the index observation point, constructs the evaluation index system of track and field training based on smart wearable devices, and explores the application of factor analysis and fuzzy comprehensive evaluation method. On this basis, teaching experiments are carried out using smart wearable devices and the evaluation system to explore the effect of smart wearable devices on the enhancement of track and field training in athletic performance. The track and field training of the students in the sample colleges and universities was of medium level, with a total score of 73.71, in which the development of students’ will quality and teachers’ grasp of the training situation still need to be improved. After training with smart wearable devices and assessment system, the practicing students got 4.09%~5.01% improvement in standing long jump, 50m run and 800m run, and there was also a significant difference in training interest with the control students (P<0.05). The smart wearable device and evaluation system can achieve real-time data monitoring and training feedback, which can help coaches and students adjust training in time and improve the effect of track and field training.

Yumin Wang 1
1Department of Economic Management, Luohe Institute of Technology, Henan University of Technology, Luohe, Henan, 462002, China
Abstract:

In today’s deepening education reform, promoting the deep integration of technology and education has facilitated the process of informatization of school education. Vocational education shoulders the important responsibility of cultivating “high-quality laborers and technical talents”, and the reform of informatization of vocational education has gradually become the focus of attention. In this study, we construct a prediction model of learning achievement based on machine learning to optimize the vocational teaching curriculum system. In this paper, before constructing the prediction model, the basic information data and learning behavior data of students are firstly subjected to feature extraction and feature selection. Then CNN combined with BiLSTM and Attention is used to construct the student performance prediction model CNN-BiLSTM-Attention. Finally, based on the performance prediction model, this study proposes the optimization path of the vocational education curriculum system to solve the problem of student employment. The model in this paper achieved the best prediction results in the performance comparison with both the single model and the integrated model, and the indicators were 0.961, 0.953, 0.985, 0.966, and 0.957, respectively. Moreover, it was found that the model had better prediction results in the process of vocational education courses at 80% and above. Among the features, the importance of the relevant features about honor acquisition is higher, all of them are above 0.8, which is an important factor affecting students’ performance. In the actual application of grade prediction, only one student had only 61.6 points in the final semester’s grade prediction, which had the risk of not being able to successfully graduate and proceed to employment. The study shows that the prediction model based on machine learning in this paper has good performance and can provide a strong basis for the reform and optimization of the vocational education curriculum system and promote the informatization process of vocational education.

Yanyan Lei 1
1College of Foreign language, Hechi University, Hechi, Guangxi, 546300, China
Abstract:

This paper discusses the application of the neural machine translation model based on language modeling technology in British Victorian literature and its linguistic adaptation. Firstly, the linguistic features of Victorian literary works are analyzed, including thematic content and social background. Then the neural machine translation model based on language modeling technology is designed, and the text style migration method based on style representation is proposed to reproduce the linguistic features of the literary works. The performance of the translation models under the three fusion style methods is compared with five baseline systems, and the BLEU value, style migration accuracy, and style migration fluency of the machine translation model using the text migration decoding module are 37.49, 0.978, and 3.59, respectively, which are all higher than those of other models. Taking the translation of Wuthering Heights as an example, there is not much difference between this model and the human translation in terms of language adaptation evaluation. It shows that the machine translation model designed based on language modeling technology in this paper has better language adaptability for translating Victorian literature.

Jun Ma1, Chunguang Zhang2, Bingzhi Chen1
1Institute of Mechanical Engineering, Dalian Jiaotong University, Dalian, Liaoning, 116028, China
2School of Electrical Engineering, Dalian Jiaotong University, Dalian, Liaoning, 116028, China
Abstract:

With the continuous development of the rail vehicle business, high-speed rail, locomotive, subway, light rail and other railroad transportation industry to reach the prosperity of the previous scene, the wheelset is an important support and walking parts of the rail train, so the detection of its geometric parameters and tread quality of the safe operation of the vehicle is of great significance. In this paper, based on the principle of binocular measurement vision, the mathematical model of bilinear structured light is used to calculate the three-dimensional coordinates of the spatial points of the wheel pairs of high-speed railways. The collected point cloud data are filtered and smoothed to eliminate the noise contained in the data. Integrate the two point data under the same coordinate system, perform data fusion on the overlapping part to complete the alignment of the point cloud. And extract its eigenvalues to realize the point cloud coordinate transformation. Through testing experiments, the accuracy of high-speed rail wheel pair data measurement and other indicators are studied and analyzed. The measurement accuracy of the journal diameter of the HSR wheelset has a deviation of about 0.003 mm compared with the CMM, meanwhile, the fluctuation range of the HSR wheelset diameter data in the left and right directions is within 0.04 mm and 0.03 mm, respectively, and the stability of the measurement data of the model is good. The point cloud rotation error is between -1.09° and 1.09°, and the first quadrant angle error is between -1.114° and 0.829°, and the model controls the error to be around 1°, and the verification of the pairing accuracy is passed, which can meet the requirements of the production and operation activities.

Yang Li 1
1School of Teacher Education, Pingdingshan University, Pingdingshan, Henan, 467000, China
Abstract:

In the field of artificial intelligence education, teaching emotion, as the main assessment basis for teaching evaluation, profoundly affects the teaching method, classroom atmosphere and teaching effect of teachers. This thesis proposes a combined network structure, CRNN, by taking advantage of CNN for speech emotion feature extraction and RNN for sequence modeling, and realizes emotion recognition of classroom discourse through DenseNet neural network to realize the crosstalk between each layer and other layers, and LSTM neural network to complete the task of speech emotion classification. On this basis, the open classroom video of the sixth grade of an elementary school is analyzed for sentiment, and the teaching practice of the application of speech emotion recognition model is carried out to study the optimization effect of the model application on the classroom atmosphere of the elementary school. The overall sentiment value of the classroom interaction video floats in the range of 0~1.9, showing a trend of first increasing and then decreasing, reflecting the feasibility of applying the speech emotion recognition model of this paper to classroom sentiment analysis. Through the teaching experiment, the positive emotional performance of the experimental group is more obvious than that of the control group, and 95.46% of the students agree that the application of the model can improve classroom interaction and the overall atmosphere. The speech emotion recognition model studied here can mobilize the classroom atmosphere, and has more important classroom guidance and application significance.

Chunhui Yang1,2, Ning Xu3
1School of Innovation and Entrepreneurship, Hebei Normal University for Nationalities, Chengde, Hebei, 067000, China
2College of Education, Capital Normal University, Beijing, 100000, China
3Office of Academic Research, Hebei Normal University of Nationalities, Chengde, Hebei, 067000, China
Abstract:

With the rapid development of science and technology, in the face of the needs of social development, colleges and universities undoubtedly need to shoulder the important task of talent training and education reform in innovation and entrepreneurship. In this paper, an intelligent learning model is constructed by using artificial intelligence technology. The model takes the subject knowledge graph as the core support, and combines the learning path recommendation algorithm to provide digital and intelligent support for innovation and entrepreneurship education. On this basis, the objectives of innovation and entrepreneurship education are formulated, and the framework of innovation and entrepreneurship education system is established based on the intelligent learning model in this paper, and the cycle model of innovation and entrepreneurship education based on the intelligent learning model is proposed, and the model is experimentally studied. The AUC values and F1 values of the proposed algorithm in the three datasets are higher than 0.85 and 0.80. Compared with the traditional model, the average value of recommendation bias decreased by 8.56, and the evaluation satisfaction increased by 0.126. In the teaching experiment, the overall average score of the innovation and entrepreneurship education model based on this paper was 4.364, which was 1.129 higher than before. Compared with the traditional innovation and entrepreneurship education, it is increased by 0.693, indicating that the innovation and entrepreneurship education model in this paper can promote the all-round development of students’ ability level and play a positive guiding role in the development and reform of innovation and entrepreneurship education.

Danqun Huang1, Yilu Ouyang 1
1Hunan Petrochemical Vocational Technology College, Yueyang, Hunan, 414000, China
Abstract:

With the booming development of large-scale open online courses, blended teaching, which combines traditional closed teaching and online open teaching, is increasingly favored by colleges and universities. In this paper, from the perspective of blended teaching of English in colleges and universities, based on the LSTM model to predict the relevant learning data in English teaching in colleges and universities, and based on the density optimization K-mean algorithm to cluster the student subjects with different learning behaviors, and then use the Apriori algorithm to study the correlation rules of the learning effectiveness and behaviors, to provide ideas for English teaching in colleges and universities. The clustering results show that the average learning scores of the first, second and third categories of learners are 92.35, 83.57 and 64.96 respectively. The results of association rule analysis show that routinely, the more active learners are in each learning session, the greater the possibility of getting better learning outcomes. The LSTM learning prediction model Precision, Recall and F1 assessment indexes trained with 4-month behavioral data are 0.899, 0.785 and 0.833 respectively, which are all greater than the corresponding index values of SVM, MLP and RF models, and have a significant advantage in prediction effect. This study provides lessons and references for improving the effectiveness of English teaching in colleges and universities.

Jingwen Zhang 1
1School of Literature and Education, Shaanxi Institute of International Trade and Commerce, Xi’an, Shaanxi, 712046, China
Abstract:

With the rapid development of technology and online social networking, the popularization of smartphones has promoted the research and development of sentiment analysis of contemporary literary texts. In this paper, the CBOW model based on Hierarchical Softmax algorithm is used to extract text sentiment features. The classification mechanism of sentiment lexicon, machine learning, and deep learning methods supported by sentiment features is discussed. According to the discussion results, a 5-layer sentiment analysis model based on CNN-BiLSTM-ATT is built based on text preprocessing, and the model design of different layering is proposed. Meanwhile, the analysis method of text themes is proposed based on LDA. In the long story dataset, the model recall rate of this paper is 83.91% and the precision rate is 83.86%, the values are higher than the other six models; the MacroF1 mean value is 83.16%, which proves that the fused and improved CNN-BiLSTM-ATT model of this paper possesses excellent performance in the sentiment analysis task. In the short story dataset, the accuracy, precision and recall are not less than 98%, and the loss rate is the lowest 34.11%, which are lower than the other six models. The model in this paper can be applied to text analysis systems and has superiority in parsing the sentiment of contemporary literature.

Qingyue Bi 1
1Liaoning University of International Business and Economics, Dalian, Liaoning, 116052, China
Abstract:

Under the background of the development of digital economy industry, more and more enterprises begin to make attempts of digital change. After constructing the financial performance index system of pharmaceutical enterprises, the study selects 30 pharmaceutical listed companies as the research samples, and evaluates their financial performance by using the principal component analysis method and the collected relevant data. On this basis, the study selects indicators of digitalization degree and puts forward research hypotheses, explores the influence of digitalization degree on the financial performance of pharmaceutical enterprises through correlation analysis, multiple regression analysis and time lag effect analysis, and then puts forward the path of digitalization development of pharmaceutical enterprises in combination with the results of the analysis. The results show that the financial performance of the sample pharmaceutical enterprises is at a medium level, with an average composite score of 0.520, among which pharmaceutical enterprises E10, E6 and E22 have the best performance, with scores above 0.9. The degree of digitization has a negative impact on the financial performance of enterprises at the 1% level, but the coefficient of digital capital investment turns from negative to positive after the lag two period, and there is a time-lag effect of digitization on the financial performance of pharmaceutical enterprises. It is recommended to promote the digitalization of pharmaceutical enterprises by encouraging the cultivation of digital talents, improving the law and cultivating thinking, and building a digital platform.

Hanqi Song 1
1School of Economics, Harbin University of Commerce, Harbin, Heilongjiang, 150028, China
Abstract:

The modernization and development of industrial chain supply chain in the era of digital economy is an important content to cultivate new quality productivity, maintain industrial competitiveness and realize industrial modernization. After the promotion effect of digital economy on the modernization and development of industrial chain supply chain, this paper takes China’s digital economy data from 2012 to 2022 as the research object, designs the evaluation index system of the development level of digital economy, and measures the development level of digital economy by using entropy value method and Kernel density estimation method. The overall situation of China’s digital economy development level is analyzed, and the dynamic evolution trend of digital economy development level is explored. Then, based on the threshold regression model, the benchmark regression and threshold effect analysis of the relevant inϐluencing factors of the digital economy-enabled industrial chain supply chain modernization and development are carried out. 2012-2022 China’s digital economy shows a steady upward trend, and its average annual growth rate reaches 1.8%, and the Kernel Density value decreases from 0.0474 in 2012 to 0.0425 in 2022, with the digital economy of each region level gap decreases. For every 1% increase in the level of digital economy development, the level of industry chain supply chain modernization and development is increased by 1.407%, and there are two threshold effects of economic double cycle and digital technology level for digital economy-enabled industry chain supply chain modernization and development. Enhancing the level of digital technology promotes the enhancement of the level of international and domestic economic double cycle, which in turn improves the level of modernization and development of industrial chain supply chain.

Peng Chen 1
1Physical Education Department, AnYang University, Anyang, Henan, 455000, China
Abstract:

Artiϐicial intelligence plays an increasingly important role in contemporary education, and it provides new possibilities for the innovation of physical education teaching mode. This paper constructs a college sports teaching integration model based on artiϐicial intelligence from ϐive aspects: educators, learners, teaching methods, educational resources and teaching feedback and evaluation. It focuses on designing a precise teaching model PLRSM based on personalized learning resource recommendation by combining learner portrait and learning resource portrait, and takes the recommendation of physical education teaching resources for physical education students as a case study to verify the effectiveness of the proposed algorithm. The results show that compared with the traditional baseline algorithm, the PLRSM algorithm still maintains a better recommendation performance when the data set co-occurrence matrix is extremely sparse, and its correct rate of physical education teaching resources recommendation is 0.80. In addition, compared with the traditional teaching model, the AIbased college physical education teaching fusion model can signiϐicantly improve the learners’ knowledge of physical education subject and course teaching, and its post-test score is higher than the pre-test score 11.525 to 15.436 points. The study provides theoretical support and practical guidance for the application of artiϐicial intelligence in physical education teaching, and provides a useful reference for promoting the innovation of physical education teaching mode.

Yuan You 1
1Geely University Of China, Chengdu, Sichuan, 641423, China
Abstract:

With the development of sharing economy, educational resource sharing has become the focus of experts and scholars to explore and practice. In this paper, from the perspective of resource sharing, a smart teaching management platform is successfully designed by combining artiϐicial intelligence technology. This research adopts YOLOv5s algorithm for face recognition and prediction in the design process, which is convenient for teaching management. Relying on the Hadoop cloud resource base, the teaching resource sharing database is designed, and the system computing logic is optimized by the distributed ϐile system HDFS. It is analyzed that the maximum number of interactions per second of the intelligent teaching management platform designed in this paper can reach 207, and the maximum interaction response time is about 68ms, and the load performance is completely better than that of the traditional teaching resource platform. At the same time, the intelligent teaching management platform can accommodate nearly 300 people to study online at the same time, which is far more than the previous mode of learning in the classroom. With the use of the intelligent platform, the development of “Internet + education” is greatly promoted.

Lin Ge 1
1School of Public Administration, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
Abstract:

With the accelerating process of urbanization development, it is urgent to optimize the national land spatial planning to promote the coordinated development of urbanization. Based on the image recognition technology, this study uses the kernel density gradient algorithm to segment the image samples of the national spatial layout and the GWO-SVM classiϐication model to classify the land use types of the national spatial layout, and ϐinally combines the Markov-FLUS model to predict the future planning of the existing national spatial layout. The research analysis found that the segmentation and classiϐication accuracy of the kernel density gradient algorithm and the GWO-SVM classiϐication model for the homeland spatial layout samples both reached more than 90%. The classiϐication accuracy using the GWO-SVM classiϐication model is improved to a greater extent than that of SVM, GA-SVM, etc. The Markov-FLUS model also maintains an accuracy of more than 80% for the prediction of future territorial spatial planning. In terms of land use types, the Markov-FLUS model shows that the proportion of residential land and industrial land will decrease after 10 years compared with 5 years, while the proportion of public facilities land will increase by about 8% after 10 years compared with 5 years. The optimization of national spatial layout is of great signiϐicance to the development of urbanization in China, and the research in this paper will promote the development of national spatial layout planning in a more reasonable direction.

Fang Yang 1
1School of Computer Engineering, Shanxi Vocational University of Engineering Science and Technology, Jinzhong, Shanxi, 030606, China
Abstract:

With the rapid development of informatization technology, the security of network data is more and more emphasized. In this study, ECDSA digital signature algorithm and PBET consensus algorithm are adopted to construct a network data security model based on blockchain technology. The system in this paper consists of three functional modules: application interaction client, federation chain Fabric module and data storage module DHT, which are further logically divided into ϐive parts: initialization, identity registration, uploading data, querying data and permission revocation. The average CPU occupation of each component of the system ranges from 0.02% to 39.96%, which consumes low resources, and the maximum value of the time used by the system for data encryption and decryption and signature authentication is no more than 41ms, which is a relatively fast operation speed, and it can support the operation of the network data security system, and the designed system has relatively high security in resisting the attack of the authentication process, and it utilizes the decentralized characteristics of blockchain to resist the attacks of the distribution process, and it utilizes the blockchain to resist the attacks of the distribution process. Centrality to resist distributed denial of service (DDoS) attacks and replay attacks. This study provides lessons and references for the application of blockchain technology in network data security.

Lijun Zhang 1
1School of Tourism Foreign Languages, Zhengzhou Tourism College, Zhengzhou, Henan, 450009, China
Abstract:

With the development of artificial intelligence technology, the learning mode of “artificial intelligence + education” has become the direction of the times. Through a questionnaire survey on students’ vocabulary learning strategies and taking students of a middle school as the research object, the study explores the level of strategy use in English vocabulary learning in terms of the frequency of strategy use and the differences in strategy use among students of different levels. On this basis, the way of English word sense processing with the assistance of artificial intelligence is summarized and the word association memory model is proposed. And two classes in a middle school are selected for teaching experiments to apply the word association memory model to English vocabulary learning and explore the effect of the model on students’ word memory. Overall the cognitive strategy (3.489) and resource strategy (3.477) of English vocabulary learning are used more frequently. The English vocabulary level model of the students in the experimental class increased after the teaching experiment, which was 8.05 points higher than that of the control class and still 5.118 points higher than that of the control class in the delayed test, reflecting the vocabulary learning effect and durability of the word association memory model. Students can improve their language cognitive learning skills in three aspects: metacognitive strategies, cognitive strategies, and communicative/influential strategies, which further promote the development of English proficiency.

Yuanliang Deng 1
1Chengdu Technological University, Chengdu, Sichuan, 644000, China
Abstract:

In the era of information technology in education, accurate analysis of individual characteristics becomes the key to personalized learning and tailored teaching, which is of positive significance to the exploration of teaching reform paths. This paper constructs a cognitive map of college English courses under the guidance of cognitive theory, and establishes a reform model of college English teaching in combination with the cognitive map, so as to realize students’ self-knowledge and cognitive construction in the teaching process. The idea of fuzzy set theory is used to quantitatively analyze the knowledge ability level of college students, and then the Logistic model and Bernoulli distribution function are used to calculate the students’ cognitive level of mastering each knowledge point and their scores of answering the questions in the college English course. The analysis of the effect of the teaching model after practice found that the students’ mastery and cognitive level of subjective and objective knowledge points in the college English course were significantly improved and higher than the ideal reference value. The correct rate of answering composition questions in subjective questions increased by 30.44% compared with that before the teaching mode was carried out. The informatization teaching mode proposed in this paper lays a foundation for the teaching reform of college English and provides an effective path for students to improve their knowledge mastery and cognitive level.

Qin Fan 1
1ShenZhen Polytechnic University, Shenzhen, Guangdong, 518000, China
Abstract:

Ideological and political education has the teaching characteristics of keeping pace with the times. In this paper, the nonlinear support vector machine is used as the ideological and political text data classification algorithm, combined with the text mining technology to collect and screen the ideological and political education data, and the ideological and political text data is divided into various clustering centers of ideological and political education, which are reflected in the research themes of ideological and political education, the hot spots of curriculum ideological and political research, and the teaching methods of ideological and political teachers. This paper analyzes the acquisition of ideological and political education resources from the perspective of students, and explores the matching degree between the acquisition of ideological and political education resources and the individual needs of students. The research objects and research hotspots of ideological and political education are divided, and the optimization strategy of ideological and political education is proposed. In the classification of research topics, the frequency of “college students” was the highest, which was 12568, and the calorific value of the research content “ideological and political education” and the research object “college students” was 8654, indicating that ideological and political education mainly revolved around “college students”. The matching degree between ideological and political education resources and students’ individual needs was 69.37%. Combined with the results of nonlinear analysis, ideological and political education can improve the effectiveness of educational content, strengthen the coupling degree between research content and research object, and strengthen the teaching factor of teachers.

Rui Zhang 1
1HANGZHOU VOCATIONAL & TECHNICAL COLLEGE, Suqian, Jiangsu, 310002, China
Abstract:

The integration of science and education is conducive to promoting the integrated development of education, science and technology, and talents, and is a key path for the high-quality development of vocational education and serving the strategy of a strong education nation. This paper explains the necessity of integrating science and technology with education, and realizes the path design of vocational education quality improvement based on the new concept of science and education integration. Then, the quality of science and education integration in vocational education is evaluated using hierarchical analysis and fuzzy comprehensive evaluation. Then, a comparison test is designed and independent sample t-test is applied to verify the practicality of the path in this paper. In the criterion layer of the established evaluation index system, the weight of industry-university-research integration is the largest, which is 23.91%, indicating that industry-university-research integration is particularly important in the path of vocational education quality improvement. In the indicator layer, the research team building has the largest weight, 10.17%, which needs to be emphasized in the implementation of the integration of science and education in vocational education. The overall rating of the quality of science and education integration in H higher vocational colleges implementing the path of this paper is 84.638, which is between good and very good, and is at a high level. And the two sided Sig value of the T-test of the evaluation score of the quality of science and education integration in the higher vocational colleges and universities using this paper’s pathway and those using the traditional education model is 0.000<0.05, which is a significant difference. It indicates the practicality of this paper's path for improving the quality of vocational education based on science and education integration. This paper provides a path paradigm for improving the quality of vocational education using science and education integration.

Hong Han 1,2
1JIEYANG POLYTECHNIC, Jieyang, Guangdong, 522000, China
2College of Business Administration, University of the Cordilleras, Baguio, 2600, Philippines
Abstract:

This paper firstly studies the current situation of rural e-commerce development in China, and then collects the gross output value of agriculture, forestry, animal husbandry and fishery, express delivery volume, rural delivery routes and so on through consulting the relevant official data of the National Bureau of Statistics, which provides an effective and reliable data basis for the construction of econometric model. Through the establishment of a fixed-effects model to analyze the empirical results, to explore the role of rural e-commerce platform development on the promotion of the economy. Finally, with the help of the spatial Durbin model to measure the spatial spillover effect, analyze whether the development of rural e-commerce can reduce the urban-rural income gap. The results show that the number of Taobao villages, kilometers of rural delivery routes, and 10,000 rural broadband access users are the explanatory variables, and the gross output value of agriculture, forestry, animal husbandry and fishery is the explanatory variable, and the coefficients are 0.0156, 0.0781, and 0.0442, with the p-value less than 0.01. Therefore, the better the development of rural ecommerce, the better the economic development is. And the increase in the level of economic development can significantly reduce the urban-rural income gap with an estimated parameter of – 0.022.

Boyu Long 1
1College of Foreign Languages and Cultures, Chengdu University, Chengdu, Sichuan, 610000, China
Abstract:

There is a close quantitative relationship between college management and college students’ employability. This paper adopts Adaboost integration algorithm to construct an employment management system that integrates graduates’ personalized recommendation. And it divides graduates according to their personal situation and analyzes the relationship between their personal ability and employment recommendation. In addition, the relationship between the management based on the system of this paper and the employment ability of graduates in colleges and universities is quantitatively analyzed by logistic regression model. A questionnaire survey is taken to assess the changes in graduates’ employability as a result of the employment management activities organized by colleges and universities. The recommendation system constructed in this paper has a higher accuracy rate of 6.92% and 16.32% than the comparison system 1 and 2 respectively when the number of job recommendations is 60. And its recall rate and F1 value are also consistently higher than the comparison system. In this paper, the system divides the sampled 200 graduates into 5 categories to provide more accurate employment recommendation for graduates of different categories. The results of regression analysis show that universities organize employment management activities can improve the employability of graduates. For example, for every unit of “Interview practice”, the employability of graduates increases by 0.349. The results of the questionnaire survey show that the employability of graduates, both individually and as a whole, improves to different degrees after participating in the management activities organized by colleges and universities. In conclusion, the construction of employment management system in universities and the organization of employment management activities can improve the quality and ability of graduates’ employment.

Jianfei Xing1, Li Sun1, Xiao Li1, Chengen Pan1
1Business&Tourism Institute, Hangzhou Vocational&Technical College, Hangzhou, Zhejiang, 310018, China
Abstract:

This paper explores the extent and direction of the impact of digital technology-enabled rural cultural tourism development by constructing fixed-effects models, GMM models, spatial Durbin models, Moran indexes, mediation effects and other models. Using the hierarchical analysis method to construct measurement indexes from two aspects of scale and benefit, and then using the coupling coordination degree model to measure the level of digital rural tourism development, to summarize the level of development and coordination of cultural tourism industry in rural areas. The results show that the difference between the minimum value and the maximum value of the rural digitalization level is about 8, and the large gap also reflects to some extent the uneven development of digital rural tourism in different regions. Digital rural tourism has a significant impact on rural economic development and non-rural non-farm employment level, and its impact coefficient is 0.138 and 0.784 respectively. in the data measurement of 2017 and 2022, the more significant the degree of aggregation is, the faster the level of development of digital rural tourism becomes. the comprehensive evaluation index of digital rural tourism in the sample area in 2021 is 0.82, which is at a high level, and the sample region’s service quality satisfaction is also at a high level. Therefore, this paper analyzes from various aspects that the development of digital tourism can promote the sustainable development of rural economy and realize rural revitalization.

Yao Lu 1
1School of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, 211169, China
Abstract:

The emergence of multimedia technology has brought unprecedented changes to the education industry, and it is of practical significance to effectively utilize multimedia technology in red culture education. After analyzing the application of multimedia technology fusion in red culture education, the study takes 240 students in a university as the research object and uses questionnaire survey to explore the application level of multimedia technology in red culture. Subsequently, the influence factors of red culture education integrating multimedia technology on the expansion of ideological education were refined, and the interrelationship between the multimedia integration practice of red culture education and the expansion of ideological education was investigated through the multiple linear regression analysis to study the regression effect among the variables.More than 70% of the students believed that the red culture classroom applying multimedia technology had better interactive effect and learning effect, but the teachers’ understanding of the However, teachers’ use of multimedia technology and proficiency still need to be improved. The red culture education integrating multimedia technology has a significant effect on the goal of moral education, timeliness of education and playing the role of technology in the expansion of ideological and political education (p < 0.001), and the effect of the goal of moral education is the most obvious. The multimedia technology integration practice of red culture education has a promoting effect on the expansion of ideological and political education.

Taofang Xia1, Chen Gao1, Yingrong Lin1, Enguo Zhu2, Yan Liu2
1Marketing Service Center, State Grid Fujian Electric Power Co., LTD., Fuzhou, Fujian, 350000, China
2China Electric Power Research Institute, Beijing, 100192, China
Abstract:

With the wide application of renewable energy sources, the impact of distributed power access units (DPGUs) on the stability of the power grid is becoming more and more important. This study aims to analyze the impact of DPGU control command issuance on the variation of grid node parameters through simulation. An advanced power system simulation software is used to construct a complex distribution grid model containing multiple DPGUs, including a simulation and analysis method of complex distribution network current characteristics and a voltage hierarchical coordinated control strategy based on inverter regulation for low- and medium-voltage distribution networks, and the process of issuing commands under different control strategies is simulated. By comparing and analyzing the voltage, frequency and power changes of each node before and after the execution of control commands, the specific influence mechanism of DPGU control on grid stability is revealed. The experimental results show that a reasonable control strategy can significantly improve the stability of grid node parameters and reduce fluctuations and mismatches. The results provide a theoretical basis for the optimization of the DPGU control strategy, which is of great significance for realizing the efficient and safe operation of the power grid.

Min Yang1,2, Hanyu Wang1, Xiaodan Wang1
1North China Sea Marine Technical Center, Ministry of Natural Resources, Qingdao, Shandong, 266061, China
2Harbin Institute of Technology, Qingdao, Shandong, 266061, China
Abstract:

Based on the wide application of satellite remote sensing in the field of ecology and environment, the study builds a remote sensing monitoring system for marine ecological reserve, processes the marine remote sensing data by using the technologies of GeoTools, NetCDF and GeoServer, builds a WebGIS system, and collects, transmits and integrates and processes the marine ecological data through the data collection module and the visualization query module. Selecting Laizhou Bay as the study area, the system of this paper was used to collect and process the ecological remote sensing data within the study area during the period of 2003-2022, and to carry out multidimensional analyses including the factors of sea surface temperature and photosynthetically active radiation, sea surface salinity and degree of eutrophication, and so on. A marine ecological health assessment index system was constructed to assess the ecological health of Laizhou Bay and explore its spatial and temporal distribution characteristics. During the period 2003-2022, sea surface temperature and photosynthetically active radiation (AWEI) in the Laizhou Bay region showed an overall increasing trend, and sea surface salinity showed a slight decreasing trend. The total area of mariculture and the total area of zizyphus culture were generally on the rise, and the eutrophication of the water body in Laizhou Bay was most serious in 2013, with the AWEI reaching the maximum value (1.00), which was mitigated after 2013, and the AWEI was reduced to 0 in 2022.The integrated health index (IHI) of the ecosystem of Laizhou Bay increased gradually in 2003, 2012 and 2022, and the health status changed to “healthy”, and the area of healthy zone expanded to “healthy”. In 2003, 2012 and 2022, the integrated health index (IHI) of the Laizhou Bay ecosystem gradually increased, the health status changed to “healthy”, and the area of the healthy zone expanded by 54.54%.

Yuehui Ye1, Ziming Ye2, Lushan Guo1, Huiqun Zhuo 1
1Horsh (Fujian) Food Co., Ltd., Zhangzhou, Fujian, 363000, China
2Academy of Arts, Minnan Normal University, Zhangzhou, Fujian, 363000, China
Abstract:

Based on digital simulation technology, this paper proposes a food packaging design model and a food production efficiency improvement model with food production as the research entry point. Establish the overall structure of the virtual reality design environment, the parameters of the packaging design process is converted into basic parameters to describe the problem, and the data is fed back to the CAD system to realize the design work. Design the hybrid optimization genetic algorithm based on annealing principle, adjust and optimize the production process and initialize the operation, simulate the annealing genetic algorithm process, and complete the production and processing scheduling sequence. Take A Food Co., Ltd. as the research object to carry out food packaging design and production efficiency improvement practice. The egg cake product packaging design scheme constructed by using the packaging design method in this paper obtains the total attention time of the subjects to be 149.3s, and the subjective score value reaches 85 points, which is better than the original packaging design. And in the real simulation of production using the production efficiency improvement method of this paper, the total production process operating time percentage is reduced from 73.8% to 35.1%, and the food production capacity is steadily increased by about 6%.

Haoyu Liu1, Yuqi Cai1, Hongbin Zhou1, Neng Wang1, Shanfeng Huang1
1Western Maintenance and Test Branch, China Southern Power Grid Energy Storage Co., Ltd., Xingyi, Guizhou, 562400, China
Abstract:

In the development process of China’s power system, automatic monitoring mode has become an important development direction. In this context, how to achieve real-time monitoring of power data in the system has become an urgent problem. In this paper, considering the current range, the controller with input current is selected to collect voltage and current data signals and detect their circuits. Through metadata integration, the semantic integration of data expression is solved to achieve the management of electric power metadata. The collected data are sequentially accessed, handled and processed, the calibration of the voltage and current signals is agreed upon, the AD-converted values are read and the electrical parameters are calculated. Using the communication protocol IEC61850, the processed electric power data is uploaded into the server to complete the electric power data reading and monitoring tasks. The real-time management platform of intelligent maintenance power box constructed in this paper is used to monitor the abnormal power data. The abnormal power data appeared at different times, and the peak value of abnormal value 1 appeared at 14:00, and the peak data was 0.91 w. The evaluation value interval of the security threat in the transmission of power data is between 100-200 g, and the energy interval fluctuates around 1000 c. The results obtained are more reasonable, and the security of the data is guaranteed.

Shuai Chen 1
1School of Marxism, Fujian Agriculture and Forestry University, Fuzhou, Fujian, 350002, China
Abstract:

This study aims to quantitatively analyze the impact of agricultural scientific and technological progress on rural economic growth. The contribution rate of agricultural scientific and technological progress in place A is measured through beyond logarithmic function model setting, data collection and processing. An agricultural carbon emission measurement model was built, in order to analyze the dynamic changes of total carbon emissions in place A. In addition, the gray correlation analysis algorithm was used to rank the correlation between agricultural science and technology indicators and economic growth in place A. Finally, a regression model is designed to analyze the impact of scientific and technological progress on rural economic growth. The coefficient of the t2 term of the contribution rate model of scientific and technological progress is 0.0013, which is greater than 0, indicating that there is scientific and technological progress in 2017-2023 in place A. The carbon emissions in place A decrease year by year with scientific and technological progress. All indicators in agricultural science and technology inputs can promote agricultural economic growth, and the gray correlation value in descending order is, T3>T9>T8>T1>T6>T4>T7>T2>T5. Scientific and technological progress has a different degree of promotion for the rural economic growth in place A.

Yan Shi1, Luxi Zhang1, Yi Zhang1, Zhiyuan Cao2, Rui Zhang1
1State Grid Mengdong Power Supply Service Supervision Center, Tongliao, Inner Mongolia, 028000, China;
2State Grid East Inner Mongolia Power Supply Service Supervision and Support Center, Tongliao, Inner Mongolia, 028000, China
Abstract:

In this paper, the modelling and fault monitoring methods of virtual power plants are investigated. Aiming at the risks faced by the virtual power plant, a virtual power plant dynamic model based on BPNN is proposed, which uses neural networks to establish the relationship between the uncertainty factors and the technical parameters of the virtual power plant, and adjusts the technical parameters of the virtual power plant in real time according to the size of the uncertainty factors. The technical parameters of the virtual power plant are optimised to obtain the parameters that maintain the optimal performance of the virtual power plant. At the same time, in order to be able to comprehensively monitor the failure of the virtual power plant, play a role in early warning, starting from the real-time database of the equipment, the data from a variety of sources to the equipment as the centre of the fusion. Multiple state parameters of the equipment are tracked in real time and displayed in the form of trend graphs, which completes the analysis of the parameters of the fault characteristics in the database and achieves a nonlinear mapping from characteristics and signs to the cause of the fault and the type of fault. Based on the BPNN dynamic model, the SMAPE is 6.51%, and after using the model constructed in this paper to monitor the virtual power plant, the failure rate of the virtual power plant decreases month by month, and the failure rate is much smaller than that before the model is used. It verifies the good performance of the method of this paper, and also shows that the method of this paper has a broad application prospect in the field of fault monitoring and warning of virtual power plant.

Yan Shi1, Siteng Wang1, Rui Zhang1, Luxi Zhang1, Yi Zhang 1
1State Grid Mengdong Power Supply Service Supervision Center, Tongliao, Inner Mongolia, 028000, China
Abstract:

With the access of multiple renewable resources to virtual power plants, hundreds of millions of power time series data are generated every day. A sparse learning-based power data compression and reconstruction processing method is designed in the study, which effectively solves the problems of low computational efficiency in the data processing centre of the virtual power plant and the waste of storage resources. According to the vector principal component analysis method, the power data are compressed. Then the data reconstruction network model is constructed based on sparse learning to achieve the reconstruction of power data. The experimental test results show that the median absolute errors of reconstruction of active and reactive power data are 4.05 MW and 0.885 Mvar, respectively, and the percentages of absolute errors are not more than 5%, which makes the reconstruction performance highly stable. The method achieves high-quality power data compression and highprecision reconstruction processing, which is of great significance for improving the computational efficiency of the virtual power plant data centre and accelerating the digital transformation of the power grid.

Wei Zhu1, Baojing Zheng 2
1Urban Vocational College of Sichuan, Chengdu, Sichuan, 610000, China
2City University of Macau, 999078, Macau
Abstract:

With the arrival of the aging society and the continuous improvement of human civilization, people pay more and more attention to the quality of existence, quality of life and happiness index, and the elderly service is becoming a hot issue of social concern. The article proposes a set of intelligent monitoring system for the elderly based on ROS service robot in the context of big health. The system is based on the machine vision following module to design the neural network-based fall detection module and the monitoring module of power consumption abnormality to realize the remote contact method between the elderly and the guardian. The article measures the quality of life and happiness index of 600 elderly people in old age through questionnaires, and systematically understands and comprehensively grasps the influence and effect of the monitoring system proposed in this paper on the quality of life and happiness index of the elderly from seven target levels and several index levels, including the quality of healthy life, economic quality of life, family quality of life, social quality of life, cultural quality of life, personal value realization and sense of identity and belongingness , with more than 97% of the elderly believing that the quality of cultural life has been improved by utilizing this AI intelligent machine.

Lixia Cui1, Lixian Xu 2
1Xi’an Fanyi University, Xi’an, Shaanxi, 710105, China
2Sanechips Technology Co., Ltd., Shenzhen, Guangdong, 518055, China
Abstract:

The traditional Japanese language teaching mode in colleges and universities has been unable to meet the requirements of Japanese language majors in various industries, and colleges and universities should use certain methods to carry out a reasonable reform of the teaching mode of Japanese language majors. Firstly, an error correction model based on UniLM model framework is proposed, using natural language processing technology to extract features, and fine-tuning training for the model after initialization. The model framework based on UniLM+CRF and the seq2seq model framework based on UniLM are built to realize the Japanese text grammar error annotation task and the Japanese text grammar error correction task respectively. Then a multi-task learning error correction method is proposed to integrate the grammar error labeling task and the grammar error correction task, so as to improve the accuracy of the error correction model. Finally, a specific Japanese grammar error correction system architecture is designed, a Japanese language knowledge base is established, and utterance synthesis rules are formulated to realize the innovative teaching of Japanese language in colleges and universities. The average grammatical error correction precision, recall, and F1 value of the model in this paper reached a good level in the students’ Japanese composition correction. The error between the average score of teacher correction and the average score of model correction is only 0.19 points, and the related experiments show that the innovative teaching model studied in this paper can effectively improve students’ mastery of Japanese syntactic ability. The above data illustrate that the Japanese error correction system based on UmiLM framework designed in this paper has certain application value and can realize the innovation of Japanese language teaching mode.

Xinming Fan 1
1School of Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, 224051, China
Abstract:

Target tracking is a fundamental task in the field of computer vision, which has a wide range of applications in real-life video image signal processing. This paper proposes target tracking optimization technique based on the principle of multi-scale convolutional neural network and multitarget tracking algorithm. The basic structure is designed using VGG16 network, the ROI align method is used to reduce the number of features for feature fusion, and the improved Hungarian algorithm is adopted to associate the fused features and obtain the target tracking results. In the tracking performance experiments, the target tracking optimization technique in this paper is more discriminative in terms of extracted features, and also has higher tracking results under challenging factors such as background clustering (BC), scale variation (SV), and out-of-view (OV). As for the target tracking experiments on mobile network video images, the average tracking accuracy and average tracking success rate of this paper’s method are 97.89% and 96.02%, which are better than DS_v2 and FFT16, and the average error between the target tracking results and the target’s actual motion trajectory is 4.12mm, while possessing the smallest error amplitude.

Ling Gu 1
1School of Humanities and Law, Gannan University of Science and Technology, Ganzhou, Jiangxi, 341000, China
Abstract:

In this paper, information theory and information metrics are used to obtain an approximate estimation of linguistic information entropy. After that, the binary model of large-scale corpus and foreign language words is established, N-Gram model is constructed, and the information entropy of modern foreign language speech is estimated. Finally, the N-Gram model was utilized to statistically analyze the results of interpreting information loss, comparing the rate of information transfer in foreign language speeches and the subjects’ interpreting performance. The results showed that the phenomenon of information loss was prominent, with many types of loss, high frequency, and serious loss situations. T assertions had 8.61%-18.95% of propositional information loss, 3.0%-7.6% of constituent information loss, and 49.68% of overall loss. The data on the information loss of each language component showed that TPO and SPE presented the most and the least frequency among the 6 propositional information losses, which were 67 and 1 times, respectively. Among the 13 types of information component loss, TFLS presented the highest frequency and TLE and SFLO presented the lowest, with their losses of 55, 1, and 1 times, respectively. In the interpreted material of English speech, the rate of narration was 2.25 words per second and the average rate was 13.45 bits per second. Among the T assertions, numbers S7, S4, and S9 have the highest propositional untranslated rate (21.8%), propositional mistranslated rate (23.5%), and propositional information loss rate (44.5%), respectively; the corresponding lowest values are at S4 (2.7%), S5 (1.8%), and S4 (2.8%).

Yuanyuan Ji1, Wei Xu 1
1Jiaxing Nanyang Polytechnic Institute, Jiaxing, Zhejiang, 314000, China
Abstract:

Virtual reality technology, as a trend of the development of the new era, has a profound impact and influence on traditional art design teaching. This paper combines virtual reality technology to construct a classroom teaching interactive analysis system to help art design teaching reform. All the objects required for the art design teaching scene are modeled in 3Ds Max, the mapping of each object in the scene is beautified using Photoshop, the FBX format file with animation effect is output, and Unity3D is used for the design and development of the VR part. Subsequently, a quantitative coding form for classroom observation, methods and rules for behavioral data collection, and a classroom migration matrix for analysis were designed to analyze the teaching interaction behaviors in the smart classroom classroom from the micro level. The characteristics of the teaching model in the art and design classroom were captured at the macro level based on the S-T analysis. A teaching experiment was conducted in a school’s art design program after the FIAS analysis. The improved art design classroom interaction increased for the blended teaching mode, and the average of the pre and posttest scores of the experimental class applying this model were 74.18 and 84.37 respectively, and there was a significant difference, which was a significant improvement over the control class. This study provides new ideas and methods for the teaching reform of art design majors in higher education institutions.

Li Jin 1
1Shanghai Xinnasongshan Biopharmaceutical Technology Co., Ltd., Qingdao, Shandong, 266100, China
Abstract:

Disease prevention has always had an important impact on the development of human life and health. The integration of complex network theory and disease has become one of the major trends in epidemiologic research. However, aspects such as individual vaccination behavior and vaccination costs are affected by social capital investment. Based on this, the article investigates a reinforcement learning model of social capital investment on disease prevention. Based on the mechanism of infectious disease dynamics on complex networks, the article investigates the Markov decision process and composition of the reinforcement learning model, and utilizes the theory related to infectious disease dynamics and reinforcement learning to study the mechanism of voluntary vaccination based on epidemic perception. It was found that when the ratio of two kinds of investment (partial investment and full investment) reaches the set maximum value, the full investment policy of targeting selection is more effective in reducing the scale of disease infection in the whole social network and reducing the total social cost, followed by partial investment, and the full investment policy of random selection brings the smallest effect. However, the results may differ for different population investment ratios and partial investment ratios, and both the full investment policy and partial investment policy can effectively control disease prevention, which is conducive to the healthy and prosperous development of the whole society.

Lei Wang1, Panpan Ge 1
1Department of Physical Education, Anhui Wenda University of Information Engineering, Hefei, Anhui, 231201, China
Abstract:

Variable speed running training method is an efficient training method targeting the improvement of athletes’ agility. In this paper, 30 male students from two badminton special classes of physical education majoring in a college of 2021 were selected as experimental subjects, and hexagonal ball reaction test, hexagonal jump test, repeated straddle test, standing bench press test, closed-eye in-situ step test, and low gravity center of gravity quadrangular running test were chosen as the evaluation indexes of agility quality. A new high-resolution multi-scale feature fusion network was designed for running stance estimation, and the effects of variable speed running training method and conventional agility training on the agility quality of young badminton players were analyzed. The performance curve of the RHPNet designed in this paper has low convergence difficulty and high recognition accuracy, which tends to 0.83, and performs much better than the LSTM network. The intergroup data after the experiments of the experimental group and the control group show that there are significant differences in the performance of the hexagonal jump test, the 20s repeated straddle test, the hexagonal ball reaction test, and the closed-eye in-situ step test. It verifies the effectiveness of the network designed in this paper in the estimation of athletes’ movements during running, and also shows that the training effect of variable speed running training is better than that of conventional agility training.

Xiaolu Xu 1
1School of Digital Tourism and Culture, Sichuan Vocational and Technical College, Suining, Sichuan, 629000, China
Abstract:

With the rapid expansion of the Internet and e-commerce, and the rapid revolution of the consumption mode, customer reviews have become the most important feedback means for customers’ preference and satisfaction level of products nowadays. In this paper, hotel customer reviews are used as the basis for predicting hotel customer satisfaction, and the TF-IDF feature word extraction method is proposed to extract review feature words. Based on deep neural network, we propose the sentiment analysis technology of hotel customer reviews, use BERT neural network to construct aspect term extraction model, realize the sentiment recognition and quantification of hotel customer reviews, and combine the fuzzy comprehensive evaluation and IPA analysis as the prediction and analysis model of hotel customer satisfaction. Taking 25837 customer reviews of XC Hotel as a research sample, we explore and analyze the satisfaction of XC Hotel customers. The secondary and primary features of the reviews were extracted by review feature words, and the themes were extracted by LDA theme mining model, which concluded that the evaluation items of concern for XC Hotel lie in location, facilities, hygiene, service, price, and food and beverage. The prediction results showed that 69.59% of customers were satisfied, 18.42% felt average and 11.99% were dissatisfied. IPA analysis of satisfaction and importance of XC Hotel and its visualization were conducted, and the intelligent service management model of the hotel was constructed based on the results of IPA analysis, and the optimization strategy of intelligent service of the hotel was proposed.

Lili Yang 1
1Fuyang Normal University, Fuyang, Anhui, 236037, China
Abstract:

This paper carries out a research on the quantitative evaluation of classroom behavior based on the maximum information entropy model, explains the theory related to information entropy and analyzes the concept of information entropy. The improved iFIAS interactive analysis system was used as the main analysis tool, and S-T analysis and time series analysis were used as auxiliary analysis methods to analyze the classroom teaching behaviors. Classroom teaching behaviors were coded and sampled, from which classroom teaching behavior related data were obtained, through which teaching behavior information entropy, redundancy, interaction mode, teaching mode and behavior category frequency were analyzed. After analysis, the overall classroom teaching characteristics of the psychoeducational quality lesson examples are teacher behavior and student behavior as the main, psychoeducational and teaching materials as the secondary, and the teacher’s behavior in the early part of the classroom accounts for a high proportion in order to drive the students into the classroom. The proportion of student behavior rises in the middle and late stages. The proportion of hybrid and dialogic teaching mode is 95.83%, which is dozens of times more than the proportion of lecture. It reveals the teaching mode of quality psychology classroom teachers, i.e., focusing on the interaction with students and replacing the pure teacher lecture student acceptance mode with interactive counseling student active learning. The teaching analysis of quality classroom teaching behaviors with the maximum information entropy model realizes the establishment of an innovative model of psychological teaching and clarifies the direction for the future development of psychoeducational classrooms.

Haizhi Wei 1
1Kexun Jialian Information Technology Co., Ltd, Hefei, Anhui, 230000, China
Abstract:

The service efficiency of intelligent customer service robots affects the service operation efficiency of enterprises and plays an important role in maintaining customer resources. This paper applies multimodal interaction technology to intelligent customer service system, takes multimodal big language model Qwen-VL as the core, proposes a two-stage relationship multimodal relationship extraction framework based on big language model, realizes multimodal relationship extraction with the help of high-quality auxiliary knowledge, integrates dynamic semantic features and static structural features to complete the multimodal emotion polarity prediction, and constructs multimodal retrieval Q&A system to improve the performance of smart robot performance. Applying the intelligent customer service system in this paper for service practice, the conversation between the intelligent customer service robot and the customer usually ends in about 50 rounds, and the service efficiency is relatively efficient. In the face of customer emotional sentences labeled as happy, complaining and angry, the recognition accuracy under multimodal sentiment analysis is greater than 99%, and the behavior of “notification” and “confirmation” service behavior accounts for the largest proportion of behaviors, and the number of behaviors reaches 560,365 times, 365976 times, which is in line with the expected service behavior of intelligent customer service robots.

Jingjing Xia 1
1College of Information Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan, 450046, China
Abstract:

In this paper, some important algorithms in the field of target detection and tracking are optimized. Firstly, Gaussian modeling is performed in the color space for the dynamic background, and the priority is set for ranking. Then introduce adaptive Gaussian component number mixing, adaptively change the weights, and adaptively change the number of mixed Gaussian components according to the pixel color change in the scene to improve the convergence speed of the complex scene. Finally, Kalman filtering and mean drift algorithm are combined to ensure the robustness of target tracking in complex scenes. The single-frame detection time, accuracy, and average tracking error of the algorithm designed in this paper are examined on the dataset, and it is found that the time consumed by the algorithm in this paper in the three scenarios is 242ms, 323ms, and 274ms, respectively, with the highest accuracy of 96.9% and the average tracking error of only 1.5 pixels. The optimization algorithm designed in this paper is able to adapt to the slight disturbance of the background scene and overcome the influence of noise and ambient lighting, which is a target detection and tracking algorithm with good robustness.

Ranran Yan 1
1Jiangxi University of Technology, Nanchang, Jiangxi, 330098, China
Abstract:

Behavior of different types of English learners tends to follow different patterns and characteristics, and the analysis of behavioral data is one of the directions for improving English learning and teaching. This study designs a set of behavioral analysis methods based on machine learning for English teaching and learners. The learners’ behaviors are firstly operated with feature extraction and quantification, and the behavioral data are clustered by using the systematic clustering method (HCM) to improve the SOM model. 1DCNN is used to process the learning time-series data and enhance the data mining and performance prediction ability by BiLSTM and attention mechanism, respectively. This paper distinguishes five categories of English learners, such as excellent, diligent, average, procrastinating and negative, and filters out the factors that are highly correlated with English performance, such as the download of learning resources and the number of times of teaching viewing. Comparison experiments show that the ACC of this paper’s achievement prediction model = 0.53, which is better than other comparison methods. Therefore, the idea of this paper based on machine learning methods to analyze the behavior of English teaching and learners has feasibility.

Hong Yao 1
1Nantong Vocational University, Nantong, Henan, 226007, China
Abstract:

In this paper, the evaluation system of college students’ innovation and entrepreneurship education is constructed and the indexes are assigned by combining the hierarchical analysis method. After that, PSO algorithm is introduced in the optimization of weights and thresholds of BP neural network, the neural network model using particle swarm optimization (PSO-BP) is constructed, and the process of PSO algorithm optimization of BP neural network is described. It was found that the combined weight of five indicators, namely, “examination results of innovation and entrepreneurship courses, entrepreneurial experience, participation in centralized entrepreneurship training camps, obtaining financial support from entrepreneurship funds, and participation in innovation or entrepreneurship clubs”, accounted for more than 10%, while the combined weight coefficients of the rest of the indicators were all below 0.1. Compared to the BP model, the PSO-BP model has better network performance and its training samples have higher correlation with the test samples. In addition, the PSO-BP model can be used for predicting data prediction after 9 iterations of training, and the maximum relative error between the actual value and the expected value of the model network test output is very small (<1.4272%), which makes the model ideal. After PSO optimization PSO-BP model has almost no prediction error (<0.34%), which can improve the evaluation efficiency and accuracy.

Wang Liu 1
1School of Marxism, Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, 421005, China
Abstract:

This study aims to explore how Hunan higher vocational colleges and universities can build a new ecosystem of industry-education integration through linear programming optimization strategies under the guidance of the strategy of developing the country through science and technology. The article evaluates and analyzes the ecosystem of industry-teaching integration in Hunan higher vocational colleges under the strategy of developing the country through science and education using linear programming method, and proposes relevant optimization strategies using the dyadic model of linear programming. The main factors affecting the efficiency of industry-teaching integration are identified through multiple regression analysis, including industry-teaching resources, incentive mechanism and management system. According to the linear programming model for maximizing the efficiency of industry-teaching integration in higher vocational colleges and universities, it is calculated that the efficiency of industry-teaching integration is maximized when Hunan higher vocational colleges and universities invest 3.6 million yuan, 0.3 million yuan and 0.5 million yuan in the resource consumption of industry-teaching resources, incentive mechanism and management system respectively. And it is proposed to build a new ecology of industry-education integration from three aspects of platform construction, cooperation docking and parenting path, respectively.

Jing Zhang 1
1Faculty of Finance and Trade, Guangdong Vocational Institute of Public Administration, Guangzhou, Guangdong, 510800, China
Abstract:

The stability of the supply chain has a significant impact on both the strategic deployment and operational efficiency of the enterprise, in order to optimize the supply chain management model for the enterprise and resolve the major supply chain risks, this paper realizes the optimization management and risk assessment of the supply chain through Monte Carlo simulation algorithm and SVM. Taking the newsboy problem as an entry point, a supply chain management optimization model is constructed, and Monte Carlo simulation algorithm is used to solve it. Using SVM regression and assessment ideas, supply chain risk regression assessment is carried out by C-SVR. Applying the supply chain management optimization model, it is concluded that when the optimal inventory of prefabricated components of the selected construction unit is 2.55×10³m³, the enterprise profit is the largest, which is 902.31×10³m³ yuan. Supply chain risk assessment of a port, the training error and prediction error of the assessment model in this paper are only 0.043% and 1.76%, which are significantly better than the BP neural network assessment method. Therefore, it proves that the work in this paper achieves the optimization and risk assessment of enterprise supply chain management model through simulation algorithm.

Jiajia Zhao1
1Wudangshan International College of Wushu, WuHan Sports University, Shiyan, Hubei, 447214, China
Abstract:

Inheriting national sports culture plays an important role in promoting national culture and national spirit. The digitization technology’s has played a role in promoting the development and dissemination of traditional sports culture. Based on the digitization model of traditional sports and related research materials and audience comments, the article extracts effective data to construct a model of the influence factors of digitization of traditional sports. Trust, cultural confidence, inheritance willingness, perceived usefulness, perceived ease of use, inheritance resistance and technology anxiety are taken as eight latent variables. Relevant hypotheses are proposed for the relationship among the eight variables. Through the questionnaire method, 321 valid data collected were validated and analyzed. Including the use of structural theory model for reliability analysis, correlation analysis, path coefficient test, etc., it is finally concluded that the mediating effect of perceived usefulness and willingness to pass on is obvious, while the mediating effect of perceived ease of use is relatively insignificant, and it cannot play a significant role of acceptance between resistance to passing on to audience acceptance. Trust, cultural confidence, perceived usefulness, perceived ease of use and inheritance willingness are positive feedback relationships to audience acceptance, and inheritance resistance to inheritance willingness and technology anxiety to audience acceptance are negative feedback relationships.

Shaohua Zhao1, Jiasheng Wu1, Chao Dong1, Junyu Zhu1, Liangrui Zhou1, Yi Yang 1
1CGS POWER GENERATION(GUANGDONG)ENERGY STORAGE TECHNOLOGY CO., LTD, Guangzhou, Guangdong, 510630, China
Abstract:

This paper constructs a three-dimensional model of energy storage power station through threedimensional visualization technology, and builds a virtual simulation environment of energy storage power station by inputting realistic environmental parameters. Four different energy storage technology routes, namely lithium-electronic battery energy storage, lead-acid battery energy storage, pumping energy storage and air compression energy storage, are selected, and the energy storage performance of the four technology routes is explored in depth based on the constructed virtual environment. At the same time, the energy storage performance of four different technology routes in the virtual environment of the energy storage power station is compared using the energy storage capacity and energy storage efficiency as the measurement indexes, and the energy storage technology routes suitable for the environment of this paper are highlighted based on the comparison results. In the energy storage simulation, the net energy storage capacities of the four technology routes in the virtual environment of this paper are 728.99MW, 724.18MW, 461.50MW and 393.45MW, respectively. Compared with the other three energy storage technology routes, the lead-acid battery energy storage capacity fluctuation is smaller, and the energy storage capacity is higher, with a higher degree of adaptability to the virtual simulation environment in this paper. At the same time, the average energy storage efficiency of lead-acid battery in four quarters is 99.71%, compared with the next highest efficiency of lithium-electronic battery energy storage efficiency increased by 14.29%, which further indicates that the lead-acid battery energy storage technology route in this paper builds the best performance of the virtual simulation environment of the energy storage power station.

Shuai Zhao 1
1School of Digital Media and Performance, Sichuan Geely College, Chengdu, Sichuan, 641423, China
Abstract:

Ethnic folk dance in Southwest China is known for its unique regional characteristics and cultural background, and the optimization of its movement choreography strategy is especially crucial for the inheritance and development of this artistic influence. In this study, an optimized graph neural network model is used to choreograph the movements of folk dances in Southwest China. The model is equipped with multi-feature fusion, spatial modeling and temporal modeling modules, which can maximize the recognition performance of the graph neural network model. Based on the model, a framework for automatic generation of folk dance movements is designed, and the model is trained and validated using Laban-16 and Laban-48 dance movement datasets. The experimental results show that the method of this paper is well tested, and the loss value and accuracy convergence algebra of the training set and the test set are basically the same, reaching 0.25 and 96%, respectively. The lower limb motion recognition rate on Laban-16 dataset is improved by 5.21%~15.81% compared with the comparison model. Under the music of different rhythms, a variety of dance movements can be reasonably choreographed to, and the feasibility score of the model by experts is between 85 and 95, indicating that the model in this paper has practical value.

Yueli Zhou1, Shaohua Zhao1, Jiasheng Wu1, Qihua Lin1, Xiaodong Zheng1, Hanfeng Bai 1
1CGS POWER GENERATION(GUANGDONG)ENERGY STORAGE TECHNOLOGY CO., LTD, Guangzhou, Guangdong, 510630, China
Abstract:

Distributed energy storage technology can effectively solve the load peak-to-valley difference and voltage quality problems faced by distribution networks. Reasonable and efficient scheduling of distributed energy storage in distribution networks is an important means to play its role. The study proposes a power prediction-based optimized scheduling strategy for distributed energy storage in distribution grids with hierarchical zoning. Firstly, power prediction is carried out using GWO-EEMDBP neural network. Then partition optimization is carried out according to distributed power and load distribution, and the energy storage scheduling strategy is formulated based on the energy storage power prediction interval. Finally, experiments and arithmetic examples are analyzed based on the data related to the distribution system of the IEEE-33 distribution node. The predicted SOC values based on GWO-EEMD-BP neural network are basically consistent with the real SOC values. After applying the energy storage scheduling strategy designed in this paper, the system power loss decreased by 260.86 kW∙h and the load volatility decreased by 67.5%. In addition, this strategy has significant advantages in terms of system operation economic efficiency and voltage quality improvement, and it is capable of scheduling distributed energy storage in the distribution network in a reasonable manner.

Yun Zhou 1
1College of Architecture and Urban Planning of Chongqing Jiaotong University, Chongqing, 400074, China
Abstract:

With the rapid development of the regional economy, the urbanization process is gradually accelerated, and the ecological safety problems of the urban water body network gradually appear, so this paper is based on linear planning to optimize the ecological landscape water body network. The study first gives a detailed description of the linear planning theory and highlights the gray linear planning model used. Based on the ecological constraints of the landscape pattern quantity optimization research, the “top-down” gray linear planning model from six aspects to build ecological constraints and objective function, through the simplex method to solve, resulting in different scenarios of the total amount of control of the optimization program. Three practicable optimization scenarios are obtained through repeated debugging of the optimization results, and the three scenarios achieve different results in terms of economic and ecological values. In this paper, effective optimization schemes are proposed for different optimization purposes, which on the one hand make the optimization results more realistically reflect the changes of the ecological landscape water body network, and on the other hand provide an optimal model for the management and development of the ecological landscape water body network, and promote the sustainable development of the region.

Ting Huang1, Yurun Li 2
1Music College, Sichuan University of Science & Engineering, Zigong, Sichuan, 643000, China
2Music and Dance School, West China Normal University, Nanchong, Sichuan, 637000, China
Abstract:

Vocal singing is a key art form of many stage singing arts, specifically including acting and singing. The study firstly is to introduce the detection principle of YOLOv5 target detection algorithm, on the basis of which the original YOLOv5 algorithm is improved by reconstructing the backbone network with the use of SENet and GhostNet, then the original YOLOv5 algorithm and the improved YOLOv5 algorithm are tested for comparison, and the test results show that on the target detection dataset Precision, Recall and mAP values reach 85.75%, 72.34% and 78.48% respectively, which are all improved compared with the original algorithm. Secondly, a high-resolution human posture estimation network incorporating multiple attention mechanisms is proposed to further extract multi-scale feature information and global feature information, and validated on publicly available datasets, CDLNet has an AP value of 0.662 and an AR value of 0.731 on the vocal singing posture estimation dataset, comparing with similar methods, the method has an MPJPE in Human3.6M The lowest is 44.6, which is suitable for use in vocal singing posture estimation in vocal singing scenarios. Finally, an action recognition model based on multi-granularity spatio-temporal graph convolutional neural network designed in this paper is used to analyze the singing gesture action recognition for singing action categories, and experiments show that the average recognition rate of MGstgcn can reach 86.5% on the HSiPu2 dataset, which meets the demand of vocal singing gesture action recognition analysis.

Siyuan Yuan1
1School of Foreign Languages, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan, 450000, China
Abstract:

This project focuses on the classroom interaction of college English and proposes a framework for optimizing college English classroom interaction by integrating big data. Taking the behavioral analysis layer as the entry point, using PSO’s improved K-mean clustering algorithm, we focus on analyzing the specific application of data mining technology in students’ learning behavior. Then we conduct experiments on two classes of students in a university, design classroom behavioral coding to analyze classroom interaction behavior, and explore the application effect of this English classroom interaction optimization pathway. The students were divided into six categories through cluster analysis, with focused learners (22%) and continuous learners (36%) having the highest fidelity scores and the largest proportion, and the analysis of students’ learning behaviors can provide a reference for teachers’ classroom teaching. The composition of the English interaction optimization classroom changes from teacher-led to student-led in the traditional classroom, the teacher-student speech curves intersect each other and both appear four peaks, showing good classroom scope and teacher-student interaction effect, and the path of interaction optimization in the English classroom based on big data is practicable.

He Jiang1, Xiaoru Li 1
1Taiyuan University, Taiyuan, Shanxi, 030012, China
Abstract:

In this paper, we understand the shortcomings of the current mainstream IoT privacy protection methods through analysis, and in this way, we propose an evolutionary and signaling game model for IoT privacy protection. The model analyzes the stabilization trend of IoT platform penalty coefficients on privacy protection and provides protection strategies. Combining the implications of the signaling game model, the degree of IoT privacy protection is measured using the Bayesian equilibrium solving algorithm. Simulation experiments are conducted to evaluate the specific effect of the model on IoT privacy protection. The increase in the detection rate of the model accelerates the convergence of the probability of malicious nodes, e.g., when the detection rate increases from 0.7 to 0.9, the convergence time is reduced by about two stages. The larger the penalty amount of the IoT platform, the model recommends more aggressive protection strategies, and the probability increases from 0.16 to about 0.4. The game parameters of the model reflect the malicious behavior in IoT, and the trust level affects the game parameters. The model in this paper reduces the attack gain by 4% to 10% compared with the comparison model when the fixed defense gain is 1500, which can better reflect the influence of protection signals on the attacker’s actions.

Guo Li1, Minghua Wang 2
1College of Intelligent Manufacturing and electrical engineering, Nanyang Normal University, Nanyang, Henan, 473000, China
2Shandong Gete Aviation Technology Co., Ltd, Jinan, Shandong, 250000, China
Abstract:

In order to strengthen the construction of network security defense system and effectively respond to new types of threat attacks appearing in the network environment, this paper constructs a network security threat prediction model using data mining algorithms. The network security threat posture needs to be assessed before the security threat prediction. Accordingly, this paper assesses the four security threat postures of services, vulnerabilities, weaknesses, and hosts on the basis of the quantitative assessment method of hierarchical security threat posture. After that, a network security threat prediction model is constructed based on the support vector mechanism, and a genetic algorithm is used to optimize the parameters of the model. The three evaluation index values of MAE, RMSE and MAPE for the GA-SVM-based cybersecurity posture prediction method proposed in this paper are 0.0106, 0.0133 and 0.0222, respectively, which are better than those of the ABC-SVM-based and PSO-SVM-based prediction methods. It indicates that the method in this paper has smaller error and higher accuracy in cyber security posture prediction. This shows that the method in this paper usually achieves better accuracy in cyber security threat posture prediction.

Mingbang Li1, Yong Wang2, Xinze Li3, Liang Wang1
1College of Physical Education and Health, Geely University of China, Chengdu, Sichuan, 610000, China
2Department of Physical Education, Sichuan University of Media and Communications, Chengdu, Sichuan, 611745, China
3School of Education, Shinawatra University, Bangkok, 10220, Thailand
Abstract:

In educational research, more and more scholars recognize the importance of teaching interaction network for learning, and they find that “interaction” is not only the method of learning, but also the learning process itself. Social network analysis provides a new way to study teaching interaction. Through the study of social network analysis, this paper proposes the construction method of teaching interaction network for physical education. In this paper, we take four real physical education courses in L school as the research object to conduct in-depth research, obtain the physical education classroom teaching interaction behavior data, and construct the teaching interaction network. The results of the study show that in the interaction network of the four physical education teaching courses, the teaching behaviors of the community network of physical education classroom 1 are significantly concentrated in B3, B4, B5, and B6, course 2 is concentrated in B4, B5, B6, B9, and B10, the teaching interactive behaviors of physical education classroom 3 are significantly concentrated in B2~B6, and the significant physical education teaching interactive behaviors of course 4 are concentrated in B2, B4, B5, and B6.From the degree-centeredness analysis, there are 33 marginal learners with the number of stored interactions less than or equal to 2 in physical education teaching interactions, which indicates that in this paper’s study of physical education teaching interactions, teachers do not pay enough attention to teaching interactions in a comprehensive way. By summarizing the theoretical basis and practical significance of teaching interaction and social network analysis, it proves that the network construction of teaching interaction in this paper is effective, and at the same time, it also provides a new idea for physical education teaching courses.

Peilan Peng1, Yuanlu Meng2
1ZHIYUAN (Liberal Study) College, Beijing Institute of Petrochemical Technology University, Beijing, 102617, China
2College of New Materials and Chemical Engineering, Beijing Institute of Petrochemical Technology University, Beijing, 102617, China
Abstract:

The traditional English teaching mode in colleges and universities has many problems in cultivating students’ language ability. This paper introduces information technology into task-based English teaching in colleges and universities and constructs a task-based English teaching mode based on SPOC technology. With the orientation of improving students’ language ability, it implements the improvement of English teaching mode in colleges and universities. Using principal component analysis to comprehensively evaluate the relevant indicators of students’ language proficiency in the process of task-based English teaching in colleges and universities, and quantify the effect of the combination of information technology and task-based English teaching on the improvement of students’ language proficiency. Ten classes of students majoring in English in a university were selected and divided into experimental and control groups, and the data related to students’ language proficiency were collected and analyzed at the end of the experiment. The data were downscaled using principal component analysis, and the principal components were extracted according to the eigenvalues and cumulative contribution rate. The comprehensive score of students’ language proficiency is calculated by the comprehensive evaluation function of students’ language proficiency constructed in this paper. The language proficiency of students in the experimental group and the control group is significantly different after the experiment, and the comprehensive scores of students in the experimental group are 53.96% and 61.96% higher than those before the experiment, respectively. It reveals that the introduction of information technology into task-based teaching of English in colleges and universities has a significant effect on the enhancement of students’ language proficiency.

Xiaojing Shen 1
1College of Basic course Teaching Department, Xinxiang Vocational and Technical College, Xinxiang, Henan, 453000, China
Abstract:

Teacher-student interaction, as the most important way of classroom interaction, its level directly affects the quality of classroom teaching. The study selected three English listening classes, three English reading and writing classes, and three English exercise classes, totaling nine English classes in a university for video recording. With the help of the Improved Flanders Interaction Analysis System (iFIAS), the study utilized classroom observation and multiple regression analysis to investigate the effectiveness of teacher-student interactions in the classroom and their influencing factors. It was found that the average value of students’ classroom discourse ratio (40.3%) was smaller than the average value of teachers’ classroom discourse ratio (48.1%), and that a reasonable structure of teacher-student language ratio was more conducive to the formation of benign interactions in the classroom and the enhancement of the overall classroom effectiveness. In addition, teaching ability, learning style, learning motivation and classroom environment all positively affect the effectiveness of English teachers’ classroom interaction in colleges and universities. Therefore, it is necessary to start from these four aspects to adjust the language ratio structure, create a positive classroom atmosphere, and enhance the integration of information technology and the classroom.

Huayang Shi 1,2
1Department of General Education, Henan Vocational University of Science and Technology, Zhoukou, Henan, 466000, China
2Doctor of Education, School of Graduate Studies, Central Philippine University, Iloilo, 5000, Philippines
Abstract:

Under the dual background of the construction of the “new liberal arts” and the digital wave, the interdisciplinary practice of combining humanities and technology continues to develop. Taking a number of Chinese language and literature works as examples, this paper selects language features from the vocabulary and sentence levels, analyzes the syntactic structure of the selected Chinese language and literature works with the help of natural language processing technology and numerical measurement method of language features improved TF-IDF method, and realizes the discussion of the lexical categories of literary works, such as word length, word frequency, word class distribution and word density, as well as the study of sentence categories such as average sentence length, sentence dispersion and sentence class distribution. It is found that most of the utterances of the selected literary works are monosyllabic words and polysyllabic words, the cumulative proportion of both of them is more than 90%, the highest frequency of occurrence is nouns and verbs, both of them are more than 22%, the average sentence length and sentence dispersion do not differ much, and the overall readability of the selected literary works is better, with a free change of syntactic structure and a stronger narrative of the text.

Qingfeng Shi1,2, Jia Hou 1
1School of Art and Design Taizhou University, Taizhou, Zhejiang, 318000, China
2College of Art Hebei GEO University, Shijiazhuang, Hebei, 050000, China
Abstract:

The argument of the article comes from the rapid development of digital technology and the urgent need for the digital protection and restoration of traditional paper horse art. For this reason, this paper proposes a method of digital protection and restoration of traditional paper horse art based on graphics processing technology. The traditional paper horse art image is collected, the image is denoised using mean filtering, the paper horse image is decomposed in gray scale through spatial conversion, and then its double histogram equalization is processed to obtain the color-enhanced image. Combined with the convolutional image restoration strategy, the paper horse art is digitally displayed. The method of this paper can enhance the color of the paper horse art image and retain the original details, and at the same time, in terms of the clarity effect, the method of this paper improves the comparison method by 25.27%~339.39%. In addition, the method in this paper has better image restoration quality with subjective evaluation rating ≥ 4 and higher PSNR and SSIM. What’s more, the scores on the evaluation dimension of digital preservation and restoration effect ranged from 4.02 to 4.48, and the overall effect performance was relatively good.

Gaofeng Su 1
1College of Arts, Beijing Union University, Beijing, 100101, China
Abstract:

Since the introduction of fractal geometry, it has set off a wave of research in the scientific community, and it has been widely used in many fields. This paper firstly introduces the landscape modeling and generating technology based on fractal geometry, and proposes the virtual landscape generating method based on fractal geometry through the study of the regular characteristics of fractal geometry. Combined with the game development of virtual landscape generation diversity, complexity needs, in the fractal Brownian motion model on the basis of the proposed optimization of the generation process for game development. In the simulation experiments of virtual landscape generation, the NME value of virtual landscape generation under the method of this paper is the smallest, which is distributed between 3 and 6, and the generation time is reduced by 31ms and 38ms compared with the average time of the traditional generation method and the SEM method, which shows that the designed virtual landscape generation is able to generate the virtual landscape more realistically. The study concludes with strategies and recommendations for the application of fractal geometry to virtual landscape generation in game development, with a view to contributing to the promotion of virtual generation technology.

Dongli Wu1, Ruo Zhang1, Lijing Zang1
1Department of Fashion Engineering, Hebei Vocational University of Technology and Engineering, Xingtai, Hebei, 054035, China
Abstract:

Intelligent thermoregulation clothing as a new type of functional clothing, the design and development of which is receiving more and more attention. PID algorithm, as a kind of classical control algorithm, realizes the precise control of the clothing temperature regulation system by adjusting the three parameters of proportionality, integration and differentiation. The control system is firstly constructed according to the principle of PID control. Then the PID controller parameters are optimized by BP neural network to improve the response speed and stability of the temperature control system. Finally, the intelligent thermoregulation garment with physical therapy and health care and portable storage is designed. Experimental verification of the parameter self-tuning PID control based on BP neural network, the BP neural network can make the temperature better maintained near the set value, the control effect is more satisfactory. The final design of the smart thermoregulation garment has a body surface temperature retention rate of 98.35% after 30 minutes at -10°C and with the heating function on. The thermal sensation evaluation of the intelligent thermoregulation garment by the subjects in different states is concentrated between “0-2”, indicating that the garment can play a more ideal temperature control effect.

Jie Wu1, Haozhe Yu1, Jing Zhang2, Jiangtao Fu3
1Department of Geographical Sciences, Shaanxi University of Technology, Hanzhong, Shaanxi 723001, China
2Innovation Team on Geo-Human Relationship and Sustainable Development in the Qinba Region, Shaanxi University of Technology, Hanzhong, Shaanxi, 723001, China
3Center for Geological and Environmental Research on Rock and Soil in the Qinba Mountains, Shaanxi University of Technology, Hanzhong, Shaanxi, 723001, China
Abstract:

This paper takes the native vegetation in Hanzhong City as the research object, and constructs a multiobjective linear programming model to optimize the distribution of the suitability of the native vegetation in Hanzhong City. The ArcGIS software was used to test the sample consistency and screen the environmental variables of the native vegetation data in Hanzhong City represented by alfalfa, and the model in the software was used to predict the distribution of alfalfa’s suitability area. Based on the prediction results, this paper constructs a multi-objective linear planning model with economic and ecological benefits as the objective function and the land area of different utilization types as the decision variables to optimize the distribution of the suitability of native vegetation in Hanzhong. At the same time, the fuzzy mathematical planning method was used to solve the constructed model. After the model optimization, the area of fitness distribution of native vegetation in Hanzhong City increased significantly, and the growth of the fitness distribution area of each vegetation by 2080 was 49.61%, 35.51%, 36.41%, 28.11%, 15.36%, 24.75%, 27.92%, 28.40%, 31.22%, and 31.52%, respectively. In addition, the optimization of the distribution of native vegetation suitability using the model of this paper can produce obvious economic and ecological benefits, which fully demonstrates the effectiveness of the model of this paper.

Bo Xia1, Shouyao Liu 1
1Department of Digital Media Art, College of Architecture and Arts, Taiyuan University of Technology, Jinzhong, Shanxi, 030600, China
Abstract:

The study firstly introduces the reinforcement learning theory, and proposes a decision-making method based on reinforcement learning to build a robot for autistic children, centered on autonomous human-robot interaction, with the purpose of serving the task of concentration training for autistic children. Among them, the goal task in the current environment is formulated based on imitation learning in the high level, and the robot’s action selection is realized based on interactive Qlearning in the low level. The decision making based on reinforcement learning to build a robot is applied to train the robot to interact with the training, and the simulation results verify the effectiveness and generalization of the designed algorithm in solving the concentration training path. Using the KANO model to analyze the needs of autistic children, based on which we design a multimodal human-computer interaction system for autistic children’s concentration training, and carry out a personal concentration intervention containing academic tasks for an 8-year-old autistic child, to verify the effectiveness of the multimodal human-computer interaction system in intervening in the concentration behaviors of autistic children, and the results of the study show that: the children’s concentration behaviors of the academic tasks in the intervention period are significantly improved compared with the baseline period compared with the baseline, and the mean value increased to 88.42%.

Xiaojing Xu 1
1Department of Tourism Management, Jinzhong University, Jinzhong, Shanxi, 030619, China
Abstract:

Teaching digitalization and integration of industry and education are developing deeply in the field of education, this study designs and constructs the digital practical training system, innovates the teaching mode of school-enterprise collaboration, and applies it to the teaching practice of tourism specialty. The performance of the digital training system for tourism majors is tested by concurrency test, business success rate test and target system thing test. Design teaching experiments to verify the teaching effect of the digital practical training system and the school-enterprise collaboration model by comparing the gaps and changes between the experimental group and the control group in the competitiveness of students’ employment, the utilization rate of resources, the tourism market research, the tourism marketing, the results of the digital practical training, and the development of tourism projects. The maximum number of users in concurrent testing of the digital practical training system for tourism majors is 20, the average number is 10.182, and all the operations of users are processed, achieving good test results. Before the experiment, there is basically no difference between the two groups in the six aspects of employment competitiveness, resource utilization, tourism market research, tourism marketing, digital practical training results and tourism project development. After the experiment, the two groups showed large differences. The scores of the experimental group were higher than those of the control group in all 6 dimensions, and the difference in the scores of each dimension was more than 5 points. The teaching effectiveness of the experimental group rose more than 4.9 points in all 6 dimensions. And the score difference between the pre- and post-test of the control group is not more than 0.5 points. In this paper, digital practical training system and schoolenterprise collaboration model have better teaching effect.

Xiaojian Chen1, Jiaqi Yuan 2
1Guangzhou Huashang College, Guangzhou, Guangdong, 511300, China
2Zhujiang College of South China Agricultural University, Guangzhou, Guangdong, 510900, China
Abstract:

With the continuous development of virtual reality technology, its application in the digitization of cultural heritage has been constantly emphasized and applied, which has an important role and significance for the protection and inheritance of cultural heritage. This paper proposes a rendering algorithm that combines LOD algorithm and occlusion rejection algorithm. The article firstly carries out theoretical research on the relevant theories and rendering processes of LOD algorithm and occlusion removal algorithm, and finally takes the cultural heritage of Shennongjia as the research object to analyze the performance of this paper’s algorithm in rendering different landscape scenes of the cultural heritage of Shennongjia. This paper concludes that in the high configuration machine, the algorithm of this paper improves the rendering performance by 587% in the resolution of 1280*720, and improves the rendering performance by 1061% in the resolution of 1920*1080. In the low configuration machine, the algorithm in this paper improves the performance by 653% in 1280*720 resolution and 770% in 1920*1080 resolution. Rendering frame rate LOD combined with occlusion culling algorithm (132.65fps) > occlusion culling algorithm (79.88fps) > LOD method (18.02fps) > without any optimization algorithm (5.32fps). The total number of rendering triangles is without any optimization algorithm (55.65) > LOD algorithm (16.78) > occlusion culling algorithm (3.64) > algorithm of LOD combined with occlusion culling (1.05).

Zaizhen Zhang1, Zhenyan Yin1, Nana Shi1
1Agriculture and Forestry Technology College, Weifang Engineering Vocational College, Qingzhou, Shandong, 262500, China
Abstract:

In order to improve the impact toughness and service life of GF/EVE composites, this paper applies the thermoplastic nonwoven fabric structure to the preparation of GF/EVE composites. The thermoplastic polyurethane was used as the raw material, and the meltblown method was used for the preparation of thermoplastic nonwoven fabrics, and then the prepared thermoplastic nonwoven fabrics were used for the preparation of GF/EVE composites through the VARTM device. For the properties of GF/EVE-TPU composites, specific test methods are given to define the moisture absorption rate and the erosion performance based on the consideration of the stress change of its hygrothermal properties, and the determination of the interlaminar fracture toughness is given.The critical damage threshold load of GF/EVE-TPU composites is 1.57kN, and its contact force increases with time, and the composites are aging in After 60 days, its moisture absorption and erosion weight loss in alkaline environment were 0.736% and 81.19%, respectively.The optimum fracture toughness value of 9g/m² thermoplastic nonwoven structure incorporated into GF/EVE composite was 0.97kJ/m², and the GIIC value of GF\EVE-TPU30 was increased compared with the GF/EVE material without interlaminar toughening by 183.83%. Combining the thermoplastic nonwoven fabric structure with GF/EVE composites can enhance the erosion resistance and interlaminar toughness of the composites and improve the service life of GF/EVE composites.

Meijing Zhang1, Yi Chen2, Dongxing Wang 3
1Key University Laborary of Fujian Province Digitalized Cyber Crime Supervision Prevention Control, Fujian Police College, Fuzhou, Fujian, 350007, China
2Collaborative Innovation Research Center of Intelligent Policing, Fujian Police College, Fuzhou, Fujian, 350007, China
3Research Center of New-quality Public Security Combat Effectiveness, Fujian Police College, Fuzhou, Fujian, 350007, China
Abstract:

In the current information age, image tampering detection technology is crucial to ensure the integrity and authenticity of digital media, and remote image tampering detection technology combined with deep neural networks has become a research hotspot. This paper adopts convolutional neural network as the main detection tool, and on the improved DPN network model, the feature fusion module based on the attention mechanism is used to fuse the two features in this paper. In this way, the image tampering detection technique based on dual-stream feature fusion is proposed in this paper. The precision, recall and F value of the detection algorithm in this paper are better than the comparison algorithm. When the image compression quality factor is reduced to 20, the precision rate, recall rate, and F value of this paper’s algorithm do not appear to be greatly reduced, and the reduction is only 0.028, 0.041, and 0.042. This paper’s image tampering detection algorithm, which fuses the frequency domain branching module and the attention mechanism feature fusion module, has a higher detection efficiency. And the Accuracy rate, Recall rate and F Value of this paper’s algorithm on image level detection are 17.8%, 15.3% and 16.3% higher than that of DCT algorithm respectively. In conclusion, the remote image tampering technique combined with deep neural network provides an effective solution to ensure the authenticity and integrity of images.

Ying Hu 1
1Shanghai Institute of Commerce and Foreign Languages, Shanghai,201399, China
Abstract:

Graph neural networks are widely used in image recognition. This paper introduces a two-node graph neural network DouN-GNN model based on a traditional graph neural network. By constructing two nodes, the features in the sample image that are difficult to extract by the shallow embedding network are extracted so that the network model can incorporate more multi-dimensional information about the sample image, thus enhancing image recognition accuracy. Aiming at the problem of the overall performance of the DouN-GNN model not reaching the ideal state, this paper adds three optimization modules to improve the DouN-GNN model and form the IGNN model. The optimized IGNN model is trained, tested, and applied to real-world scenarios such as agricultural weed recognition, natural resource enforcement, and video surveillance to explore the performance of the IGNN image recognition model constructed in this paper in real-world applications. The model achieves the highest accuracy of 98.39% in agricultural weed image recognition, and the classification accuracy for weeds is also high. In natural resources law enforcement and video surveillance, the model in this paper performs better than other image recognition models and can effectively meet the requirements of image recognition in practical application scenarios.

Zheng Yuan 1
1Zhengzhou Academy of Fine Arts, Zhengzhou, Henan, 450000, China
Abstract:

Ceramics have many applications, covering scientiϐic research, medical, industrial, jewelry, etc. Ceramic materials are stable and have a silk-like touch. Ceramic 3D printing technology is based on laser curing molding as a rapid manufacturing technology. This paper proposes a personalized design strategy for ceramic artwork, determines the degree of inϐluence of ceramic process parameters on the quality of laser 3D printed ceramic artwork by calculating the Pearson’s correlation coefϐicient, and adopts numerical simulation to obtain the ceramic 3D printing quality data, calculates the error of the number of printed layers, and controls the quality of the printed ceramic artwork. The ceramic quality parameter optimization model is established. Five algorithms of SVR support vector regression, BP neural network, RF random forest, RBF radial basis function, and Kriging model are used to set up the relevant parameters of 3D printing, input the six ceramic process parameters that have been processed by the uniϐied magnitude, and complete the optimization of the quality ceramic process parameters of laser 3D printing. Through the investigation and analysis of the effect of ceramic artwork design, the ceramic color designed in this paper makes the user generate positive emotions; a total of 235 positive emotions were generated, accounting for nearly 60%. The mean value of user preference for ceramic samples is analyzed. The samples with the highest user preference are sample 4, sample 6, and sample 1, and the mean values of preference are 3.425, 3.245, and 3.148, respectively.

Mingzhi Qi1
1College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong, 266044, China
Abstract:

Currently, the severity of information leakage is increasing, and attacks and protection against cryptographic devices have become a research hotspot in the ϐield of information security. In order to increase the security of SM4 algorithm structure against side channel attack, the paper focuses on the protection scheme of adding masks to cryptographic circuits to resist DPA attack, and proposes a cipher algorithm design method of ϐinite domain additive coding. Experimentally, it is proved that the additive coding SM4 algorithm used in this paper can correctly and efϐiciently perform encryption, and the encryption efϐiciency is improved by 56.54%~82.42% than the general SM4 algorithm. Meanwhile, it has the security against 1st-order and 2nd-order side-channel attacks, and the success rate against attacks reaches 93.67%, which is higher than that of the compared algorithms by 5.34%~21.00%. It also proves that the scheme has high security against side channel attacks and can provide a reliable solution for the information security of wireless LAN.

Yan Hou1, Yan Xiang1, Xin Xiao 2
1School of Intelligent Construction and Environmental Engineering, Chengdu Textile College, Chengdu, Sichuan, 611731, China
2Minmetals Land Limited (Chengdu), Chengdu, Sichuan, 610039, China
Abstract:

People’s performance requirements for air conditioning along with people’s requirements for indoor air quality also continue to improve, air conditioning heat exchanger as an important part of the refrigeration system in the air-conditioning products in the largest proportion of space. Therefore, this paper is based on ϐinite element analysis of air conditioning heat exchanger optimization design, oriented to the needs of air conditioning heat exchanger, heat transfer to the mechanism of depth analysis. The ϐinite element analysis is used to study the heat transfer simulation theory of air conditioning heat exchanger, and the heat transfer optimization design method is proposed, and the heat transfer model based on ϐinite element analysis is constructed. Through the physical model and its numerical simulation method for veriϐication, the numerical simulation value and experimental value of the pressure drop and convective heat transfer coefϐicient error of ± 6.50W/m² ℃ and ± 12.7Pa, respectively, which veriϐies the model of this paper and the feasibility of numerical simulation method for. Comparing the performance of the optimized air conditioning heat exchanger, the optimized heat exchanger in this paper improves the cooling capacity by 0.04~0.50kW and the total pressure drop by 11.19~50.84kPa compared with the comparative models, which proves that the optimized heat exchanger in this paper has better performance and can meet the performance and reliability index requirements of engineering applications.

Dongyun Lin 1
1Guangzhou College of Technology and Business, Guangzhou, Guangdong, 510000, China
Abstract:

This paper studies the application of numerical simulation in visual communication design from two perspectives of artistic expression and technical application, and explores the facilitating effect of numerical simulation method on the intersection of artistic expression and technical application. Based on the improved K-means method, the extraction of the main color of the image is completed, and the extraction results are input into the color matching model integrating visual aesthetics as the label of the color palette. The visual communication design method is constructed based on image processing technology, and the method is realized through numerical simulation, so as to test the effectiveness of the technology application in visual communication design. Compared with other algorithms, the improved K-means algorithm in this paper can effectively realize the extraction of the main color of the image. The visual aesthetics score in the color matching model ϐluctuates within the range of [1.10,7.09], and the main color extraction result of the improved K-means algorithm combined with this score as a parameter can realize the coordinated matching of colors. At the same time, the visual communication design method based on image processing technology shows superior performance in terms of communication success rate and communication consumption time. According to the role of numerical simulation method in artistic expression and technical application, this paper explores the intermingling of artistic expression and technical application, highlighting the important inϐluence of numerical simulation method in the process of intermingling.

Jiani Wang1, Jiahui Zhang2, Jun Wang 3
1International College, Hebei University, Baoding, Hebei, 071000, China
2School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518100, China
3School of Economics and Management, Shanghai Zhongqiao Vocational and Technical College, Shanghai, 201514, China
Abstract:

As a key link in international trade, the price volatility of container transportation has a profound impact on the global supply chain, and uncertainty shocks are one of the main causes of price volatility. With this topic, this paper measures the level of uncertainty at the policy level through the uncertainty index construction method, which lays the foundation for subsequent research. Dynamic correlation and impulse effect analyses of container transportation market prices under uncertainty shocks are conducted using DCC-GARCH and SVAR models. China’s economic policy uncertainty index showed four stages of significant increase in 2001, 2008, 2015 and 2019. The overall price volatility of the container transportation market shows an upward trend, and in 2023, the transportation price is 23,835 yuan. Container transportation prices are affected by the uncertainty of China’s economic policies as well as China’s trade policies, with correlation coefficients ranging from -0.69 to 0.60. The influence of China’s economic policy uncertainty index on container transportation price does not have a long time lag effect.

Wei Xu1, Yu Sui1, Yabin Chen1, Huazhen Cao1
1Power Grid Planning Research Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, 510220, China
Abstract:

Aiming at the shortcomings of traditional relay protection, an adaptive multi-area protection coordination model is studied and designed. Firstly, combining different control strategies such as master-slave control and sag control, a method of AC/DC distribution network trend calculation and network loss analysis based on the alternating iteration method is proposed and realized to ensure that the adaptive relay protection can act correctly. The proposed method is analyzed for AC/DC hybrid distribution network trend calculation, and the alternating iteration solution method is used for trend analysis and calculation, and the effectiveness of the proposed method is veriϐied by two examples of AC/DC hybrid distribution networks. Then the adaptive Agent with reinforcement learning is introduced, and its constructed multi-agent system has more system adaptive capability. The adaptive current interruption protection is compared with the traditional current interruption protection, and its protection principle and protection scope are analyzed, on the basis of which an adaptive coordinated protection method based on MAS grid is proposed to realize the MAS adaptive current interruption protection, and its simulation is veriϐied. The experimental results show that the method of this paper can signiϐicantly improve the ϐlexibility, effectiveness and stability of AC and DC distribution network operation.

Guozhen Ma1, Xiangming Wu2, Po Hu1, Hangtian Li1
1Economic and Technology Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, 050000, China
2State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, 050000, China
Abstract:

The rapid development of the electric power market makes the scientificity and rationality of grid investment decision-making particularly important. In this paper, firstly, we design a grid investment benefit assessment method based on fuzzy comprehensive evaluation. And taking the grid investment benefit of M city in 2022 as an example, the fuzzy comprehensive evaluation method is used to quantitatively evaluate the grid investment benefit. Based on the evaluation results, the weaknesses of power grid investment in M city are found. Then the multi-level optimization strategy of grid investment is further proposed to achieve the maximization of investment benefits. The strategy considers the objectives and constraints of different levels, such as grid structure, power supply reliability, operation efficiency, and power sales revenue, and coordinates the interests between all levels by establishing a multi-objective optimization model to achieve the global optimization of the grid investment decision. Finally, after adjusting the allocation ratio and the allocation amount by the multi-level optimization strategy, the overall evaluation of the city’s grid investment efficiency in 2023 is improved from “average” to “excellent”. It shows that the multilevel optimization strategy designed in this paper can provide scientific guidance for grid investment decision-making.

Guozhen Ma1, Xiangming Wu2, Po Hu1, Hangtian Li1
1Economic and Technology Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, 050000, China
2State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, 050000, China
Abstract:

According to the decision-making process of power grid investment, this paper sets the objective function and constraints, realizes the construction of optimization model, and selects genetic algorithm as the solution algorithm of optimization model. Under the requirement of evaluation index principle, 16 secondary indexes and 4 primary indexes are screened, thus forming the evaluation index system of power grid project investment efficiency. The experimental conditions are set to evaluate and analyze the optimization of investment decision and multidimensional benefits of power grid project respectively. Along with the reduction of voltage data, the diversity of optimal solutions for grid project benefits begins to materialize, and the diversity of optimal solutions of GA algorithm is higher than that of PSO algorithm, indicating that the use of genetic algorithm to calculate optimal solutions for grid investment benefits is more effective. In addition, the closeness of the seven projects to the optimal solution is 0.4613, 0.5044, 0.4681, 0.5398, 0.6342, 0.5759, 0.4116, respectively, of which project 5 has the best investment benefit.

Guowei Liu1, Hao Dai1, Hao Deng1, Lisheng Xin1, Longlong Shang1
1Distribution Network Management Department, Shenzhen Power Supply Co., Ltd., Shenzhen, Guangdong, 518000, China
Abstract:

The study proposes a multi-stage dynamic resilient recovery strategy based on multiple energy storage to cope with distribution network failures after a disaster in a coastal city, and the post-disaster recovery of the urban distribution network is planned in phases, which is divided into the first stage of emergency response, the second stage of energy storage recovery and the third stage of economic optimization. Then the post-disaster defense measures of the coastal city are improved by optimizing the recovery strategy. After the calculation example design, the post-disaster recovery and resource scheduling effects of this paper’s multi-stage dynamic recovery model are examined through simulation experiments. The multi-stage dynamic recovery model of this paper takes 261 minutes to recover the urban distribution network, which is shorter than the 273 minutes of the traditional recovery model, and the post-disaster resilience is improved. The proposed dispatching scheme based on the multi-stage dynamic recovery model in this paper uses only 13 vehicles, which is the least number of vehicles among all dispatching schemes. The traveling path of mobile emergency resources of this paper’s scheme is most consistent with the post-disaster restoration scenario. The combined level of load reactive power and active power restoration of this paper’s scheme is optimal.

Yu Yu1, Yu Wang1, Shucui Tan2, Shining Chen1, Yuqian Mo1
1Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning, Guangxi, 535000, China
2Yulin Power Supply Bureau of Guangxi Power Grid Co., Ltd., Yulin, Guangxi, 537000, China
Abstract:

At present, digital twin technology has been developed in many fields and plays a very important role. In this study, digital twin technology is applied to remote control of power system to build a set of remote control system of power system, which contains perception layer, data layer, operation layer, function layer and application layer. In order to make the power system remote control system more reliable and effective, a power system fault diagnosis method based on MRPSODE-ELM is proposed using deep learning technology. The method combines PSO algorithm and DE algorithm to construct a multiple stochastic variation particle swarm differential evolution algorithm, and it is used for the optimization seeking ability of the number of neurons in the hidden layer of the limit learning machine. The experimental results show that the MRPSODE-ELM model performs superiorly in detecting different fault types in terms of accuracy, recall and F1 score, with the results of each index above 95%, and the fault diagnosis accuracy is improved by 4.77% and 3.36% over SVM algorithm and DNN algorithm, respectively, and possesses a smaller training time consumption. The fault detection method proposed in the study can be applied to the remote control of power systems based on digital twins.

Hengjie Liu1, Jie Tang2, We Li 1
1Zhengzhou Electric Power Co., Ltd., Zhengzhou, Henan, 450064, China
2Department of Mechanical Engineering, Henan University of Science and Technology, Zhengzhou, Henan, 450064, China
Abstract:

The environment near substations is complex, and electrocution accidents of operators occur from time to time during on-site operations, and the development of safety detection models for substation operations has received more and more attention. The article proposes a safety distance detection model for substation operation, which is mainly composed of binocular stereo matching perception model and safe area detection model. The binocular stereo matching perception is based on the PSMNet network model, combined with the parallax regression calculation to obtain the threedimensional coordinates of the operation area in the process of substation operation, and the threedimensional reconstruction of the substation operation process. The spatial context inference algorithm is utilized in the safe region detection model to detect the edge of the safe region, and the image segmentation of the safe region of the substation operation scene is performed by the improved OTSU algorithm. Then the three-dimensional coordinates obtained from binocular stereo matching perception and the three-dimensional coordinates of safe region detection are solved for the Euclidean distance, and then the safe distance detection of substation operation is realized. The EPE result accuracy of binocular stereo perception matching on the dataset is reduced by 0.71px compared with CRL, and the resulting mismatch pixel rate is between 0.83 and 1.48%. The average time-consuming image segmentation of the improved OTSU threshold segmentation method is 6.34ms, and the average relative error of the safety distance detection for substation operation is only 0.85%, and the maximum absolute error of the safety distance detection is only 0.13 m. Combining the spatial contextual reasoning algorithm with the deep learning technology can realize the effective detection of the safety distance for substation operation in multiple scenarios, and fully ensure the operation of the substation workers’ safety.

Zhan Zhang 1
1Faculty of Natural, Mathematical and Engineering Sciences, King’s College London, London, WC2R 2LS, United Kingdom
Abstract:

This study analyzes the aerodynamics of fluttering flight of birds through their body structure characteristics. A convolutional neural network is combined with a bird-like flight aerodynamic model. By analyzing the symmetric and asymmetric motion laws of birds in flight, the three-dimensional model and equations of motion of the wing-fluttering motion are established, the aerodynamic simulation study of bird wing-fluttering flight under Computational Fluid Dynamics(CFD) and train it by convolutional neural network. When the model trained to 12 rounds, the loss values on both the training and validation sets converge to about 3.5%, the training effect is good. The predicted values of the lift-to-drag ratio by the model in this paper are close to the CFD calculated values, and the average relative errors of the validation set test set are 0.483% and 0.486%, respectively. In addition, the model predicts the pressure coefficient of the flow field better, and the prediction error of the vast majority of the positions is less than 1.2%. In conclusion, the convolutional neural network can significantly improve the performance of bird flight aerodynamic simulation model.

Yutong Chen 1
1International School, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Abstract:

In this paper, with the help of the real-time state observation property of the Kalman ϐilter method, we propose to use the Kalman ϐilter method for channel estimation of OFDM wireless communication system. The linear interpolation method is used to deal with the fading process of data symbol positions, and the Kalman ϐilter estimation expression of the fading process is obtained. And considering the computational complexity of the channel estimation algorithm, the channel estimation is optimized by adding the 1st order AR model into the channel model. The Doppler frequency is used as the simulation parameter to analyze the operational performance of the Kalman ϐilter channel estimation method under different Doppler frequencies. To further broaden the applicability of the proposed method in this paper, a MIMO-OFDM system is introduced, and numerical simulations are conducted to analyze the relationship curves between the outage probability and the SNR performance under the OFDM channel processing module for both the random channel and the random channel with OFDM modulation. In the massive MIMO multipath random transmission channel, the better the SNR performance of the channel, the smaller the probability of generating interruptions. Meanwhile, in the presence of the same non-ideal factors (hardware impairments, interference noise) interruption probability impairments of the channel, the SNR in OFDM-ideal state is about 10 dB more than the OFDM-hardware impairments simulation value.

Jiarui Ai 1
1School of Economics and Management, Maanshan University, Maanshan, Anhui, 243000, China
Abstract:

Corporate ESG disclosure quality is a key condition to optimize industrial structure and a realistic path to reach sustainability performance. Based on the theoretical knowledge of Bayesian network model, the research program of corporate ESG disclosure quality and sustainability performance influence path is designed. According to the current status of enterprise development, 11 research variables are set, which contain explanatory variables, interpreted variables, and control variables. Mathematical statistics and Bayesian network modeling are adopted to parse the mutual influence mechanism between the two. In the forward Bayesian inference, the probability of enterprise sustainability performance being in a good state is 49.3%, and the probability of the explanatory variables being in a good state is increased to 58.7% by changing the state probability of other variables. In order to provide a comprehensive overview of the relationship, backward Bayesian inference was also performed, and when the probability of sustainability performance being in a good state was 100%, the probability of the board concurrent position being in a good state was the highest with a value of 72.3%. This study enhances the most effective corporate ESG disclosure quality control program for companies to maximize the possibility of sustainability performance.

Shibo Liu1, Zhiqing Chen 2
1School of Materials Science and Engineering, Shenyang Aerospace University, Shenyang, Liaoning, 110000, China
2School of Management, Chongqing University of Technology, Chongqing, 400054, China
Abstract:

In this paper, for the influence of non-metallic inclusions on the contact fatigue performance of steel, based on the finite element method and rolling contact fatigue theory, the contact fatigue model of U26Mn2Si2CrNiMo bainitic austenitic steel containing non-metallic inclusions is established. The characteristics of non-metallic inclusions and U26Mn2Si2CrNiMo bainitic austenitic steel are analyzed. To investigate the changes in the composition, density and size of each inclusions during the production steps of U26Mn2Si2CrNiMo bainitic austenitic steel by using the inclusions detection technique in steel, the stress and strain response algorithm and the thermodynamic calculations (deoxidization equilibrium calculations of the steel liquid). To analyze the range of fatigue damage concentration caused by non-metallic inclusions by characterizing the distribution of subsurface fatigue damage in the RCF process of U26Mn2Si2CrNiMo bainitic austenitic steel. Explore the effect of the distribution depth of individual non-metallic inclusions on the contact fatigue life of U26Mn2Si2CrNiMo bainitic austenitic steel, and the role of the angle of arrangement of dual nonmetallic inclusions on the properties of U26Mn2Si2CrNiMo bainitic austenitic steel. When circular alumina inclusions with a radius of 5 m are located at different depths of the bainitic austenitic steel, the von Mises stress reaches a maximum value of 770.0 MPa at a depth of 0.53 mm (0.67 Hb ) of inclusions, which is increased by 18.5% compared to the case without inclusions (650 MPa). When the spacing of the two inclusions is 2.5 r (12.5 m ) and the depth is 0.5 mm, the arrangement of the nonmetallic inclusions affects the predicted fatigue life, and the two inclusions reduce the predicted fatigue life around them to different degrees.

Haiyun An1, Qian Zhou1, Xiaorong Yu2, Bingcheng Cen1, Yuqi Hou3
1Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing, Jiangsu, 211103, China
2State Grid Jiangsu Electric Vehicle Service Co., Ltd., Nanjing, Jiangsu, 210004, China
3Tianjin University, Tianjin, 300072, China
Abstract:

The reasonable division of power supply grid plays an important role in the feasibility and stability of power grid operation. This paper mainly explores the feasible methods of power supply grid division under the dynamic change of grid load. The grid load prediction model is constructed by the improved long and short-term memory network algorithm (ILSTM) based on expert rules to visualize the dynamic changes of the grid load. Based on the study of hierarchical architecture of power supply grid, the objective function is constructed using hierarchical recursive method, and the power supply grid division model is constructed with adjacent connection as the basic constraint. The power consumption information of JH urban area is selected as the data source of this paper, and the method of this paper is used to forecast the grid load of JH urban area and perform the power supply grid division. The power supply network in JH city can effectively meet the objective function and constraints set in the model, and the average number of faults in the power supply network decreases by 94.8% compared with that before the grid demarcation, which fully ensures the safety and reliability of the power supply network operation.

Ying Chen1, Cen Peng1, Chenghui Wang 2
1School of Foreign Languages, Zhixing College of Hubei University, Wuhan, Hubei, 430011, China
2WH Auto Parts Industries Inc., Wuhan, Hubei, 430223, China
Abstract:

Students have the problems of insufficient self-control, insufficient learning motivation and unplanned and unsystematic for independent learning of university French. In order to solve this problem effectively, this study proposes the reform of French blended education model guided by POA theory. In this paper, we design a hybrid intelligent teaching mode of university French guided by the output-oriented approach, improve it based on the mutation operation in the genetic algorithm, propose the adaptive mutation genetic algorithm, and optimize the BP neural network with this algorithm. The GA-BP neural network is trained through simulation experiments to verify the performance of the algorithm. Using SEM structural equation modeling, the measurement model of six dimensions, namely, learning effect, teaching effect, learning input, objective learning conditions, subjective learning factors and learning ability, is established, integrating factor analysis and path analysis, and relevant research hypotheses are proposed. The feasibility of the hypotheses is verified one by one through empirical research. The path coefficients between each variable in the model and the path coefficients of the factor loadings are all at the significant level of 0.000, and all of them are positive, the path coefficients’ validity is within the acceptable range, and the hypotheses proposed in this paper are all supported. Compared with the default path, 69.78% of the students in the recommended path for learning French think that the knowledge of the recommended learning path is easy to understand, and the learning path constructed on the basis of the educational resources of the output-oriented method can better satisfy the learning needs of the students compared with the default learning path.

Ning Zhao1, Kang Guo1, Qian Li1, Siying Wang1, Ziguang Zhang1, Lei Fan1
1State Grid Shijiazhuang Electric Power Supply Company, Shijiazhuang, Hebei, 050000, China
Abstract:

This topic is centered around temperature and stress, and describes the theory of electric power thermal characteristics. There are usually two methods for thermal coupling analysis, for direct coupling and sequential coupling. Considering that the stress field of the cable does not have much influence on the temperature field, it is proposed to use the sequential coupling method for the calculation of the thermal characteristics of the cable. The calculated and solved cable temperature and stress distribution values are put into the Lap-ML-ELM algorithm for training. When the contact coefficient k=1, 4, 7, 10, 13 and 15, the cable joints and surfaces produce a monotonically increasing law of temperature, and the stress exhibits the same situation.During the training of the model on the thermal characteristics of the cables, it is found that the accuracy curve of the thermal characteristics detection of the Lap-ML-ELM algorithm is higher than that of both the RNN network and the CNN network, which shows that in the detection of the thermal characteristics of cables, the Laplace Multilayer Extreme Learning Machine fusion algorithm performs more obviously.

Yongjia Huang 1
1School of Foreign Languages, Zhengzhou Shengda University, Zhengzhou, Henan, 451191, China
Abstract:

The development of globalization has contributed to the increasing demand for cross-language communication, and machine translation, as an effective language conversion tool, has improved the quality and efficiency of English translation. The article discusses the syntactic optimization and semantic reconstruction strategies for English translation based on machine learning. The machine translation model of English syntax optimization and semantic reconstruction based on EM algorithm is constructed by using key technologies such as EM algorithm and multi-head attention mechanism. The model adopts a joint learning method, combining the Transformer model with the EM algorithm. The dependency between any two words in the input sequence is captured using the multi-head attention mechanism, and the new translation corpus is generated by multi-task joint training algorithm. The training phase of this paper’s model has good translation effect, and the model of this paper gets the highest BLEU score of 32.86 when the number of multi-head attention layers is 1. The distribution of semantic features of translation reconstruction under this paper’s method is basically consistent with the simulation results, and the error elimination rate of semantic reconstruction is 99.64% when the number of samples is 500. The method in this paper is more effective in syntactic structure optimization, with the highest BLEU scores on “Chinese to English” and “English to Chinese”, and the syntactic correctness rate on English long sentences of different topics reaches 88.69%~96.57%.

Nannan Wu1, Wenxiao Dong1, Xiangjin Wu2, Jianxun Li3, Hongsheng Qian 1
1School of Tourism and Planning, Pingdingshan University, Pingdingshan, Henan, 467000, China
2Hefei Urban Planning and Design Institute, Hefei, Anhui, 230000, China
3Chatone Smart Technology Co., Ltd., Beijing, 100000, China
Abstract:

In order to better realize the automatic classification and change detection of remote sensing images, this paper proposes an automatic remote sensing image classification model based on CNN and migration learning, and constructs a remote sensing image change detection model by combining CNN and Transformer network. In the remote sensing image classification model, DenseNet network and Inception network are used as the backbone network, combined with the new channel attention module to mine the image features of remote sensing images, and then realize the accurate classification of remote sensing images. In the remote sensing image change detection model, the convolution operation of CNN with different sizes of void rate and expansion rate is utilized to better guide the feature map to focus on local information. Combined with the dynamic deformable Transformer to provide more accurate remote sensing image location information and detail information, to reduce the impact of background interference on remote sensing image change detection, and to improve the model’s ability to recognize the target of remote sensing images. The parameter count and floating-point computation of the remote sensing image classification model are 7.69MB and 1.89GB, respectively, which are smaller than the parameter count and floating-point computation of the single network model. The RSICD models mF1 and mIoU are 1.66% and 0.58% higher than the optimal ones. Through the effective integration of convolutional neural networks and many different types of deep learning techniques, automated classification and change detection of remote sensing images can be realized.

Jipei Zhang 1
1School of Foreign Languages, Henan University of Urban Construction, Pingdingshan, Henan, 467036, China
Abstract:

The expansion of English vocabulary is the foundation of college students’ English learning and the key to improve English learning. This project centers on the quantitative analysis of college English vocabulary learning efϐiciency improvement, through the questionnaire survey to understand the use of English vocabulary learning strategies of students. The inϐluencing factors of English vocabulary learning efϐiciency improvement are selected, correlation analysis is carried out, and then multiple regression model is used to explore the role of each variable on the improvement of English vocabulary learning efϐiciency. The results show that students most often use the metacognitive strategy of preplanning (3.674), and that students who are good at learning are more inclined to adopt the metacognitive strategy to control vocabulary learning from a macro perspective. Multiple selfelements and environmental elements together positively affect the improvement of English vocabulary learning efϐiciency (p < 0.01), with the most signiϐicant effects of learning strategies (0.482), teaching methods (0.457) and learning strategies (0.416). It is recommended to promote the efϐiciency of English vocabulary learning through innovative teaching methods, combining word class memorization, expanding the scope of reading, and vocabulary association learning.

Lanyue Pi1, Yangzi Mu1, Lanyu Pi 2
1Zhengzhou Vocational College of Finance and Taxation, Zhengzhou, Henan, 450048, China
2China International Telecommunication Corporation HE NAN Communication Service Co., Ltd., Zhengzhou, Henan, 450016, China
Abstract:

In recent years, with the development of science and technology, image enhancement has become a very important topic in scientific research, become an indispensable part of machine vision, and has a wide range of applications in various fields of computer vision. In this paper, the image gradient enhancement algorithm is first improved based on the image gradient field, and its enhancement effect on low quality (low resolution) images is found to be poor through experiments. For this reason, the study constructs a multi-scale feature image enhancement model (LIEN-MFC) by convolutional neural network to further optimize the image enhancement effect. By comparing with different algorithms, the average PSNR of the model is 21.80 and the average SSIM is 0.8767, and it outperforms other compared algorithms in both PSNR and SSIM. In addition, the ablation experiments demonstrate that the enhancement effect of the LIEN-MFC model is further improved on the basis of the improved image gradient enhancement algorithm. The results show that the image enhancement model algorithm with multi-scale features proposed in this paper has a significant image enhancement effect and the improved image gradient enhancement in image enhancement of convolutional neural networks improves the model performance to some extent.

Haijun Su1, Huajun Xu1, Lei Ma 1
1State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia, Gansu, 731100, China
Abstract:

For national grid power line infrastructure construction construction, quality management and control can ensure improved safety standards, long-term reliability and cost savings through avoiding rework. In this paper, a high-definition image of a transmission line is collected from multiple viewpoints by a UAV, and a model for recognizing surface defects on infrastructure lines is proposed to reduce the computational complexity to improve the YOLOv8 algorithm. The model uses ResNet50 as the feature extraction backbone network and fuses convolution and attention mechanisms to enhance global and local feature extraction. A multi-scale feature aggregation diffusion module is added to the neck network of the model to enhance the detection of small targets on infrastructure lines. Finally, the classification loss function combined with the PIOU bounding box loss function is introduced to further enhance the recognition accuracy of infrastructure line surface defects. The experimental results show that the mAP of the infrastructure line surface defect recognition model is up to 0.935, which is 2.41% higher than that of the baseline model, and the performance is significantly better than that of some of the current mainstream defect recognition models. Therefore, from the computational complexity, combined with the target detection YOLOv8 algorithm can realize the accurate recognition of surface defects on infrastructure lines, and provide reliable data support for improving the timely repair of grid infrastructure lines.

Zixin Jiao 1
1School of Music and Dance, Bengbu University, Bengbu, Anhui, 233000, China
Abstract:

Music therapy is the treatment of college students’ psychology through various techniques and methods of music, and this paper focuses on researching and analyzing the improvement effect of music therapy on college students’ mental health in the context of cultural education. Students’ physiological data are collected and denoised, and machine learning models are used to realize the multimodal fusion of all kinds of physiological signal features to obtain the objective psychological state assessment values of college students. The subjective assessment results of the mental health assessment scales were then combined to analyze the improvement effect of mental health of college students in the music therapy intervention. The analysis of the psychological health status of the students before and after the intervention experiment revealed that the objective assessment values of the psychological state of the college students in the intervention group gradually tended to be positive with the music therapy, and the subjective assessment results of the psychological health scales of the students in the intervention group were signiϐicantly better than those of the nonintervention group after the experiment (P<0.05). Music therapy has a signiϐicant role in intervening in the mental health of college students and resolving their psychological malaise, which is of great practical and guiding signiϐicance in improving the psychological tolerance and health of college students.

Na Yan 1, Jian Zeng1, Hui Wang1, Yunzhang Yang1, Shuzhong Li 2
1State Grid Shaanxi Electric Power Company Limited Research Institute, Xi’an, Shaanxi, 710065, China
2WLSL ELectric Energy Star, Inc Electric Energy Star Co., Ltd. (Chongqing), Chongqing, 400039, China
Abstract:

Energy level fluctuations in Distributed Generation (DG) systems and Electric Vehicles (EVs) sometimes exceed the carrying capacity of typical distribution network topologies, which may lead to inefficiencies and lack of reliability. Based on this, this paper introduces a new Levy flight-electric eel foraging optimization (LF-EEFO) method for adapting network topology reconfiguration for new power systems. The DG output power, EV charging power, distribution network loss power, and switch lifetime cost cost are taken as the objectives, and the tidal current, voltage, branch power, network topology, and switching action are set as the constraints, in order to construct a multi-objective optimization model for distribution network topology reconfiguration. In the optimization phase, a Levy flight strategy is used to optimize the local search capability of the EEFO algorithm to obtain the optimal solution of the multi-objective optimization model for distribution network topology reconfiguration. In order to ensure the efficiency of the LF-EFO algorithm in optimizing the distribution network topology reconstruction model, an IEEE-33 node test system was established for simulation analysis. The results show that this research can significantly reduce the operating cost and improve the operational reliability of distribution networks, while promoting the development of electric vehicles.

Zhengqiang He1, Yuanyuan Gu 1
1College of Art, University of Sanya, Sanya, Hainan, 572000, China
Abstract:

Currently, the development of cultural tourism has become a new trend of urban development, and how to use modern technology to realize the innovative development of urban cultural tourism has become a key issue to be considered in the process of urban construction. The research combines the Web domain ontology to construct a multi-level user portrait master model, which mainly includes four sub-models: retailer static attribute vector model, retailer domain dimension model, retailer marketing ability model and retailer social dimension model. The FCM algorithm based on the improved AP algorithm is utilized to cluster the user portraits, and the user portrait clusters obtained by the method studied in this paper perform well with an average number of iterations and an average time consumed of 21.3 and 60.35 compared with the traditional K-Means algorithm, the improved KMeans algorithm, and the traditional FCM algorithm, respectively. Then a personalized recommendation method for tourism products based on MAGFM is proposed, which achieves Top-N recommendation of tourism products by calculating the total interest value of users and the comprehensive similarity of tourism products. And test and analyze in the tourism e-commerce platform, the results show that the recommendation algorithm proposed in this paper has higher effectiveness compared with the traditional recommendation algorithm. Finally, the research content builds a personalized recommendation system for tourism cultural and creative products.

Shuo Wang1, Zhicheng Xu 2
1School of Accounting and Finance, Taizhou Vocational College of Science & Technology, Taizhou, Zhejiang, 318020, China
2School of Bussiness Administration, Zhejiang Gongshang University, Hangzhou, Zhejiang, 310018, China
Abstract:

Supported by the theory of economic growth convergence, this paper takes the eastern, central and western regions as the research object during 2010-2020, analyzes the economic growth convergence of the eastern, central and western regions of the country, and verifies the relationship between the regulation of fiscal policy and the high-quality development of the regional economy. Analyze the relationship between regional economic development, fiscal policy and economic convergence, and put forward the analytical view that fiscal policy affects regional economic convergence. The combination of dynamic panel model and absolute  convergence analysis is used to derive the results of the absolute convergence test of regional economic growth. Convergence role test for fiscal expenditure variables, transfer payment variables. It is brought to the western region to analyze the role of government expenditure in the western development policy on the convergence of the western region’s economy. Convergence  as well as absolute  convergence is conducted for each of the eight comprehensive economic zones, and the regional economic high-quality development policies are adjusted. Relative to 2010-2015, there is no convergence in economic growth in the western region in 2016-2020, and there is a tendency to divergence, which suggests that the fiscal policy of western development has limited effect on economic convergence among regional provinces. Absolute  convergence exists for the whole country and the eight comprehensive economic zones, and the convergence coefficient is significantly negative at the 1% level. However, the speed of convergence varies for high-quality economic development.

Ying Zhang 1
1Accounting Financial Institute, Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, 310018, China
Abstract:

In response to the greening and decarbonization of economic development and in search of a path to improve the corporate efficiency of resource-consuming enterprises, the study explores the impact of the financial sharing model on the efficiency of resource-consuming enterprises. The research hypothesis is formulated after the preliminary analysis of related theories such as financial sharing and accounting information. After completing the selection of research samples and data collection, the research variables are defined, the regression analysis model of the impact of financial sharing model on enterprise efficiency is constructed, and empirical analysis is conducted. The research hypotheses proposed in the previous section are verified through regression analysis. Monte Carlo method is used to simulate the financial sharing model and resource-consuming enterprise efficiency, and the net present value of resource-consuming enterprises is simulated during the construction period and the operation period of the financial sharing model, respectively, so as to understand their enterprise efficiency. The results of the empirical study show that financial sharing can realize the improvement of enterprise efficiency. Enterprise efficiency can increase with the improvement of accounting information transparency and accounting information consistency. During the construction and operation periods of the financial sharing model, the mean enterprise NPV after five years of operation is $608.4 and $2,327.4 million, respectively, and the probability of positive NPV is 68% and 94%, respectively.

Zhidan Zhang 1
1Dazhou Vocational and Technical College, Dazhou, Sichuan, 635001, China
Abstract:

This paper establishes a specific path for the realization of AI-enhanced learning on the content of Civic and Political Education, starting from the relevance, quality, novelty and intuitiveness of the teaching content. Through HTML parsing and other crawler technology to obtain the Civics education data on the news network, and extract the data characteristics of the Civics material, using the clustering rule algorithm, to classify the material. Decision tree calculation based on random forest is performed to dynamically expand and integrate the material, on this basis, using reinforcement learning recommendation algorithm, the Civic and political education content recommendation model is constructed, and the recommendation results of the algorithm are verified using simulation experiments. The experimental results show that the average success rate of the research-designed recommendation algorithm in the last 10 groups of experimental data is 25.218%, which is higher than that of the MK recommendation algorithm (18.03%), and the average time of the research-designed recommendation algorithm in the last 10 groups of data is 5.095s, which is more efficient than that of the MK recommendation algorithm (11.903s). After integrating the enhanced learning content recommendation in the Civics education, the students’ humanism scale score was 100.56±12.364, with a p-value of less than 0.05, which was significantly higher than that before teaching.

Jinhua Ma1, Rong Zhu 2
1Department of Economics and Management, Shandong Vocational College of Science and Technology, Weifang, Shandong, 261000, China
2College of Economics and Management, Qingdao Institute of Technology, Qingdao, Shandong, 266300, China
Abstract:

In enterprise operations, multi-objective optimization involves multiple conflicting objectives such as cost escalation control, customer satisfaction, and production efficiency. Based on reinforcement learning algorithm, the article deals with multi-objective optimization problem in enterprise operation through the interactive learning between intelligent body and environment, for which a multi-objective operation efficiency improvement path for enterprise based on Q-learning scheduling is designed. The simulation data is utilized to generate the PDR tree structure, and subsequently, the intelligent body is prompted to complete the multi-objective operation learning of the enterprise through several iterations. On this basis, the intelligent body completes all the actions and generates scheduling strategies to improve operational efficiency. The model proposed in this paper can predict the demand changes of enterprises in the future time window and make the best decision to improve the operational efficiency. Under the model of this paper, the mean values of pure technical efficiency as well as scale efficiency of 10 firms in 2024 are 0.9 and 0.933, respectively, and they are predicted to continue to grow in 2025. The model reduces the firms’ average operating costs and administrative expenses, while employee compensation and fixed assets increase by 49.58% and 19.48%. Since the survey period, the TFP index of all 10 companies is greater than 1, which indicates that, the application of the model in this paper improves the operational efficiency of the companies.

Gaofeng Huang1, Xiangjun Xu1
1School of Electronic and Information Engineering, Wuhan Donghu University, Wuhan, Hubei, 430212, China
Abstract:

The article uses the appropriate equipment for research data, designing the face and physiological signal emotion recognition network respectively, and putting its recognition features into the random forest classifier for training in order to realize the construction work of emotion recognition model. In-depth interpretation of the random forest algorithm based emotion recognition model in the application of information systems, combined with the research data, respectively, the emotion recognition model and system safety performance testing assessment. The emotion recognition model of this paper based on the 25% retention method has a recognition rate of 96.16% for the 14- dimensional B emotion features, which has the highest recognition efficacy and can well meet the system emotion recognition needs. The experimental group is found to be significantly different from the control group, and it is concluded that by introducing the emotion recognition model into the traditional information system, all three security performance indicators of the system are significantly improved.

Xinli Han 1
1School of Electronic and Information Engineering, Wuhan Donghu University, Wuhan, Hubei, 430212, China
Abstract:

The purpose of this study is to evaluate the comprehensive ability of students objectively by constructing the evaluation system of compound music talents based on multi-objective planning, so as to promote the quality improvement and excellent cultivation of compound music talents in higher vocational colleges. The selected evaluation indexes of composite music talent cultivation are empowered by using the combination assignment method, and the construction of multi-objective planning model for cross-border composite music talent cultivation is realized based on the setting of objective function, constraints and model solving method. The article forms an index system covering 6 dimensions and 24 indicators, successfully divides the interval length of five evaluation levels, and obtains the distribution of students in each level, with the largest proportion of students in level 3, which is 40.77%. In addition, the ratings of the level 1 indicators are 2.47 to 3.31, which are in the middle to lower level. According to the student groups of different grades and the evaluation results of the indicators, we can clarify the level of student cultivation, improve the music talent cultivation system, coordinate and improve the elements and resources of each dimension, and promote the cultivation of cross-boundary composite music talents in higher vocational colleges and universities.

Hui Luo 1
1Academic Affairs Office, Geely University, Chengdu, Sichuan, 641423, China
Abstract:

The article aims to accelerate the growth and progress of young teachers in private applied colleges and universities and improve their teaching ability, combining with the knowledge graph, and designing a recommended algorithm based on deep reinforcement learning to improve teachers’ ability. Firstly, the growth and progress process of young teachers in private applied colleges and universities is defined as a dynamic development process, i.e., for different latitude abilities such as teacher ethics, professional knowledge, preteaching preparation, communication and cooperation, teaching ability training needs to be carried out gradually and in a certain order. Then the Knowledge Graph Teacher Competency Enhancement Recommendation Algorithm (KGDR) based on deep reinforcement learning and knowledge graph algorithm is constructed by combining deep reinforcement learning and knowledge graph algorithm. When performing top-𝑘 recommendation, the diversity value of the model at 𝑘 = 20 is 0.7876, and the model can provide more diverse paths for teacher ability improvement. After the application of the dynamic development mechanism of young teachers’ competence based on KGDR, the competence improvement of young teachers is significant and can reach the grade of “excellent”. The mechanism designed in this paper can be used as a reference for other colleges and universities.

Linjie Cai 1
1Shanghai Technical Institute of Electronics & Information, Shanghai, 201411, China
Abstract:

Innovation and entrepreneurship, as an important part of social and economic activities, has received more and more widespread attention. Based on the characteristics of the digital era, the study uses artificial intelligence to empower innovation and entrepreneurship education in colleges and universities. Optimize the allocation of innovation and entrepreneurship education resources in colleges and universities through multi-objective optimization algorithm. Construct an optimization model of resource allocation for innovation and entrepreneurship education in colleges and universities, and verify its resource optimization and allocation performance. Taking 13 colleges and universities in C city as the research object, the optimization of their innovation and entrepreneurship education resource allocation is processed. The MSS cumulative values of this paper’s multi-objective optimization model on the CPLX problem and the MATP problem are -1.400 and -1.033, respectively, which are the smallest among all models, with the best performance and ranked the first in resource allocation efficiency. After optimization, the resource allocation level of innovation and entrepreneurship education in all 13 colleges and universities has been improved, and the resource allocation among the colleges and universities is more balanced.The resource utilization efficiency of innovation and entrepreneurship education in the 13 colleges and universities has been improved by 17.02% on average.

Yongjun Wang 1
1Law School, Henan University of Urban Construction, Pingdingshan, Henan, 467036, China
Abstract:

This paper analyzes public interest litigation and its salient features, and organizes the audit rules for the electronic transformation of litigation evidence. Aiming at the phenomenon of varying text length in litigation evidence, a joint CTC-Attention decoding model (HCADecoder) based on bigram hybrid labeling is proposed. Based on the existing research on computer vision technology for target number prediction, the stacked object occlusion problem existing in special scenes is proposed, and an algorithm for predicting the number of stacked objects combining planar density map and depth map is proposed. Combined with the public interest litigation evidence document corpus dataset, we analyze the recognition of basic elements of litigation evidence by text label recognition algorithm, and select the commonly used precision rate P, recall rate R and F1 value to evaluate the recognition results of basic elements. Subdivide the text length of litigation evidence and analyze the recognition accuracy of each algorithm on different text lengths. Bring the text label recognition algorithms into real cases to analyze the element extraction. For this paper, we propose monocular image target counting algorithm, which is brought into different scenarios for performance testing. This paper proposes text label recognition algorithm with evidence image target counting algorithm for litigation evidence text image recognition with mean value at 80%.

Zhanying Wang1, Guangshuo Liu2, Yutong Liu2, Haiwei Jiang2, Fei Pan3, Shuchang Pan 3
1State Grid Liaoning Electric Power Co., Ltd., Shenyang, Liaoning, 110055, China
2Economic and Technical Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang, Liaoning, 110015, China
3Shanghai Puyuan Technology Co., Ltd., Shanghai, 200240, China
Abstract:

This paper is based on the definition of novel distribution system panoramic perception technology under the perspective of generative artificial intelligence. The preprocessed data are put into forward GRU neurons and reverse GRU neurons as model input variables for multi-task assisted training, and the model outputs distribution system perception results to complete the task of constructing a new distribution system panoramic perception model based on BiGRU. When the distribution system current and voltage data is zero, it will lead to a reduction in the current and voltage prediction accuracy of the distribution system of the ELM model, for this reason, it is proposed to use the genetic algorithm to optimize the ELM model, to achieve the modeling of the new distribution system prediction model based on the ELM-GA algorithm. Using the model constructed in this paper, panoramic perception and prediction analysis of the new distribution system is carried out. When the BiGRU model is deployed in the new distribution system, the BiGRU network’s system perception accuracy and error rate are 95.00% and 5.00%, respectively, which fully meets the user experience requirements of the new distribution system, and the relative errors of fault voltage and fault current prediction based on the ELM-GA algorithm for the new distribution system are less than 5%, which indicates that the ELM-GA distribution system prediction model has the characteristics of high robustness and high accuracy.

Lei Wang 1
1Hunan Communications Vocational and Technical College, Changsha, Hunan, 410132, China
Abstract:

This paper carries out a research on patients’ lower limb posture capture strategy based on the lower limb rehabilitation of patients with sports function injury. The study is based on the posture filtering algorithm and designed a lower limb joint localization model based on the quaternion Kalman filter. The model utilizes five IMUs to capture the patient’s lower limb movements to determine the posture of the patient’s critical limbs in three-dimensional space and establish the joint coordinate system. Based on the filtered pose quaternions, the joint coordinate system of the lower limb is solved to obtain the optimal estimation of the lower limb pose. The results of simulation experiments show that the algorithm of this paper can make the motion data smoother and satisfy the motion requirements. The valuation of this paper’s algorithm on the Z-axis in the single-axis rotation experiment is stable from – 90° to 90°, while the valuation on the X-axis and Y-axis is near 0°. And the error in the ankle motion trajectory is small, with a mean value of 1.36°. The example results illustrate that the rehabilitation system equipped with the algorithm of this paper is basically consistent with the thigh elevation curve of the optical method in the patient’s lower limb motion monitoring during walking, and the error is within 6°. The research in this paper provides a new technical means for lower limb rehabilitation training, which helps to improve the personalization and precision of rehabilitation training.

Yanru Li 1
1International Business College, Chengdu Polytechnic, Chengdu, Sichuan, 610041, China
Abstract:

The planning of green logistics networks has gradually become the focus of attention in both academic and business circles, as it has been increasingly emphasized on environmental protection. This study aims to explore how to combine machine learning and carbon emission constraints to construct a more efficient and environmentally friendly green logistics network planning strategy. A machine learning-based logistics demand forecasting model is constructed by Support Vector Regression (SVR) machine, and the model parameters are optimized using genetic algorithm to improve the model accuracy. Analyze the sources of carbon emissions in the logistics network and establish a carbon emission calculation model. Construct a green logistics network planning model considering carbon emission constraints, and analyze the feasibility of the model through practical examples. The method of this paper can effectively measure the carbon emissions in the transportation and storage phases of the logistics network. Under the condition of considering carbon emission constraints, positioning the upper limit of carbon emission below 270,000 can realize a stable balance of economic and environmental benefits.

Honghe Li1, Guopeng You 2
1Humanities and Arts Media Department, Changzhi, Shanxi, 047100, China
2Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, 361000, China
Abstract:

The aim of this study is to construct a deep learning-based biomechanical model of musical instrument playing action that integrates skeletal pose estimation and action recognition techniques. PHRNet-based human pose estimation can extract the skeletal key points of a player from video data, and these key points provide basic data for instrumental performance action recognition and analysis. The human skeletal action recognition method based on diversity rewarded reinforcement learning framework (DDRL-GCN) classifies the extracted key point sequences into specific playing actions, and the musical instrument playing actions are successfully modeled. The biomechanical model of musical instrument playing action designed in this paper is applied to recognize the playing action of five different musical instruments, and the recognition accuracy can reach more than 90%. This paper is designed to distinguish between different musical instruments, the recognition effect is more satisfactory.

Hui Wang 1
1Tourism Management Department, TAIYUAN TOURISM COLLEGE, Taiyuan, Shanxi, 030032, China
Abstract:

With the in-depth promotion of ecological civilization education, the grassland natural ecology study course has become an important way to realize the comprehensive development of students. The article takes the design idea of grassland study course as the entry point, analyzes the design links of grassland ecology study course development, and proposes the design process of grassland ecology study course development based on this. Taking the second grade students of the first middle school in X city as the research object, the decision tree algorithm is used to optimize the relevant variables in the development of the grassland ecological study course, and the optimization model of the study path of the grassland ecological study course is established starting from the shortest distance and the least time. Based on the variables optimized by the decision tree algorithm, a DT-SVM model is built by combining the support vector mechanism to solve the learning path of the grassland ecological study course. Through the simulation and example data, it can be seen that the convergence accuracy of the DT-SVM algorithm shows the optimal accuracy under all experiments, and its mean value can be up to 3.652, and the average time consumed for obtaining the optimal learning path of the grassland ecology research course is only about 3.17min. And more than 65% of the students agreed with the teaching effect of the grassland ecology study course. The grassland ecology study course can significantly improve students’ core literacy in geography, enhance their independent living skills, and better realize the school’s teaching goal of promoting moral education.

Fei Yuan1, Xianzhong Jiang1, Jingkui Li 1
1Wuxi Vocational and Technical College of Commerce, Wuxi, Jiangsu, 214153, China
Abstract:

In recent years, it has become the frontier and hotspot of research in the field of intelligent robotics. In this study, a robot vision-guided unloading system is designed, and a robot grasping control method based on fuzzy mathematical method is proposed for the robot unloading problem under the uncertain information environment, and the particle swarm optimized fuzzy PID control algorithm is introduced into the grasping force control field. Comparison experiments of robot joint trajectory tracking, position and control inputs are carried out in the simulation environment, and the method in this paper can realize accurate tracking of motion trajectory and weaken the vibration phenomenon. The robot unloading experiments show that the success rate of single target and multi-target grasping and placing are both above 91% and 85% respectively, which verifies the effectiveness of the particle swarm fuzzy PID control algorithm of this paper in robotic grasping, and it has a certain value of engineering application for robotic unloading control.

Changzhu Wang1, Jixiang Chai1, Hongtu Xu1, Zhiquan Liu 2
1CCCC Third Highway Engineering CO., LTD., Beijing, 050000, China
2Bridgee Engineering Consulting of Shanghai, Shanghai, 200084, China
Abstract:

Bridges as the basic transportation facilities in the national daily life, with the development of road transportation, the number of urban viaducts and cross-sea bridges grows year by year, and the role of bridges in the transportation network becomes more and more significant. The article selects the Qingshuihe Bridge demolition project as the research object, and designs the Qingshuihe Bridge demolition implementation program based on the engineering characteristics. For the mechanical property changes and structural residual bearing capacity calculation during the demolition process of Qingshuihe Bridge, this paper constructs a finite element model of Qingshuihe Bridge based on ANSYS software, and analyzes the structural reliability of Qingshuihe Bridge after the demolition project by combining with structural reliability indexes. Under the same mid-span displacement condition, the cracking load error between the finite element simulation values and the experimental values is relatively small, and its fluctuation range is between 2.12%% and 6.58%. In the first 5 years after the demolition of the bridge section, the residual load capacity of the bridge structure increased from 5.23*103kN to 5.51*103kN. The reliability index value of the bridge over the Qingshui River changed relatively slowly in the early stage, and began to decline gradually in the later stage, and the greater the strength of the steel girders the higher time-varying reliability index of the bridge over the Qingshui River. In this paper, the design of the Qingshui River bridge demolition implementation program has a strong feasibility, in order to ensure that the Qingshui River bridge structural residual bearing capacity on the basis of the completion of the demolition of part of the bridge abutment, and can guarantee the safety of the bridge operation.

Da Yin 1,2,3, Liguo Wu 1,2,3, Yanna Li 1,2,3, Hongwei Bi 1,2,3, Tianye Guan1,2,3
1Harbin Forestry Machinery Research Institute, State Forestry and Grassland Administration, Harbin, Heilongjiang, 150086, China
2Key Laboratory of Forestry Mechanical Engineering, National Forestry and Grassland Administration, Harbin, Heilongjiang, 150086, China
3Engineering Research Center of Forestry Equipment, National Forestry and Grassland Administration, Harbin, Heilongjiang, 150086, China
Abstract:

Due to global warming and drought, land desertification has become more serious all over the world, and desertification control has become the focus of global attention. Salix sand barriers as a desert wind and sand area engineering sand control mechanical sand barriers have been more widely used. For this reason, this paper analyzes the demand for sand willow sand barriers, starting from the causes of desertification research, and for the sand willow sand barriers laying method for in-depth analysis. Taking the research on the governance of salix sand barriers as the theoretical basis, the self-propelled salix sand barriers horizontal laying machine insertion cutter head is designed, in order to improve the efficiency of salix sand barriers laying and the survival rate of salix spike insertion. Through the test data, the driving force, resistance, and tractability parameters of the laying machine when traveling were theoretically calculated. And according to the different slip rate when the drive seeks to come up with the slip rate when the whole machine is actually working, and finally determine whether the tractor meets the requirements of the passability. Through simulation calculations, it can be seen that the cutterhead laying machine designed in this paper can overcome the resistance generated by acceleration and climbing in the application of sand willow sand barrier laying, and has good versatility in the desert environment.

Liangzhi Xu1, Xin Zhang2
1School of Economics, Tongling University, Tongling, Anhui, 244000, China
2School of Mathematics and Computer, Tongling University, Tongling, Anhui, 244000, China
Abstract:

In recent years, the green economy has been developing rapidly, and the environmentalization of industries has been widely popularized in various industries. This paper carries out an in-depth study on the relationship between agricultural carbon finance and carbon emission reduction, and after understanding the theory related to carbon finance and carbon emission, it adopts the method of system GMM estimation to construct a dynamic panel model for the study of agricultural carbon finance and carbon emission, and selects research variables. The development of agricultural carbon financial innovation and carbon emission in 30 provincial-level administrative regions in China from 2014 to 2024 is studied, and regression analysis is carried out using system GMM so as to obtain the relationship between the impact of agricultural carbon financial innovation on carbon emission reduction, and the robustness test is carried out. The maximum values of agricultural carbon financial scale, carbon financial efficiency, carbon financial structure, and per capita carbon emission are 16.942, 7.052, 1.926, and 128.945 respectively, while the minimum values are 0.965, 0.048, 0.079, and 0.145 respectively, and the maximum values are 17.56, 146.92, 100.33, and 889.28 times of the minimum values. There are large differences in the development of agricultural carbon financial innovation and carbon emission reduction effects among different provinces. Per capita carbon emissions are reduced by 22.5%, 20.5% and 24.5% for each unit increase in carbon financial scale, carbon financial efficiency and carbon financial structure, respectively. The parameter estimates of carbon financial scale, carbon financial efficiency, and carbon financial structure are significant at the levels of 10%, 1%, and 5%, respectively. It indicates that the innovative development of agricultural carbon finance can effectively promote carbon emission reduction.

Yanping Yang1, Peng Zhao 2
1Nanjing Tech University Pujiang Institute, Nanjing, Jiangsu, 210000, China
2Purple Mountain Laboratories, Nanjing, Jiangsu, 210000, China
Abstract:

In the context of urban elderly human resource development, differential evolutionary algorithms can be used to optimize the development strategy and improve the efficiency of resource utilization. The study constructs a multi-objective scheduling optimization model for human resources based on an improved differential evolutionary algorithm, which searches for the optimal development strategy by simulating the mutation, crossover, and selection operations in the process of biological evolution. In addition, the model combines a multi-objective feature selection algorithm to capture the data information of urban elderly resource development more accurately and ensure the scientific and practicality of the strategy. The pareto front of this paper’s algorithm on the optimal solution test function is more in line with the real frontier, and the GD value is between 0.00171 and 0.0325, which has better convergence. The execution time of this algorithm for elderly manpower resource scheduling is shortened compared to the comparison algorithm, and the convergence of different task sizes is accomplished when iterating to 110~150 rounds. The ADE-MOFS algorithm has the lowest running cost and the shortest completion period on elderly manpower resource scheduling. The research in this paper shows new ideas and methods for the rational development and utilization of urban elderly manpower resources, which has important theoretical and temporal significance.

Xinjie Chen 1
1School of Economics, Jinan University, Guangzhou, Guangdong, 510630, China
Abstract:

In this paper, the autoregressive moving average model (ARMA) and LSTM deep neural network are first introduced, and the time series are decomposed into high volatility components and low volatility components by MA filtering method. Then the time series forecasting model ARMA and deep neural network LSTM are combined on the basis of MA filtering method to form ARMA-LSTM combination model based on MA filtering method, and the application effect of this model in financial market volatility forecasting and risk response is verified through empirical evidence. The results show that the ARMA-LSTM_t model will achieve relatively good results in predicting the GDP_IG of the current year using the data of the 12 months of the current year and the last month of the previous year, and the training and prediction sets of the ARMA-LSTM combination model proposed in this paper have the best results. In addition, there is a positive relationship between investment-related indicators and GDP_IG, and the addition of investment network search data improves the estimation accuracy of the model, obtains smaller prediction errors, and improves the prediction accuracy of the ARMA-LSTM model in the short and medium term.

Tingting Zhang1, Hanhua Chen 1
1Institute of Arts, Chongqing College of Humanities, Science & Technology, Chongqing, 401524, China
Abstract:

In this paper, a personalized scheme recommendation method for dance movements based on ontological similarity is proposed. An ontology model of trainers is established, and in order to explore the interactions between trainers’ attribute features and their influence on core parameters, SWRL rules are established using Jena inference engine for the inference of core parameters of training programs. The similarity degree is calculated according to the different types of user variables respectively, and the artificial neural network model is used to determine the degree of similarity between different trainers, in order to complete the recommendation of personalized training programs for dance movements. And then the requirements of the system are summarized to achieve the framework construction of the personalized dance movement training program recommendation system to achieve the health management in the training process. The recommendation effects presented by the similarity calculation method of this paper have reached the design goal of this paper, and the personalized recommendation system of this paper has also significantly improved the physical fitness level and the performance effect of dance skills of the experimental group of dance trainees, and the success rate of the kicking back leg movement has reached 91.67%. However, the system’s function of improving health knowledge and health awareness needs to be further upgraded.

Shuqiao Chen1, Peng Zhang1, Hui Ma1, Shuo Zhou1
1Mengdong Concord Zalutqi Wind Power Co., Ltd., Tongliao, Inner Mongolia, 029100, China
Abstract:

Due to the complex structure of multi-dimensional anthropomorphic wind turbine and the harsh operating environment, in order to reduce its maintenance cost, it has become a popular research hotspot to get fast and effective condition diagnosis and fault early warning through big data mining and analysis of wind turbine condition monitoring. The article clarifies the basic mechanism and typical faults of multi-dimensional anthropomorphic wind turbine, and after analyzing the characteristic frequency of faults on the transmission chain of multi-dimensional anthropomorphic wind turbine, it proposes the anomaly detection method of wind turbine condition monitoring data based on the auxiliary eigenvectors improved density clustering (DBSCAN), which realizes the accurate identification of different types of normal data, valid anomalous data containing fault information, and invalid anomalous data in the monitoring data. It realizes the accurate identification of different types of normal data, valid abnormal data with fault information, and invalid abnormal data in monitoring data. Subsequently, the actual historical data of the wind farm is used as the experimental data set to realize the identification of the operating status of the wind turbine. Finally, the DBN-Dropout wind turbine fault identification method is proposed by combining Deep Confidence Network and Dropout technique. The experimental results indicate that the recognition rate of this paper’s model for nine faults is as high as 99.88%, and the superiority and accuracy of this paper’s model in feature extraction and fault diagnosis are verified by comparing its performance with other fault detection models.

Tingting Jin1
1Boda College of Jilin Normal University, Siping, Jilin, 136000, China
Abstract:

Rural tourism, as an important part of the tourism service industry, the study of the spatio-temporal evolution and influence mechanism of rural tourism flow has also become a hot topic at present. This paper takes Jiangsu Province of China as the research area, proposes the heat measurement and identification method of rural tourism based on network data, constructs a heat measurement model, takes standard deviation ellipse analysis, average nearest neighbor index method, kernel density analysis as the core method of spatial analysis, and proposes the hotspot identification method on the demand of spatial relevance analysis, so as to provide the method and means for the analysis of the spatio-temporal evolution of the rural tourism flow. In the analysis of the influence mechanism of rural tourism flow, the QAP model is used as a research tool to explore the influencing factors of rural tourism flow.The value of rural tourism hotness was low during 2014-2017, and it has rapidly increased and maintained a high growth trend since 2018. The Gini coefficient of rural tourism hotness increased from 0.51 in 2014 to 0.72 in 2018, and then fell back to 0.65 in 2023, and the degree of spatial difference of rural tourism hotness showed a weakening trend, and the hotspot areas of rural tourism were increasing. The structure of tourism flow is affected by a variety of factors such as spatial proximity, tourism income, and the impacts produced by the factors change somewhat in different time periods.

Yongliang Xia 1,2
1Graduate School, Central Philippine University, Iloilo, 5000, Philippine
2School of Economics and Management, Henan Vocational University of Science and Technology, Zhoukou, Henan, 466000, China
Abstract:

Giving full play to the vitality and autonomy of inter-governmental departments can improve the national governance system and enhance the modernized governance capacity. This paper conducts a relevant research on the coordination mechanism between multiple government departments. According to the network analysis method, the relationships of departments in different service items and fields are studied, and the causes of the problems of power distribution and coordination mechanism in the network of departmental relationships are explained, and the analysis of the influence mechanism of departmental coordination is completed by using the random forest algorithm. The analysis results show that the power of government departments in the fields of housing security, social insurance, labor, employment and entrepreneurship, public education, and health care is more concentrated, and the Ministry of Civil Affairs and the Ministry of Human Resources and Social Security have an active position in the coordination process. Cultural cognitive bias, imbalance of power and responsibility, lack of coordination system guarantee and insufficient support of coordination environment are the causes of problems in the coordination mechanism. In addition, ambiguous coordination responsibilities, imperfect institutionalized coordination and lack of supervision system are important factors affecting the multisectoral coordination mechanism.

Lili Deng1, Xue Zhou 1
1Zhengzhou Technical College, Zhengzhou, Henan, 450121, China
Abstract:

The aim of this study is to develop a near-infrared photothermally controlled nano-retarded release system loaded with the anticancer drug Adriamycin optimized based on numerical simulation calculations. Firstly, the instruments, agents and experimental methods for the preparation of selfassembled albumin-loaded nanoparticles were introduced.The cumulative absorption wavelengths of the albumin nanoparticles were investigated by UV and IR spectroscopy, and it was found that the maximal absorption wavelengths of DOX and BDC were distributed at 487 nm and 435 nm, and that the UV maximal absorption wavelength of CUR was 435 nm.In the in vitro slow-release performance, it was found that the cumulative release rate of DOX reached 97.36% when pH 5.0 was used, and that when CUR was used, the cumulative release rate of DOX reached 97.36%. The cumulative release rate of DOX reached 97.36% at pH 5.0, while it was only 59.15% and 30.81% at pH 6.0 and 7.0. The cumulative release rates of CUR at the three pH values were 58.69%, 29.98% and 16.81%, respectively, which were basically the same trend of the retardation curves of the two drugs. The nanoparticles degraded morphology showed the widest and narrowest particle size distribution in PBS buffer solution at pH=5.0 and 7.0, respectively. The loading capacity of the optimized model showed good consistency of effect on measured (11.03%) and predicted (10.87%) values.The photothermal conversion experiments of DOX nanoliposomes were found to have concentration and time dependent photothermal conversion effects. In this paper, from the optical characterization of albumin drugcarrying nanoparticles, it was found that UV light was able to excite PFNSNO for photodynamic therapy as well as NO release through the fluorescence resonance energy transfer process.

Na Zheng 1
1School of Education, Shanghai Donghai Vocational and Technical College, Shanghai, 200000, China
Abstract:

As the father of musical instruments, the piano is commonly used in solo, repertoire, accompaniment and other performance processes. In the process of piano playing, the quality of its sound is closely related to the playing skills. The article analyzes the structural composition of the piano as well as the physical mechanism of sound generation, and summarizes the characteristics of the four elements of piano music, namely pitch, intensity, timbre and duration, on the mathematical basis of the twelve equal temperament laws and the vibration equations of the strings. Subsequently, we analyze the time and frequency domain characteristics of the piano’s musical technique evolution, and calculate the main physical parameters that can affect the piano timbre. Finally, based on the theoretical study and characterization, the corresponding result evaluation experiments were conducted. It is concluded that by analyzing the root-mean-square and mean values of the vibration time-domain signals of piano soundboards excited at different points, it can be seen that, for different structures of piano soundboards, there are excitation points that can maximize their vibration signals. At the same time, the time-domain characteristic index crag factor is analyzed, and it is found that there is no obvious pattern in the crag factor value of the vibration signal of the soundboard with different point excitations.

Yanze Wang1, Xuming Han 1
1School of Information Science and Technology, Jinan University, Guangzhou, Guangdong, 510632, China
Abstract:

In the era of artificial intelligence, the technology of speech conversion has developed rapidly and has gradually become a hot topic of research in the field of speech processing. This paper explores the problem of speech signal extraction and generation based on Wave RNN model, and constructs a speech conversion generation model driven by artificial intelligence. First, the short-time Fourier transform is utilized to convert and preprocess the speech signal in the time-frequency domain. Second, a stepwise speech enhancement model is proposed to enhance the perceived strength of the speech signal. Then, a speech generation model based on improved self-attention mechanism and RNN is designed to realize the generation of speech signals. Finally, the model effect is evaluated for application. The time-frequency domain feature that mixes time-domain features and frequencydomain features is able to capture the characteristics of speech signals more comprehensively than a single time-domain feature and frequency-domain feature, which corresponds to a higher recognition accuracy and a lower training loss value. Meanwhile, after speech enhancement, the average accuracy of model A~D speech recognition is improved by 19%~25%, which indicates that the stepped speech enhancement model used in this paper can substantially enhance the perceptual strength of speech signals. In addition, the language conversion model in this paper outperforms other speech conversion models in both MCD and RMSE, and its advantage in rhyme mapping is obvious, and the pitch of the output speech is more accurate and natural. The model in this paper has high practical value in speech signal generation and conversion.

Yan Liu 1
1School of Music, Henan Polytechnic University, Jiaozuo, Henan, 454003, China
Abstract:

How to form a personalized shortest learning path for vocal skills based on learners’ individual characteristics is the key to improve the efficiency of vocal music teaching. In this paper, on the basis of dynamic key-value memory network, a gating mechanism is used to update students’ knowledge mastery status, and a knowledge tracking model based on dynamic key-value gated recurrent network is proposed to realize the accurate assessment of students’ vocal music level. On this basis, after searching the suboptimal path using the particle swarm algorithm, the shortest path is searched using the ant colony algorithm, which solves the shortcoming of the blindness of the initial search direction of the single ant colony algorithm, and constructs a recommendation model for optimization of the learning path of vocal skills. The results of simulation experiments show that the model AUC and ACC on the ASSIST2015 dataset are 0.7468 and 0.7654, respectively, which are much higher than the highest 0.7281 and 0.7528 in the baseline model. Path optimization was achieved for both ordinary and excellent vocal students, and the average optimization was 4.297 and 3.242 on ASSIST2009, and 3.819 and 3.044 on ASSIST2015.This paper makes an innovative exploration to improve the quality of vocal music teaching.

Min Qin1, Yang Li2, Jihua Cao 3
1School of Design and Creativity, Guilin University of Electronic Technology, Guilin, Guangxi, 541000, China
2Basic Teaching Department, Guilin University of Electronic Technology, Guilin, Guangxi, 541000, China
3Student Work Office, Guilin University of Electronic Technology, Guilin, Guangxi, 541000, China
Abstract:

There is a close relationship between adolescent mental health and physical health, so it is of great practical signiϐicance to explore the speciϐic inϐluencing factors and early warning model of students’ mental health. In this paper, the early warning model of students’ mental health risk is constructed. Firstly, the association rules and Apriori algorithm are used to explore the relationship between the important inϐluencing factors of students’ mental health and common psychological problems, and then the CMA-ES-XGBoost prediction model is proposed to address the defects of the XGBoost prediction model that has high complexity and low prediction accuracy. It adopts the hyperparameters of CMA-ES optimization algorithm to ϐind the optimal hyperparameter solution, and solves the fuzzy phenomenon existing in the early warning of mental health risks by fuzzy logic method, which reduces the error of prediction results. It is experimentally veriϐied that the mental health prediction method based on CMA-ES-XGBoost performs well on the task of students with mental health risk, and the prediction accuracy is 89.66%, which is better than the comparison model. It can accurately detect the mood ϐluctuations of students with different types of personality when they are exposed to multiple extroverted stimuli, and accurately predict the emotional risk. It shows that the model in this paper realizes the function of predicting students’ mental health status and achieves the expected goal of model design.

Yisen Wang1, Hongwei Wang2, Feng Bian3
1Southwest Petroleum University, Chengdu, Sichuan, 610000, China
2Development and Planning Department, Dagang Oilfield Company, Tianjin, 300280, China
3No.4 Oil Production Plant, Dagang Oilfield Company, Tianjin, 300280, China
Abstract:

In this paper, a pressure distribution model of seepage field based on complex reservoir conditions is established based on a finite element mathematical model. Due to the non-homogeneity and multiple flow characteristics of the reservoir, the mathematical model of fractured horizontal wells based on reservoir and fracture is established by solving the finite element equations of oil-phase pressure and water-phase saturation under the two-dimensional oil-water two-phase finite element model. Through numerical simulation of the coupling between the permeability change of the fractured fracture and the bedrock in the oil seepage field, the influence of different fracture parameters on the pressure distribution is analyzed, and each parameter is optimized. Investigations of stress-strain, porosity and permeability in time and space in low-permeability reservoirs found that in the region near the bottom of the well, each parameter varies more, while the farther away from the bottom of the well region the less affected it is. The relative position of the fracture to the well has a large effect on the production of fractured horizontal wells, but this parameter can be artificially regulated. Repeated fracturing cumulative oil incremental analysis found that “fracture network bandwidth, main fracture half-length and main fracture inflow capacity” have the greatest influence on the high permeability strip, the factors of angular wells and low permeability zones, and the repeated fracturing cumulative oil incremental simulation of each fracture parameter has the greatest effect on the fracture network bandwidth, main fracture half-length and main fracture inflow capacity under the coupled model of Well 3 (23.25%), and the optimal values of the parameters are 100m, 100m, 100m, 100m, 100m, 100m and 100m respectively. optimal values of the parameters are 100 m, 150×10-3μm2·m, 20 m and 45×10-3μm2·m, respectively.

Wenshu Li1, Yichen Yang1, Chunsong Li 1
1School of Urban Construction, Beijing City University, Beijing, 101309, China
Abstract:

Increasing the degree of mixed use of urban land and building diverse and multifunctional urban spaces are important ways to shape urban vitality and promote healthy development of neighborhoods and social inclusion. Taking the urban area of City A as the research object, the article screens and classifies the collected POI data, and realizes the division and identification of functional areas in the core urban area of City A by calculating the degree of chaotic urban land use in parcels based on entropy under the fine-grained grid scale of the road network. Subsequently, the calculation methods of spatial weights and bandwidths of the model based on ordinary least squares and the Moran’s index eliciting the GWR model are introduced. Finally, eight factors that have an impact on neighborhood and social inclusion were selected as explanatory variables, and an empirical study of the spatial distribution of neighborhood and social inclusion and the influencing factors was carried out using the geographically weighted regression model. The study found that the functional mixing degree in the main urban area of City A generally shows the spatial distribution of high-mixing degree plots of land with “center clustering and multi-point scattering”, and locally shows the characteristics of piecewise clustering in the central area, linear clustering along the main roads, and pointwise clustering around the subway stations. The four influencing factors of common habits, psychosocial distance, social contact behavior and external behavioral interference are positively correlated with the changes of neighborhood relationship and social inclusion.

Amuersana 1, Shi Jin1, Lu Chao1, Xuan Li1, Danping Wang1
1Meteorological Disaster Prevention Center, Hohhot Meteorological Bureau, Hohhot, Inner Mongolia, 010010, China
Abstract:

Frequent lightning activity has the potential to cause damage to man-made facilities, cause forest fires and other hazards, and the prediction of lightning activity can help to avoid the occurrence of these disasters. In this paper, based on the lightning activity data of a region, the distribution pattern of lightning activity is identified at different elevations and latitudes and longitudes. Then geodetic distance and contributing nearest-neighbor similarity are introduced, and a GS-DBSCAN clustering algorithm is proposed to realize the spatial prediction of lightning activity by using the method of leastsquares fitting of prediction equations. The lightning activity directions after data clustering show topographic correlation, and the overlap between lightning activity directions and topography is about 35%. Combined with the prediction images, it is found that the lightning activity prediction results of this paper’s method are closer to the real value than other algorithms, with an average offset error of less than 1.1km, an accuracy rate of >85%, and a false alarm rate of <35%, which reflects a good prediction performance.

Shansheng Fan 1
1The School of Marxism, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, Zhejiang, 322100, China
Abstract:

Knowledge mapping technology can effectively integrate and manage knowledge, and fully show the relationship between knowledge. Based on this, knowledge mapping is applied to the construction of the resource base of the ideology and politics course to explore its association with the teaching content. After sorting out the relevant concepts and construction methods of knowledge mapping, this paper proposes the design method of course ideology based on knowledge mapping. The web crawler tool is utilized to crawl the text data of the Civics material and preprocess the data. The seven-step method and Protégé, an important tool for ontology modeling, were used to complete the construction of the ontology model of the curriculum Civics and Politics domain. Finally, BERT, GGAT, CRF, and graph pooling techniques are combined to construct the general architecture of the Civics knowledge extraction model to realize the extraction of Civics knowledge. The method of Civics knowledge relation extraction in this paper performs well in the comparison experiment, and the AUC value of the method reaches 41.59%. More than 90% of the students express their liking and agreement with the teaching model based on knowledge graph, which verifies that the teaching model based on knowledge graph proposed in this paper has a positive and active effect on the learning aspect of students’ Civics knowledge.

Guoxi Lv 1
1Neijiang Normal University, Neijiang, Sichuan, 641100, China
Abstract:

With the arrival of the big data era, a huge amount of text data of college language is generated, and how to manage these text data efficiently and mine useful information has become the focus of many scholars. The study first preprocesses and represents the university language text data, proposes a feature screening method based on Shannon entropy and JS-scatter, and then combines the principal component analysis algorithm with the dimensionality reduction of the extracted features on this basis. Subsequently, a pre-trained high-dimensional word vector spatial mapping model is introduced to generate richer semantic representations, and a pre-trained high-dimensional word vector spatial mapping model based on the pre-trained high-dimensional word vector spatial mapping model is designed. Finally, the method proposed in this paper is tested experimentally. Under different feature dimensions, the macro-averages of this paper’s method are 72%, 44.2%, 67.1%, and 3.3% higher than those of IG, PMI, ANOVA, and JS methods. At the feature dimension k=350, the macro-mean of this paper’s method is 0.853, when the classification effect reaches the optimization. In the spatial mapping relationship of word vectors, the accuracy of the mapping of this paper’s method also reaches 11.2% for the words with word frequency sorted from the first 5000 to the first 6000. This proves the effectiveness and feasibility of this paper’s method.

Feifei Gao1, Benyang Dou 2
1Department of Photovoltaic, Xuancheng Vocational & Technical College, Xuancheng, Anhui, 242000, China
2Administration of Technical Education, Xuancheng Vocational & Technical College, Xuancheng, Anhui, 242000, China
Abstract:

Wireless sensor networks, which integrate a variety of technologies such as sensors, microelectromechanical systems, wireless communications, and distributed information processing, have become a cutting-edge field for studying the behavior of intelligent autonomous self-governing systems in groups. This paper explores distributed sensor networks in intelligent buildings, uses QoS routing algorithm based on ant colony optimization to implement the strategy of energy efficiency regulation of distributed sensor networks, and conducts experimental analysis on the performance of the algorithm as well as distributed sensor networks. Compared with the PCCAA algorithm, the node degree variance and channel percentage variance of this paper’s algorithm are smaller, the network link distribution and channel allocation are more balanced, and the topology is better. Meanwhile, the average power of this paper’s algorithm is slightly larger than that of the PCCAA algorithm, which is able to increase the robustness of the network while reducing the energy consumption and BER to ensure the network performance. In addition, the variance of the node energy consumption of this paper’s algorithm in different networks is smaller than that of the PCCAA algorithm, which indicates that this paper’s algorithm can make the node energy consumption of the whole network more balanced, and then improve the energy efficiency of the whole network. Simulation experiments prove that the algorithm in this paper effectively allocates node bandwidth through the quantization mechanism, thus reducing the amount of inter-node communication, while the corresponding sampling interval extension strategy can save the overall energy consumption of the network. The algorithm proposed in this paper has important practical value for energy efficiency regulation of sensor networks in intelligent buildings.

Xiaoqiang Tang1, Kai Wang1, Chengbo Lu 2
1PowerChina Road & Bridge Group Co., Ltd., Beijing, 100160, China
2Xinjiang Agricultural University, Urumuqi, Xinjiang, 830000, China
Abstract:

Bridge construction is an important link in the construction of transportation infrastructure, which plays a key role in ensuring the smoothness and safety of road traffic. This paper systematically organizes the process of laser point cloud technology in bridge quality monitoring, and proposes an improved adaptive hyperparametric RANSAC point cloud segmentation algorithm to realize the bridge quality monitoring. Firstly, the basic process of RANSAC algorithm is sorted out, and the mean downsampling operation is adopted to replace the center of gravity downsampling method, which improves the point average degree of downsampling. Next, the FPS algorithm is combined with the method of selecting seed points to expand the range of selected values of seed points under the premise of meeting the relevant requirements. After splitting multiple fitting surfaces, the split fitting surfaces are combined to optimize the unfitted points and improve the fitting rate of the algorithm. The detection accuracy of the bearing flatness of bridge number 3 under the method of this paper is improved by 78.26%, and the maximum deviation of the detected bridge constitutive point offset is only 0.623m, which is within the acceptable range of bridge error monitoring. The feasibility of laser point cloud technology for bridge quality monitoring is verified.

Wenqian Cui1, KieSu Kim1
1Department of Industrial Design, Silla University, Busan, 46958, South Korea
Abstract:

This paper constructs the evaluation index system of city image IP brand communication efficacy, and utilizes hierarchical analysis and fuzzy comprehensive evaluation to construct a comparison matrix to assign and quantify them. Then, it constructs a regression model to analyze the influencing factors of city brand image communication efficacy with city brand image communication management power, communication power and relationship power as independent variables and city brand image perception as dependent variable. With empirical factor analysis, the chi-square degrees of freedom ratio CMIN/DF is 1.034, and the root mean square of approximation error RMSEA is 0.017, the assessment model has a good fit, which verifies the scientificity of the communication effectiveness assessment framework system. The communication effect of a city’s brand image is assessed and found to have a comprehensive score of 86.16. The city brand image communication management power, communication power and relationship power all have a positive influence on the city brand image communication effectiveness.

Jing Fan 1
1College of Music and Dance, Fuyang Normal University, Fuyang, Anhui, 236000, China
Abstract:

In this paper, the problem of piano practice time allocation is categorized as an integer planning problem, and focuses on 0-1 integer planning in integer planning. Based on the advantageous information in the 0-1 integer programming problem, the value of feasible solutions and the index set corresponding to the feasible solutions are proposed to realize the piano practice time allocation based on integer programming. For the evaluation of piano playing effect, a piano playing effect evaluation method based on the extraction of musical melody features is proposed, which adopts the base note cycle extraction algorithm based on the short-time autocorrelation method to extract the base note of the musical melody, and improves the linear scaling algorithm to solve the problem of uneven playing speeds and so on. In the piano practice practice allocation experiment, the average allocation time of player A applying the time allocation method of this paper is 2516s, which is higher than that of player B with the traditional allocation time, and the average concentration time accounts for 98.53% of the average time, which is better than that of player B’s 95.43% share. Compared with the traditional manual evaluation method, the evaluation results of this paper’s piano playing effect evaluation method in different test times sum up to 1, and the evaluation effect is better.

Jing Wang 1
1Sichuan Vocational and Technical College of Communications, Chengdu, Sichuan, 611130, China
Abstract:

In order to optimize the pattern design method in lacquerware decoration design, this paper first analyzes the discrete and continuous situation of the pattern in time and frequency by Fourier transform method, and explains the mapping principle of Fourier variation. After that, the original image is processed such as sharpening and smoothing under the Fourier transform algorithm, and the lacquer decorative pattern after automatic deformation is obtained through interaction on the basis of 2D affine transformation technology. Finally, the geometric deformation of the lacquer decoration design from 2D to 3D is simulated and verified. The results show that in this paper, the threshold value, brightness and contrast of the lacquer decorative design patterns can be obtained by the geodesic distance deformation algorithm under the Fourier transform in MATLAB software to get the geometric patterns of the lacquer decorative design with the main color of the appropriate filler blocks. The corresponding blue values of the four patterns are 418, 38, 104 and 256; the optimal values of green are 256, 100, 87 and 405; and the optimal values of red are 256, 57, 63 and 117. 3-D imaging simulation experiments show that the average absolute error, root mean square error and maximum absolute error of the depth of the geometric patterns of the 3-D imaging method and the geometric patterns proposed in this paper are all significantly reduced, and the depth of the geometric patterns in the 20- mm depth range are reduced significantly. and the advantages of this paper’s method are more obvious in the depth variation range of 20mm. It can be seen that the algorithm of this paper can improve the deformation effect of geometric patterns in lacquer decorative design.

Zhirong Zhao 1
1Physical Education College, Luoyang Normal University, Luoyang, Henan, 471934, China
Abstract:

College students’ physical fitness is an important part of national health, and analyzing physical fitness data in college physical education teaching helps to dig out the factors affecting students’ physical fitness and adjust the teaching plan in time. The article reviews some basic regression tools and selects variables such as BMI dietary habits for logistic regression analysis to analyze the factors affecting students’ physical fitness. The similarity, uncertainty and dissimilarity between students and their friends are calculated by Top-N recommendation set algorithm, and the physical education teaching program is dynamically adjusted with the new SFD recommendation algorithm. Finally, values were assigned to different movement banks and risk factors, and the experts’ agreement with the new adjusted program was examined. The intensity of physical activity had the greatest relationship with passing or failing physical fitness among all factors (regression coefficient = 0.927, p70%), reflecting the rationality and feasibility of this study.

Qipin Cheng1, Zhongqi Cai1, Yujie Liu 2
1School of Humanities and Social Sciences, Shanghai Lida University, Shanghai, 201609, China
2School of Nursing, Shanghai Lida University, Shanghai, 201609, China
Abstract:

Teachers and students will form a variety of dependent behaviors and interactions centered on teaching activities in the teaching process, thus, the teaching process can be regarded as a typical game process. This paper invokes game theory, takes teacher-student behavioral interaction as the research object, constructs a game model of teacher-student behavior in the process of English teaching, and proposes a teaching optimization strategy for English flipped classroom. At the same time, numerical simulation of the teacher-student game model is carried out to explore the dynamic game equilibrium under the cooperative behavior of teachers and students. The simulation results show that in the teacher-student game network, the strategy choices of teachers and students change over time, and different benefit-loss parameter μ, additional gain parameter β₀, and cost-saving parameter ψ have a greater impact on the replication of the strategy choice behaviors of the game parties. In addition, the increase of the parameters of the gain PT obtained by the instructor’s conscientious instruction, the gain PS obtained by the student’s conscientious learning, and the loss KS of the punishment that the student receives for not learning conscientiously are conducive to the promotion of the instructor and the student’s strategy evolution towards cooperation (conscientious instruction, conscientious learning), while the increase of the instructional cost CT of the instructor’s conscientious instruction and the learning cost CT paid by the student’s conscientious learning are not conducive to the promotion of the two parties’ cooperation. And when the proportion of instructors and students initially choosing cooperation is larger, the likelihood of both parties evolving toward cooperation is greater. This paper provides theoretical support for the optimization of English teaching process.

Yuyang Guo 1
1Zhengzhou Railway Vocational & Technical College, Zhengzhou, Henan, 451460, China
Abstract:

In this paper, time series analysis is used to monitor and predict the performance of athletes in sports training. A smooth time series model ARMA p q   , model is established, a fixed-order method based on autocorrelation function and partial correlation function is proposed, and the parameters of the model are estimated, and least squares prediction is used for model prediction. The monitoring test data of hemoglobin (HGB) in sports performance of Z athletes of a club were used as the research object, and the smooth time series test was conducted to determine the ARMA (1,1) model as the optimal time series fitting model, and the fitting effect was tested. In the application of blood oxygen saturation (BOS) index, ARMA (1,1) model can predict the trend of BOS of athlete Z with good application effect. Based on the prediction of athletes’ performance by ARMA (1,1) model, this paper further proposes the integrated neuromuscular training method (INT), and integrates it with physical training will to develop the INT physical education training strategy. In the application experiment of INT physical education training strategy, the test results of the experimental group of athletes applying the INT physical education training strategy in the six events of T-test sensitive running, agility ladder, vestibular step, blindfolded one-legged standing, 30-meter sprint running, and 60-meter sprint running presented P<0.05, and the athletes' performance was significantly better than that of the control group.

Meiying He 1
1School of Humanities, Zhejiang Guangxia Vocational and Technical University of Construction, Dongyang, Zhejiang, 322100, China
Abstract:

This study aims to investigate the influence of university language education on students’ expressive ability, and uses a questionnaire to collect the relevant factors affecting the relationship between students’ expressive ability and university language education. The key principal factors were extracted from many variables by principal component analysis to simplify the data structure and retain the main information. Subsequently, a multiple linear regression model was constructed and the least squares method was applied to estimate the model parameters in order to quantitatively analyze the linear relationship between each principal component and students’ expressive ability. In this paper, four principal factors, namely, “language organization ability, communication ability, language use ability and intonation ability”, were identified under the principal component analysis technique, and their total variance explained reached 56.326%. It is found that the average score of students’ expression ability is in the middle normal level, but the extreme difference of score between different students is as high as 27, which shows that there is a big gap between students’ expression ability. The correlation coefficient between students’ expressive ability and university language education is 0.8947, and the correlation coefficients of the four sub-dimensions of the two sig values are less than 0.01, indicating that the stronger the university language education, the higher the level of students’ expressive ability. And the regression equation of students’ expression ability and university language education is obtained as Y=0.893X-15.874.

Zhengwan He 1
1Public Foundation College, Anqing Medical College, Anqing, Anhui, 246000, China
Abstract:

The field of education is paying more and more attention to the fundamental task of education by establishing morality, and ideological and political education has become a major project in which all the teaching and learning links cooperate with each other and are accomplished in a concerted manner. This study explores the method of organic integration of ideological and political education and teaching and data visualization technology to enhance the effect of ideological and political teaching. Firstly, the method of portrait construction is introduced, combined with the student behavior dataset, and the student behavior data is preprocessed. Using the user portrait construction method as a hub, a gradient boosting decision tree model was used to predict the students’ Civics learning performance. The improved K-prototypes clustering algorithm was used to categorize student groups, which facilitated teachers to develop targeted learning strategies. Finally, group portraits and feature labels are extracted from the students to further help teachers accurately determine the types of student groups and carry out personalized teaching. The classroom teaching model in this paper classifies students into four categories with obvious behavioral characteristics, which increases teachers’ understanding of students, and the model not only improves students’ academic performance in Civics, but also significantly improves students’ level of course Civics and increases students’ classroom active response rate by 19.625%. The Civics education data visualization technology proposed in this paper reveals the rules of Civics education and improves teachers’ work efficiency.

Bo Gao1, Hengxin Jiang1, Jianwei Xu2, Yangguang Chen 3
1College of Science, Wuhan University of Technology, Wuhan, Hubei, 438300, China
2School of Mechanical and Electrical Engineering, East China Jiaotong University, Jiujiang, Jiangxi, 332004, China
3School of Mechanical Engineering, Lushan College of Guangxi University of Science and Technology, Liuzhou, Guangxi, 545000, China
Abstract:

The mechanism study of steel pipe welding in Dianzhong water diversion project is very complicated, and there are many process parameters affecting the temperature distribution of high-frequency heating of welded steel pipe, and the degree of influence and the influence law are not the same. In this paper, Abaqus software is used to carry out the finite element analysis of the steel pipe welding process, and the displacement variational method (i.e., Ritz method) is introduced to derive the radial displacement of the steel pipe when it is subjected to the action of the centralized force, so as to realize the finite element simulation of the welding process of the steel pipe. At the same time, the optimization of the welding process parameters of the steel pipe is realized by combining the radial basis function neural network (RBF) and particle swarm algorithm (PSO). The simulation results show that the Von mise equivalent residual stress at the weld seam reaches the nominal yield strength of the material on both the internal and external surfaces of the steel pipe, while the axial residual stress has a very different distribution law on the internal and external walls of the steel pipe, which belongs to the tensile stress and weld residual compressive stress at the weld seams on the internal and external walls of the steel pipe, which are about 0.4 times the yield strength of the material and 0.7 times the yield strength of the material, respectively. The ring residual stress distribution law of the steel pipe is similar to the axial residual stress, but both reach the nominal yield strength of the material. Through parameter optimization, this paper determines that when the opening angle is 5°, the current frequency is 217.35 kHz, and the distance from the coil to the V-point is 252 mm, the corresponding optimization target values are all smaller, and the welding quality of the corresponding weld seam is better. The research in this paper provides a theoretical basis for further improving the welding quality of steel pipe in Dianzhong water diversion project.

Zhengrong Liu 1
1School of Humanities and Arts, Hunan International Economics University, Changsha, Hunan, 410000, China
Abstract:

Personalized learning, in which learners set their own pace and select their own resources according to their own learning needs and characteristics, is the trend of Chinese education and teaching. In this paper, we design a personalized teaching path recommendation model for Chinese education based on reinforcement learning. The knowledge tracking prediction model LTKT is designed to integrate multiple knowledge points as information dimensions for model learning in the data preprocessing stage. The sparse self-attention mechanism is introduced into the encoder and decoder structure and embedded with location coding containing absolute and relative distances to enhance the model’s perception of location information. Finally, the RL4ALPR algorithm is designed to model the changing knowledge level, the candidate learning item filtering algorithm is used to narrow down the scope of the recommended learning items, the reinforcement learning algorithm assumes the role of a recommender, and the degree of change in the knowledge level of the learner is regarded as a reward for the improvement of the reinforcement learning recommendation strategy. Simulation experiments are conducted on datasets such as ASSISTments and compared with baseline models such as KNN, GRU4Rec, Random, etc. The model in this paper has an F1 value and an AUC of 0.635 and 0.956 respectively in the evaluation of learning effect, which are the highest among the models. The study makes a useful exploration for the informatization of Chinese education and teaching.

Ying Jin 1
1Art Department, Fushun Vocational Technology College, Fushun, Liaoning, 113122, China
Abstract:

In order to improve the attendance rate of students and optimize the quality of teaching, this paper proposes a method of predicting the attendance rate of students in colleges and universities based on multivariate regression analysis. Firstly, we obtain the factors affecting students’ attendance rate through sample survey and conduct correlation analysis, and then summarize and refine the three dimensions of students, teachers and schools. The above dimensions are used as independent variables to construct regression equations, and the regression equations are used to predict the attendance rate of students, so that teaching managers can optimize the management. The analysis found that the factors such as the college to which the truant students belonged, the reason for truancy, and the grade level showed diversity and complexity. Overall male students have more truancy rates than female students, and lecturers with higher titles have lower truancy rates. Regression modeling and prediction of truancy rate found that the prediction results are closer to the real results. Therefore, the method of this paper can be combined to optimize and adjust the attendance rate from the aspects of regulations, work allocation, teaching management and ideological education.

Jiping Liu1, Mei Huang 1
1Art College, Wanxi College, Lu’an, Anhui, 237012, China
Abstract:

The application of artificial intelligence on the field of art can be used to assist the creation of musicians and provide new creative ideas for musicians. In this paper, firstly, an ARIMA model is established for the prediction problem of opera style, which is used to predict the trend of the development of opera style sequence, and the best model is selected according to the minimum information criterion and Bayesian criterion. Then an automatic music melody generation method based on the generative adversarial network framework is proposed, which applies the trained natural language generation model to music generation to textualize the music melody and reduce the model running time. In addition to this a barization music melody generation method is also used, which divides a large music melody into melodic segments and generates them segment by segment, reducing the difficulty of the model in generating the music melody. Finally, the Fourier transform method is used to extract the features of the music melody and complete the visualization of the music melody. The model ARIMA(2,1,1)(2,1,0)12 that best fits with the time-series prediction of the development of opera styles was identified through empirical analysis. The PB value of Leak-GAN_2 model in this paper is improved by 41.38% compared with MusicGAN. It shows that both the opera style prediction model and the music melody multimodal generation model constructed in this paper have better effect and certain advancement.

Jingjing Wang 1
1Organization Department, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, Zhejiang, 322100, China
Abstract:

The introduction of performance evaluation in the educational management of colleges and universities is conducive to the formation of result-oriented concepts and management methods of student educational management. In this paper, we select the indicators of educational management conditions, processes and results to design the performance evaluation index system of educational management. Using the hierarchical analysis method, the eigenvectors and maximum eigenvalues are calculated to determine the weights of each index element of the index system. Then apply the gray correlation method to evaluate the educational management performance of the five universities by calculating, one by one, the absolute difference between each indicator sequence (comparative sequence) and the corresponding element of the reference sequence of the object to be evaluated after the data are dimensionless. The analysis found that, according to the formula for calculating the degree of correlation between the actual level of educational management performance and the ideal educational management performance situation, the comprehensive correlation degree of each sample of colleges and universities in the five stages is Z = (0.3333, 0.3951, 0.4600, 0.5031, 0.5946, 1.0000), and the rankings of colleges and universities in terms of the performance of educational management from the highest to the lowest are Academy 4, Academy 2, Academy 5, Academy 3, Academy 1. HEI 3 and HEI 1 should reflect on the shortcomings, enhance the digital construction of teaching informationization, deepen the collaboration between schools and enterprises, and improve the performance of educational management of colleges and universities.

Qiang Li1, Peiwen Yu1, Danni Chen1
1School of Human Settlements and Civil Engineering, Xi’an Eurasia University, Xi’an, Shaanxi, 710065, China
Abstract:

Large-span steel structures are prone to wind vibration under wind loads, which affects the safety and performance of the structure, and wind vibration control is the key to its design. This paper takes the large-span steel structure as the research object, firstly introduces the theory and method related to wind vibration control analysis, constructs the topology-optimized inertial capacitance damper controlled wind vibration response dynamic equation of super high-rise building to analyze the influence law of wind speed and wind direction on the dynamic characteristics of the structure, and then further strengthens the vibration control ability of the structure through reasonable arrangement and parameter adjustment. The deformation of ETABS model in y-direction is larger than that in xdirection under 50-year wind load, and the maximum displacements in y- and x-directions are 18.72 mm and 11.65 mm, respectively. The y-direction interstory displacement angle meets the code requirement limit (2.65×10-4). The amplitude of the acceleration time-range curve of its top floor structure is between ±0.08, which meets the requirements for comfort. The optimization of the reinforcement layer using continuum topology optimization is better than the optimization of the optimal location arrangement according to finite element software. The results of node displacements and inter-story displacement angles of each story of the modified structural model under wind load meet the limits of top story displacement and inter-story displacement angle, and the performances are similar to those of the extended-arm truss structural model.

Fei Xie 1
1School of Software, Beihang University, Beijing, 100000, China
Abstract:

Service Oriented Architecture (SOA), as a distributed computing architecture, is widely used to build efficient, maintainable and scalable information systems. This paper focuses on SOA design optimization based on reinforcement learning and cloud computing to achieve resource scheduling optimization with a view to improving the service quality of SOA applications. The asynchronous dominant action evaluation algorithm (A3C) based on policy gradient is used as the decision core of the cloud resource scheduler, and the residual recurrent neural network (R2N2) is introduced to construct the cloud resource scheduler based on the A3C-R2N2 algorithm to promote resource scheduling optimization. In the resource scheduling deployment strategy performance experiments, the median average latency of the stochastic dynamic scheduling strategy based on policy gradient learning proposed in this paper is reduced to 9.99% and 56.25% of the direct deployment, respectively, and the CPU utilization rate is also improved by 20.72% compared to the direct deployment. The loss function and reward function of the A3C-R2N2 algorithm in this paper begin to converge after the number of practice reaches 10,000 times and the number of training episodes reaches 300, respectively. Compared with random deployment and nearby deployment strategies, the deployment strategy based on A3C-R2N2 algorithm in this paper has an average service response time of 9.3622s, which is optimal.

Teng Zhang1, Guoqiang Hao1, Zhenhua Zhang2, Chenyu Song2, Chenxin Cui 2
1Economics and Management School, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China
2Software School, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China
Abstract:

Market economy is characterized by the uncertainty of supply and demand, so enterprises can realize the optimization of inventory cost control only by reasonably forecasting the demand of supply chain. This paper studies a supply chain demand forecasting method based on machine learning. The factors affecting supply chain demand are collected and analyzed, and the ARMA model, which combines autoregressive model and moving average model, is used to forecast supply chain demand. Then, through the introduction of procurement cost, storage cost and time cost, a multi-level inventory model is established, and the immune genetic algorithm is used to solve the model to find the optimal inventory cost. The experimental results show that the prediction model has good forecasting performance. After using the optimized scheme, the total inventory cost of the enterprise supply chain is reduced by 17.35% and 13.69% respectively. It can be seen that, on the whole, the method in this paper has a good effect of supply chain demand forecasting and cost control.

Ru Zhao 1
1Department of Management Engineering, Anhui Communications Vocational & Technical College, Hefei, Anhui, 230051, China
Abstract:

In the era of artificial intelligence, human-computer collaborative teaching has become a new picture of future development in the field of education. Based on the theory of human-computer collaboration and the theory of production-oriented approach (POA), this paper constructs a university English POA teaching model based on human-computer collaboration. It also combines the speech recognition algorithm, S-T behavioural analysis method and social network analysis method to conduct a case study on the current situation of college English classroom teaching under this instructional design model. Meanwhile, a teaching experiment is designed to verify the effectiveness of the constructed POA teaching model. The results of the case study show that most of the university English courses favour the lecture mode, with less interaction between students, and the classroom is dominated by teacher lectures and teacher-student interactions, but at the same time, many teachers begin to experiment with the discussion mode, which increases teacher-student interactions and student-student interactions in the classroom. In addition, the experimental group adopts the POA teaching mode and the control group adopts the traditional lecture mode, and its independent samples t-test results show that the experimental group is significantly better than the homogeneous control group in the dimensions of interest, ability, attitude, and test scores in English literacy after the experiment (P<0.05), which suggests that the combination of AI technology and the production-oriented method can effectively improve the effectiveness of the design of university English literacy teaching and achieve better teaching effectiveness and has potential application value.

Guohao Zou 1
1School of Humanities and Arts, Nanchang Institute of Technology, Nanchang, Jiangxi, 330099, China
Abstract:

AI technology can accurately capture and feedback user emotions in digital media interaction to realize precise interaction. In this paper, we design an AI emotion interactivity enhancement model based on multimodal fusion, and apply the neural network model of Bi-GRU and dual attention mechanism to fuse the long and short-term emotion classification results of the tested samples at the decision level to obtain the final emotion classification results. Then the weight coefficient vector of each sentiment category is calculated based on the sentiment classification confusion matrix of the classifier, which is used as the a priori knowledge for multimodal sentiment analysis for decision fusion. The performance is examined on the MOSI dataset and the AI-based interaction design strategy in digital media is proposed. Analyzing the interaction design effect, the interaction design applying the model of this paper has better user experience sense, emotional arousal, pleasure level, and emotional feedback effect in subjectivity evaluation than the control group, and 75% of the experimental subjects think that the feedback-adjusted digital media has a better pleasure level.

Baoqun Wang 1
1School of Fine Arts and Design, Huainan Normal University, Huainan, Anhui, 232038, China
Abstract:

With the rapid development of computer vision technology, image enhancement technology involves an increasingly wide range of research content. At the current stage, picture hierarchy enhancement technology is a research hotspot in the field of image enhancement. This paper proposes an oil painting image enhancement network based on positive probability distribution guidance. The multidimensional spatial information of the samples is obtained through the multibranch information extraction architecture in the network structure, and the probability distribution estimation module estimates the probability distribution through the obtained multidimensional spatial information. In addition, a new image enhancement method based on the RGB color balance method is proposed, which combines the multi-scale Retinex enhancement algorithm with color recovery and the RGB, Lab color space histogram adaptive stretching algorithm, to further improve the effect of oil painting image display. The experimental results show that the method has a better image color bias correction effect compared with the existing techniques. In terms of subjective evaluation, the average subjective score of this paper’s method in three different aesthetic levels reaches 9.15, obtaining a high evaluation. The samples enhanced based on this paper’s algorithm all obtained high aesthetic index scores, indicating that the oil paintings under this paper’s algorithm are in line with the public aesthetics, which is of great significance to the work of oil painting artists.

Xinyu Gong1, Siqi Mao2, Shixian Wu1
1Faculty of Shipping and Ship Engineering, Chongqing Jiaotong University, Chongqing, 400074, China
2Hohai College, Chongqing Jiaotong University, Chongqing, 400074, China
Abstract:

In order to enable ships to operate stably for a long time under complex sea conditions, all kinds of ships have an urgent need for gyroscopic rocking reduction devices. This paper takes the double gyro rocking reduction device with better rocking reduction effect as the research object, establishes its corresponding nonlinear dynamic equations, adopts the energy method to establish the differential equations of motion, and deduces the dynamic model of the rocking reduction double gyro. A parameter optimization model is established with the main objective of improving the shaking reduction effect, and the key components of the shaking reduction double gyro are optimized. The bacterial foraging optimization algorithm is selected to solve the model, and the multi-objective parameter optimization model is established. For one to five wave classes, the middle value of the wave height of the meaningful wave is selected for the dynamic simulation experiment of the double gyro. When the wave level is less than three time level, the rocking reduction performance of the rocking reduction double gyro reaches 87.5%, 78.1% and 77.78%, respectively, and the transverse rocking reduction performance is good. Under the simulation environment of sea state I (wave height 2.5m, average period 7s) and sea state II (righteous wave height 2.5m, average period 12s), the rocking reduction efficiencies of the ship after parameter optimization are improved by 6.44% and 10.09%, respectively.

You Chen 1
1Guangdong University of Science and Technology, Dongguan, Guangdong, 523083, China
Abstract:

The problem of English education quality is worth exploring in depth, and quantifying the indicators of English education can help to understand the problems in teaching and improve the quality of teaching. The study firstly establishes the English education quality evaluation index system, including five first-level indexes of teaching resources, teaching content, teacher quality, teaching effect and teaching quality feedback and 15 second-level indexes, such as network resources, book resources and comprehensive teaching content. On this basis, the combination weights are determined by fusing the G2 method and the projection tracing method through the combination assignment method to eliminate the one-sidedness problem of adopting a single assignment method, and then the cloud model theory is introduced to establish the English education evaluation model based on the cloud model. Problems and shortcomings of multi-objective linear programming weight allocation in English education evaluation system are found through the evaluation results, which lead to low multi objective linear programming weight allocation in English education evaluation system.

Xiaoyang Meng1, Yujing He 1
1School of Accounting, Jiaozuo University, Jizozuo, Henan, 454100, China
Abstract:

Financial performance optimization is an important embodiment of enterprises to improve operational efficiency and optimize management level. The article proposes a method of financial performance optimization and evaluation using group intelligence algorithm in order to optimize the financial performance of enterprises. EVA is introduced to establish the evaluation index of enterprise financial performance. The financial performance prediction model is constructed according to the propagation process of BP neural network, and the IPSO-BP algorithm is utilized to avoid BP from falling into local optimum and improve the prediction accuracy. In the learning ability test, the relative errors of the EVA value, EVA payoff and EVA rate of the IPSO-BP algorithm are controlled within 6%, 8% and 10% respectively, and the average relative error of the model application results is 3.87%. The model in this paper can achieve more accurate financial performance assessment and prediction, which is conducive to the optimization of financial performance management of enterprises.

Shiyi Xu 1
1The College of Educational Science and Technology, Anshan Normal University, Anshan, Liaoning, 114000, China
Abstract:

Aiming at the needs of reconstructing the structure of calligraphic seal cutting strokes and virtual display, this study designs a GAN technique that integrates three models, namely, “WGAN, DCGAN and CGAN”. The Cycle GAN model is used to obtain the mapping relationship between learning and style migration by utilizing its cyclic consistency loss. Adaptive pre-morphing technique is introduced to process the input image to capture the outline information and morphological features of calligraphic seal carvings, and a Generative Adversarial Network-based Generative Model for Structural Reconstruction of Calligraphic Fonts (CRA-GAN) is proposed. Meanwhile, an online virtual display system is designed to provide users with a good sense of experience in the virtual display of calligraphy. The results show that the CRA-GAN model can better capture the details and global information of the fonts, and its recognition rate of the eight calligraphic fonts ranges from 90.42% to 97.38%, and the MOS rating value of the text image is > 8.5 points, and its recognition results are in line with the observation characteristics of the human eye for calligraphic images. The FID calculation result of the CRA-GAN method ( 204.361) of the CRA-GAN method is much lower than that of other methods, which obviously improves the diversity and visual quality of the generated calligraphic fonts. This paper evaluates the user’s experience of the system from five aspects: narrative experience, emotional experience, sensory experience, cognitive experience and interactive experience, and calculates that the final score of the system is in the range of 80-100, which indicates that the user’s satisfaction is very high after actually experiencing the virtual display system.

Yuan Wang1, Jing Jin1, Meiling Ye2, Tingting Tao 2
1School of International Studies, Maanshan Teacher’s College, Maanshan, Anhui, 243041, China
2Department of General Education, Maanshan Teacher’s College, Maanshan, Anhui, 243041, China
Abstract:

At present, machine translation performs better in the general domain translation effect of large-scale bilingual corpus, but the translation effect in specific domains still needs to be improved. In order to optimize the accuracy of machine translation in the domain of English translation of professional terms, this paper proposes a translation model that incorporates syntactic knowledge and terminology. Aiming at the problem of more limited translation domain knowledge in the RNMT and Transformer models based on the self-attention mechanism, an optimization method is proposed. According to the domain characteristics of English translation of professional terms, English syntactic keywords are incorporated into the model training process, the special information contained inside the text of professional terms is learned, and the lexical properties of each word in the dataset are recognized before they are input into the model. Then attempts are made to incorporate the specialized terminology into the model to enrich the parallel corpus required by the model. The experiments confirm the excellent performance of the optimized translation model in this paper on the De→En terminology translation task, which improves 22.67 BLEU values compared to the base model. And the fluctuation of its BLEU value with the change of sentence length is small, which further indicates that the method optimizes the accuracy of the machine translation model in the English translation of professional terms.

Shunwuritu Na 1
1Inner Mongolia Preschool Education College For The Nationalities, Inner Mongolia, 017000, China
Abstract:

The traditional Chinese culture contacted in history education has many common points with the Civic and Political Education, which has become a new method of value penetration of Civic and Political Education. This paper reveals the value penetration of traditional Chinese culture in Civic and political education from the perspective of innovative cultural topology, and puts forward three strategies to innovate the concept of Civic and political education, such as enhancing the effect of aesthetic connotation of Civic and political education. On this basis, variables are designed, structural equation model is constructed, and the role of teaching concept and other variables on the value penetration of traditional Chinese culture Civic and political education is analyzed through the reliability test and factor analysis. Combined with system dynamics, the system causality diagram is drawn according to the causal feedback relationship between internal and external factors to explore the causal relationship affecting the value penetration of Civic and Political Education, and then explain the mechanism of the role of traditional Chinese culture Civic and Political Education. It was found that all five paths among latent variables passed the significance level test of 0.001, and the teacher’s mission and ideal belief in teaching philosophy had the most significant effect on the value penetration of traditional Chinese culture Civic and political education, with path coefficients of 0.98. In the process of Chinese traditional culture civic education, it is necessary to reflect the unity of humanistic spirit and modern spirit, the unity of professional ethics and values, and to form the style of course civic education and course civic education characteristics with Chinese traditional culture.

Lijuan Zhang1
1Luoyang Institute of Science and Technology, Luoyang, Henan, 471023, China
Abstract:

In the face of the traditional agricultural marketing model is difficult to continue the status quo, agricultural marketing competition – cooperation relationship for agricultural enterprises of commodity marketing and long-term development is also increasingly important. In this paper, game theory is introduced into the study of competition and cooperation strategy of agricultural products marketing, the strategic behavior of two agricultural products enterprises in the agricultural products industry cluster is constructed into the corresponding matrix, and evolutionary dynamic stability analysis is carried out to establish the replication dynamic equations and Jacobi matrix to solve the evolutionary stability strategy (ESS), so as to provide reference for the formulation of the competition and cooperation strategy of the enterprise’s agricultural products marketing. Using simulation to explore the influencing factors of the evolutionary direction of the marketing competition and cooperation strategy of agricultural products enterprises. When the probability of winning the joint bidding is greater than 0.8, it will evolve into a cooperative strategy, and when it is less than 0.7, it will evolve into a competitive strategy, and with the increase of the allocation coefficient of the investment amount of the project construction, the agricultural products enterprise 1 and the agricultural products enterprise 2 will gradually shift from a competitive strategy to a cooperative strategy. The lower the cost allocation coefficient is, the higher the probability that enterprises will evolve to cooperative strategy. The increase of cooperative transaction cost then accelerates the evolution of enterprise 1 and enterprise 2 to competitive state.

Yu Hang1, Guanqun Zhang 2
1College of Continuing Education, North China Institute of Aerospace Engineering, Langfang, Hebei, 065000, China
2Hebei University of Engineering Science, Shijiazhuang, Hebei, 050000, China
Abstract:

With the rapid development of artificial intelligence technology, the research on personalized learning in the field of ideological and political intelligence education is increasingly active. In this paper, an improved locust optimization algorithm is proposed, which is applied to the intelligent grouping strategy of ideological and political education. Then a knowledge state-oriented hypergraph self attention knowledge tracking model is proposed, which consists of a hypergraph module and a self attention module, and is capable of predicting students’ future interaction sequences through their past interaction sequences. In order to realize students’ personalized test question matching needs, a Civics test question recommendation algorithm based on the neural graph model is proposed, based on which a personalized Civics test question recommendation exam system is designed and implemented. The intelligent grouping strategy based on the optimized locust algorithm achieves a total score accuracy of 100% in the Civics grouping task. The knowledge tracking model accurately predicts students’ knowledge status, and the attention weights of students’ learning paths based on this paper’s recommendation algorithm are all higher than 0.5. It shows the effectiveness of this paper’s strategy of automatic generation of Civics education content based on the locust optimization algorithm and the personalized test question matching model on the students’ in-depth understanding of the Civics knowledge and improvement of learning efficiency.

Chuan Liang 1
1Mental Health Education Center, Shangqiu Medical College, Shangqiu, Henan, 476000, China
Abstract:

This paper constructs a comprehensive evaluation system based on the CIPP model, covering multiple dimensions such as input evaluation and outcome evaluation, in order to comprehensively measure the effect of college students’ mental health education in the new media environment. In terms of weight determination, the subjective weights are obtained by hierarchical analysis method, then the objective weights of each index are calculated by entropy value method based on the actual data, and then the combination assignment method is used to organically combine the subjective and objective weights to obtain the ϐinal indexes. The relationship matrix was constructed on the basis of a large amount of collected data, and the fuzzy comprehensive evaluation method was used to comprehensively assess the implementation effect of college students’ mental health education. The results of the study show that the overall level of the effect of college students’ mental health education is good, with the ratings of 79.54 and 78.28 for their mental health knowledge and ideological awareness evaluation, respectively, and that the mastery of mental health methodology and the awareness of proactively seeking psychological help are the main factors affecting the mental health of college students. In addition, the mastery level of college students’ mental health practice ability is average (69.52), and there is an obvious deϐiciency in their theory to practice, which also adds difϐiculty to the construction of college students’ mental health. Therefore, the fuzzy comprehensive evaluation method can be used to optimize the evaluation system of college students’ mental health education in the new media environment.

Haonan Wu 1
1School of Future Technologies, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China
Abstract:

Agent technology is widely used in intelligent manufacturing and digital workshop as a new method to solve complex, dynamic and distributed artiϐicial intelligence application problems. This paper ϐirstly summarizes the application steps of Agent technology in 3 aspects of modeling, simulation and monitoring of intelligent manufacturing system on the basis of a brief description of multi-agent system. Then, based on reinforcement learning theory, a multi-agent collaborative algorithm SRL_M3DDPG based on state representation learning is proposed.Finally, the algorithm model is tested and applied to the smart shop scheduling problem. The learning curve of the SRL_M3DDPG algorithm in the example remains relatively stable after the 3400th round, and the maximum completion time of the scheduling is 29. Comparing with other composite scheduling rules, the delay rate of this paper’s algorithmic model is the lowest, which is only 15.47%, which indicates that the algorithm is able to signiϐicantly reduce the delay rate of the workpiece. In addition, this paper’s algorithm achieves better results in adaptive intelligent manufacturing workshop scheduling, ϐinding the shortest machining completion time of 221 unit time, which can adapt to the dynamic intelligent manufacturing workshop environment.

Xianfeng Zhang1, Shouqi Cao 1
1Mechanical Engineering, Shanghai Ocean University, Shanghai 201306, China
Abstract:

The article firstly establishes a mathematical model of the FMS shop floor planning process problem, and combines the rescheduling strategy and rolling scheduling strategy for solving the FJSP problem. Subsequently, the simulated annealing genetic algorithm is improved by relying on genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm, and the application of hybrid optimization algorithm in problem solving is proposed. The simulated annealing algorithm is incorporated into the crossover and mutation operations of the genetic algorithm to strengthen the local search capability, and then the global annealing operation is incorporated into the new individuals obtained. The overall design of the mixed reality-based FMS virtual simulation system was tested with a view to optimizing the external tool library tool limitation problem in the FMS shop floor planning process. The results of the simulation experiments show that although the algorithm of this paper, SaDE and CoDE algorithms can reach the optimal solution, the convergence speed of the algorithm proposed in this paper is significantly better than the other two algorithms. Based on the experimental results, the article finally constructs a mixed reality-based FMS virtual simulation system to solve the external tool library tool limitation problem in the FMS shop floor planning process.

Yan Gao 1
1Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu, 223301, China
Abstract:

Chinese oil painting art is an important carrier of contemporary Chinese cultural identity features, the identification and quantitative study of the color and texture of the picture can help to understand the characteristics of the oil painting works more deeply. Therefore, this paper proposes a feature recognition method for oil painting art based on deep learning method. The Otsu threshold method and DeeplabV3+ network model based on DeeplabV3+ are selected for image graying and segmentation processing. The global color histogram and ring LBP are used to extract the color and texture features of the picture respectively, and the oil painting feature recognition is completed based on the regularized limit learning machine. In several sets of quantitative results, the methods in this paper all have better oil painting color and texture feature recognition, among which the RELM algorithm has the highest detection accuracy at low correlation features. It shows that the deep learning based Chinese oil painting art and cultural identity feature recognition method can effectively extract oil painting features and realize the quantitative research on oil painting.

Siqi Chen 1
1Chongqing City Management College, Chongqing, 401331, China
Abstract:

At present, drilling fluid leakage in oil and gas drilling engineering in complex formations is a worldwide technical problem. The study explains the mechanism of dense pressure-bearing plugging at the bottom of the fracture, explores the influencing factors of the pressure-bearing capacity of the leakage prevention and plugging working fluid, and establishes a mathematical model by using multivariate nonlinear regression analysis. Based on the machine learning technology, the support vector machine algorithm is selected as the prediction method of the particle size of the working fluid for leakage prevention and plugging, and the system model of the ultra-high-temperature dense pressurized leakage prevention and plugging working fluid is constructed. It is found that the established multivariate nonlinear regression analysis has good fit and accuracy, and the average relative error is only 2.9%, and the seam width (-0.694) and formation pressure (0.502) have the greatest influence on the pressure-bearing capacity of the working fluid for leakage prevention and plugging. The prediction accuracy of the support vector machine model for the working fluid particle size was 95.36%, and the prediction F1 values on multiple datasets were all greater than 0.9, showing excellent prediction results. The constructed mathematical model can be used to guide the field operation, which is conducive to the long-term stable plugging and scientific leakage prevention of fissure-based leakage.

Yixing Bao 1
1Department of Data and Systems Engineering, The University of Hong Kong, Hong Kong, 999077, China
Abstract:

As an environmentally friendly and efficient public transport, the optimization of the operating frequency of electric buses is of great significance for improving passenger satisfaction and reducing operating costs. This paper proposes an optimal electric bus frequency setting method that combines LSTM prediction and two-layer planning. First, LSTM neural network is utilized to predict the passenger flow of electric buses. Second, a two-layer planning model is constructed, with the upper model aiming at frequency optimization and the lower model aiming at electric bus frequency setting. Finally, this two-layer planning model is solved by genetic algorithm to obtain the optimal electric bus frequency setting. The inbound and outbound passenger flow data of the 5th station of 363 electric bus in Q city are used for practical verification. The prediction results of the LSTM model on inbound and outbound passenger flow on weekdays and natural days are basically consistent with the actual values. The optimal frequency of 62 trips was solved using genetic algorithm. The maximum deviation of the actual capacity supply from the actual capacity demand curve is only 0.09% when the frequency setting is verified under the scenario of thousands of passenger flows. From the above analysis, it is shown that it is practical to design the optimal electric bus frequency using LSTM prediction and two layer planning model.

Yaan Xing1, Nannan Dong1, Jie Du 1
1School of Business, Ningbo University, Ningbo, Zhejiang, 315211, China
Abstract:

This paper synthesizes relevant theoretical knowledge and construction principles, selects 20 evaluation indicators to constitute the evaluation system, and divides the evaluation system into two subsystems in order to more intuitively demonstrate the relationship between international trade network optimization and regional economic synergy. Setting the source of research data, due to the initial data outline is not uniform, the research data for the dimensionless processing. Then the weight values of each index are calculated with the help of entropy weight method, and their values are substituted into the coupled synergy model of the fusion evolutionary algorithm. It is calculated that the synergy level of international trade network optimization and regional economy is medium in the period of 2014~2016, the coordination level of the two has been significantly improved in the period of 2017~2021, and the coordination level is good, and the coordination level of international trade network optimization and regional economy rises to excellent in the period of 2022~2023.

Pengfei Zheng1, Ting Qin 2
1Shanghai Customs University, Shanghai, 201204, China
2Liuzhou Institute of Technology, Liuzhou, Guangxi, 545616, China
Abstract:

Achieving high-quality development has become the core essence of tourism industrialization, and is also a necessary step for the construction of ecological civilization to make new achievements. The article establishes the index system of China’s tourism high-quality development, and uses the entropy weight-TOPSIS model to measure the tourism high-quality development of China’s tourism in each region from 2013 to 2021. On this basis, it comprehensively applies density estimation, Dagum Gini coefficient and convergence modeling methods to analyze the regional differences and convergence of China’s tourism development. The study shows that the level of high-quality development of China’s tourism industry is gradually rising, and the regional differences in high-quality development of tourism are generally narrowing, with insignificant changes in intra-regional differences and narrowing of inter-regional differences, though. The overall trend of wave height in the central region is increasing, the wave height in the western region is decreasing and the width is increasing, and the wave height in the northeast region is increasing and the width range is decreasing. At the same time,  convergence coefficient shows that the gap between the level of high-quality development of tourism economy in the eastern, central and northeastern regions shows a trend of convergence, while the western region increases from 0.373 in 2012 to 0.388 in 2021, that there is no trend of convergence.

Huanyong Zhang1, Jinghan Lin 1
1School of Business, Jiangnan University, Wuxi, Jiangsu, 214122, China
Abstract:

New energy vehicles have a broad market, and the pricing and after-sales service of new energy vehicle enterprises have become the effective competitiveness of new energy vehicle enterprises. Therefore, this paper studies the pricing and after-sales service decision-making of new energy vehicles on the basis of game theory, and the study first gives a brief overview of game theory. Then, in the context of new energy vehicle subsidies, the optimal pricing under different sales modes is studied using game theory models. It also studies the utility of service stores of the same level of new energy vehicles with the support of game theory, and finally puts forward service suggestions from four aspects: optimizing offline service stores, expanding online services, developing service projects, and developing personalized services. This study can also provide valuable references for the pricing and service marketing of new energy vehicle enterprises, improve the competitiveness of after-sales service at the same time, and also put forward feasible suggestions for the future after-sales marketing methods of new energy vehicle manufacturers.

Tong Su1, Da Ji 2
1School of Innovation and Entrepreneurship, Shandong Huayu University of Technology, Dezhou, Shandong, 253000, China
2School of Sociology, Sanya University, Sanya, Hainan, 572022, China
Abstract:

The development of artificial intelligence has brought new development opportunities for modern enterprises, but employees present a certain degree of resistance to the introduction of AI technology. The author tries to dissipate employees’ resistance and improve their acceptance of AI through organizational training. After researching organizational training and employees’ perceived awareness of AI, organizational training and employees’ acceptance of AI are taken as antecedent and consequent factors to construct a structural equation research model of the two. The research hypotheses are proposed based on the theoretical study of the two. Regression analysis of the effect of organizational training on employees’ AI acceptance is conducted through structural equations. The regression results show that training investment, employee motivation and knowledge training in organizational training all have a significant positive effect on both employees’ AI perceived ease of use and AI perceived usefulness. Employee AI perceived ease of use and AI perceived usefulness have a positive effect on employee behavioral intention to use AI for knowledge creation and automation. Employees’ behavioral intention to use AI for knowledge creation will have a positive effect on AI for knowledge creation, and behavioral intention to use AI for automation will have a positive effect on AI for automation.

Junting Yang 1
1School of Foreign Studies, Wenzhou University, Wenzhou, Zhejiang, 325000, China
Abstract:

This topic obtains the data of featured vocabulary under the technical architecture of big data platform and saves it in the form of dataset. Standing on the perspective of the principle of translation of featured words in foreign propaganda, the improved K-means algorithm and attention mechanism are utilized to design the translation model of featured words. The model of this paper is validated and analyzed from two aspects, namely, performance indexes and application effect, respectively. In the six performance indexes, this paper’s model performs better compared to the other two control models. After the experience, the control group and the experimental group show a significant difference, i.e., the introduction of data mining algorithm is more effective in translating the featured vocabulary on the traditional model.

Yi Yu1, Li Ma2, Xiao Chen1, Yichao Zhong3
1HangZhou Animation & Game College, Hangzhou Vocational & Technical College, Hangzhou, Zhejiang, 310000, China
2College of Art, Krirk University, Bangkok, 10220, Thailand
3New Media Content Center, Hangzhou Bicheng Digital Technology Co., LTD., Hangzhou, Zhejiang, 310000, China
Abstract:

In today’s digital era, user interface (UI) design is crucial for enhancing user experience and strengthening user engagement. The study uses heatmap analysis, K-means clustering algorithm and random forest regression algorithm to comprehensively analyze the characteristics of user behavior in UI pages. The predicted results of user behavior in UI pages are visualized and analyzed through heatmaps. Cluster classes are divided according to user behavioral characteristics to generate user profiles with the same behavior. Combine Random Forest and Logistic regression algorithm to get the key indexes of UI optimization design and predict their impact on user behavior experience. The research results show that the MAE and SMAPE values of Random Forest regression algorithm on user behavior prediction are 133.55 and 8.18%, respectively, with an R² of 0.96, and the accuracy rate of behavior prediction is more than 80%, which shows a good performance of user behavior prediction. The clustering algorithm divides the user behavioral characteristics into 6 clusters based on their behavioral characteristics, including cluster class 1 (browsing and exploring class), which accounts for 11.5% of the number of investigators. The weight of the top 8 of the importance of UI optimization design obtained by the random forest regression analysis algorithm is 70.26%. And the user behavior experience can be improved by 5.377~9.925 times when each element is improved by one unit.

Qi’ao Li 1
1College of Music and Dance, North Minzu University, Yinchuan, Ningxia Hui Autonomous Region, 750000, China
Abstract:

This paper adopts research methods such as literature method and questionnaire survey method to take the cultural inheritance and development of Lanzhou Taiping Drum as the research object, and conducts in-depth discussion on the characteristics, social background and development of Lanzhou Taiping Drum. The research and analysis of the influence of the inheritance of Lanzhou Taiping Drum was also carried out by using principal component analysis and stepwise regression method in combination with the actual situation. It is found that many factors of Lanzhou Taiping Drum itself and government factors have significant influence on its inheritance. On the basis of the results of this study, we explore the ways and contents of the protection and inheritance of Lanzhou Tai Ping Drum, and put forward the digital inheritance of Lanzhou Tai Ping Drum and the path of cultural ecological reconstruction in terms of the influencing factors.

Cong Ma1, Mei Sun 1
1Department of Design, Taishan University, Taian, Shandong, 271000, China
Abstract:

The emergence of artificial intelligence has changed the traditional visual communication design mode to a great extent. This study aims to conduct an in-depth theoretical discussion and empirical analysis of the intersection of artificial intelligence and visual communication design, for the generative design application of AI technology in visual communication design, based on the AttnGAN algorithm, designing the adaptive word attention module and feature alignment module, constructing the ACMA-GAN text image generation model, and evaluating its visual communication design by combining quantitative and qualitative experiments to assess its The effect of ACMA-GAN on visual communication design is evaluated by combining quantitative and qualitative experiments. Combined with OLS algorithm, the empirical analysis of the effect of AI technology on visual communication design is carried out, and the ACMA-GAN model achieves excellent performance in the evaluation of assisted visual communication design, with the BLEU-3 and CIDEr scores higher than the next highest scores by 7.48% and 7.35%, and the average scores of each qualitative index are over 4.5, which indicates the feasibility and good utility of AI technology in assisting visual communication design. AI technology can positively act on visual communication design through image recognition and analysis, image generation and creation assistance, personalized design and workflow optimization.

Yishu Liu1, Xiaowen Lv2
1School of International Business, Xi’an FanYi University, Xi’an, Shaanxi, 710105, China
2School of Management, Qilu Medical University, Zibo, Shandong, 255213, China
Abstract:

Aiming at the difficulties faced by traditional industries, this paper formulates a smart blockchain solution for sustainable industrial digitalization. Through the theoretical analysis of blockchain technology integration into industry, it provides theoretical support for the application of intelligent blockchain technology in industrial digital transformation. Combining the above three algorithms and the actual situation of industrial digitalization development, an industrial digital transformation scheme integrating intelligent blockchain technology is designed, and a case study of the scheme is conducted. The delay mean value of this paper’s scheme is within the allowable range at the maximum throughput, indicating that the scheme can promote the sustainable development of industrial digitalization. In the actual application scenario, the CD-PBFT consensus algorithm performs more prominently, and in addition, it can be seen that the industrial blockchain solution, which can enhance the product recycling rate, well practices the concept of sustainable development.

Junhong Zhu1
1Chengdu Jincheng College, Chengdu, Sichuan, 611731, China
Abstract:

This paper improves the prediction accuracy of financial crisis of listed companies by optimizing the traditional Z-score model and taking the financial warning indicators as the input features of the neural network. The study selected the financial data of listed companies in a certain place from 2017 to 2023 as a sample, compared and analyzed the early warning performance of multiple traditional machine learning algorithms with this paper’s method, and assessed the reliability of this paper’s model in the early warning of financial quality by combining with cases. The neural network-based Zscore model has an AUC value of 0.914 on financial quality early warning, which is close to 1, and the prediction results are reliable. The model’s overall financial quality early warning accuracy in year t-1 is elevated by 16.61% to 19.35% compared with the comparison algorithm, and has a faster error has convergence speed. The Z-value calculation predicts that three companies will appear to have financial quality risk in 2017, which is consistent with the actual results. The algorithm of this paper predicts that company 9 has a Z-value of 3.79 in 2031, which may have financial quality risk. The results of this paper are reliable and show the early warning method of financial quality of listed companies in a new perspective, which is an important reference value for investors and managers.

Li Fu1, Yi Yao 2
1School of Economics and Management, Taiyuan Normal University, Jinzhong Shanxi, 030619, China
2School of Economics and Management, Xinzhou Normal University, Xinzhou Shanxi, 034000, China
Abstract:

As one of the important stakeholders in ecotourism, community residents play a crucial role in ecotourism development. This study takes interactive emotional cognition, social exchange theory and the concept of psychological carrying capacity as the guiding theories, and designs the community residents’ questionnaire from the aspects of emotional cognition and psychological carrying capacity, respectively. Correlation analysis and regression modeling were used to test the influence of interactive emotional cognition on the psychological carrying capacity of ecotourism community residents. The calculation results show that the psychological carrying capacity of ecotourism community residents is positively correlated with positive interactive emotional cognition (r>0) and negatively correlated with negative interactive emotional cognition (r<0). It was also found that community residents' proud emotional perception of tourism development had the highest degree of influence on the psychological adjustment capacity variable (R²=0.299). This study verifies the mechanism of community residents' interactive emotional cognition on their psychological carrying capacity and enriches the theoretical research on promoting ecotourism development.

Bingqi Yin1, Yanxue Wang2,3
1School of Drama, Film and Media, Dalian Art College, Dalian, Liaoning, 116699, China
2 School of Communication, Baicheng Normal University, Baicheng, Jilin, 137000, China
3Center for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, 43600, Malaysia
Abstract:

The process of innovative education is not only a purely intellectual activity process, it needs innovative emotion as a driving force, such as strong interest, strong passion, the motivational function of evaluation, harmonious teacher-student relationship and other non-intellectual factors cultivation, in order to obtain a comprehensive effect. This study is oriented to the intelligent distribution platform of journalism and communication content to study its teaching value and innovation emotion. The Information Adoption Model (IAM) was adopted as the theoretical basis for the study of content intelligent distribution platforms, the characteristics of the platforms were summarized, and the impact of the platforms on teaching value was studied using regression analysis. The result table of the study found that the content intelligent distribution platform’s exhaustiveness, readability, and objectivity had a significant positive correlation on the usefulness of educational value, and that the influence of interactivity on perception and participation did exist and had a certain impact on educational usefulness. Finally, this paper also takes S colleges and universities as an example to assess and calculate the innovative emotion and innovative ability of the platform’s teaching value, further analyzes the teaching value of the intelligent distribution platform, and provides suggestions for the cultivation of the innovative emotion in combination with practical research.

Yuan Sun 1
1School of Mathematics and Information Engineering, Puyang Vocational and Technical College, Puyang, Henan, 457000, China
Abstract:

In order to alleviate the problems of short supply of parking spaces and traffic congestion, intelligent driving solutions have emerged. Automatic parking has now become the first application scenario for driverless driving due to the more fixed scenario and lower traveling speed. In this study, the traditional A* algorithm is improved using the cost function, and the hybrid algorithm of parking space path search and planning is designed by combining the improved A* algorithm with the Reeds-Shepp curve, and then combined with the collision constraints to improve the algorithm’s path planning performance. The results of simulation experiments and in-loop test experiments show that the maximum lateral error and heading error are low in parallel and perpendicular parking scenarios, and it is found that the average lateral error during the whole parking process is only 0.177m in the in loop test, which is a good tracking effect for vehicles. The path search and planning algorithm designed in this paper can better realize the autonomous parking function and has high tracking accuracy and stability in the simulation scenario.

Jiayi Xu1, Chenchen Shan1, Yixuan Lv1
1HBU-UCLan School of Media, Communication and Creative Industries of Hebei University, Baoding, Hebei, 130600, China
Abstract:

In the context of the digital economy driven by the Internet of Everything, the dissemination of cultural heritage is facing the challenge of transitioning from traditional to digital media. The study develops an introduction to the visual SLAM system, models the binocular camera configuration and the indoor and outdoor dense 3D reconstruction process, and designs a complete set of algorithms based on the calibration of the actual binocular camera, image correction, binocular stereo matching algorithm (SELAS), and real-time dense point cloud 3D reconstruction. Based on the real laboratory scene, the original ELAS algorithm is compared with the improved method for experiments, and the results show that the mean value of the deviation of the optimized S-ELAS algorithm is -0.046m, and the algorithm accuracy is remarkable. Then a virtual cultural relics museum based on the combination of visual SELAM system and VR technology is designed to realize close interaction with S-ELAS stereo matching algorithm. In order to test the performance of the designed cultural relics museum system, the users are firstly acclimatized, and then the screened users are tested to experience the virtual museum system, and the MOS scores are made after the test. The MOS scores show that the virtual cultural relics museum system has better interactivity and experience.

Wei Tong1, Xiaomeng Liu1, Gang Wang2, Zuohu Chen2, Zhenguo Peng 2
1State Grid Gansu Electric Power Company, Lanzhou, Gansu, 730000, China
2Gansu Tongxing Intelligent Technology Development Co., LTD., Lanzhou, Gansu, 730050, China
Abstract:

This paper constructs the key business index system of electric power system consisting of electric power supply, electric power transmission, electric power distribution, electric power equipment and electric power system management. By evaluating the validity optimization, reliability optimization, and redundant indicator removal based on the neural network analysis method of the indicator system, a new power system key business indicator system is formed, and the weights of the optimized indicators are calculated. The power system key business indicator control program is designed based on the weight parameters, and a new power system key business indicator control platform is developed. Extract power data using the weighted FCM clustering algorithm, and classify user power data on the cloud platform. Resource utilization and performance response analysis are performed on the power system key business index control platform. The power system key business index control platform designed by index weights developed in this paper is able to meet the transaction demand under different concurrent user numbers, and always maintains a memory utilization rate within 10, with good operating conditions.

Zongpeng Xu 1
1School of Management, Anshan Normal University, Anshan, Liaoning, 114000, China
Abstract:

The development of blockchain technology in modern business and finance is of great importance. The study delves into the blockchain-based shareholder voting system and the role of blockchain on corporate governance. On this basis, relevant research hypotheses are formulated. After completing the definition of research variables, the research model is constructed to empirically investigate the impact of blockchain-based shareholder voting system on corporate governance. The research hypotheses are tested through regression analysis and the robustness test is utilized to ensure the reliability of the research findings. The minimum value of blockchain-based shareholder voting and corporate governance level are both 0, the maximum value is 4.954, 0.624, and the average value is 0.821, 0.089, respectively. There is variability in shareholder voting and corporate governance level across companies. Before and after the control variables, the coefficients of blockchain-based shareholder voting system are 0.225 and 0.247 respectively, and both are significantly positive at 1% level. Blockchain-based shareholder voting system can improve corporate governance.

Zhen Xia 1
1School of Digital Eeconomy & Trade, Wenzhou Polytechnic, Wenzhou, Zhejiang, 325035, China
Abstract:

Under the background of globalization and knowledge economy, the importance of innovation and entrepreneurship education for college students is becoming more and more prominent. This paper combines fuzzy logic and decision tree algorithm to construct a cultural confidence recognition model of innovation and entrepreneurship education. Feature selection and classification are carried out on the salient features of the collected data information on innovation and entrepreneurship education. First, eight types of statistical features, such as the degree of integration of excellent traditional culture, the degree of value leadership and moral cultivation, the innovative power of grounded cultural knowledge, and the effect of social responsibility cultivation, are extracted as inputs to the C4.5 algorithm, and a decision tree is constructed for feature selection. Then, according to the constructed decision tree, the affiliation function and IF-THEN rule of the fuzzy inference model are designed. Finally, the designed fuzzy inference model is used to classify the degree of cultural confidence. The method achieves 100% accuracy in recognizing the lack of cultural self-confidence in innovation and entrepreneurship education, and more than 90% in recognizing the overall effect of general cultural self-confidence and rich cultural self-confidence. The experimental results show that the combination of decision tree and fuzzy inference modeling is feasible for the detection and classification of college students’ innovation and entrepreneurship education, and has strong practical application value.

Wenshao Li1, Baolu Wang1, Guipin He1, Hongyuan Liu1, Paiyi Li1, Wei Liu 2
1Liuzhou Cigarette Factory, Tobacco Guangxi Industrial Co., Ltd., Liuzhou, Guangxi, 545005, China
2 School of Civil Engineering, University of South China, Hengyang, Hunan, 421001, China
Abstract:

This paper presents the design and implementation of the non-electric contacting power supply system for the electronic scale, which mainly focuses on improving power transfer and measurement accuracy. The whole system architecture includes electromagnetic coupling, an advanced algorithm of control, and safety. Simulation results have shown that, under standard conditions, it is possible to reach a high power transfer efficiency higher than 90% while keeping voltage regulations within ±1% and limiting current ripple to below ±1.8%. Therefore, this provides a measurement resolution of ±0.1g for the system while granting stable performance for a variety of conditions in both coupling and load. Protection mechanisms set within the system ensure reliable operation; fault detection time is less than 10μs. The proposed method represents a relatively good guide for non-contact power supply towards precision measurement, thus solving the challenge of WPT in an electronic scale system.

Qingyun Ge1, Jing Zheng1, Fulian Yang1, Caimei Li 2
1School of Architecture and Civil Engineering, West Anhui University, Lu’an, Anhui, 237012, China
2Gates Winhere Automobile Water Pump Products (Yantai) Co., LTD., Yantai, Shandong, 712000, China
Abstract:

This research proposes a new optimization technique for reinforcement concrete filled structural tubular columns using genetic algorithms and unified strength theory. A complete theoretical model to determine the axial bearing capacity of reinforced CFST columns incorporating modified confinement coefficients and enhanced steel section properties was developed. The optimization procedure deals with performance of the structure, materials usage and construction convenience as the optimization goals. Experimental validation for ultimate bearing capacity of five full scale specimens was carried out and the deviation was found out to be 5.2% which was found to be predicted by the theoretical model accurately. Internal stiffeners are likely to increase axial capacity by about 15.7%-23.4% over traditional CFST columns. The relationship between stiffener parameters and performance of the structure was found to be critical with optimal height to thickness of the stiffener to be in the range of 30 to 45 and space to diameter ratio no greater than 0.5. The problem sets out such mathematics as is nowadays simply necessary for the modern construction world to have at their disposal, as well as reasons for designing reinforced CFST columns.

Rao Li1, Yaxiong Tao1, Lingfeng Chen 2
1College of Communication Engineering, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China
2School of Information Engineering, Chongqing Vocational and Technical University of Mechatronics, Chongqing, 400000, China
Abstract:

By improving the standard U-Net architecture, this paper proposes a novel semantic segmentation model, which incorporates multiple attention mechanisms to enhance the model’s capacity to capture multi-scale features. Specifically, we introduce the Efficient Multi-Scale Attention Module with CrossSpatial Learning (EMA), Spatial and Channel Squeeze and Excitation (SCSE), and Squeeze-andExcitation (SE) mechanisms into the standard U-Net network. These modules assist the network in learning significant information from feature maps at multiple scales while suppressing interference from irrelevant background. Experimental results demonstrate that incorporating attention mechanisms effectively enhances the prediction accuracy of the standard U-Net network for lane line semantic segmentation. The new model outperforms the standard U-Net model on our custom dataset, with particularly significant improvements in lane detection accuracy in scenarios with certain interference.

Huawei Xie1,2, Weijun Li1,2, Jinzhou Su1,2, Shuliang Tu 3
1Department of Forensic Science, Fujian Police College, Fuzhou, Fujian, 350007, China
2The Engineering Research Center, Fujian Police College, Fuzhou, Fujian, 350007, China
3Longyan Public Security Bureau, Longyan, Fujian, 364000, China
Abstract:

In order to solve the problems of traditional traffic accident scene investigation, such as taking a long time, evidence easily lost and difficult to save in case of bad weather, low survey accuracy, and field measurement data, DJI Mavic 3E UAV is used to convert the collected data into digital two-dimensional ortho image and three-dimensional model by using DJI Intelligent map software, such as mid-way point flight, map construction aerial photography and oblique shooting. One-stop help traffic accident investigation comprehensively improve the efficiency of scene investigation, standard forensics, improve the accuracy of accident scene investigation, in order to quickly restore traffic order, ease the demand for police, and improve the identifiability, safety and timeliness of traffic accident scene investigation.

Li Zhang1
1School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, Zhejiang, 325016, China
Abstract:

Focused crawlers are targeted to search the internet for web pages on specific topics. Its main task is to collect preprocessed and topic related web pages and ignore irrelevant web pages. Traditional focused crawlers have limited success in achieving multi-text categorization of web pages. Due to the large amount of unstructured data present in web pages, the correct classification of web pages based on a given topic is the main practical challenge for focused crawlers.The main objective of this work is to design an improved focused crawling approach using web page classification. In this paper, a text classification model based on the combination of GloVe word vector model and TF-IDF weighting technique is proposed to improve the accuracy of web page classification. The GloVe-based text classification model is further utilized to guide focused crawlers to classify web pages.The proposed GloVe and TF-IDF text categorization models are validated on 10 different datasets and the results are compared with traditional machine learning algorithms as well as different methods based on Naive Bayes, Bag-of-Words and Word2Vec. According to the experimental results, the proposed text classification model is 7-12% better than traditional machine learning algorithms.

Jianrong Sun1, Bo Xun1, Zhangling Chen 1
1Financial Sharing Service Center, Yunnan Power Grid Co., LTD., Kunming, Yunnan, 650000, China
Abstract:

In response to the shortcomings of traditional enterprise financial management information platforms in data processing and analysis efficiency and decision support capabilities, this study introduces intelligent decision support systems to fundamentally improve these issues. In this study, we automated data collection through API (Application Programming Interface) technology, used ETL (Extract, Transform, Load) tool for data format conversion, and strictly performed data cleaning and standardization to ensure data quality. The article uses association rules and support vector machine machine learning algorithms for in-depth analysis and prediction of financial data, and optimizes decision-making scenarios based on multi-criteria decision analysis, Monte Carlo simulation and linear programming techniques. Evaluation results show that the system significantly improves the speed and accuracy of data processing, with an increase in processing efficiency of more than 70% and a decision-making accuracy rate of up to 95%. The intelligent decision support system effectively improves the informatization level of enterprise financial management and provides more scientific and reliable decision support for the enterprise leadership.

Kexiu Yu1,2, Qiong He1, Peng Chen1
1School of Accounting, Dalian University of Finance and Economics, Dalian, Liaoning, 116622, China
2School of Accounting, Dongbei University of Finance and Economics, Dalian, Liaoning, 116622, China
Abstract:

In enterprise cost accounting and control research, traditional activity-based costing (ABC) relies on detailed activity tracking data and complex cost allocation models, which makes data acquisition difficult, has low-cost allocation accuracy, ignores dynamic changes, and has the problem of insufficient flexibility. This paper constructs an improved ABC application framework, builds an activity-driven cost accounting model, analyzes the daily activity data of the enterprise, determines the key factors related to cost, and establishes a mapping relationship between activity and cost. This paper introduces a dynamic adjustment mechanism to adjust the weights and parameters in the cost accounting model in real time according to changes in the external environment and internal operations, thereby improving the flexibility and accuracy of cost accounting. It can integrate the ERP (Enterprise Resource Planning) system with the cost accounting model, integrate the company’s financial data, production data and sales data, use information tools to automatically update activity costs, and provide timely feedback to the cost control system; it can closely combine cost accounting and control, monitor and adjust costs in real time during the accounting process, and take timely control measures when abnormalities occur. Experiments show that in terms of cost allocation accuracy, the average SE (Standard Error) of the improved ABC in enterprises with different employee sizes is 2.1, and the average MSE (Mean Squared Error) is about 5.5. It is more stable when processing enterprise data and can better reflect the actual cost allocation. The response time of the improved ABC is 5.7 seconds when the raw material price increases by 25%. It can make adjustments faster, with better flexibility and dynamic adaptability; the experiment proves the effectiveness of this paper in the research of enterprise cost accounting and control.

Aming You1, Xiaolong Zhang 1
1Resources and Environment, Shanxi University of Finance and Economics, Taiyuan, Shanxi, 030006, China
Abstract:

As an indicator of climate change, the change of vegetation cover directly reflects the ecosystem dynamics of the region. In this paper, the spatial and temporal characteristics of vegetation cover in the headwaters of the Fen River and the effects of temperature, precipitation, GDP and population on the changes of vegetation cover were statistically analyzed by using the Theil-Sen median slope and the Mann-Kendall test and Pearson’s correlation coefficient from 2000 to 2020. The results showed that: (1) from 2000 to 2020, the vegetation cover of the Fen River headwaters showed an overall upward trend, and the mean value of NDVI was 0.55. The fluctuation increased from 2000 to 2011; the significant increase was observed from 2011 to 2013; and the fluctuation of the value of NDVI from 2013 to 2020 was relatively small   p  0.01 . (2) Climate change affects changes in vegetation cover. On the time scale, the 2000-2020 mean NDVI values are positively correlated with temperature and precipitation, but the correlation is not significant   p p     0.053 0.05, 0.185 0.05 . On the spatial scale, vegetation cover was weakly negatively correlated with air temperature as a whole, while positively correlated with precipitation as a whole. (3) The influence of human activities on vegetation cover was dominant, NDVI and GDP were positively correlated, with only 5.13% negatively correlated in the central and northeastern part of the region, and NDVI and population were strongly positively and negatively correlated, with alternating distribution in the study area. (4) The vegetation cover of the Fen River headwaters area shows an increasing trend, but there are still ecological and environmental problems, and it is necessary to continue to improve the implementation of the relevant ecological protection policies in order to achieve the goal of sustainable development. The results of the study can provide scientific references for the restoration of vegetation cover and protection of fragile ecosystems in the transition zone of semi-arid and semi-humid climate.

Huiqian Zhang1, Dongchuan Xue1, Jiahui Zhang1, Li Wang1
1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650093, China
Abstract:

Karst water plays a vital role in meeting daily population needs. Determining groundwater sources, understanding chemical changes, and accurately evaluating flow paths and evolution stages are essential for the protection and sustainable use of groundwater resources in mining areas.This study collected 10 sets of karst groundwater and surface water samples from the Anle Village mining area. Using multivariate statistical analysis, Piper trilinear diagrams, Gibbs diagrams, and isotopic techniques, we analyzed the hydrogeochemical characteristics of both contaminated and uncontaminated water samples.The results show that uncontaminated groundwater and surface water are slightly alkaline and dominated by Ca2+ and Mg2+ cations, along with HCO3 and SO42− anions. Hydrochemical facies include HCO3-SO42−-Ca2+-Mg2+ and HCO3-Ca2+-Mg2+.Uncontaminated samples contain high levels of impurities, with dominance of Ca2+, Mg2+, and SO42−. These waters are mainly recharged by atmospheric precipitation and influenced by evaporation. Their chemical composition is primarily driven by the weathering and dissolution of carbonate, sulfate, and silicate rocks.Nitrate (NO3) concentrations in surface water suggest influence from agricultural fertilizers, while contaminated groundwater is closely linked to mineral resource development.These findings are significant for understanding the circulation and evolution of karst water in Anle Village and for informing the protection and utilization of local water resources.

Qiaoning Zhang1,2, Han Liu 3,4
1College of Business Administration, Lyceum of the Philippines University, Batangas, 4200, Philippines
2School of Education and Management, Bozhou Vocational and Technical College, Bozhou, Anhui, 236000, China
3 College of Business Administration, Lyceum of the Philippines University, Batangas, 4200, Philippines
4School of Economics and Trade, Shanghai Modern Chemical Industry Vocational College, Shanghai, 310116, China
Abstract:

Adapt to the new competitive environment, the supply chain concept and management model of horizontal integration and cooperation between enterprises have begun to rise, and continuously demonstrate enormous competitive strength and superiority. However, the existing enterprise supply chain management (SCM) system has problems of low security, low efficiency, and high management costs. In view of the above problems, this paper studied the enterprise supply chain management and its information assurance mechanism based on the error back propagation algorithm. By analyzing the problems in enterprise supply chain management and introducing error back propagation algorithm as an optimization method, the efficiency and accuracy of the supply chain have been improved. At the same time, corresponding guarantee mechanisms were proposed to address the importance of information security in the enterprise supply chain. The research results indicated that the information leakage rate of the supply chain information protection mechanism based on the error back propagation algorithm was below 3.21%, and the average leakage rate of 20 experiments was 2.654%. For supplier management in enterprise supply chain management systems, the selected users scored the system based on error back propagation algorithm at least 8.84 points, and the average score of 10 users was 8.995 points. Enterprise supply chain management and information assurance mechanism based on error back propagation algorithm can effectively improve the effect of supply chain management and enhance the security of information.

Yubao Zhang 1
1School of Design and Communication, Zhejiang Fashion Institute of Technology, Ningbo, Zhejiang, 315211, China
Abstract:

The development of society has led to the continuous development and progress of artificial intelligence technology, and has also led to an increasing demand for graphic design. In order to better solve the problems of color deviation, poor design effect, and high design cost in traditional graphic design, this article applied artificial intelligence image identification system to graphic design to overcome the problems of traditional graphic design. The elements extracted from the graphic database were denoised and enhanced by means of mean filtering and histogram equalization; after image preprocessing, Deep Learning (DL) algorithms were used to construct an image identification system, and the modules and visualization interfaces of the system were introduced. Through experiments, it could be found that the average expert rating of the graphic design scheme designed by the DL based image identification system was 8.818 points, and the satisfaction rate of the 20 users selected for the DL based image identification system was above 93.4%. In summary, using DL to construct an image identification system and applying it to graphic design could effectively improve the overall effect of graphic design and increase user satisfaction with the designed graphic scheme.

Yuening Wang1, Linlin Zhang1, Hao Tang 2
1Planning and Finance Department, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, 650000, China
2Information Center, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, 650000, China
Abstract:

In the current process of social development, reimbursement has become a generally accepted phenomenon. With the improvement of economic level and the improvement of people’s living standards, all walks of life have developed rapidly, which also provides new ideas for the financial reimbursement system and financial management. At present, most of the financial reimbursement processing is conducted manually, which can not meet people’s requirements for work efficiency. Moreover, there are many limitations, which are very unfavorable for enterprises. Therefore, it is necessary to take reasonable and effective measures to strengthen the improvement and optimization of the financial reimbursement system, so as to ensure the safe and efficient operation of funds. Image recognition technology is an indispensable and important means of modern information management. It can automatically extract data information and analyze statistics, which brings great convenience to financial reimbursement. This paper mainly studied the problems related to financial reimbursement based on the process of image recognition and denoising, and put forward some suggestions for the design of financial reimbursement image recognition system. It is hoped that it can promote its better application in practical work, so as to achieve the purpose of improving economic efficiency and ensuring the security of funds, and at the same time help further promote the healthy and orderly development of enterprise construction. This paper compared the traditional manual reimbursement method with the financial reimbursement automatic entry system based on image recognition. The results showed that the error of automatic input system was smaller than that of manual mode, and the degree of automation was higher; in addition, the accuracy rate of reimbursement voucher identification and review had also increased by about 6.34%. Therefore, this method has good advantages and practicability, and this method is conducive to reducing the workload of staff and facilitating the follow-up work. To sum up, electronic imaging technology can analyze and process data with the help of image processing means, thus obtaining corresponding results. It is convenient to adjust the accounting process as needed and timely in the process of financial management, so as to make the overall financial reimbursement work more standardized and unified.

Peijun Liu 1
1Department of General Education, Foreign Language Teaching and Research Section, West Anhui Health Vocational College, Lu’an, Anhui, 237000, China
Abstract:

Artificial Intelligence (AI) is increasingly used in medical research, especially in the analysis and interpretation of medical data. In this study, based on the traditional CARS model, we built a framework for thesis abstract language step research by categorizing fuzzy steps into optional steps and adding appropriate key steps to the language steps. With the help of artificial intelligence technology, an extraction model of key elements of abstracts incorporating the attention mechanism is constructed, aiming at screening the elemental utterances in abstracts. Finally, by collecting data from medical related papers in CNKI, Web of Science and other databases, the CARS modeling strategy based on artificial intelligence is implemented in the comparative analysis of medical paper abstracts in English and Chinese. Through the comparative analysis, it is found that the number of sentences in English abstracts is concentrated in 6-7 sentences, while the number of sentences in Chinese abstracts is scattered in 2-8 sentences. The percentage of the use of Chinese sentences on English abstract writing is the highest, with an average percentage of 45.24%. The frequency of the first 20 words of fuzzy restrictive phrases in English abstracts was significantly higher than that in Chinese abstracts. The organization of Chinese and English abstracts was mostly in the structure of “introduction method-results-discussion”, which accounted for 54% and 71%, respectively. In addition, the frequency of steps indicating gaps in the research area is higher in English than Chinese abstracts.

Man Qin1, Hua Wang 1
1 School of Foreign Studies, Suzhou University, Suzhou, Anhui, 234000, China
Abstract:

In this paper, on the basis of relevant theories, based on the adversarial training of BERT-PGD-BiLSTMCRF entity recognition model and relationship extraction technique to complete the entity extraction and relationship extraction, and then use the entity linking method that fuses attribute and semantic features (BERT+CBOW+CLS) to complete the construction of the knowledge graph and the supplementation of the knowledge graph, and the data is imported into the Neo4j Storage and Display. The symbols contained in the above knowledge graph for the city cultural image in translanguaging practice are divided into three hierarchical symbols, and the symbols are analyzed in terms of flow. In terms of single language usage, English has the highest proportion (22.57%), and Chinese has the best proportion (63.19%) in the process of urban cultural image construction, highlighting the dominant position of Chinese in urban cultural image construction. During the twenty-year period from 2004 to 2023, the trend of social behavioral symbols growth is significantly higher than that of material and spiritual symbol layers, which fits well with the current social development trend.

Siyuan Sheng1, Bing Yan1, Min Xiao2, Chengze Tang 3
1School of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, Sichuan, 610000, China
2 The 7th Geological Brigade of Sichuan Province, Chengdu, Sichuan, 610000, China
3Sichuan Yunlixiang Construction Engineering Co., Ltd., Chengdu, Sichuan, 610000, China
Abstract:

The Three Gorges Reservoir Area is a hotspot for landslide disasters, with many landslide development patterns and influencing factors remaining unclear. The slip zone soil, a weak interlayer between the sliding mass and the bedrock, has inherently low strength, which is a critical factor in landslide occurrence. Water is one of the most active elements reducing the shear strength during the formation of the slip zone. Given the particularity of reservoir bank water-related landslides, the mineral composition and geochemical characteristics of the slip zone and its surrounding rocks and soils exhibit significant variations across different geological periods and environments. These changes reveal the mechanisms and extent of water-rock interactions, further clarifying the fundamental reasons for the reduction in shear strength of the slip zone. The results show that in the Liujiaobao landslide in Quchi Township, Wushan County, Chongqing, within the Three Gorges Reservoir area, the composition of minerals and the content of major chemical elements in the slip zone soil and its surrounding rocks and soils indicate that the slip zone and surrounding rocks and soils form the material basis for the slip zone. During its formation, the groundwater in the slip zone is closely connected with external hydraulic forces, continuously influenced by groundwater, leading to changes in the physical properties of the rock and soil mass. This is accompanied by the hydrolytic mudification of marl debris, dissolution of calcite, and interconversion among clay minerals, which are the main reasons for the attenuation of the shear strength of the slip zone soil.

Chengcheng Zhu 1
1Accounting College, Zhengzhou Vocational College of Finance and Taxation, Zhengzhou, Henan, 450000, China
Abstract:

The article solves problems such as personalized investment, and then achieves the expected effect of investment decision-making. The article firstly designs an investment decision support model based on collaborative filtering, elaborates the implementation path to realize investment decision support from the perspective of machine learning, and then combines the user image technology to design the user image labeling system and model construction. Finally, the effectiveness and rationality of the proposed method in this paper are verified through experiments. Experiments on a corporate investment decision support task on a company’s dataset reveal that the method proposed in this paper has good performance on all metrics, with the highest value of 0.6985 on AUC.This gives an indication of the effectiveness of the financial data analysis and investment decision support model proposed in this paper.

Lisha Zhang1, Yan Liu 2
1 Hunan Mass Media Vocational and Technical College, Changsha, Hunan, 410000, China
2Changsha Preschool Education College, Changsha, Hunan, 410000, China
Abstract:

Cheerleading events are flourishing in China, the level of competition is rising, the number of competition groups and programs is increasing, the competition is becoming more and more intense, and the innovative research on formation design is an inevitable demand for the development trend of cheerleading. The study designed a multi-objective path planning model based on the intensity of willingness and consultation strategy, so that college cheerleading can avoid conflicts and reach the goal point of cheerleaders in the complex environment. Then an improved multi objective particle swarm algorithm (MOPSO-CA) based on meta cellular automata is proposed and applied to college cheerleading formations to realize the design of college cheerleading formations. The simulation results show that the MOPSO-CA algorithm can re-select the optimal movement direction angle according to the real-time positions of the moving obstacles and moving targets, which illustrates the effectiveness of the algorithm. Secondly the feasibility of the formation design conditions are suggested as: keeping the originality of the movement, the use of the moving route of the formation and the space of the venue, and the type of formation change.

Yuchen Wang1, Qin Shi 2
1Music School and Dance, Chongqing Institute of Foreign Trade and Economics, Chongqing, 400000, China
2Department of Global Convergence, Kangwon National University, Chunchuan, 24341, Korea
Abstract:

How to give full play to the clarinet in the symphony orchestra in the sound advantages and characteristics of the role, undoubtedly is an important topic of the current music research. Combined with years of working practice and learning experience in the symphony orchestra, the author explains the tonal advantages and characteristics of the clarinet in the symphony orchestra. For the study of the relationship between its tonal advantages and characteristics and the symphonic concerto, the author combines the finite element method in the music education environment, through the method of computational simulation, to explore the symphonic performance conditions, as well as the main discussion on the analysis of the boundary conditions with the vibration velocity and sound-absorbing materials, in order to achieve the purpose of improving the clarinet’s musical and artistic level in the symphony orchestra. Through the study, we found that the numerical simulation of the relationship between the clarinet technology and the symphony orchestra concerto is analyzed by the local fundamental solution method with high computational accuracy, which lays the foundation for the successful application of this method to the numerical simulation of the sound field of the complex music education environment.

Ruijuan Hu1, Xiaoli Chen2
1College English Department, Henan Finance of University, Zhengzhou, Henan, 450046, China
2Zhengzhou Business University, Gongyi, Henan, 451200, China
Abstract:

The study proposes a dynamic resource allocation model suitable for English language teaching, which combines learner characteristics, learning progress and resource availability to achieve real-time optimal allocation of resources through mathematical optimization algorithms. A multi-objective optimization model is constructed based on the key factors in resource allocation for English teaching. Facing the optimization objectives of maximizing learning efficiency and minimizing resource idleness, NSGA-II algorithm is used to construct a non-dominated solution to achieve global sorting, and combined with congestion calculation to complete global quality population screening. At the same time, the branch delimitation algorithm is utilized for local search of optimal solutions, and merged with the population of NSGA-II to generate the new generation of optimal populations. The optimization probability of the combined algorithm in this paper is 0.85, and the average convergence error is only 0.01081, which has excellent optimization performance. The resource allocation delay of this algorithm is around 0.1ms, and the allocation efficiency is more than 95%, and the comprehensive effectiveness is better than the comparison algorithm. The dynamic allocation model of resources in this paper improves the balance of resource allocation of English teaching and auxiliary room area, the number of teaching materials, the number of full-time teachers and teaching equipment. At the same time, it prompted the average English score of the experimental class to exceed 80, which was significantly higher than that of the control class.

Zhe Xu 1
1China-Korean Institute of New Media, Zhongnan University of Economics and Law, Wuhan, Hubei, 430000, China
Abstract:

Following the footsteps of the times, an excellent and complete movie cannot be separated from the application of digital modeling. In this paper, we mainly use 3D modeling, motion capture, rendering and other related technologies to edit and produce the character’s physique, proportion, contour, etc., design the character’s expression, color and action, and build the film and television scenes in 3D space. Thus, it realizes the characterization and emotional expression in film and television. Will be through the traditional 2D film and television and three-dimensional film and television control experiments, from the experimental data can be seen, in the frame rate, three-dimensional modeling technology film and television than the traditional 2D film and television on average 14% to 20% higher. There is also a leading edge in the number of textures. The data color emotion analysis indicated that the color shift and strong contrast connects the plot and the audience’s feelings. The quantitative survey of emotional experience through questionnaires shows that the audience in the 3D film and television group is higher than the traditional 2D film and television in terms of immersion experience, interaction experience and learning and enjoyment experience. Therefore, 3D modeling technology plays an important role in the creation of film and television art.

Yujing Xiong 1
1Institute of Civil Engineering, Hunan Software Vocational and Technical University, Xiangtan, Hunan, 411100, China
Abstract:

In order to improve the teaching effect of dynamic structural behavior simulation in structural engineering teaching, this study develops a dynamic structural behavior simulation teaching model combined with the finite element method to explore the effect of its application in teaching. This paper first introduces the process of applying the finite element method to simulation teaching and the steps of structural engineering system development. After that, it introduces the common structural engineering analysis functions under ANSYS software and its application in various aspects of structural engineering teaching. Then the construction process of the dynamic structural behavior simulation teaching model is briefly described, and the finite element principle is combined with the actual engineering problems through the integration of case teaching to realize the deep integration of theory and practice. Finally, the teaching model of dynamic structural behavior simulation is constructed and the teaching evaluation system after applying the model. The results of teaching practice show that more than 95% of the students maintain a positive attitude towards the use of the model in this paper. Under the teaching mode of the simulation model visualizing dynamic behavioral characteristics, the average grade of students in the experimental group was significantly higher than that of the control group by 14.96 points, and the difference between the grades of students in the two classes was significant (P=0.000). It can be seen that the use of the model can improve the students’ understanding of dynamic structural mechanical behavior and the application of finite element analysis tools, which provides an efficient platform for combining theory and practice for structural engineering teaching.

Changhong Xie 1
1College of Information, Mechanical and Electrical Engineering, Shanghai Normal University (SHNU), Shanghai, 201418, China
Abstract:

In order to improve the accuracy and efficiency of medical image segmentation, this paper designs and proposes a medical image visualization method containing Sobel edge detection operator and 3D UNet network based on deep learning and edge detection. The 3D U-Net network is used to capture the morphological and edge features of medical images on the public dataset, and the image binarization is performed on the result of its operation. The binarized image processed by corrosion and expansion algorithms is multiplied by the corresponding elements of the matrix with the medical image to obtain the visualization of the medical image. Different comparison algorithms and data sets are selected to verify the effectiveness of the optimized 3D U-Net network module and feature fusion module. Parameter settings are carried out, and the LIDC-IDRI dataset is used as the algorithm training base data to analyze the segmentation accuracy of the image processing method that fuses the edge detection operator with the 3D U-Net network. The algorithm ablation experiments are carried out according to different pruning degrees and training methods. The algorithm in this paper can achieve more than 80% segmentation accuracy on LIDC-IDRI dataset, in which the segmentation accuracy of liver reaches 97.1%.

Qian Wang 1
1Department of Music, Sichuan University of Science and Engineering, Zigong, Sichuan, 643000, China
Abstract:

The integration and development of Sichuan’s rural music and cultural tourism industry is of great signiϐicance in the context of rural revitalization strategy. The purpose of this paper is to construct a multilevel regression model to deeply explore the inϐluencing factors and role mechanisms of the integration of the two. Through theoretical analysis and empirical research, the research variables are clariϐied, and the null model, random effect model and complete model are constructed and data validation and analysis are carried out. The results show that the richness of rural music resources, the level of cultural and tourism industry, policy guidance and support, market demand and human resources have a signiϐicant positive impact on the integration of rural music and cultural and tourism industry in Sichuan. The results of the full multilevel regression model show that the same level of rural music resource abundance has different impacts on the integration of rural music and cultural and tourism industries due to regional differences. The results of the study provide theoretical support for the development of cultural tourism industry in Sichuan Province, and deeply help the implementation of rural revitalization strategy in Sichuan Province.

Xiaoyang Meng1, Ying Jin 2
1School of Accounting, Jiaozuo University, Jiaozuo, Henan, 454100, China
2College of Continuing Education, Jiaozuo University, Jiaozuo, Henan, 454100, China
Abstract:

The higher the corporate financial transparency, the more it can reduce the information asymmetry, which can enhance the market trust and improve the corporate performance. In order to improve corporate financial transparency, the study constructs a financial fraud identification model by improving the machine learning model based on XG Boost algorithm from the financial fraud factors. Based on the XG Boost algorithm, the model integrates the decision rules through the weighted fusion method to generate a new decision tree to determine the financial fraud. In order to improve the ability of enterprise performance assessment, the baryon support vector machine method is used to classify the performance of enterprise employees, and the nonlinear baryon support vector machine is used to establish the enterprise performance assessment model. In the process of verifying the effect of the two models, text indicators are extracted using big data technology to provide a rich feature set for the financial fraud identification model. The data from ERP, CRM and other systems are integrated to provide a comprehensive and high-quality data set for the enterprise performance assessment model. After empirical analysis, the combination of big data and machine learning can improve the effect of financial fraud identification, and then effectively improve the transparency of corporate finance. The enterprise performance evaluation model provides a scientific and efficient quantitative evaluation tool for enterprise managers, and effectively improves the enterprise performance evaluation capability.

Menghe Tian1, Xiangyang Bian 1
1Collage of Fashion and Design, Donghua University, Shanghai, 200050, China
Abstract:

Dress metaphor is a very important way of expression in the novel text of Ming Dynasty, and the recognition and interpretation of the metaphor play a very important role in really understanding the novel text. This paper proposes a dress metaphor recognition model based on Transformer and graph convolutional neural network, and a dress metaphor interpretation method based on Seq2seq framework. The apparel metaphor recognition model performs feature extraction of global and local information of apparel metaphor sentences by Transformer. Graph Convolutional Neural Network is utilized to obtain syntactic structure information and sentence dependencies, in order to complete multi-word dress metaphor recognition. Then the obtained deep metaphor features and syntactic structure information of the sentence are input to the classification layer. The metaphor decoding method carries out costume metaphor understanding through the encoder-decoder, which chooses the LSTM network structure for both encoder and decoder to better obtain the semantic features of the novel text. The dress metaphor recognition model improved the recognition correctness on the dataset by 17.97% and 7.28%. The dress metaphor interpretation method based on the Seq2seq framework elaborates the interpretation content and can more accurately interpret the dress metaphors in Ming Dynasty novels. It verifies the practicality of the metaphor recognition and interpretation model in this paper in the task of interpreting dress metaphors in Ming Dynasty novel texts.

Pan Li1,2, Xu Song3, Hui Yuan 3
1The Business School, Anyang Normal University, Anyang, Henan, 455000, China
2School of Software Engineering, Anyang Normal University, Anyang, Henan, 455000, China
3Anyang Water Conservancy Project Operation Support Center, Anyang, Henan, 455000, China
Abstract:

In order to more comprehensively study the influencing role mechanism of consumer behavioral decision-making process in the digital economy platform and explore the influencing factors of consumer behavioral decision-making, this paper constructs a model of consumer behavioral decision-making process based on Bayesian network. With the help of Netica software to construct the Bayesian network topology, using EM algorithm to learn the parameters of the Bayesian network model, and proposed to use the Bayesian network to carry out sensitivity analysis and probabilistic inference, and formulate the corresponding Bayesian network model framework. Subsequently, the influencing factors of channel search willingness and purchase willingness and their relationships in the consumer behavioral decision-making process in the digital economy platform environment are analyzed. The structural equation model is introduced, the measurement equation and structural sub equation calculation methods are determined, and the sample data are collected by means of questionnaires to carry out the test and analysis of the model of consumer behavioral decision-making process. The CR value of each variable in the model of this paper is higher than 0.7, and the AVE values are all greater than 0.5, and the model performs well in terms of intrinsic quality. The exogenous latent variables such as perceived benefits, channel trust, and transfer costs have a significant positive effect relationship on the endogenous latent variables such as search behavior and purchase intention (P<0.05).

Jing Li 1
1School of Foreign Languages, Wuhan College of Arts and Science, Wuhan, Hubei, 430345, China
Abstract:

As artiϐicial intelligence technology becomes more and more mature, it is both a challenge and an opportunity for English speaking teaching. Aiming at the poor generation of virtual English teaching resources due to the training problems of traditional generative adversarial network, dual generative adversarial network is used to optimize the above problems and select the virtual English teaching resources that meet the requirements with the help of Pielou. At this level, the HTC VIVE suite, high performance computer system, Unity 3D development engine, and joystick control are integrated to jointly complete the work of English speaking teaching scene design. Combining the research data and evaluation indexes, the practical application efϐicacy of the scenario is analyzed. From the overall performance of different methods in the four datasets, this paper’s method is superior to the other four methods, that is, this paper’s method is able to generate high-quality virtual spoken English teaching resources. And the practical application efϐicacy in terms of test scores, learning effects, satisfaction, and English speaking teaching background is better than traditional multimedia, which is more conducive to promoting the development of English speaking teaching.

Qiliang Hu 1
1School of Foreign Studies, Changsha University of Science & Technology, Changsha, Hunan, 410114, China
Abstract:

Based on the background of information technology, this paper proposes a multimodal blended learning model of English listening based on “WeChat+Classroom+TED-Ed”. It focuses on the experimental teaching of multimodal learning and English listening comprehension, and describes the object of the study, the design of the study and the process of the study. Based on the research idea, the experimental variables were designed, and the empirical analysis was carried out by using multiple linear regression model. The teaching effect of multimodal teaching is examined by comparing the differences in the total English listening scores of the two groups of students before and after the experiment. With the help of Pearson correlation analysis, the correlation between the experimental variables is explored. The value of R² was determined through the multiple regression model to determine the magnitude of the explanatory power of multimodal learning on English listening comprehension ability. The results showed that the scores of the control class improved by 1.19 points and the experimental class improved by 4.19 points in the experimental posttest, with a significance (two-tailed) p-value = 0.008<0.05. The explanatory power of the combined three modalities of learning on English listening performance was 15.4%, and classroom learning had the highest level of significance in terms of its explanatory power on listening comprehension, and the test of regression coefficients reached the level of significance (t=3.862, p= 0.002<0.05).

Qi Liu 1
1Department of Art and Technology, School of Music and Dance, Communication University of Zhejiang, Hangzhou, Zhejiang, 310018, China
Abstract:

In this paper, DAG is utilized to represent the dependencies between musical features, and a topological sorting algorithm based on layer order relationships is used as the sampling algorithm for AI music generation models. The feature de-entanglement mechanism of VAE is utilized to learn multiple feature representations, and Transformer-XL is used as the encoder and decoder of the model to design the Control-VAE model that manipulates the latent variable representations to change the music structure. Statistical autocorrelation coefficients, spectral analysis, and diversity auto assessment metrics data were used to evaluate the model performance in terms of three dimensions: melody, timbre, and diversity. The feasibility of Control-VAE model AI music generation and melody optimization is examined through the evaluation of practical application effects. The results show that the autocorrelation coefficients and frequency amplitudes of the music generated by Control-VAE model are basically consistent with the original music, and reach human-like PPL values, seIf-BLEU values and Zipf coefficients near p=0.95.The music pieces generated by Control-VAE model have a certain degree of musicality, and the melody-optimized music is clear, accurate and novel and interesting.

Qi Chen1, Ying Chen2, Junxiao Tang2, Yan Tu3, Hongyang Hu 1
1School of Physical Education, Yichun University, Yichun, Jiangxi, 336000, China
2School of Politics and Administration, Tianjin Normal University, Tianjin, 300000, China
3College of Life Sciences and Resource Environment, Yichun University, Yichun, Jiangxi, 336000, China
Abstract:

National security education in the new era puts forward new and higher expectations on the scope, degree, speed, and object of knowledge dissemination, while presenting new dissemination characteristics such as all-media and group emergence.Based on graph theory algorithm, this study proposes a dissemination model with credibility constraints about national security education knowledge.Text mining is used to analyze discussions of social network users on national security education knowledge from Sina Weibo and Baidu Search. The dissemination mechanism of national security knowledge is explored through text analysis. Based on this, different expectations of information dissemination are set to conduct numerical simulation. The simulation results show the model is highly sensitive to parameter changes. In the case of R < 1, with the increase of β, the time for S to reach the steady state decreases, and the time for I to reach the maximum value decreases, while the maximum value increases.When β = 0.03, Max I = 39.86; and when μ = 0.3, Max I = 37.23. The model plays an important role in controlling and managing knowledge dissemination.The proposed graph theory-based knowledge diffusion model achieves an average knowledge stock of 0.924 under regular networks and 0.726 under scale-free networks. In terms of knowledge diffusion rate, this model outperforms both the traditional knowledge diffusion model and the random diffusion model.

Jinlong Zhuang1, Taoming Qian1, Li Liu 2
1Graduate School, Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang, 150040, China
2 The First Affiliated Hospital, Heilongjiang University of Traditional Chinese Medicine, Harbin, Heilongjiang, 150040, China
Abstract:

ECG and PCG reflect the activity characteristics of the heart, and the combination of the two can record the electromechanical activity information of the heart more comprehensively. In this paper, we design a heart failure prediction model based on Transformer, and utilize Transformer Encoder to complete the feature fusion of ECG and PCG. Feature classification is performed using ResNet-18 to achieve the prediction of nine typical arrhythmias. Evaluate the classification results on the dataset to explore the performance level of the proposed model. Obtain ECG and PCG data in real situations, and select entropy analysis and heart rate variability metrics to quantify the physiological signal time series complexity. The model classification accuracy, specificity and sensitivity are compared to analyze the effect and superiority of the proposed model in practical applications. The results show that the average accuracy of the model on the four datasets reaches 92.28%, and the highest average F1 score is 0.930. In practical applications, the classification accuracy, specificity and sensitivity of the proposed model in this paper are 96.79%, 97.47% and 96.77%, respectively. Through the fusion analysis of ECG signal and heart sound signal characteristics, the model fully reflects the HRV change characteristics of heart failure patients and can effectively predict heart failure.

Zhe Zhang1, Changdong Shao 2
1School of Art and Design, Bengbu University, Bengbu, Anhui, 233000, China
2EI Fire Electronics Co., Ltd., Bengbu, Anhui, 233000, China
Abstract:

As a key component of urban environmental resources, the design of landscape paths and facility layouts of urban public environments is not only related to the overall aesthetics of the city, but also to the quality of life of urban residents. In this paper, from the perspective of landscape layout, the ecological landscape spatial network is constructed by calculating the ecological landscape environmental adaptation degree and the ecological landscape pattern index. On this basis, the traditional ant colony algorithm is introduced and its heuristic function and path selection are improved, and the adaptive adjustment factor and angle guiding factor are added to improve the diversity and efficiency of path searching, so that the landscape layout optimization model based on the ant colony algorithm is obtained. Using this model to design a landscape layout optimization scheme for a scenic spot, the average fulfillment time of the optimized landscape path is 20.73 minutes, which is 19.52 minutes shorter than the average fulfillment time of the original planning scheme, indicating that the model in this paper is able to carry out the landscape layout optimization design effectively.

Wen Li1, Ruiqian Su 2
1Ideological and Political Theory Course Teaching Department, Xiamen Institute of Technology, Xiamen, Fujian, 361021, China
2School of Foreign Languages, Xiamen Institute of Technology, Xiamen, Fujian, 361021, China
Abstract:

In this paper, K-prototype algorithm is chosen to cluster and analyze the data of students’ behavior in the educational field. Further, a model of students’ employment interest is constructed based on the job rating data of different classes of students. The timeliness is introduced in the model to improve the recommendation accuracy. Synthesize the algorithm and model to build an employment support system. Apply the system to the clustering study of college students’ behavioral data to verify its career recommendation value. Set up comparison experiments to find the optimal similarity fitting parameters and number of neighbors to improve the system recommendation accuracy and judge the system recommendation effect. Preliminarily divide students into 3 categories by analyzing students’ online behavior and book borrowing behavior. Preliminarily categorize students into 4 categories based on their grades. Combined with the performance labels and grade categories of professional courses, the employment direction of students was finally clustered into four categories, namely “postgraduate entrance examination”, “civil servant application”, “company work” and “others”. The highest accuracy of the system job recommendation is achieved when the similarity fitting parameter λ = 0.5 and the number of neighbors N = 50.The RMSE value of the K-prototype algorithm ranges from 0.6011 to 0.731, and the recommendation effect is better than the comparison algorithm.

Yuan Jin1, Chenyu Zhang1
1School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China
Abstract:

Due to the complexity of the ship product structure and process, long production cycle and other factors, ship enterprises are plagued by the problem of profitability. Strengthening cost prediction and budget control is a very important means for ship enterprises to improve their profit margins. By analyzing the cost structure of shipbuilding, this paper proposes a rolling forecast model of shipbuilding cost based on long and short-term memory neural network (LSTM) as the estimation method of shipbuilding cost. Meanwhile, the traditional earned value method and target cost method are combined to sort out the shipbuilding cost control process and prepare the cost control plan as the control strategy of shipbuilding cost. Then we take the manufacturing data of a shipyard as the experimental object, use this paper’s model for data mining, compare the data performance of this paper’s model with similar algorithms, and verify the feasibility of this paper’s model. Finally, the model of this paper is applied to real cases. In the comparison of the estimation results between this paper’s model and the commonly used algorithms, the average error of cost estimation of this paper’s model is ±4.95%, which is better than the average error of the commonly used algorithms. The superior accuracy of this paper’s model in shipbuilding cost estimation is verified.

Yuting Wang 1
1Applied Technology College, Anshan Normal University, Anshan, Liaoning, 114000, China
Abstract:

In order to solve the problem of vagueness and uncertainty, which is difficult to deal with in traditional education assessment, this paper introduces the theory of fuzzy matrix logic, and constructs a multilevel assessment model of education quality by means of the affiliation function and multilevel weight allocation. Through fuzzy reasoning and cognitive estimation techniques, combined with knowledge graph visualization, the cognitive level of learners is accurately estimated to achieve personalized learning resource recommendation. The quality assessment of physical education teaching in colleges and universities is taken as an example to verify the application value of the model. The constructed PE teaching quality evaluation index system contains 3 level 1 indicators, 11 level 2 indicators, and 38 level 3 indicators.The initial index scoring result of the PE classroom by 5 raters is an average score of 100.8, which is 89.2 points different from the full average score.The weights of the indicators within the 3 levels do not differ much. Students’ levels of knowledge of the 6 initial physical education concepts ranged from 0.53 to 0.86 points. The maximum inter-conceptual influence strength was 0.86 and the minimum was 0.18. After the interference of the resource recommendation, the cognitive level increased to between 0.67-0.98 points. The maximum inter-conceptual influence intensity reaches 1. The Sig value is greater than 0.05, and the results of the model calculations have reliability and can be used for education quality assessment and dynamic learning planning method improvement.

Xiaoyu Chen 1
1College of Film, Television & Media, Guangxi Arts University, Nanning, Guangxi, 530022, China
Abstract:

As an emerging form of cultural communication, microshort dramas have emerged in the audiovisual industry. In order to explore the optimization method of international communication of short microdramas, this paper takes the selected short micro-dramas of an international video platform as samples, selects the influencing factors of the international communication effect of short microdramas, constructs the optimization model of international communication of short micro-dramas by using Bayesian network, and adopts the Great Likelihood Estimation Algorithm as its parameter learning method. The performance of the Bayesian network model is explored through model comparison, node sensitivity analysis and scenario simulation. The results show that the Bayesian network model has good prediction performance, and its AUC value is greater than 0.8 in both training and testing results. The entropy reduction percentages of publisher’s fan number, video duration and localized creation are all greater than 0.07%, which have the most obvious influence on the effect of international dissemination of microshort dramas. Scenario simulation verifies the influence of each variable on the optimization of the international dissemination effect of micro-short dramas, and the probability value of the obtained optimal solution with a strong dissemination effect is 83.5%. It is recommended to actively guide the creation of high-quality products, carry out in-depth localized creation, accelerate the integration of art and technology, and strengthen the comprehensive governance of the industry, so as to promote the global dissemination of China’s online micro short dramas.

Meijian Wu 1
1Academy of Fine Arts, Zhaoqing University, Zhaoqing, Guangdong, 526061, China
Abstract:

As a conventional technique in lacquer painting, the abrasion painting technique is widely used in the creation of modern lacquer painting. In order to promote the digital innovation of the abrasion painting technique in the creation of lacquer paintings, a fusion scheme of the abrasion painting technique and color distribution in the creation of lacquer paintings is formulated. According to the relationship between color and gray scale, the color mapping of image coloring algorithm is proposed under the framework of energy optimization algorithm to realize algorithm-driven lacquer painting color generation. In addition, with the technical support of the renderer, the color distribution of lacquer paintings is integrated with the milling technique according to the principle of texture mapping. With the help of evaluation indexes and experimental platforms, we simulate and analyze the techniques and colors in lacquer painting. In the color generation of lacquer paintings, the indicators of this paper’s method are 34.09, 0.964, 0.025 and 4.28 in order, which verifies the application effect of this paper’s method in the color generation of lacquer paintings. In addition, the speed of this paper’s rendering method (42-86FPS), fully meets the requirements of real-time drawing, this method better promotes the fusion of grinding and painting techniques and color distribution in the creation of lacquer paintings, which is of great significance to the digital dissemination of traditional culture of non-heritage.

Jing Li 1
1School of General Education, Hunan University of Information Technology, Changsha, Hunan, 410000, China
Abstract:

The study combines hierarchical Bayesian model and adversarial neural network according to the model architecture of neural machine translation, and introduces the domain generalization method based on cross-domain gating to solve the domain generalization problem, and constructs the neural machine translation system based on hierarchical Bayesian model. Translation performance experiments are conducted on this translation system to test the cross-domain generalization performance of the neural machine translation system based on hierarchical Bayesian model in this paper. The translation method of this paper significantly outperforms the baseline system of statistical machine translation in the direction of translation for all the inter translated languages and medial languages of the European Parliament corpus. The statistical machine translation model and the standard neural machine translation model have maintained a stable performance during the growth of the interpolation coefficients, while the performance of this paper’s hierarchical Bayesian-based neural machine translation system grows rapidly to the maximum when the interpolation coefficients grow to 0.3 or 0.4, and its overall average BLEU value always outperforms that of the statistical machine translation model and the standard neural machine translation model. The BLEU values of the hierarchical Bayesian-based neural machine translation system are 35.26% and 34.28% for bidirectional Chinese-English translation, and 26.42% and 25.96% for bi-directional Chinese-Western translation, which are better than those of the neural machine translation based on the attentional mechanism and variational scoring. And the hierarchical Bayesian-based neural machine translation system has strong stability on the translation of low-resource languages.

Axin Huang1,2, Mary Jane C.Samonte 3
1School of Graduate Studies, Mapua University, 1002 Metro Manila, Philippines
2GongQing Institute of Science and Technology, Gongqingcheng, Jiangxi, 332020, China
3 School of Information Technology, Mapua University, 1002 Metro Manila, Philippines
Abstract:

The article proposes a novel cross-modal adversarial learning framework for analyzing the emotional dynamics of non-English learners during classroom engagement and predicting their individualized behaviors. The framework combines multilevel feature extraction and Transformer CNN-LSTM integrated model to handle multimodal data more efficiently and capture the complex relationship between emotions and behaviors. Low-level and high-level multilevel features are then extracted from the raw multimodal data. Meanwhile, Transformer is utilized to mine long-distance dependencies between multimodal data, CNN extracts local features, and LSTM is used to model dynamic changes in time series. In addition, the framework introduces adversarial training to learn shared features across modalities. Before 50 rounds of training, the CL-Transformer model loss function, emotion recognition accuracy, and behavior prediction accuracy converge, showing the fastest training speed and training results. The algorithm in this paper has more than 90% precision, recall, and F1 scores for emotion recognition and behavior prediction, and the recognition accuracy for different emotions is up to 0.96. In the fifth stage of the case study, the classroom emotion conversion rate and arousal is up to 0.66, and the model predicts that the probability of cell phone playing behavior is the highest for learners who are in angry moods, which is 64.7%. The learners’ classroom emotional acceptance as well as behavioral integration have an impact on their classroom engagement.

Fangfang Zhang1, Zhou Zhou1, Zishuai Zhou 2
1School of Accounting, Anhui Wenda Information College, Hefei, Anhui, 231200, China
2School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
Abstract:

Under the current development trend of global economic integration, countries around the world are interconnected and influenced by each other in international trade, and the connection of world trade forms a complex network. This paper constructs a global trade network based on global trade theory and social network analysis theory, and selects indicators such as the number of network nodes and network diameter to characterize the topological structure of the global trade network. The Transformer model is designed based on the gating mechanism unit and dynamic attention mechanism to analyze the multimodal, high-dimensional and heterogeneous global trade time series data. The empirical analysis finds that the characteristics of the global trade network structure change over time, the trade network between countries and regions becomes more and more close, and there is an impulse effect of the country’s GDP and other influencing variables on the structure of the global trade network. This paper reveals the multi-path influence effect of global trade network through empirical analysis, and improves the related research on the structural change and positive evolution of global trade network, with a view to providing useful reference and guidance for the formulation of national trade countermeasures.

Xiaohan Du 1
1School of Smart Finance and Economics, Anhui Vocational and Technical College, Hefei, Anhui, 230011, China
Abstract:

Financial fraud, as a global problem in the financial industry, brings huge economic losses to financial institutions and customers. In this paper, a multi-task financial fraud detection model is constructed based on heterogeneous graph neural network with deep reinforcement learning, combined with variational self-encoder. In this model, the variational self-encoder is combined with graph convolutional network to construct the node input representation coding module, as a way to enhance the multi-task financial fraud data and better mine the structured features of different nodes. The attention mechanism is then introduced to build the relation-aware attention, which deeply mines the input node features, further acquires the neighbor-generated features of different nodes in the network, and combines the mutual information to measure the nonlinear correlation between different random nodes. Then the financial fraud node representation is mapped into the highdimensional space by the multilayer perceptron, and then the financial fraud prediction confidence of the model is obtained, and different types of loss functions are set to ensure the detection efficiency of the model. The results show that the F1-macro and AUC values of the financial fraud detection model on the self-constructed FFD dataset are 0.749 and 0.925, respectively. Relying on the heterogeneous graphical neural network and the variational autocoder, a multi-task financial fraud detection model can be constructed, which provides a new idea for solving the suspected fraud and money laundering cases that may exist in the field of finance and economy.

Manlu Kong 1
1School of Art, Anhui Wenda College of Information Engineering, Hefei, Anhui, 230000, China
Abstract:

In order to optimize the performance of generative adversarial networks on automatic advertisement image generation, this paper combines the variational self-encoder with generative adversarial networks, which consists of four parts: encoder network, decoder network, target-to-be-attacked network, and discriminator network to form a new adversarial sample generation method based on GANs, i.e., AdvAE-GAN model. To make the generated samples more clear and natural, the adversarial learning mechanism and similarity metric (PCE) are added to the AdvAE-GAN model. To obtain the performance of the model in diverse image coloring, multiple methods are elicited for subjective and objective qualitative evaluation and model complexity analysis, respectively. Combining the four standard datasets of AWA, CUB, SUN and FLO, zero-sample image recognition, generalized zero-sample learning experiments are carried out sequentially to derive the loss value curve of the model. The visual effects of animated advertisements generated by AdvAE-GAN model are rated using questionnaire research. For the product effect of animated advertisements generated by AdvAE-GAN model, the category diversity, design diversity, animation contour completeness, and image clarity indexes with scores above 7 account for 70.47%, 85.82%, 76.73%, and 84.02%, respectively. The animated advertisement generation model based on improved generative adversarial network is recognized by the market as well as the society and can be deepened.

Yongguan Ai1, Juan Wang1, Nianfang Xu2, Yuanjun Zhang 1
1School of Public Administration, Anhui Vocational and Technical College, Hefei, Anhui, 230011, China
2School of Computing and Information Technology, Anhui Vocational and Technical College, Hefei, Anhui, 230011, China
Abstract:

The aggravation of population aging makes the demand for elderly care expanding. In this paper, we propose an integrated care model based on deep learning to build an intelligent service robot system for elder care organizations by integrating sentiment analysis and knowledge reasoning techniques. The model is driven by the dynamic needs in long-term care scenarios, and two modules are innovatively designed. In the sentiment analysis module, multimodal sensors (facial expression, audio state, textual content) and graph attention networks are integrated, and global contextual information is modeled on these features to identify long-distance emotional dependencies of the elderly. In the knowledge inference module, graph representation learning is combined with knowledge graph temporal inference to construct an inference model to speculate the care needs of the elderly. The experiment shows that after the system performs long-term service, the depression condition of the elderly is significantly improved, and the nursing care safety risk perception shows a significant difference from that before the system is used (P<0.001). The integrated care model studied in this paper provides a practical technical solution to the problem of aging care resource shortage.

Linghao Pan 1
1School of Music, Nanjing Normal University, Nanjing, Jiangsu, 210000, China
Abstract:

Along with the fast developing of IT, it is more and more popular to apply the modem interaction technique to the educational domain, particularly in the college musical educational potentiality. Based on the perspectives of psychology and interactive technology, the author analyzes the latest progress of interactive technology in human-computer interaction, emotional computing, and design psychology, as well as its impact on music education in universities. It is found that the educational effectiveness of MCAI has been maintained at 92 percent and that of the others has been rising. However, there are some differences between them and the new system. Interactive technology can not only optimize the learning experience and enhance teacher-student interaction, but also provide personalized and intelligent learning support for students through emotional computing and ubiquitous computing technology, thereby enhancing learning effectiveness and artistic creativity. By building a student-centered teaching ecosystem, the deep integration of technology and art education will help promote innovation and improvement in music education in universities in the information age.

Gaoya Li 1
1 Department of Accounting, Xinzhou Normal University, Xinzhou, Shanxi, 034000, China
Abstract:

In the current context of China’s economic transition, focusing on the issue of corporate innovation performance can lay a solid foundation for the acceleration of the digital transformation process as well as the improvement of corporate innovation performance. This paper selects the relevant data of a listed enterprise from 2018 to 2023 as a research sample for empirical analysis. Combined with the DIT model to test the role of digital transformation on innovation performance, and on the two perspectives of financing constraints and intellectual property protection, it specifically studies the mediating effect and adjustment mechanism between digital transformation and enterprise innovation performance. Finally, from the perspective of enterprise heterogeneity (whether stateowned or not, enterprise size, geographical policy), the actual impact of digital transformation on performance under different enterprises is specifically analyzed. The results show that digital transformation has a positive effect on enterprise innovation performance, and digital transformation can reduce financing constraints to a certain extent, ensure sufficient financial support for enterprise operations, and contribute to the improvement of enterprise innovation performance. Research on the moderating mechanism shows that intellectual property rights have a positive impact on digital transformation to promote the enhancement of enterprise innovation performance. Further heterogeneity analysis shows that digital transformation has a more prominent effect on innovation performance in large-scale enterprises.

Yufeng Xiao1, Shuqing Xiao2, Yanxing Xue 3, Zuoteng Wang 4
1Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, 50000, Malaysia
2School of Modern Service Management, Shandong Youth University of Political Science, Jinan, Shandong, 250000, China
3Faculty of Education, The National University of Malaysia, Kuala Lumpur, 50000, Malaysia
4Institute for Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business University, 400067, Chongqing, China
Abstract:

Foreign direct investment plays a more important role in China’s economic development. This paper examines the impact of FDI on China’s GDP and analyzes regional variability through OLS and quantile regression models. Then the spatial correlation-Moran, I scatter plot is used to visualize the clustering pattern of regional units. The analysis shows that FDI has a significant positive effect on China’s high economic growth at the 25% quantile. However, the higher the economic growth rate, the margin of positive effect of FDI on economic growth gradually decreases. China’s regional economic development is characterized by a dualistic structure. The elasticity coefficient of FDI in the eastern region is 0.099, and that in the western region is 0.05. Therefore, FDI has a greater impact on the eastern region than on the western region. With the development of China, foreign investment began to discrete, gradually spreading from coastal areas to inland areas.

Ning Feng 1
1School of Management, Henan University of Urban Construction, Pingdingshan, Henan, 467036, China
Abstract:

Traditional construction project cost estimation methods rely on expert experience and statistical models, which are difficult to handle complex data and multimodal features effectively and have low prediction precision. This paper constructs an intelligent building engineering cost estimation model that combines subtractive clustering, a self-learning mechanism, and convolutional neural networks (CNN) to address this problem. In the data preprocessing stage, subtractive clustering is applied to optimize multimodal data, screen key features, and eliminate redundant information. Subsequently, the model parameters are dynamically adjusted according to the error feedback through a self-learning mechanism to improve its adaptability to diverse construction projects. In the feature extraction and estimation stage, the CNN module is combined to extract deep features from images, texts, and numerical data to achieve high-precision estimation. The experimental results show that the model in this paper outperforms traditional methods in terms of MSE (mean-square error), MAE (mean absolute error), R² (coefficient of determination), MAPE (mean absolute percentage error), with the mean values being 73.18, 8.33, 0.9477, and 5.33%, respectively. In summary, the model in this paper demonstrates superior precision, adaptability, and robustness in construction project cost estimation.

Ruoyan Jiao 1
1School of Economics and Management, Shanghai Sport University, Shanghai, 200438, China
Abstract:

This paper focuses on the coupling and coordinated development of provincial sports industry and tourism industry. In view of the integration trend of the two as the pillar of the tertiary industry and driven by relevant policies, in view of the insufficient quantitative and regional comparison of existing studies, data from 31 provinces from 2014 to 2021 were selected for analysis. The connotation mechanism of coupling coordination is explained from the economic, social, ecological and cultural levels, and the system including industrial scale and structural indicators is constructed, and the coupling coordination degree model is used to calculate. The results show that the coupling coordination degree of the country is rising in a step, with the eastern starting point being high, the central part making great progress and the western part growing fast. The types of industrial development vary between regions and over time. The global Moreland index shows that there are significant autocorrelation and clustering in the space, the local “high-high” cluster in the east and part of the middle, and the “low-low” cluster in the west. Further, suggestions were put forward to strengthen policy guidance, optimize industrial structure, promote the development of talents and technology, and strengthen the protection and utilization of ecological culture, so as to provide decision-making reference for industrial upgrading and sustainable development of regional economy.

Lipeng Cui1,2, Yu Yu3, Mingzhu Tang3, Zhao Wang4, Jianyou Ouyang4
1School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China
2School of Electronic Information and Automation, Tianjin Light Industry Vocational Technical College, Tianjin, 300350, China
3School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha, Hunan, 410114, China
4Department of Energy Technology, Changsha Electric Power Technical College, Changsha, Hunan, 410131, China
Abstract:

A fault diagnosis method for wind turbine gearbox based on adaptive probability random forest is proposed to address the issue of noise pollution in SCADA data of wind turbine gearbox. Firstly, SMOTE oversampling is used to balance sample categories, and then CART is trained and classified by constructing multiple balanced subsets. The sample error rate represents the weight of sample ambiguity, and the label uncertainty is determined. Monte Carlo simulation is used to calculate the mean distribution of features, which is fused with each sample instance to obtain the uncertainty of sample features. Utilizing adaptive labels and sample uncertainties as inputs to probabilistic random forest can enhance the ability to manage feature noise and label noise, thereby improving the robustness of fault diagnosis. Conduct an experimental evaluation using the SCADA dataset of wind turbine gearbox. The results show that this model outperforms other methods in terms of false alarm rate, false alarm rate, and F1 rating metrics when dealing with missing values, Gaussian noise, and label noise in the dataset, as compared to other methods. This method is of great significance for improving the accuracy and robustness of wind turbine gearbox fault diagnosis.

Si Fang1, Chaohui Tian2, Xiongbin Wu1
1School of Economics and Management, Fujian Chuanzheng Communications College, Fuzhou, Fujian, 350007, China
2School of Automobile, Fujian Chuanzheng Communications College, Fuzhou, Fujian, 350007, China
Abstract:

Rural digitalization and rural tourism are important tasks to achieve the goal of rural revitalization strategy, and researching whether there is a connection between them and the degree of association is helpful to accelerate the transformation of rural digitalization and promote the quality and upgrading of rural tourism. This paper constructs an evaluation system of rural digitalization and rural tourism, adopting 253 counties in China as samples to measure the development differences between regions of the two systems. A coupled coordination model is applied to explore the relationship between the two systems and reveals the distribution characteristics of the level of coupling and coordination in China. The findings show that the difference in the overall score of rural digitalization between counties is greater than that of rural tourism industry. There is a high degree of coupling between rural digitalization and rural tourism systems, and the two systems are currently at a barely coordinated stage in China. In addition, the degree of coordination varies significantly between counties, presenting a phenomenon of higher coupling coordination in the eastern coastal region, intermediate in the central and western inland regions, and lower in the northwest. This paper supports and validates some results of rural development projects in the research area to provide theoretical and decision support for coordinating rural digitalization and rural tourism services.

Fang Han1, Lijun Liu1
1School of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an, Shaanxi, 710021, China
Abstract:

This paper studies integrated process planning and scheduling (IPPS), a typical workshop scheduling problem, and mainly investigates the uncertain problems in the actual industrial production process. Then, we introduce the theoretical knowledge of interval numbers and adopt the interval number comparison method. Specifically, interval numbers are used to replace the determined processing time, and uncertain IPPS problems are modeled based on the interval number theory. Based on this, a hybrid particle swarm algorithm is proposed to solve the uncertain IPPS. Meanwhile, the genetic operator is introduced to improve its ability to deal with combined optimization problems. The above theoretical results are applied to the process planning and scheduling of a mechanical workshop, thus verifying the effectiveness of the proposed method.

Yi Zuo1,2, Peng Wang3, Zhaofang Duan4, Hui Fan4, Minjie Wu4
1School of Economics, Peking University, Beijing, 100871, China
2Economics and Technology Research Institute, China National Petroleum Corporation, Beijing, 100724, China
3PetroChina Natural Gas Marketing Company, China National Petroleum Corporation, Beijing, 100028, China
4 Economics and Technology Research Institute, China National Petroleum Corporation, Beijing, 100724, China
Abstract:

The Tradable Green Certificate (TGC) system scientifically guides renewable energy investment by internalising the positive externalities of renewable electricity. With the promotion of energy transition, the demand for TGC has increased significantly, and the scale of market players has gradually expanded. Market players will imitate other players’ trading strategies for reasons such as herd mentality, which is manifested as herd behaviour. If TGC market players ignore high-quality information and blindly imitate the behaviour of other players, it will limit the diffusion of effective information in the market and reduce the pricing efficiency of the market. Therefore, this paper explores the emergence law of herd behaviour in the TGC market based on a hybrid system dynamic model, with a view to providing theoretical and methodological support for the immediate identification of market risk. This paper portrays the emergence process of herd behaviour of TGC trading subjects, and analyses the emergence law through multi-scenario computational experiments. The results show that (1) herd behavior will emerge from all kinds of strategy subjects and there is a positive feedback relationship between the emergence speed and the return difference between subjects. (2) The emergence of herd behaviour of fundamental strategy subjects has scale and structural effects, and only when the initial imitation scale of such subjects reaches 40% or the market share is less than 50%, will the emergence of herd behaviour, and the depth of its emergence shows an ‘S’ type growth. (3) The herd mentality and the weakening of cognitive bias of TGC trading subjects will reduce the emergence speed of herd behaviour, but have almost no effect on the depth of emergence.

Wenjing Yan1, Jiajia Yin1, Tengyu Ma1, Yu Tian1, Haixin Sun 1
1College of Life Sciences, Qingdao University, Qingdao, Shandong, 266071, China
Abstract:

Background: Ultraviolet radiation (UVR) causes premature skin aging. Litchi seed (LS) is considered a natural plant extract with potential antioxidant, anti-aging and anti-inflammatory properties. However, the mechanisms of LS’s protective effects on skin photoaging remain unclear. Objective: This study aims to perform a rapid and efficient virtual screening of the main targets and possible mechanisms of the protective effect of LS on skin photoaging through network pharmacology, bioinformatics and molecular docking. Methods: The primary active compounds and their corresponding targets of LS were obtained from the TCMSP, STP, and UniProt databases. Concurrently, photoaging-related targets were mined from the GEO, GeneCards, and OMIM databases. “LS-photoaging” targets were identified using Venn diagrams created with R software. Protein-protein interaction (PPI) networks and “compound-target-disease” networks were constructed and analyzed using Cytoscape. GO and KEGG pathway enrichment analyses were then performed to predict the protective mechanisms of LS against skin photoaging. Finally, key targets and active compounds were validated through molecular docking using AutoDock Vina. Results: The screening identified 368 targets of LS active compounds and 872 photoaging-related targets. Network topology analysis revealed 87 common targets, with AKT1, IL6, TP53, and CASP3 as core targets. Enrichment analysis reveals that LS can modulate the ROS/MAPK/AP-1 pathway, thereby inhibiting inflammatory responses and reducing oxidative stress, which leads to a decrease in pro-inflammatory factors. Additionally, it promotes collagen restoration by suppressing the expression of MMPs. Molecular docking validation demonstrated a strong binding affinity between the core targets and the key compounds. Conclusion: LS shows potential for treating photoaging by counteracting inflammation and oxidative stress, regulating collagen and lipid metabolism, and inhibiting apoptosis.

Pan Shao1, Daowang Ren2, Guoqi Ma3
1China Waterborne Transport Research Institute, Beijing, 100088, China
2Shandong Gangtong Engineering Consulting Co., Ltd, Yantai, Shandong, 264000, China
3Rizhao Transportation Bureau, Rizhao, Shandong, 276800, China
Abstract:

With the rapid development of the global cruise transportation industry and the worldwide increase of cruise ship transportation year by year, fire accidents on passenger and roll-on/roll-off ships (P/ROCs) pose a serious threat to economic properties. The article establishes a fire model of a passenger-roller ship carrying electric vehicles using the basic equation of dynamics, a large eddy simulation model, and a mixed fraction combustion model. The mesh division is used to improve the solving accuracy of the kinetic equations. The fire simulation conditions of the electric vehicle carried by a passenger-roller ship are designed to analyze the fire combustion characteristics of the passenger-roller ship transported in terms of wind speed, fire intensity, and ignition power in multiple dimensions using the FDS simulation software as a carrier. Based on the YOLOv5s network and combined with the improved non-great suppression algorithm, a statistical model for target detection of electric vehicles carried by a passenger-roller ship is designed, and the corresponding loss function is designed. When the external ambient wind speed was increased from 0.5 m/s to 6.5 m/s, the maximum temperature at the fire center of the electric vehicle carried by the passenger-roller ship was reduced from 883.93°C to 748.57°C. The improved YOLOv5s model has the highest mAP of 96.67% on the target detection of EVs after fire damage and an accuracy of 92.96% for counting the number of EVs after fire. The state of electric vehicles after fire damage can be obtained under fire dynamics simulation, and the target detection and quantity counting of electric vehicles can be effectively realized by combining deep learning technology.

Yuchen Jin1, Yining Chen1, Yetong Huo1
1International School of Hebei University, Baoding, Hebei, 071000, China
Abstract:

The construction of information resource management system is a promotion for upgrading industrial structure and enhancing independent innovation capability. Based on the city-level data of a region from 2010 to 2022, the evaluation index system of information resource management system and regional economic development mode is established, and the variables are measured according to the collected data using gray correlation analysis method. Then double machine learning method is applied to explore the influence effect of information resource management system on regional economic development model. The baseline regression analysis reveals that the information resource management system can promote the regional economic development model, with a regression coefficient of 0.029, and the conclusion still holds after the stability test. The heterogeneity results show that regions with better economic foundation (0.067) and peripheral cities (0.036) are more significantly affected by the positive spillover effect of the information resource management system. This paper combines machine learning algorithms with traditional causal inference to explore the role path of information resource management system to promote regional economic development model, which provides empirical evidence and decision-making reference for promoting regional economic development.

Yuance Yang1, Hongye Fan1, Nianlu Ren1
1School of Management and Economics, Tianjin University, Tianjin, 300072, China
Abstract:

Transportation demand is gradually increasing and road traffic congestion is becoming more and more serious. Traffic state prediction is one of the important bases for accurate traffic management and control. This paper investigates a traffic state prediction method based on a deep learning algorithm fusing spatio-temporal graphical convolutional networks, and explores the law of path selection decision-making of pedestrians under different traffic flow prediction and guidance strategies, and analyzes the effect of the implementation of the information guidance policy by traffic managers in realistic scenarios using evolutionary game theory. The simulation results combined with the traffic simulation model show that the traffic state prediction method proposed in this paper is more effective compared with other models. The evolution results are more reasonable when the value of the path adjustment rate in the replicated dynamic model is the inverse of the number of iterations. In the perceptual error analysis, when the value of perceptual error 1 is taken to be too large, i.e., when the perceptual error of the first type of travelers is small and small, it tends to be a deterministic choice. Finally, a traffic simulation model is implemented to validate the performance of the proposed model and propose congestion mitigation strategies.

Ruilin Liu 1
1School of Physical Education, Wuhan University of Science and Technology, Wuhan, Hubei, 430000, China
Abstract:

The study is based on the important role of graph theory in the teaching of physical dance and aesthetic education, integrating the concept of graph theory into it and designing the training path of physical dance and aesthetic education based on graph theory. Taking two classes in a university as the research object, the teaching experiment is conducted to compare their physical quality and course performance after the experiment, and the aesthetic education evaluation index system is constructed, and the index weights are measured using the combination assignment method to carry out the comprehensive scoring. After the experiment, the students improved in physical quality, course grades and aesthetic effect, and as far as the students of traditional teaching class are concerned, the experimental students improved in course grades and aesthetic effect by 18.17% and 7.52% respectively. The teaching practice of integrating the concept of graph theory and the curriculum of physical education dance and aesthetic education not only embodies the concept of cross-disciplinary teaching, but more importantly improves the physical quality, physical education dance level and aesthetic effect of students in colleges and universities, and provides a reference for the teaching reform of physical education dance and aesthetic education in colleges and universities.

Peng Hu 1
1Army Logistics Academy Chongqing, Chongqing, 401311, China
Abstract:

Under the environment of plateau alpine region, the new model of substitute construction separating government construction and management functions has gained great development in barracks construction, which significantly improves the risk management level of barracks facilities to some extent. From the significance of barracks facilities construction guarantee in highland alpine area, the article proposes a risk identification framework for the substitute construction unit of Someplace facilities in highland alpine area based on the whole life cycle of engineering projects. Combined with the risk identification framework, the risk evaluation index system of the agency construction unit is constructed, and then the AHP hierarchical analysis method is introduced to solve the weight of the indexes, and combined with the fuzzy comprehensive evaluation method, the AHP-FCM evaluation model is constructed. A barracks facilities project in a camp area is selected as a case study, and Company T is used as the research object to carry out data analysis of its risk degree using the AHPFCM model. In the construction of barracks facilities in highland and alpine areas, the biggest risk faced by the construction unit is the project implementation stage, the weight of which reaches 29.93%, and the fuzzy comprehensive evaluation of Company T’s risk score is 3.182, which is between medium and large risks. Therefore, the agency needs to examine and check its own risk factors in time, in order to lay a solid foundation for ensuring the smooth implementation of the agency project of barracks facilities in highland alpine areas.

Wencheng Lv 1
1Faculty of Education, Shaanxi Normal University, Xi’an, Shaanxi, 710000, China
Abstract:

The rapid development of information technology has put forward higher requirements for teachers, and the traditional training model is difficult to meet the demand. The article constructs a teacher digital competency framework based on the ASTD model, realizes the division of teachers’ professional competence, and explains the professional core connotation of teacher digital competency in detail. A personalized resource recommendation model for teachers is constructed using artificial intelligence technology, which provides accurate recommendations for teachers through candidate resource extraction and learning resource screening. At the same time, with the help of Google Cloud Services digital tools, the design of teachers’ digital teaching and research activities was accomplished, and communication and cooperation with users in the virtual community was promoted. The combination of the two is integrated into the development of teachers’ professional skills to enhance their teaching competence. The mean values of accuracy, applicability, timeliness, personalization, and diversity of learning resource recommendations under artificial intelligence technology ranged from 4.123 to 4.544, with good recommendation performance. The Google Cloud Services platform can promote teaching and research exchange activities among teachers. The use of artificial intelligence and digital tools makes teachers improve their professional skills in knowledge base, instructional design, teaching and research between 24.04% and 91.00%, and with their intervention, teacher competency shows significant improvement.

Lin Cen1, Zhengwei Luo1, Meng Du1, Xiaojuan Zhang2, Zhijie Gao3
1High Pressure Branch, State Grid Sichuan Ya’an Electric Power (Group) Co., Ltd., Ya’an, Sichuan, 625000, China
2 Department of Science and Mathematics, State Grid Sichuan Ya’an Electric Power (Group) Co., Ltd., Ya’an, Sichuan, 625000, China
3 High Pressure Branch, State Grid Sichuan Ya’an Electric Power (Group) Co., Ltd., Ya’an, Sichuan, 625000, China
Abstract:

Power system simulation training is one of the important means to improve the quality of operators and ensure the safe and effective operation of power systems. Research based on digital twin technology, combined with configuration algorithms to give the substation integration diagram model generation method, developed a smart substation virtual training system. The intelligent monitoring is studied, the digital twin-based substation output voltage anomaly detection method is designed using the tracking differentiator method, and finally the simulation test of the intelligent substation virtual training system is carried out. The analysis shows that the voltage anomaly detection method in this paper is highly accurate and can extract the voltage anomaly waveform, and the offset rate of its collected signal is significantly lower than that of the comparison method (11.58%~14.84%), which is only 0.54%. The training test of fast distance protection, differential protection and zero sequence protection verifies the feasibility and effectiveness of the virtual training system in practical application. The platform can effectively promote the reform of applied electric power practice courses and provide a backbone for the training of new power system talents.

Ganbin Xu 1
1Zhejiang Police College, Hangzhou, Zhejiang, 310000, China
Abstract:

At present, the physical training of public security police has not formed a unified training system in the country, and various places ignore the cultivation of other aspects of the ability to take skill training as the leading role, and solve the problem of how to train through the construction of the system, so as to ensure that the physical training of public security police is carried out effectively. This paper explores the impact of physical training on college students’ professionalism in public security colleges, constructs the K nearest neighbor classification algorithm, and introduces the relevant activation function to deal with more complex students’ physical training exercise trajectories. ATT-DAN multitarget tracking model is constructed to extract the feature information of college students’ physical fitness training, obtain the target movement trajectory, and parameterize the representation of students’ physical fitness training programs. The correlation ranges of frequency, average score, highest grade score of physical fitness training and occupational ability were between 0.415~0.632, 0.452~0.769, 0.412~0.715, respectively, and the credibility and stability of the occupational ability characteristics were good. Meanwhile, the linear regression of the two showed that the correlation P value of age, 30-second deep squat, pull-up, 3200 meters, and 15-second repetitive straddle with occupational ability was less than 0.05, and there was a positive correlation between the two.

Xuxin Li1, Shishuo Chen1, Xiaoyun Tang1, Yuhang Qiu1, Zhiping Ke1
1Chaozhou Power Supply Bureau Guangdong Power Grid Co., Ltd, Chaozhou, Guangdong, 521000, China
Abstract:

This paper proposes a real-time computational method for multidimensional dynamic data fusion (VIO-SLAM) for intelligent monitoring of seat belts in the grid construction environment. In this paper, the optical flow method is first used to process and track point features, and the geometrically constrained line matching algorithm is utilized to improve the accuracy of feature matching. Combined with IMU modeling and pre-integration techniques, it effectively reduces the computation of high frequency IMU data and improves the system efficiency. At the same time, a real-time lightweight semantic segmentation system is constructed to achieve fast semantic understanding of the construction scene. The real-time and accuracy of data processing is further improved by sliding window method with BA optimization. On this basis, a VIO-SLAM algorithm based on EKF fusion of multidimensional dynamic data is proposed to realize real-time monitoring and localization of seat belt status. The results show that when a dangerous collision occurs in a complex power grid construction environment, the protection performance of shoulder belt, neck bending moment force and head acceleration of the construction personnel under the method of this paper is much higher than that of the traditional seat belt. In the process of emergency collision avoidance, the VIO-SLAM algorithm is able to tighten the seat belt in advance for the construction personnel, which has better protection performance and can achieve the purpose of “collision avoidance and damage reduction”. The pre-tensioning force for eliminating the gap in the webbing of seat belts and the pre-tensioning force for somatosensory warning reminders are also determined to improve the protection performance of construction workers.

Zhen Wang1, Linhao Xu1, Ao Feng 2
1 China Southern Power Grid Co., Ltd. Ultra High Voltage Transmission Company Electric Power Research Institute, Guangzhou, Guangdong, 510555, China
2Wuhan University, Wuhan, Hubei, 430072, China
Abstract:

As the core equipment of high-voltage direct current transmission system, the operation status of the converter valve directly affects the safety of the power grid. In this paper, we first construct a multisource data fusion system to realize the error-free fusion of fault information parameters. Then, combined with the random forest algorithm, the time-varying law of the electrical characteristics of the converter valve based on harmonic theory is extracted. Finally, the collected time-varying laws of electrical characteristics are input into the constructed Random Forest particle swarm optimization model, and the trained model is used to monitor the status of the converter valve. In the simulation experiment, the ±800kV UHV DC transmission system is built by PSCAD/EMTDC software, from which the current waveforms are collected when the converter valve fails, the time domain features of the current are extracted, and the obtained converter feature indicators are selected using the Random Forest algorithm, and 10 important features will be finally identified to construct the converter valve feature indicator set, and input into the Random Forest Particle Swarm Optimization model and the other comparative models for training and testing. The accuracy of this model is 97.5%, which is better than other comparative models. The study provides a high-precision solution for converter valve condition monitoring and effectively extends the application of multi-source data fusion in power equipment.

Qinze Yang1, Yi Jiang1, Hai Jiang1, Haiyang Chu1, Hongnie Cai1, Ao Feng2
1Tianshengqiao Bureau, Ultra-High Voltage Transmission Company of China Southern Power Grid Co., Ltd., Xingyi, Guizhou, 562400, China
2Wuhan University, Wuhan, Hubei, 430072, China
Abstract:

Sparse decomposition has been generally emphasized in signal processing theory. In this paper, a nonelectrical signal feature dataset of key components of high-voltage DC converter valve is established by using principal component analysis to streamline the data volume. The compression-aware feature extraction algorithm based on polynomial matrix sparse coding is used to extract and collect the nonelectrical signal parametric data. Through the performance over the experimental signal analysis, it can be known that the eigenvalues of a total of 10 parameters, including the infrared temperature measurement results, the appearance, the presence of corrosion or dirt, and the presence of abnormal vibration and sound, are all greater than 1. Therefore, these 10 parameters are identified as the key parameters. When the number of measurement points is between 64 and 200, the algorithm in this paper can satisfy the need of feature extraction when the signal length is insufficient, compared with the traditional approach. In the empirical analysis of the vibration signal as an example, the method of this paper can effectively extract the frequency and time domain of the vibration signal.

Lina Zhang 1
1School of Life and Health, Zhengzhou Vocational and Technical College, Zhengzhou, Henan, 450000, China
Abstract:

With the development of big data and education informatization, education reform and talent cultivation mode are facing digital reform. In this paper, the important feature selection algorithm based on random forest is used to select the relevant features that affect the application effect of teachers’ practice teaching cultivation and innovation mechanism, which lays the foundation for constructing the practice teaching data mining model based on Light GBM. Then the data processed by feature selection is preprocessed and standardized, and then the processed data is partitioned and the model is trained in turn to get the prediction results. The Light GBM-based practical teaching data mining model was compared with other classification models in different datasets, and the experimental results showed that the model in this paper has an advantage over other classification models in a number of evaluation indexes, with the highest accuracy rate of 13.07%, and the model data mining results accurately locate the open innovation experimental indexes that have a lower score of importance to students’ development, and provide a good basis for the optimization of teaching paths and students’ development. , which provides ideas for the optimization of teaching paths and the improvement of the impact of students’ future development.

Yuli Hu 1
1School of Jewelry and Art Design, Wuzhou University, Wuzhou, Guangxi, 543000, China
Abstract:

Using digital back camera to complete the traditional national costume image acquisition work, and then with the help of VOLO model to segment and colorize the image, the traditional national costume elements were successfully extracted. By fusing them with smart wearable devices, a detailed fusion implementation scheme is developed, which contains constraints and objective functions. In the context of numerical computation optimization, the fruit ϐly algorithm (FAO) is used to explore the fusion design scheme of the two in depth. The values of the four objective factors of the fusion design are 0.233, 0.232, 0.348, 0.144, and the ϐinal value of the objective function is 0.957, which indicates that the results of this paper not only can improve the comfort of the device and the user’s experience, but also can provide a new idea and method for the fusion of the apparel industry and the wearable device industry.

Yan Shen1, Qinghua Zhu 2
1Department of Physical Education and Research, Hunan Institute of Technology, Hengyang, Hunan, 421002, China
2College of Physical Education, University of South China, Hengyang, Hunan, 421001, China
Abstract:

The article explores the method of diversified modeling of college sports track and field data, aiming to provide a basis for scientific training of college sports track and field. In this article, the diversified modeling of college sports track and field data is carried out by using multiple linear regression model, testing method and mathematical statistics method in order to analyze the sports characteristics and training needs of college track and field athletes. Using multiple linear regression model to analyze the influencing factors of track and field special movement patterns, then, on the basis of clarifying the training needs of track and field special movement patterns, combining the theoretical study of functional movement screening with the actual practice of track and field sports, carrying out the FMS test of the research object, and proposing the optimization plan of college sports track and field training after analyzing the results of functional movement screening of different track and field events. By using the multi-dimensional modeling method of college sports track and field data proposed in this paper to analyze the influencing factors of athletes’ track and field special action patterns, it is found that there is a significant medium correlation between the “torso forward swing and hip and knee rotation speed” in the buffer action link and the “torso extension speed” in the kick and stretch action link and the in-situ jump height. At the same time, there was a significant correlation between the common factor “trunk forward swing and hip and knee rotation speed” and the “trunk extension speed” in the push and extension link.

Gao Zhang 1, Yajuan Wang 2, Junpeng Kang 1, Jia Li 3
1 College of Architectural Surveying and Mapping, Shaanxi Energy Institute, Xianyang, Shaanxi, 712000, China
2College of Intelligent Mechatronics, Shaanxi Energy Institute, Xianyang, Shaanxi, 712000, China
3School of Construction Machinery, Chang’an University, Xi’an, Shaanxi, 710064, China
Abstract:

In this paper, the strain law of natural gas pressure vessel steel fatigue is firstly analyzed through the stress-strain curve and steel fatigue life curve, and the finite element model of natural gas pressure vessel is constructed by combining ABAQUS simulation software, and the fatigue performance of natural gas pressure vessel steel is analyzed from the cyclic softening behavior of the material and SN curve. Then the stress intensity factor theory combined with Paris formula is introduced to calculate the crack expansion rate, and ABAQUS and FTANC3D are jointly simulated to study the crack expansion law. Finally, the fatigue yield strength of the natural gas pressure vessel was analyzed based on the elastic-viscoplastic constitutive model combined with the finite element model. It is found that the stress intensity factor along the path under hydrostatic loading is larger than that under stresscontaining loading, but the difference in stress intensity factor is only about 1.42%. When the cracks of the natural gas pressure vessel extended to the vicinity of 20 mm, its crack extension rate showed a sharp downward trend. When the temperature comes to 900°C, the yield strength value of the steel of natural gas pressure vessel is only 280.42 MPa.Exploring the steel fatigue performance and crack extension rate of natural gas pressure vessel can help to better ensure the stable and safe operation of natural gas pressure vessel.

Yihong Chen 1
1College of Art, Quanzhou Kindergarten Normal College, Quanzhou, Fujian, 362000, China
Abstract:

The article calculates the average image entropy of the image domain, quantitatively analyzes the information has richness asymmetry in the task of digitally generating ink paintings, and constructs an asymmetric cyclic coherent ink painting digital generation model based on graphical algorithms. The model integrates a generative adversarial network, and the generator is centered on the Dense Block and replaces the residual block with a dense block to improve the characterization ability. The position fusion attention network is utilized to capture the main body region of the ink painting and combined with the edge extraction technique to extract the significant main body edges of the image and simulate the salient features of the ink painting strokes. The model is integrated into the teaching of “Children’s Ink Painting” course in a high school teacher, and students are instructed to use the algorithm to generate digital ink paintings to further explore the effectiveness of the teaching method. In this paper, the model is iterated for 30 times, and the total objective function converges to the minimum value of 0.85, and the measured values on PSNR, UIQM and UCIQE are improved by 4.44, 0.3 and 0.68 respectively compared with the optimal values of the comparison model, and the model can obtain the highest evaluation score (8) of the generated image at the fastest convergence speed (50 epochs), and the degree of overlap with the real image on the LPIPS distance is higher. After the experiment, the dimensions of digital pedagogical literacy level of the experimental class increased by 3.37 to 7.63 points compared with the control class and showed significant differences. As for the satisfaction of learning experience, students’ satisfaction with digital teaching resources is the highest, which is 4.70 points. The experimental results show that the model constructed in this paper has good performance of ink painting image generation and can be used as a digital teaching method for children’s ink painting course in high school teachers.

Shanli Wu 1
1 Department of Fine Arts, Quanzhou Preschool Education College, Quanzhou, Fujian, 362200, China
Abstract:

In order to optimize the design effect of cultural and creative products with non-heritage patterns, this paper uses image reconstruction algorithm and image recognition algorithm to process non-heritage problem patterns. By combining the processed non-heritage cultural patterns with consumer demand for cultural and creative products, non-heritage cultural pattern cultural and creative products are designed to meet market demand. On the basis of recursive network, we add multi-scale feature extraction module and attention feature fusion module, choose L1 loss function to optimize the details of image reconstruction, and construct image super-resolution reconstruction algorithm based on multi-scale recursive attention feature fusion network. And the image feature extraction network containing MSA module is designed, which is the fine-grained image recognition network based on multi-scale attention. The non-heritage cultural pattern dataset is established, and in order to optimize the recognition rate of non-heritage patterns, the image reconstruction based on multi-scale recursive attention feature fusion network is carried out on the non-heritage cultural pattern data. In view of the creative design strategy of non-heritage culture, the evaluation indexes of non-heritage cultural and creative product design are obtained from the consumer research, and the implementation suggestions of non-heritage pattern cultural and creative product design are derived based on the ranking of the importance of the evaluation indexes. The multi-scale recursive attention feature fusion network proposed in this paper achieves 34.89dB and 90.52% indicator scores on the Set5 dataset. For the design of cultural and creative products with non-heritage patterns, consumers make more suggestions in terms of functional differentiation, having a response rate of 21.58%.

Zhige Lyu 1
1Guilin Normal University, Guilin, Guangxi, 541199, China
Abstract:

Mental health issues have become a global concern. Aiming at the complexity of individual facial emotion expression in the task of analyzing mental health status, this study proposes a face emotion recognition method oriented to psychological intervention. The method integrates image recognition and sentiment analysis techniques, adopts Adaboost algorithm for face detection, generates an emotion region suggestion network based on face image recognition, and constructs an image sentiment classification network through feature map mapping and shared convolution. The method is then applied to the mental health recognition system. The model in this paper avoids the effects of individual and illumination differences. It has good face emotion recognition on several datasets, and the prediction accuracies are above 90%, especially for Happy emotion. In the comparison with other recognition methods, the recognition accuracy of this paper’s model is improved by 12.92% to 22.95%. The experiments show that the proposed face emotion recognition method can effectively predict the emotion of facial expression data in the mental health recognition system, and promote the assessment of individual mental health status and emotion management.

Yifang Hu 1
1Dean’s Office, Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, 310000, China
Abstract:

This study focuses on the “blockchain + education” perspective, focusing on the integration of edge computing in the higher education resource sharing system. Through the benign interaction between blockchain and edge computing in the system data management system, the security and efficiency of data storage and transmission of shared resources in the system can be improved. In order to improve the performance of the system’s educational resource sharing, this paper utilizes the node identification model on the basis of the traditional PBFT consensus algorithm for the selection of master nodes and the monitoring of malicious nodes. Meanwhile, in order to ensure the balanced allocation of educational resources within the sharing system as much as possible, this paper utilizes the differential evolution (DE) algorithm for the balanced allocation of system resources and the educational resources within the system. The results of experiments and system tests show that the improved PBFT consensus algorithm (NR-PBFT) in this paper shows obvious superiority in tests such as throughput and latency. Although the educational resource allocation model performs poorly in the allocation of resources with larger technology such as digital books, the results for the allocation of teacher resources can effectively prove the effectiveness of the resource allocation model in this paper. In addition, the system test results also show that the system in this paper has good performance, and the introduction of edge computing can significantly reduce the packet loss rate of resource sharing, which has considerable application value.

Wenbo Shi1, Haiyang Liu2,3,4, Jie Liu3, Changyou Li5
1International Engineering College, Shenyang Aerospace University, Shenyang, Liaoning, 110136, China
2College of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China
3Department of Research Engineering, AVIC SAC Commercial Aircraft Co., Ltd, Shenyang, 110000, China
4Key Laboratory of Fundamental Science for National Defense of Aeronautical Digital Manufacturing Process of Shenyang Aerospace University, Shenyang, Liaoning, 110136, China
5School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Abstract:

In this paper, the Gamma process is used to describe the change of cutting force coefficient and analyze the time-varying stability of chattering, and then the time-varying reliability model of chattering of turning machining system is established. The optimal Coupla function model is selected by the AIC criterion, and the reliability analysis of the turning machining system is carried out by using the Monte Carlo method and the VC-MCS method which introduce the Coupla function, and at the same time, the fuzzy factors of the turning machining process are taken into consideration, and the fuzzy optimization mathematical model of turning machining is set up with the goal of the lowest machining cost, and then the model is solved by using the multi-objective particle swarm optimization algorithm, which realizes the fuzzy optimization in the aerospace manufacturing. Then the model is solved using a multi objective particle swarm optimization algorithm to realize the reliability optimization of turning machining process in aerospace manufacturing, and the fuzzy optimization mathematical model of turning machining is experimentally verified by taking common plane milling and cylindrical turning as an example. The experimental results show that the analysis results of the VC-MCS method and the Monte Carlo method with the introduction of Coupla function are almost the same, which verifies that ignoring the correlation between the parameters affects the turning reliability results, and secondly, the turning machining system operates well at full rotational speeds when the turning width b=0.63mm. Finally, according to the case results, the effectiveness and feasibility of the proposed optimization method is proved, which can provide certain optimization objectives for improving the efficiency of turning machining.

Songyao Feng1, Zhengyan Huang1, Junhao Song1, Xuexia Quan1
1Information Center of Guangxi Power Grid Co., Ltd., Nanning, Guangxi, 530012, China
Abstract:

Smart grid technology is developing rapidly around the world and is gradually applied to the operation and maintenance management of power systems, and its main advantage lies in its integration capability, which can effectively realize the high efficiency, security and reliability of power system operation and maintenance. This paper explores the integration of grid operation and maintenance by integrating computing and information theory using multidimensional data mining and analysis methods. The operation data of smart grid is first preprocessed, including resampling and PCA dimensionality reduction of multidimensional data signals. Then, a CNN-based power operation state prediction model and an R-CNN-based grid fault diagnosis model are constructed to ensure the stable operation and timely maintenance of the smart grid, and the predicted and actual values of the smart grid operation state of the CNN model are basically consistent with each other, with the MAE, MSE, and RMSE of 0.00104, 0.00014, and 0.012, respectively, and the prediction results are good. The effect is good. Compared with CNN and SVM, the performance of R-GNN model is better, and after PCA dimensionality reduction, the fault identification rate of R-GNN model is as high as 98.91%. And the delay of the R-GNN method for fault diagnosis is only 0.04s, while it can realize the comprehensive and accurate localization of the fault area. This paper provides methodological reference for the utilization of multidimensional data mining and analysis technology to realize the operation and maintenance integration of smart grid.

Yuanli Liu 1
1School of Foreign Studies and Trade, Hubei University of Automotive Technology, Shiyan, Hubei, 442002, China
Abstract:

Higher-order cognitive computational modeling focuses on the large amount of data generated by learners during their educational activities in order to make predictions and inferences and obtain their cognitive characteristics. In this paper, the original ant colony system algorithm is improved. Considering learners as ants, through state transfer probability calculation, pheromone updating, and continuous iteration of multiple ants with the same cognitive characteristics, the optimal teaching path suitable for the learner can be derived. After analyzing, it can be seen that comparing with the data of other GA and ACO algorithms, the improved ACO algorithm in this paper achieves the optimal training effect. By setting up the experimental group and the control group, it can be found that the teaching paths of the five students who did not use the method of this paper were all longer. Therefore, a concise and precise teaching path can be designed from the complicated learning resources and activities. Compared to the control group, the students in the experimental group presented more significant grammar scores and grammar learning attitudes (p<0.001).

Chong Gao1, Xinghang Weng1, Yao Duan1, Zhiheng Xu1, Junxiao Zhang 1
1 Grid Planning & Research Center, Guangdong Power Grid Co., Ltd., CSG, Guangzhou, Guangdong, 510000, China
Abstract:

In order to solve the adverse effects of uncontrolled charging of electric vehicles on the distribution network, the study constructs a Monte Carlo-based uncontrolled charging load model to calculate the effects of uncontrolled charging on the electric vehicle side on the distribution network load and voltage. Based on this, the electric vehicle trip chain is modeled by Bayesian network so as to manage the charging options of electric vehicles. The charging loads of EVs managed by the Bayesian network at different sizes and different charging locations are predicted to explore the impact of the Bayesian network on EV charging and distribution grid loads. The peak weekday grid base load occurs at 11:00 AM (3695 kW) and 20:00 PM (3656 kW). On weekdays, the grid base load occurs at 12:00 pm (3495 kW) and 20:00 pm (3725 kW), and the peak load increases significantly with the increase of penetration rate and the time is gradually advanced. The end node 18 has the lowest voltage and the lowest value of voltage at node 18 is 0.9135 and 0.9140 on weekdays and bi-weekdays respectively when only the base load is present. At 100% penetration, the minimum voltage is 0.9015 and 0.9008 on weekdays and bi-weekdays, respectively. When the penetration rate of electric vehicles is 20% and 30%, the average value of peak load of electric vehicle charging power increases to 150.05kW and 220.85kW. When the charging scheme of residential charging + office charging is used, the peak load of EV charging in residential areas is reduced by 60.3%.

Shiyuan Ni1, Guilian Wu1, Sudan Lai1, Lu Tang1
1State Grid Fujian Economic Research Institute, Fuzhou, Fujian, 350012, China
Abstract:

With the rapid development of distribution networks in China and the increasing penetration of renewable and traditional energy sources, it is necessary to study the optimal allocation of capacity and optimal operation for the two stages of pre-planning and practical application of distribution networks. In this paper, the probability density function is used to model the uncertainty of “source” and “load” respectively, and the optimal allocation model of distributed power supply capacity of distribution network system is constructed by the equipment models of “wind generator”, “photovoltaic generator”, “diesel generator” and “battery”. Comprehensive cost and power supply security are taken as the objective function and constraints, respectively, to improve the distributed power supply capacity optimization, and adaptive sparrow search algorithm is applied to solve the model. In the comparative analysis of source-load synergy, source-load synergy and energy storage system joint optimization configuration scheme, the joint planning of DPV and ESS enhances the installed capacity of DPV by about 13.45%, and the average power generation of the joint planning scheme is 88.35 kW/h. The joint planning obviously enhances the installed capacity of DPV under the condition of slightly increasing the DPV curtailment. Examples are examined to verify the practical application of the proposed adaptive sparrow search algorithm in configuring the power supply capacity of the hybrid generation system, and the cost of using the cyclic charging operation scheme is 81,067 yuan lower than that of using the load-tracking scheme, and the economic effect has been significantly improved.

Wenbo Ma 1
1Sports Industry Management, Hunan First Normal University, Changsha, Hunan, 410000, China
Abstract:

The employment and entrepreneurship career choice planning of college students is an important constituent module of the talent training system of colleges and universities in the new era. Aiming at the traditional ant colony algorithm with poor realm adaptability and a large number of inflection points, this paper proposes an ant colony algorithm based on Sigmoid statistical iteration. The Sigmoid activation function distribution strategy is adopted to reduce the blindness of the algorithm’s presearch, and the heuristic function is dynamically adjusted by the introduction of the adaptive factor to reduce the convergence time of the algorithm, and finally the pheromone update function is dynamically adjusted according to the number of iterations to construct the career choice path planning model and apply the model to the career choice planning path recommendation system. When the number of users is 1000, the average response time of the proposed system is only 322ms, the throughput is 394, and the pass rate is 100%, and the CPU occupancy and memory usage are lower than those of the traditional system (35.32% and 39.83%).

Chunyu Zhang 1
1Management School of Northwestern Polytechnical University, Xi’an, Shaanxi, 710129, China
Abstract:

The development of communication technology and the rapid growth of the number of mobile network service users have made the competitive situation in the market of communication service increasingly fierce, and maintaining the stock of users is of great significance to the sustainable development of telecommunication enterprises. In this paper, we collect relevant data features of telecommunication users, and after pre-processing the features with RFM model, we use XGBoost model to analyze the importance of each user’s feature value. Then we use the secondary classification Stacking integration model that combines the base learner and the meta-learner to predict the telecom subscriber churn. Comparative validation reveals that the prediction model in this paper shows excellent prediction performance in all four datasets. Practical application results show that the effectiveness of churn maintenance efforts by telecom companies is improved after applying the model, and the average maintenance response rate reaches 50.63% in the first quarter of 2024. The prediction model proposed in this paper based on the binary classification method can assist telecommunication companies to manage the stock of subscribers, optimize the maintenance work plan, and reduce the subscriber churn rate in the telecommunication work period.

Zhaoming Huang1,2, Rui Hu1, Chenchen Zhu 1,2
1Aircraft Strength Research Institute of China, Xi’an, Shaanxi, 710000, China
2National Key Laboratory of Strength and Structural Integrity, Xi’an, Shaanxi, 710000, China
Abstract:

This paper designs the system structure to meet the impact test of aircraft landing, and utilizes finite element calculation to derive the maximum impact stress of the impact platform and the maximum bearing stress. Analyze the attitude combination measurement system, based on the coordinate transformation theory to build a digital level, attitude probe and inclination sensor combination of attitude measurement model, the horizontal attitude angle of the object to solve the calculation. And the robustness overall least squares method is applied for plane fitting. The overall flow of the attitude measurement experiment is designed to analyze the stability and accuracy of the spatial attitude measurement system based on the combination of multi-sensors, and analyze the measurement error of the measured target in different states (translation or deflection). Different attitude solving algorithms are used to measure the attitude angle of the dynamic simulation experiment, and the measurement errors of the roll angle, pitch angle, heading angle and the root-mean-square error are compared. The RMS errors of the roll angle, pitch angle and heading angle measured by the attitude solution model in this paper are 0.2982, 0.2214 and 1.0333, respectively.Comparing with the data in the charts and graphs, it can be seen that the measurement errors and RMS errors of the attitude solution algorithm used in this paper are smaller, which are more in line with the requirements of the target spatial attitude measurement.

Yan Xiao1, Dongming Yao 2
1School of Engineering, Guangzhou College of Technology and Business, Guangzhou, Guangdong, 510850, China
2Guangdong Nonferrous Industrial Construction Quality Inspection Station Co., Ltd., Guangzhou, Guangdong, 510725, China
Abstract:

With the rapid development of China’s economic level and the significant improvement of people’s living standard, the quality issue of peaches has become more and more strict. In this paper, based on deep learning algorithm, we propose the recognition method of peach fruit color, size and fruit shape features, combined with near-infrared spectroscopy detection technology, to quantify the peach fruit components and discriminate its maturity. Differential algorithm, standard normal transform, and multiple scattering correction are applied to pre-process peach fruit data. Based on M-YOLOv5s target detection framework, spectral analysis and image characterization techniques were used to jointly detect the degree of peach fruit disease. The distribution of peach fruit quality parameters was investigated, and the test results showed that 39.19% of the samples with measured values of fruit size were concentrated at 1.60-6.40 cm, and 61.79% of the samples with predicted values were concentrated at 2.50-7.50 cm, which was located at around the mean value of 4.763 cm.The classification accuracies of the information modeling set and validation set for the combination of the spectral analysis and image eigenvalue detection techniques were 91.439% and 88.487%, respectively, and the combined use of the two techniques had a high accuracy for the differentiation of diseased peach fruits. Based on the experimental results, the application of spectral detection technology in food freshness detection as well as pesticide residues and illegal additives is explored.

Weijun Tang1, Shaohong Gu 1
1School of Business, Jiangnan University, Wuxi, Jiangsu, 214122, China
Abstract:

Service quality is the key for takeaway platforms to maintain their advantages in the fierce market competition. In this study, we construct a mathematical model to solve the takeaway delivery problem by ant colony algorithm, so as to realize the takeaway delivery path planning based on ant colony algorithm. The grey neural network model is used to predict the order demand in the takeaway platform, and the fruit fly algorithm is used to fine-tune and optimize the parameters in the grey neural network model to avoid the model from falling into the local optimum and to improve the accuracy of the model in predicting the takeaway demand. Through simulation experiments, it is found that the planning algorithm in this paper can successfully realize the reasonable planning of takeaway delivery paths when the initial positions of merchants, users and delivery workers are known. The gray neural network optimized using the fruit fly algorithm is also able to accurately predict the takeout demand of platform users based on the order data provided by the takeout platform. Using the method of this paper for the improvement of the service quality of the takeaway platform can significantly improve the delivery efficiency of takeaway orders and develop personalized service strategies according to user demand, thus enhancing user satisfaction with the takeaway platform.

Rongyao Li1, Jinghui Wang 2
1 Hebei Sport University, Shijiazhuang, Hebei, 050041, China
2Hebei Vocational College of Public Security Police, Shijiazhuang, Hebei, 051433, China
Abstract:

For enterprises, development is ultimately reflected in the task completion performance of employees, and in order for employees to create higher task performance, it is necessary to consider not only their education and knowledge level, but also their emotional management ability. This study first collects data related to employees’ emotion management ability and task completion efficiency improvement through questionnaires, and then analyzes the statistical data by using the potential impact identification model designed based on Bayesian neural network model to obtain the potential impact probability of each dimension of emotion management ability on task completion efficiency improvement. The analysis of the forward and reverse inference probabilities of the Bayesian network model indicated that the most important potential influence factor leading to the improvement of task completion efficiency was the emotion expression ability, with a forward and reverse inference probability of 36.2% and 59.4%, respectively, followed by the emotion regulation ability and emotion acceptance ability. The results of this study reveal the important potential influence of emotion management ability on task completion efficiency enhancement, and the formulation of task completion efficiency enhancement strategies based on the perspective of emotion management ability can effectively enhance employee task performance, which in turn promotes the overall development and competitive advantage of enterprises.

Hui Wang 1
1Tourism Management Department, TAIYUAN TOURISM COLLEGE, Taiyuan, Shanxi, 030032, China
Abstract:

In recent years, the development of study activities is in full swing. In order to study the eco-education effect in national park study activities, this paper introduces Bayesian network and constructs an ecoeducation effect assessment model based on Bayesian inference. In the comparison of the absolute error of the assessment value with other assessment models, the assessment accuracy of the Bayesian inference assessment model in this paper is obtained. After constructing the ecological education effect assessment index system and completing the assignment, the level of ecological education that should be achieved in the national park study activities is obtained through Bayesian inference diagnosis. Finally, according to the results of education effect assessment, the probability of each indicator being in various states is obtained by simulation using Monte Carlo method. The mean absolute error of the Bayesian assessment model is 0.26 points, which is smaller than other comparative assessment models and has the highest assessment accuracy. The model’s ecosystem principles, anthropogenic intervention impacts, ecological disasters and ecological protection measures should be guaranteed to reach 75.6, 64.8, 67.9 and 69.4. The ecological operation rules (59.4→79.8), climate change (50.6→70.2), biodiversity reduction (52.2→69.8), and pollution prevention and control (56.4→78.3) have the highest accuracy for the ecosystem principle, anthropogenic intervention impacts, ecological disasters and ecological protection measures, respectively. , anthropogenic intervention effects, ecological disasters and ecological conservation measures, and ecological education effects had the greatest impact. The overall score of ecological education effect was 84.1, and the scores of ecosystem principle, human intervention impact, ecological disaster and ecological protection measures were 83.8, 85.2, 83.0 and 84.2.

Lixia Wang1, Xiaoping Song2, Zewei Su1
1School of Architecture and Surveying & Mapping Engineering, Shanxi Datong University, Datong, Shanxi, 037003, China
2Datong Architectural Design and Research Institute, Datong, Shanxi, 037006, China
Abstract:

Ultra-low energy buildings for building energy efficiency development, compared with traditional buildings have obvious advantages. This paper simulates ultra-low-energy residential buildings in severely cold regions through Software PHES, and calculates the energy-saving results of ultra-lowenergy residential buildings. The carbon emission factor method is analyzed, and the carbon emission factor is calculated at different stages in the life cycle of the building. Select ultra-low-energy residential buildings in cold regions for modeling, input meteorological parameters, indoor environmental parameters and internal disturbance settings, building envelope, and combine with heat recovery system to simulate the operation of ultra-low-energy residential buildings in cold regions. Analyze the indoor and outdoor temperature and humidity values of traditional houses and compare them with those of ultra-low-energy-consumption houses to verify the advantages of ultralow-energy-consumption residential buildings. Calculate the energy-saving efficiency of ultra-lowenergy residential buildings. Using the 9# residential building of Ruihu·Yunshanfu in Datong as a practical verification case, this ultra-low energy residential building has a total life-cycle carbon emission of 171.078 tCO₂/a, with a unit area carbon emission of 16.415 kgCO₂/m²·a. Compared to the energy-saving design standards implemented in 2016, the carbon emission intensity is reduced by 60.02%, fully confirming the carbon reduction benefits of ultra-low energy residential buildings in severe cold regions.

Chenglin Yang1, Guang Xing1, Weiqian Ma1, Jia Tai1
1School of Art and Design, Anhui Broadcasting Movie and Television College, Hefei Anhui, 230011, China
Abstract:

Generative artificial intelligence represented by ChatGPT has attracted wide attention in the field of education because of its powerful generative ability, both personalized learning, understanding the learner’s motivation, and providing personalized tutoring and feedback for education. With the advent of the Education 2.0 era, smart classroom has become a strategic choice for the construction of education modernization, and is widely used in higher education and vocational education. Generative AI enlightens students’ engineering thinking, computational thinking, design thinking and systems thinking, which not only helps students to master their professional courses, understand what they have learned, and improve their academic performance, but also assists teachers in updating their course content, keeping abreast of students’ learning trends, improving their teaching efficiency, and simplifying their work. However, generative AI is faced with expertise gaps and uncertainty about the existence of generated content in its application, as well as ethical issues, and this study proposes that the needs and values of education should be respected, with the aim of efficient and convenient services, and that data-driven and ethical ethics should be emphasized in future development. Smart classroom and enlightened thinking with the application of generative AI is a new way of thinking about educational change, which can help teachers and students to effectively carry out multiple interactions, enable teachers to better understand students, play the role of human beings in education, and truly allow technology to be used for teaching and promote classroom teaching reform.

Duanyang Cai1, Huafeng Zhuge1, Ru Wang1, Cong Wu1, Guo Zhang2
1Zhejiang Haining Rail Transit Operation Management Co., Ltd., Haining, Zhejiang, 314400, China
2Chengdu Tangyuan Electric Co., Ltd., Chengdu, Sichuan, 610000, China
Abstract:

With the rapid expansion of high-speed railway network, the real-time monitoring of trackside equipment becomes particularly important. To detect trackside equipment information more accurately, a YOLO-R algorithm grounded on the improved You Only Look Once v3 (YOLOv3) algorithm is proposed, and the trackside equipment identification and detection model is constructed. By introducing feature pyramid network and adaptive Bessel curve network, the new model can effectively identify and locate different types of trackside equipment such as switch machine, derailer, and shaft counter. The experiment findings denote that the new model is superior to the existing technology in all aspects of on-orbit equipment recognition and detection, the computer resource occupancy rate is only 22%, the image recognition accuracy rate is more than 98%, and the processing speed is up to 200 images/second. This research not only raises the automation level of trackside equipment monitoring, but also provides a powerful technology for railway safety operation.

Yishu Liu1, Xiaowen Lv 2
1School of International Business, Xi’an FanYi University, Xi’an, Shaanxi, 710105, China
2School of Management, Qilu Medical University, Zibo, Shandong, 255213, China
Abstract:

Big data is an important foundation in social economy, science and technology, life and other fields, which also becomes a strategic emerging industry and has a crucial impact on the development of enterprises. As a new business model, its development is greatly limited due to the huge amount of data and difficult management. At present, there are many problems in power trading enterprises, such as backward management and low efficiency. The development of big data and blockchain technology would provide new management models for power trading enterprises and eliminate data inconsistency. It can improve data quality and help improve work efficiency, so as to reduce operating costs. Therefore, this paper introduced big data and blockchain based on fuzzy algorithm into the research of digital transformation of enterprise management. Blockchain technology provided technical support for enterprise data management. By starting from the concept of big data and blockchain, this paper would study and analyze how to promote the digital transformation of enterprise management. The research results showed that big data and blockchain based on fuzzy algorithm could promote the digital transformation of power enterprise management and improve the digital transformation process of power enterprises. This was about 11% higher than the digitalization process of traditional enterprises, and the satisfaction score was about 14.7% higher. Through data governance, the speed of digital transformation of power enterprise management was improved.

Youcheng Peng1, Sihan Chen1
1Northeastern University at Qinhuangdao, Sydney Smart Technology College, Northeastern University, Qinhuangdao, Hebei, 066004, China
Abstract:

In today’s rapid development of information technology, the big data industry has ushered in explosive growth, and big data analysis has become an important research topic in the cross-cutting field of computing. This study constructs a big data prediction base model based on deep learning, and uses the improved butterfly optimization algorithm with OGRU model to realize feature selection and classification processing of big data. Then the Adam algorithm is used to optimize the parameters of the model, and finally the classification and prediction model of big data based on deep learning is constructed. Simulation and empirical analysis results show that the model proposed in this paper has excellent classification and prediction performance, and can meet the efficiency requirements of big data classification and prediction. The prediction errors of distribution network load data and smart charging pile operation data are lower than 9% and 16%, respectively, which have high practical application value. This study is of great significance to the research related to big data classification and prediction in different fields, and provides an effective method for data prediction in complex scenarios such as industrial as well as power grid scheduling.

Yuhang Liu1
1School of Intelligent Equipment, Shandong University of Science and Technology, Tai’an, Shandong, 271001, China
Abstract:

In recent years, intelligent control has realized rapid development in the field of electrical engineering, the article initially studied the principle of electrical intelligent control, accordingly built the electrical intelligent control system, and designed the system hardware, the system module is divided into the main control module, the expansion module, the digital input and output module and the mounting rail. Based on the working principle of fuzzy control, design the software of the electrical intelligent control system, and optimize the traditional fuzzy controller by using fuzzy adaptive hybrid genetic algorithm, so as to improve the fuzzy control accuracy of the electrical intelligent control system in this paper. The electrical control system of this paper is applied to greenhouse greenhouse temperature and humidity control, substation air conditioning energy consumption control and subway station illumination control, and the control effect of the electrical intelligent control system of this paper is known through three experimental data. The system of this paper can effectively deal with the dissimilar data in the greenhouse temperature control experiment. Under the steady state environment, the temperature deviation of this paper’s fuzzy control method and conventional single structure fuzzy control is within 0.1℃ and 1℃ respectively, and the humidity deviation is within 5%RH and 10%RH respectively. Obviously, the fuzzy control method in this paper has higher control accuracy. In the substation air conditioning energy consumption experiment, the annual power consumption of this paper’s electrical intelligent control system and the traditional ventilation and air conditioning system are 32,660 degrees and 45,620 degrees, respectively. The electrical intelligent control system in this paper can save 22,000 yuan per year. The output illuminance of the subway station of the fuzzy control system in this paper increases with the comfort of the light environment and the density of the crowd, which achieves the expected effect.

Meng Qin1
1 Business School of China University of Political Science and Law, Beijing, 100000, China
Abstract:

This paper firstly constructs a coupled evaluation index system based on three primary indicators, seven secondary indicators and 29 tertiary indicators for agriculture, culture and tourism. Then the entropy weight-TOPSIS method and the coupling coordination degree model are selected to measure the development of agriculture, culture and tourism industry and the coupling level in Yijun County respectively. Finally, 23 spatially related villages in Yijun county area are selected to reveal the reasons for their spatial differences with spatial measurement model, and analyze the factors affecting the coupled and coordinated development of agricultural, cultural and tourism industries in Yijun county area. From the comprehensive evaluation results, the trend of the development level of agriculture, culture and tourism in 2017-2023 was generally upward, in which the agriculture industry had the highest growth between 2022 and 2023, with an increase of 0.0445. After analyzing the factors influencing the development of the coupled agriculture, culture and tourism industries in the Yijun county region by applying the spatial Durbin model, it was found that the general budget expenditures, human capital, infrastructure construction, fixed asset investment and education investment in the region at a significant level of 0.01 correlation of 0.211, 0.03, 0.082 and 0.085, and education investment in the region at a significant level of 0.1 correlation of 0.211. These five factors significantly affect the Yijun County region agriculture, culture and tourism industry, and deepen the development of integration of tourism industry.

Hongqiang Wu1, Shaohua Wang1, Xinlong Tan 1
1Inspur Yunzhou Industrial Internet Co., Ltd, Jinan, Shandong, 250101, China
Abstract:

Blockchain theory and its key technologies are developing rapidly, and the industrial internet combined with blockchain technology is driving the realization of safe and reliable comprehensive connectivity in multiple fields. In this paper, we propose a resource optimization allocation method for industrial internet that integrates edge computing and blockchain to reduce the task computing energy consumption and computational overhead of the system while improving the efficiency of the consensus process. This optimization problem is constructed as a Markov decision process, and a deep reinforcement learning algorithm is used to solve the optimal resource scheduling strategy under a single edge node. The effectiveness of the proposed resource optimization allocation method for industrial internet fusing edge computing and blockchain is verified by simulation validation. The method is able to obtain better and smoother convergence under the premise of harvesting high total rewards, effectively reduces the computational energy consumption and computational overhead of the device, and at the same time effectively improves the consensus efficiency of the blockchain.

Nianlong Chi1, Liping Yan1
1College of Civil Engineering, Fujian Chuanzheng Communications College, Fuzhou, Fujian, 350000, China
Abstract:

Particle swarm algorithm, as a kind of population intelligent optimization algorithm, shows great potential in solving multivariate and nonlinear optimization problems due to its simple and efficient characteristics. The article constructs a concrete ratio optimization model in construction engineering technology, which is supported by particle swarm algorithm as the main technology. The model also integrates the least squares support vector regression algorithm, which makes it not only simple ratio optimization, but also has the function of concrete performance prediction. The relative error of the model in predicting the physical properties of concrete is small, less than 5%, which improves the reliability of concrete proportioning. The concrete samples generated by the model with five different ratios have better physical properties for daily needs. In the durability test, the concrete sample with proportion 4 showed the best performance in terms of mass loss rate and impermeability, which were 3.52% (after 400 cycles) and 156.44C (after 56d), respectively. And all the concrete samples used were in the range of proportional qualification and the cost was 5.99% to 28.61% lower than the comparison method.

Ping Yan 1
1 Taiyuan Tourism College, Taiyuan, Shanxi, 030000, China
Abstract:

As the most popular mode of out-of-school education in recent years, study tour plays an important role in the comprehensive ability improvement and overall development of students. Based on the path planning problem of study tour, this paper proposes a travel route optimization model with time optimization as the goal orientation, aiming to plan the time-optimal path for students in the study tour process. The particle swarm algorithm is used to improve the genetic algorithm for solving the travel route optimization model. The effectiveness of the optimization model and the hybrid algorithm is verified through the analysis of an actual case of a study tour, and the experimental results are substantially optimized compared with the traditional planning path, reducing the time spent by 2.2 days. Then we use qualitative comparative analysis method to explore the efficiency improvement of the curriculum of study tours, and obtain four grouping paths, which can cover more than 85% of the cases. The research in this paper not only helps to enrich the academic research of cross disciplines in the form of “travel + education”, but also provides theoretical basis and practical reference for the development of study tours to a certain extent.

Chao Yin1, Peibiao Liu1
1School of Business Administration, Shandong Women’s University, Jinan, Shandong, 225030, China
Abstract:

Encouraged by the strategy of rural revitalization, rural areas in many places are exploring the development path of characteristic industries. The article embeds the multi-objective optimization model into the development of rural characteristic industries to optimize the current rural industrial development path. The multi-objective optimization model of rural characteristic industry development is constructed, and the ACO-PSO algorithm is used to solve the model, in order to realize the organic unity of economic, social and ecological benefits of rural industry development. The multi objective optimization model is used to optimize the industrial development of village S. The total regional output value of village S in 2035 is 2.08 times of that in 2025. The proportion of output value of primary industry and secondary industry decreases by 20.29% and 18.50% respectively. The proportion of tertiary industry output value increases by 38.79%, and the industrial structure becomes more and more reasonable. After the multi-objective optimization, Village S changes the development mode at the expense of resources and the environment, and maintains the survival of the ecological environment by appropriately slowing down the economic development. After the multi objective optimization, the total output value of the primary industry and the per capita income of farmers in Village S increased by 17,412 and 205.76 yuan respectively. The total output of tourism in the tertiary industry is 465,222,000 yuan, which is 126% higher than that before optimization.

Chengcheng Zhu 1
1Accounting College, Zhengzhou Vocational College of Finance and Taxation, Zhengzhou, Henan, 450000, China
Abstract:

In the face of the requirements of the financial management system, enterprises need to accelerate the digital transformation of finance and realize the “data-driven” management decision-making operation mechanism. The article constructs a new quality productivity-based finance-driven enterprise digital transformation path, and makes it clear that enterprises need to play a new type of labor objects, labor materials and laborers to achieve digital transformation. Based on this transformation framework, a system dynamics approach is used to construct an enterprise financial dynamic planning system, which consists of five parts: a financial analysis subsystem, a target gap adjustment subsystem, an income statement subsystem, a balance sheet subsystem, and a production and operation subsystem, and analyzes the driving factors that affect value growth. The feasibility of the model is determined through the methods of structure test and sensitivity test on the dynamic financial planning model. Taking Group A as a case study object, the financial data for the five-year period from 2019-2023 are analyzed, and the operation of the enterprise is reflected through the financial indicators of each system, which proves the validity of the model and promotes the realization of the digital transformation of the enterprise, which contributes to the management of enterprise value.

Zhiwei Luo1, Junyi Li 2
1Guangxi Fuhe Expressway Co., Ltd., Hezhou, Guangxi, 542800, China
2Guangxi Transportation Science and Technology Group Co., Ltd., Nanning, Guangxi, 530000, China
Abstract:

Transportation electromechanical engineering has an important role in the process of transportation engineering construction. After studying the basic situation and problems of the current transportation electromechanical engineering, the author selects the Q city subway line 1 as a test of the effect of quality control of its transportation electromechanical engineering construction, so as to assess its performance. Ten monitoring points are randomly selected, and four groups of control quantities between the monitoring points are detected, and their qualification rate is judged by the gap between the detection results and the standard value. Then, optimize the transportation electromechanical engineering by using big data and emerging detection technology, etc. Construct the performance evaluation index system of traffic electromechanical engineering to evaluate the performance of the optimized traffic electromechanical engineering scheme. The pass rate of the preoptimized traffic electromechanical engineering in the four groups of control volume testing is 100%, 22%, 80% and 70%, respectively, and the construction performance is poor. The scores of the equipment layer indicators of the optimized traffic engineering scheme were all above 80 points, and 19 indicators scored more than 90 points. The subsystem index scores are between 87.1 and 96.2 points, and the comprehensive score of traffic E&E engineering performance is 92.2 points, which shows that the optimized traffic E&E engineering has achieved more excellent performance evaluation results.

Chang Liu 1, Lin Xu1, Qian Xie1, Hua Zhang1, Hua Yang1, Shu Fang2, Wei Wang3, Shixuan Lv3, Yinzhang Cheng3, Guanliang Li3
1Electric Power Research Institute of State Grid Sichuan Electric Power Company, Chengdu, Sichuan, 610041, China
2State Grid Sichuan Electric Power Company, Chengdu, Sichuan, 610041, China
3Electric Power Research Institute of State Grid Shanxi Electric Power Company, Taiyuan, Shanxi, 030002, China
Abstract:

Rapid and accurate assessment of power network loss in the power system has become a key research topic for the vast and diverse dataset of power grid operation. This study integrates data mining techniques with typical scenario modeling concepts and innovatively designs a distribution area power network loss rate multi population parallel spectral clustering evaluation strategy that incorporates distribution characteristics. Firstly, clustering attributes are determined for power network loss evaluation, and a power network loss evaluation framework based on clustering algorithms is proposed. Based on power flow calculation, the distribution characteristics and indicator system of each node’s output are analyzed; Secondly, in order to improve the clustering accuracy of power network loss evaluation, spectral clustering algorithm is introduced, and automatic algorithm design is carried out to address the issue of manually setting the initial number of clusters and cluster centers. Then, multi cluster partitioning and parallel computing methods are used to significantly improve the computational efficiency of spectral clustering algorithm; Finally, to verify the practicality of this method, a provincial power grid was selected as a case study. The results showed that this method not only has high accuracy in evaluating power network loss, but also has excellent computational efficiency, demonstrating good feasibility in practical engineering applications.

Jiayong Liu1, Lan Jiang2
1School of Economics and Management, Yan’an University, Yan’an, Shaanxi, 716000, China
2School of Economics and Management, Xi’an Aeronautical Institute, Xi’an, Shaanxi, 710077, China
Abstract:

The article is based on Cite Space software for bibliometric analysis of the impact of artificial intelligence on economic development. Literature information comes from CNKI Knowledge Network database, identifying the hotspots and characteristics of the research related to artificial intelligence and economic development from the perspective of the number of articles issued, core authors, keywords, etc., and comprehensively analyzing 3,340 pieces of literature during the period from 2013 to 2023. The study shows that the number of published articles on the research on the impact of artificial intelligence on economic development increases year by year, and by 2021, the number of published articles is more than 600. Most authors publish related articles in the range of 3-7 articles, and there are fewer collaborations between authors. There are 16 keywords that appear more than 30 times in the field of the impact of AI on the economy between 2013~2023, which is statistically accounted for the total of 15.41%. The keyword clustering is divided into 7 cluster classes, and the clustering module Q=0.781, S=0.877, which has a high feasibility degree. The keyword with the highest intensity of emergence (3.91) in the field of research on the impact of artificial intelligence on economic development after 2018 is “research and development applications”.

Xiangyu Xie 1
1University of Bristol Business School, University of Bristol, Bristol BS8 1TH, United Kingdom
Abstract:

The rapid development of the economy in recent years has brought convenience to enterprises, but also made the competition between enterprises more intense, enterprises want to stand firm in the fierce competition not only to improve financial performance, but also from a multi-dimensional integrated perspective. For this reason, this paper launched a multidimensional financial comprehensive evaluation research for enterprises. Based on the Harvard analytical framework, the study firstly emphasizes the financial performance of enterprises and at the same time combines the social responsibility perspective to screen the indicators. Then the quantitative evaluation method of this paper is proposed, i.e. the entropy weight method and gray correlation method are combined to analyze the development status of multidimensional financial performance from an objective point of view. Then the entropy weight method and gray correlation method model are introduced respectively, and the modeling method of combining the two applied in this paper is explained. Finally, by analyzing and evaluating the results of the sample company M, it can be obtained that (1) the results of the correlation degree of company M from 2017 to 2022 are 0.722, 0.473, 0.398, 0.389, 0.426, and 0.496 respectively, and the results of the multidimensional financial synthesis evaluation of company M during these six years are optimal in 2017. (2) The overall performance of the financial capital status of Company M from 2017 to 2022 is gradually deteriorating. (3) Overall, the performance of Company M’s responsibility to its employees is evolving from 2017 to 2022. (4) The company’s performance of responsibility to consumers and government during the six years from 2017 to 2022 is good, but ecological responsibility is at a medium level and has some room for development. This paper provides a multidimensional and comprehensive evaluation of the financial indicators of the company from a scientific point of view, which provides some reference for investors and business managers.

Yayue Li1, Bingxiang Hu 2
1Woosong University Daejeon, 34606, South Korea
2Weifang University, Weifang, Shandong, 261061, China
Abstract:

In recent years, study travel has become a popular way to expand teaching outside the classroom. Based on the trajectory of the development of study travel, the article conducts an in-depth study of the current development of study travel in the context of the new era, and explores the 4.0 model of regional study travel development. Introducing big data and new technologies into study travel and designing a digital platform for study travel. Construct the evaluation index system of study travel, and evaluate the study travel 4.0 mode through questionnaires. Detect the study effect of the study travel 4.0 mode by comparing the impact of the study travel 4.0 mode and the traditional study travel mode on students’ disciplinary literacy. The comprehensive score of the evaluation of the study trip was 4.17, and the study trip 4.0 mode achieved excellent evaluation results. The experimental group and the control group did not show significant differences before the experiment, and significant differences were produced after the experiment. The experimental group’s scores on each dimension of geographic literacy increased by 6.35, 5.56, 7.57, 5.01, 7.89, 5.75, and 38.13 points after the experiment, showing significant differences (p<0.05), while none of the control group's scores increased by more than 1.5 points, with p-values of greater than 0.05. The research and study trip 4.0 model has a significant positive effect on improving students' disciplinary literacy. At the same time, under the background of regional study tours, the cultural innovation strategy is put forward.

Sifa Qian1, Kehong Li 2
1Anhui Wanbei Coal and Electricity Group Co., Ltd., Suzhou, Anhui, 234000, China
2Yunding Technology Co., Ltd., Jinan, Shandong, 250000, China
Abstract:

The study applies machine vision technology to the production and operation process of energy enterprises, and constructs a fire detection model based on improved YOLOv4 from the real-time monitoring of fire emergency safety scenarios. Based on the original YOLOv4 algorithm, the model lightens the feature extraction and feature fusion networks, and introduces CA attention mechanism in the bottom layer of the feature extraction network to improve the accuracy of target detection. An intelligent fire alarm system is built on this basis as a response method for emergency security scenarios. Comparison with the basic YOLOv4 algorithm reveals that the improved YOLOv4 algorithm reduces the parameter amount by 45.97%, improves the FPS by 27.75, and improves the mAP value by 14.10%, which achieves a better detection accuracy on the basis of greatly reducing the amount of computation and parameter count, and also achieves a better Loss value and mAP in the comparison with other detection methods. Intelligent Fire Alarm The system integrates intelligent detection, intelligent alarm, intelligent alarm receiving and intelligent alarm dispatching, and can complete the fire alarm process within 6s. In summary, it shows that the method proposed in this paper can be used in real-time monitoring of emergency security scenarios and can provide timely warning at the early stage of security hazards.

Yongkang Cheng1, Dongqi Yue1, Lili Yan2, Qian Tong1, Jiarui Zhang1, Yiwen Zhao1, Kunhan Li1
1JiaXing NanHu University, Jiaxing, Zhejiang, 314000, China
2JiaXing University, Jiaxing, Zhejiang, 314000, China
Abstract:

Flipped classroom teaching puts forward new requirements on the enthusiasm of students’ independent learning, however, the traditional independent learning lacks scientific aids and cannot meet the individual needs of students in the process of self-study. Therefore, this paper exploits the neural network technology in intelligent computing technology to extract the deep implicit semantic representation, combines the implicit semantic indexing (LSI) to improve the traditional collaborative filtering algorithm, and explores an optimized implementation path of the flipped classroom teaching mode. The improved ICF algorithm outperforms the comparison algorithm in terms of recommendation accuracy, average recall, and average coverage in the three datasets. The computational time consumed is reduced by 44.85%, 57.34%, and 73.68%, respectively, compared with UCF. Incorporating the learning resource recommendation model constructed in this paper in a traditional flipped classroom, it is found that the post-test scores of the experimental class in Moral Education are significantly higher than those of the control class (p<0.01), and its post-test scores are significantly higher than its pre-test scores (p<0.01). The collaborative filtering algorithm optimized by intelligent computing technology facilitates students' personalized independent learning, innovates the general flipped classroom teaching mode, and receives the expected results.

Abstract:

The technical analysis of conventional tennis sports basically focuses on individual studies, with less research on the basic theory of tennis, and the theoretical analysis of tennis trajectory is even rarer. In this study, based on the calculation equations of the main forces during tennis movement, the dynamics analysis of tennis serve movement is carried out, and the three-dimensional trajectory equations of tennis serve are established. Then, based on the ODE dynamics engine technology, the simulation platform of tennis serve is built to realize the simulation and visualization analysis of tennis trajectory. Since the simulation system beat frequency is 1000Hz, the time difference between tennis simulation and actual movement is the smallest, so the frequency of 1000Hz is chosen for the simulation study of tennis serve trajectory. The simulation results show that under the same hitting height and ball angle, the larger the initial velocity of the tennis ball is, the farther the X-axis landing point is from the center line. In addition, under the consideration of air resistance and Malnus force, the difference between the Y-axis landing point of tennis ball when the initial serve angle is 30° and 60° is 1.81098 m. The present study provides a certain reference for the in-depth study of the serving strategy of tennis ball, and at the same time, it also provides a certain theoretical basis for the improvement of the tennis players’ training method and technical playing style.

Abstract:

Curriculum Civics refers to the integration of Civics elements into the teaching of professional courses, so that courses other than Civics courses can also play the role of Civics teaching. In this paper, we study a knowledge mapping-based content generation technology for teaching course Civics and Politics, so that the knowledge of Civics and Politics courses can be integrated and visualized. The knowledge points, concepts, definitions and other information of the course Civics and Politics are extracted in the form of Civics and Politics knowledge triples. Through the extraction of the knowledge entity of curriculum Civics and politics, the relationship between semi-structured data and unstructured data is extracted to realize the integration of knowledge and content generation. After achieving content generation, the generated content is personalized through a deep reinforcement learning recommendation algorithm based on diversity optimization. Taking the two courses of Engineering Cost Management and Engineering Economics in the engineering management specialty as an example, it is found that the proposed knowledge graph construction method has an accuracy rate of 96.2%, which is able to effectively establish the knowledge association between the civic elements and the elements of professional knowledge, and realize the mining and generation of the civic elements. Meanwhile, the DDRL-Base recommendation algorithm achieves the optimum in accuracy, recall and F1 value indexes, and optimizes the problems such as cold start and sparse data in resource matrix, which improves the effect of recommending the Civics and Politics teaching content of the course.

Abstract:

The study adopts a detection followed by tracking paradigm. In the detection stage, the BiFormer dynamic sparse attention module is embedded in the YOLOv8 network model, while the original nearest neighbor interpolation upsampling is improved by replacing it with the lightweight upsampling operator CARAFE. In the target tracking stage, a multi-vehicle steering trajectory tracking algorithm based on particle filtering is proposed, and the particle filtering algorithm is improved by combining the target motion direction weighted resampling algorithm. The two improved algorithms are combined for multi-vehicle detection and tracking in tunnel scenarios, and the average tracking accuracy can reach 97.3%. Compared with the traditional YOLOv8 combined with particle filtering algorithm for tracking, the method in this paper is more advantageous.

Abstract:

Since the financial crisis, the economies of all countries have been affected by the recession triggered by global events, and the uncertainty brought by the changes in economic policies has also become a risky shock, and the uncertainty of economic policies has been climbing worldwide. This paper firstly briefly analyzes the mechanism of economic policy and financial market, in order to comprehensively study the changes of market economic liquidity, this paper starts from the return of the market economy, and adopts the symbolic time series analysis method to analyze the prediction of the financial market by taking the stock market as an example. Then construct the regression model, and then study the impact of economic policy uncertainty on market liquidity. The regression coefficient of economic policy uncertainty is 0.064, which is significant at 1% level. Secondly, when GDP growth rate and inflation level are added as control variables, the regression coefficient of economic policy uncertainty obtained is 0.108, which is still significant at 1% level, implying that a rise in economic policy uncertainty brings about a decline in market liquidity. This study provides an effective analytical tool for the impact of economic policies on market liquidity. It also provides a basis for the government to improve market liquidity and enhance market vitality.

Abstract:

In this paper, the image parameters are preprocessed by the gray scale histogram statistical image parameters, which reflect the gray scale distribution information of the plant images, using the zero-mean normalization formula. According to different lighting conditions, the plant image is segmented, and the texture feature information in the plant image is extracted by using the improved grayscale covariance matrix. The hyperspectral linear mixing model is constructed, and the MVSA algorithm meta-decomposes the mixing model to solve the solution optimization problem. Using the natural gravity embossing method, produce plant embossed flowers and analyze the features and spectral curves of different parts of the embossed flowers to evaluate the comprehensive use of the embossing method proposed in this paper. The ROI images of 1200 embossed pattern petals were calculated to obtain the sample spectral matrix of embossed petals, in which the reflectance of the central petal was the highest among the three parts at a wavelength of 450 nm, with a reflectance of 0.46487, and then decreased, and then gradually increased to one place after the wavelength was equal to 694, with a reflectance of 0.8. The reflectance of the Shaanxi Weixiang (Weixia), the single side-embossed Yuanbaosi (Yuanbao maple), the hammered elm (fruits), and the pachypodium (Green) obtained a full score of 35 in the comprehensive evaluation after drying, which is a perfect embossed plant material, and all the plant materials embossed using the method proposed in this paper averaged above 30, and the comprehensive effect of plant embossing was good.

Abstract:

This paper applies smart technologies to urban rain garden design and analyzes the hydrological effects based on urban smart rain garden technologies. The SWMM model is used to simulate runoff water quantity and quality under the environment of long-term and continuous rainfall events in urban areas. Building area A is selected as the case study object of this paper, and its geographic location and precipitation data are analyzed to preliminarily explain the hydrological conditions of the case study area. Based on the SWMM model, the model pipe network generalization and other operations are carried out to establish the SWMM model of the study area. The SWMM model is calibrated in terms of the calibration of the model’s parameters and the feasibility of the structured network SWMM model to verify the validity of the SWMM model of the study area and its catchment delineation method. Based on the urban smart rain garden technology, the LID module is added to the SWMM model of the study area and the hydrological effects under different scenarios such as combined LID are analyzed. Each LID measure can have a certain reduction effect on the combined runoff coefficient and total runoff. The combined LID measures in this paper have the best reduction effect, with the reduction rate of the integrated runoff coefficient over 35% and the total runoff over 50% in the 2h rainfall event. The combined LID scheme has the best reduction effect on the flood peak, and the reduction rate can reach more than 40% in both 2h rainfall events.

Abstract:

With the increasing complexity of the financial market, corporate financial fraud events occur frequently, posing a serious challenge to investors and market regulators. Aiming at the limitations of traditional financial fraud recognition methods, this paper constructs a financial fraud recognition model MCN based on the topological data analysis method. The model consists of two parts: the Mapper algorithm and one-dimensional convolutional neural network (1DCNN), which combines the global topology extracted by the Mapper algorithm with the local features of the IDCNN to realize the effective identification of financial fraud samples. In order to evaluate the recognition performance of the model, this paper controls the topological feature extraction method unchanged and the classifier unchanged respectively, and compares the performance of the MCN model with other financial fraud recognition models. The results show that the Acc and F1-score of the MCN-based financial fraud recognition model in this paper are 98.69% and 97.64%, respectively, which are better than other models in both perspectives, proving the superiority of the financial fraud recognition model based on topological data analysis constructed in this paper, and thus providing powerful technical support for the regulation of the financial market and the risk management of enterprises.

Abstract:

This paper examines the differences and convergence of regional real estate markets based on panel statistics of 28 provinces, autonomous regions and municipalities directly under the central government in China from 2010 to 2023. Relevant variables such as urban construction land area, population and economic growth are set and the data are processed. The data show that the degree of industrial convergence and circulation costs have a positive spatial correlation and an upward trend from 2015 to 2021. From the viewpoint of regional real estate market divergence, the proportion of the real estate industry in GDP has remained above 5% since 2015, and this proportion is larger in the eastern region, for example, it was 8.74% in Beijing in 2015, but it has slightly decreased in some provinces and cities. The proportion in central and western provinces and cities has been rising faster year by year. The extreme deviation and standard deviation coefficient of the eastern region are relatively large, with the extreme deviation of the eastern region being 4.35% and the standard deviation coefficient being 1.45529 in 2021, indicating that the internal development is not balanced. From the analysis of convergence, the rate of convergence in the absolute convergence test is 3.66%, and the rate of convergence in the conditional convergence test is 2.89%, with a half-life of about 23.8 years. It indicates that the regional real estate market differences are shrinking, showing a trend of convergence, but the convergence process is relatively slow, which provides an important basis for an in-depth understanding of the characteristics of the regional real estate market.

Abstract:

Tunnel gas and shallow natural gas overflow have been a major problem plaguing the safe construction of tunnels and one of the main types of common diseases in tunnel engineering. The article chooses the tunnel construction of Funci Highway as the research object, and collects the rock and gas data in the research area on the basis of analyzing the distribution characteristics of shallow natural gas. Based on the AVO analysis technique, the PP wave reflection coefficient is approximated as a linear combination of longitudinal wave velocity, transverse wave velocity, density and other elastic constants to construct a pre-stack AVO inversion model to analyze the shallow natural gas distribution in the Funci Highway Tunnel construction. The porosity of the rock layer in the tunnel construction area ranges from 4.5% to 12%, with an average porosity of 8.93% and a maximum permeability of 0.004 μm². The longitudinal wave impedance distribution of the non-reservoir surrounding rock ranges from 1.48 to 2.01, and the error between the longitudinal wave velocity and density obtained by the inversion and the original logging curves is up to only 2.04%. Combined with the logging data, it can realize the comprehensive evaluation of the oil and gas geological environment of Funci Highway tunnel construction, and provide data support for ensuring the safety of Funci Highway tunnel construction.

Abstract:

In order to accurately assess the financial status of a company and identify potential anomalies, this paper first implements unsupervised classification of financial transaction data based on Support Vector Machines, which automatically classifies the data into normal and abnormal categories. Histograms are introduced in combination with LightGBM to quickly fuse data from multiple sources. The most suitable first layer is selected by different algorithms, and the outputs of these algorithms are combined with industry-wide common abnormal features as inputs for LightGBM’s second layer identification. With this two-layer structure, the model not only takes into account the industry characteristics, but also the common anomaly features. Empirical results show that in the accuracy of smart financial statement generation, the sensitivity of this paper’s model iterates to 99.99% at 41.25% specificity, and the accuracy of this paper’s model is as high as 0.98 when dealing with financial private information, macroeconomic, and market information.In the identification of financial transaction anomalies, the number of anomalous weeks is identified to be 24, 29, 34, and 36, and the fusion of multi-source data effectively identifies the large amount of financial transactions, fluctuating transactions and other suspicious abnormal transactions.

Abstract:

The integration of modern information technology and civil litigation promotes the electronic civil litigation, online litigation as a kind of litigation behavior, promoting the development of traditional trial mode. This paper starts from analyzing the relationship and conflict between civil e-litigation and traditional civil litigation, and organizes the relationship between online trial mode and traditional court trial, and the relationship between civil online trial mode and traditional trial mode respectively. Based on the influencing factors of civil trial, the time proof consensus algorithm and data security transmission algorithm are respectively proposed to combine the network nature of online litigation and blockchain storage data information to optimize the online litigation electronic evidence storage. Summarizing the litigation efficiency of online trial and traditional trial under different control variables, from the point of view of the complexity of the case, the litigation efficiency of online trial mode for more complex cases is significantly higher than that of traditional trial mode. For non-complex cases, the efficiency increases but the difference is not significant. The online litigation mode is a part of the civil online trial mode and serves the traditional civil trial mode.

Juan Wang1,2, Qiang Li3, Yanyan Wang4
1School of Management Science and Engineering, Shandong Technology and Business University, Yantai, Shandong, 264003, China
2School of Business, Qingdao University, Qingdao, Shandong, 266071, China
3Safety and Emergency Department, Yantai Engineering and Technology College, Yantai, Shandong, 264006, China
4Yantai Vocational College of Culture and Tourism, Department of Tourism management, Yantai, Shandong, 264005, China
Abstract:

In order to be able to study in-depth image recognition technology for the detection of emergencies, this paper firstly adopts the image processing technology image processing, removes the noise in the image, improves the clarity of the image, and reduces the distortion of the image. Secondly, the signal in the image is extracted, and the network transmission algorithm is used to detect the signal in the image and calculate the corresponding transmission energy value. Finally, a standard threshold is set according to the calculation results, and once the transmission energy exceeds the threshold, it is an abnormal event. The analysis of the emergency event detection model based on image recognition technology shows that the image contrast effect is good, around 8.5 points, indicating that the image quality obtained based on image recognition technology is good. For the third emergency detection, the value based on image recognition technology is 93.3%, the detection results are more accurate, the response speed is faster, the fastest can reach about 1.1s, can real-time feedback on the results of the detection of the emergency situation in a timely manner to deal with the emergency situation, to reduce the loss of personnel, and to improve the efficiency of the management of the smart community emergencies of public health events.

Bihua Ou1, Baomin Wang 1
1Law School, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China
Abstract:

The ontological issues such as the concept, features, and attributes of smart contracts written in code and running on the blockchain have been the focus of research in the academic community. In this paper, we first construct a smart contract illegal behavior determination model based on the C4.5 decision tree algorithm, which realizes accurate prediction and determination of illegal behaviors existing in smart contract transactions by extracting multiple attribute features of smart contract transaction data. Then, the correlation between smart contract features and contract risk is analyzed by Pearson coefficient, and the risk assessment evaluation system of smart contract performance is constructed by using hierarchical analysis. Finally, the fulfillment path of smart contract is proposed by synthesizing all the analysis results. Among the 24 randomly selected samples, the total prediction probability of the illegal behavior determination model based on the C4.5 decision tree algorithm reaches 95.83%, which is able to effectively identify the illegal behavior of smart contracts. The Pearson chi-square value between smart contract features and contract risk is 224.6317, and the Sig.(two-tailed) value is 0.000, indicating that there is a significant correlation between the two. By constructing a smart contract risk assessment index system, this paper designs a dynamic monitoring model of smart contract fulfillment risk level, and proposes a smart contract fulfillment path from the aspects of reasonable allocation of legal responsibility and legal regulation of contract fulfillment.

Ming Lu1, Rongfa Chen1, Xiuzhe Meng1, Kai Yang2
1College of Management and Economy, Tianjin University, Tianjin, 300072, China
2Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
Abstract:

In order to be able to accurately identify user behavior and emotional tendency, this paper firstly adopts the neural network structure to build the emotion analysis model, and divides the model into four parts to analyze the text and emotion in social media, and obtains the information of semantics and emotion-related content in social media text. Secondly, from the semantic and emotional symbol content of the text in social media, the public emotional tendency model is built, and the sharing content and behavior of a large number of users in social media are analyzed. Finally, the association rule mining algorithm is used to extract the text and emotional symbols in social media, to improve the accuracy of the user’s emotional tendency analysis model, and to be able to accurately derive the user’s behavioral habits. In order to verify the analytical effect of the model, the model was tested, and the training speed of the BLSTM model was fast, and the training time was 1.5 hours in the first iteration of the test with a data set of 1 million. The model is more accurate in analyzing the user’s positive emotions, with accuracy and precision around 85% and 90% respectively, and the results obtained are more accurate, meet the user’s needs, and enhance the user’s experience.

Xiaoyi Guo 1
1Beijing Chengpeng Automobile Sales and Service Co., Ltd, Beijing, 100000, China
Abstract:

With the proposal of sustainable development of energy, countries begin to develop from fuel vehicles to new energy vehicle market. Firstly, we construct a consumer purchase behavior recognition model based on XG Boost algorithm, simulate the gradient enhancement process of purchase behavior recognition, obtain the approximation value based on function calculation to become the learning target of the overflow value, and at the same time, give higher learning weight to the samples with unsatisfactory accuracy in the last round, and after continuous iteration, gradually correct the purchase behavior recognition bias. According to the number of purchase behavior features identified correctly, the number of features that do not have purchase behavior features, and the number of features that are not identified, invalid users are eliminated to improve the accuracy of the algorithm. The Cronbach’s alpha coefficients of the four factors are found to be 0.891, 0.895, 0.813, and 0.800, all of which are greater than or equal to 0.800, indicating that the factors are internally consistent. And the relationship values between the factors and purchase intention are 0.439, 0.406, 0.430, 0.387, which are all greater than 0. Therefore, there is a prominent relationship between all four dimensions of consumer purchase behavior factors and consumption impulse, and the identification of purchase behavior patterns has a guiding role in electric energy vehicle marketing strategy.

Ming Gao1
1School of Economics and Management, Beijing Institute of Technology, Beijing, 100081, China
Abstract:

In order to satisfy consumers’ needs, enterprises must conduct in-depth research on consumers’ purchasing behaviours and design and develop marketing strategies based on the characteristics of consumers’ needs. The article takes 4P marketing theory and SOR model as the guide, and establishes a consumer purchase intention model in combination with the consumer behaviour model. The questionnaire is designed from the product value, price range, channel optimisation, and promotional efforts of the enterprise marketing strategy, and the validity of the questionnaire is tested by principal component analysis. Then meta-analysis method was used to explore the correlation of each variable, and the SEM model was combined to explore the influence path of corporate marketing strategy on consumer purchase intention. The Q-value of the hypothesised relationship of consumer purchase intention ranges from 446.137 to 814.535 and is significant at 1% level, and the correlation coefficients of each variable in the model with consumer purchase intention are more than 0.35. The indicators of model fit, CMIN/DF and RMSEA, are 1.076 and 0.015 respectively, and the path coefficient of the value of the product in the marketing strategy on the purchase intention is the largest at 0.076. The path coefficient of product value on consumers’ purchase intention in marketing strategy is 0.369, and the development of enterprise marketing strategy needs to actively expand marketing channels and design differentiated product and service programmes, so as to enhance consumers’ recognition of the enterprise brand to stimulate their purchase intention.

Jie Chen1, Yajuan Zhang1, Zhiguo Zheng1, Xiaowei Zhao1, Jianhao Dong1
1College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, Hainan, 571126, China
Abstract:

With the development of Internet of Things (IoT) technology, improving the interactivity of IoT communication teaching has become an important research content. This paper firstly constructs the IOT communication teaching system on the basis of service layer, network layer and teaching layer, through which the teaching information is ensured to be delivered timely and accurately. Secondly, the group intelligence algorithm teaching interactivity is optimized and designed to optimize the teaching environment, network, and teaching layer to get the optimized server resource allocation scheme to achieve the optimization of different levels in the teaching of Internet of Things communication. When the number of iterations reaches 20 and 45, the adaptability of this paper’s algorithm is maintained between 100-10-1, and the optimization of the algorithm improves the student participation, the depth of understanding of knowledge, the accuracy of data, the speed of transmission, the efficiency of management, and the teaching effect by 28.6%, 41.7%, 4%, 100%, 18.8%, and 20%, respectively. In the delay analysis, when the number of terminals is 10, 20, and 30 respectively, the delay of the teaching system in this paper is the lowest among all the compared systems, which is 10ms, 40ms, and 230ms respectively.This study can lay the foundation for improving the quality and effect of IoT communication teaching and promote the cultivation of teaching interactivity between teachers and students.

Xinyue Qi1, Chen Dong 2
1School of Economics, Shanghai University, Shanghai, 201800, China
2School of Finance, Anhui University of Finance and Economics, Bengbu, Anhui, 233030, China
Abstract:

In order to study the role of digital economy on the transformation of regional economic structure, firstly, the mechanism of the role of datatized economy on the change of regional economic form is elaborated, and on the basis of the analysis of theoretical model, the structure of the distribution of capital factors in each industry and the ideal factor are determined. Determine the index system and weights of regional economic structure transformation through the selection of weight indicators, and complete the measurement of the data-based economic situation under the construction of the index system of data-based economic situation. Two hypotheses are proposed that digital industrialization can have an ideal effect on the structural transformation of local development but the shape of the effect is inverted U-shape, and that industrial digitalization can have an ideal effect on the structural transformation of local development. The empirical analysis finds that the Moran’s I index of structural transformation of local development from 2008 to 2020 is prominent in the 1% case, and the FP and UE within, central, eastern, western regions of China and the level of structural transformation of local economy is prominent in the 1% case. It is concluded that there is a prominent spatial isotropic relationship between the datadriven economy on regional economic structural change resilience in the whole region, and the constructed research model has a good robustness.

Jingjing Fu 1
1Art and Media School of Fujian Polytechnic Normal University, Fuzhou, Fujian, 350300, China
Abstract:

The aim of this paper is to improve the advertisement display effect and realize accurate placement in the market. Firstly, the convolutional neural network is used to select the advertisement keywords, and optimize the click rate, conversion rate and so on when the number of iterations reaches a certain value. Next, the established hierarchical analysis model is used to conduct a comprehensive evaluation of online advertisement release forms, and select the advertisement form that best suits the needs of the enterprise and the market environment. The weight of the webpage and the similarity between the center of mass of the webpage and the advertisement are used to calculate the final score, and the advertisements are sorted to achieve the improvement of the display effect and placement accuracy of the advertisements. The final analysis found that for short-term user behavior, the weight of text link ad clusters is as high as 0.66, which can improve the accuracy of ad placement. For long-term user behavior, the multi-objective optimization algorithm can accurately identify and assign high weights when users continue to visit specific web pages, for example, the cluster of web banner ads reaches 0.64. Meanwhile, it can be adapted to different application scenarios, and the weight of text link ads cluster is significantly increased from 0.14 to 0.758 when the freshness factor is increased from 0 to 1. The optimal F1 value of the advertisement delivery effect is 97.24, which is the highest F1 value of AIGC. The AIGC ad placement strategy provides a new method for the intelligent development of the advertising industry.

Chao He1, Shi Cheng 2
1Asset and Laboratory Management Department, Nantong University Xinglin College, Nantong, Jiangsu, 226000, China
2School of Artificial Intelligence and Computer Science, Nantong University, Nantong, Jiangsu, 226000, China
Abstract:

This paper establishes a solution model for resource scheduling optimization in university laboratories, and sets the corresponding constraints and objective functions. The genetic algorithm under the heuristic algorithm is used to solve the resource scheduling optimization problem. On this basis, the pyramid model is constructed, the population evolution and variant strategy are proposed respectively, the model genes are labeled with scheduling cost adaptation, and the genes are generated in series. The framework of scheduling algorithm is proposed, and the dynamic scheduler is constructed to realize the scheduling of university laboratory resources. Through simulation experiments and algorithm analysis, the effectiveness of the use of the model is verified. The experimental results show that when the number of simulation is 10 times, the fitness of the population is 20, 100 and 200 respectively. After the implementation of scheduling for college laboratory resources, the utilization rate of laboratory equipment is increased by 16.3%, 34.6% and 18.4% respectively.

Huan Wan1, Ye Wu1, Chuan Tian1
1Department of Economic Management, Jiangxi Tourism & Commerce Vocational College, Nanchang, Jiangxi, 330100, China
Abstract:

Product awareness can be spread to a wider audience through advertisements, and the introduction of social media platforms has made it easier for marketers to spread brand advertisements and fully attract consumers to generate corresponding purchasing behaviors. Based on fuzzy set theory, the article establishes a fuzzy evidence theory through evidence-weighted fusion, and calculates the utility value of social media advertisements in order to achieve the optimal evaluation of social media advertisements. Then, it explores the influence of social media advertisements on consumers’ purchasing behavior with OLS-LR model, and combines the VAR model to study the dynamic correlation between different types of social media advertising channels and consumers’ purchasing behavior. Without considering the control variables, the regression coefficient of social media advertising on consumer purchasing behavior is 0.438, which is significant at 1% level. With the fourth-order VAR model, CICs social media advertisements have a significant short-term effect on consumer purchasing behavior, while FICs advertisements show a long-term effect. Based on the fuzzy evidence theory, the utility value of social media advertisements can be calculated, and based on the sorting of the calculation results, the construction of optimization paths of social media advertisements can be realized, which provides a new research basis for improving the efficiency of corporate advertising and marketing.

Ye Wu1, Chuan Tian1, Huan Wan1
1 Department of Economic Management, Jiangxi Tourism & Commerce Vocational College, Nanchang, Jiangxi, 330100, China
Abstract:

In the context of big data, with the accelerated development of digital technology, enterprises are facing the pressure of digital transformation, and at the same time, big data computing system provides technical support for the digital transformation of enterprises. In this paper, we propose a data analysis system based on iterative computing for the digital transformation of enterprises. In order to avoid the resource consumption caused by unnecessary repeated calculations in iterative computing, this paper proposes optimization based on Spark fault-tolerant mechanism and constructs an enterprise data analysis system based on iterative computing model, which provides technical support for enterprise digital transformation. On this basis, this paper also provides optimization strategies in terms of organizational structure and cultural coordination for enterprise transformation, which provides an effective path for realizing comprehensive digital transformation of enterprises. Through the test of this paper’s iterative computing data analysis system, the speed of Spark optimization based on this paper is increased by nearly 2 times, which illustrates the usefulness of this paper’s optimization based on Sparl fault-tolerant mechanism. Meanwhile, the cache misses of the data analytics system are in the range of 46% to 60%, which provides better performance performance in terms of cache hits and time overhead. In this paper, we provide practical and feasible transformation paths for enterprise digital transformation from three aspects, including digital technology, enterprise organizational structure and culture, and promote the development of enterprise digital transformation.

Jing Ma1
1College of Child Education and Development, Hangzhou Campus, Zhejiang Normal University, Hangzhou, Zhejiang, 321000, China
Abstract:

Based on the view that artistic style is mainly reflected in sculpture and painting, the sculpture and painting style of Giacometti is analyzed in depth. Starting from the scope of application of big data technology, the theoretical knowledge based on information theory is proposed to explore the differences in the styles of Giacometti and his contemporaries, and the basic concepts used in the processing are defined, including Shannon entropy, conditional entropy, and interactive information. Redundancy, orderliness, and complexity are set as eigenvalues that can characterize the style of art works, and the eigenvalues of the style of Giacometti and contemporaneous artists are analyzed. The minimum, maximum, and average values of the complexity of Picasso’s works are 207, 991, and 596, respectively, while the values of the three indexes of the complexity of Giacometti’s art works are 446, 990, and 718, respectively, and on the whole, the complexity of Picasso’s works is smaller than that of Giacometti’s works. This paper comprehensively reveals the stylistic differences between Giacometti and his contemporaries through the analysis of quantitative characteristic indexes.

Xiaopeng Pei 1
1College of Humanities and Education, Hebi Polytechnic, Hebi, Henan, 458030, China
Abstract:

Teaching curriculum design is centered around the three dimensions of affective attitudes and values, processes and methods, and knowledge and skills, which fit with the affective learning model composed of emotion, learning and cognition. This paper brings affective analysis into art curriculum design and proposes a learner affective model for teaching art courses driven by multiple teaching objectives. Through multi-objective optimization, we give an interactive decision-making method based on a hierarchical affective cognitive model to simulate learners’ affective decision-making under multi-objective-driven teaching. Analyze the teaching process of incorporating affective learning strategies in an art course, and examine the interrelationship between affective engagement and learners’ knowledge construction in three rounds of learning activities. To analyze the impact of affective learning strategies on students’ learning outcomes. The experimental group (affective learning strategy group) significantly outperformed the corresponding creativity abilities of students in the control group in the three components of surprise, originality and challenge after the teaching of the art course, and the affective learning strategy succeeded in stimulating students’ creativity. The combination of affective learning model and curriculum design can enhance the effectiveness of art education.

Lulu Hao 1
1School of Foreign Languages, Luoyang Normal University, Luoyang, Henan, 471000, China
Abstract:

The application of technologies such as big data, mobile Internet, artificial intelligence and so on has triggered a major change in the field of education and promoted the classroom reform in colleges and universities. Taking deep learning theory as the research perspective, this paper constructs a college English teaching model based on deep learning, and applies the model to actual teaching practice, with a view to promoting students’ English learning level and enhancing their intercultural communication ability. Among them, the K-means algorithm improved by the whale optimization algorithm is also used to cluster and stratify the English proficiency of students in a class to illustrate the specific application of deep learning in English teaching. The results classified the sample students into four categories, A, B, C and D. The English level of students in category A is the highest and the largest, accounting for 35.56%, and teachers can design differentiated teaching based on the results of student stratification. After carrying out the experiment of the teaching model, the practicing students’ English scores improved by 4.01%, and at the same time, they gained 18.87%~28.45% and 18.82%~39.01% of competence in the personal domain and the communicative domain, respectively, which confirms the effect of the constructed English teaching model on the enhancement of the students’ English learning level and cross-cultural communicative competence.

Yu Wang1
1School of Music, Shanghai Normal University, Shanghai, 200233, China
Abstract:

Since the 21st century, the rapid development of artificial intelligence technology, artificial intelligence in many fields have achieved remarkable research results and applications, the integration of AI technology and music has also gradually become an emerging research field. In this paper, first of all, the generation principle of vocal interpretation AI model is studied, in order to realize the digital conversion of vocal interpretation this paper constructs a converter model so as to facilitate the application of artificial intelligence algorithm model. In this paper, in order to match the generated opera vocal music with the given opera performance background, the rhythmic relationship between opera and vocal interpretation is established, and the relationship between motion salience and note intensity is constructed. On this basis, the generator model is changed to a model with a loop structure, and the music theory is mathematically modeled to propose an adversarial network model based on improved multi-track sequence generation. Finally, for the prediction problem in the vocal interpretation AI model, this paper is optimized based on support vector regression. Through empirical analysis, the improved model in this paper has a smaller gap with the real dataset on the metrics of pitch use, pitch shift, note interval and polyphony rate within the track. Meanwhile, the TD distances of this paper’s improved model on the three datasets are 0.655, 0.784, and 0.685, respectively, which is the smallest in the experimental data, and the quality of the improved model’s vocal music generation is excellent. The pitch distribution of this paper’s improved model and the original vocal data basically match, indicating that this paper’s model has better effect on pitch adjustment. In addition, the improved model of this paper generates vocal music with better musicality effect, which has higher musicality while avoiding the generation of more invalid notes. The research work of the paper proves the feasibility of the AI model for opera vocal interpretation and provides a new solution for the current field of vocal music generation.

Jing Xia 1
1School of Marxism, Chongqing Vocational and Technical College of Industry and Trade, Chongqing, 408000, China
Abstract:

Social media as a new type of media has become an important channel for people to obtain information and communicate, which brings new opportunities and challenges for English education. In this study, the Markov chain model is improved by using the weighted ward system clustering method and the fuzzy set theory to improve the prediction performance of the model. Then the ARIMA model and the improved Markov chain model are combined to construct an improved time series prediction model to realize the prediction of the interaction efficiency of social media in English education. The performance of the improved prediction model is superior compared to other comparative models, providing reliability for the subsequent prediction results. The prediction results show that the interactive efficiency of social media interaction data in English education shows an upward trend over time, and the number of readings and playbacks of English courseware resources as well as video resources increases from 18477 and 18147 to 88629 and 84571 in six months. The predicted results of this study indicate that social media has good interactive efficiency in English education, which can be utilized in the future to expand the dimension of education, build an English education platform, expand the teaching space and extend educational thinking, and play a percolating role in English education.

Li Huang 1
1 Hunan High-speed Railway Vocational and Technical College, Hengyang, Hunan, 421200, China
Abstract:

The study constructs a prediction model to predict the mental health status of innovative entrepreneurs. The real data of mental health assessment of innovative entrepreneurs in S province in 2023 is chosen as the data source. The recursive random forest feature elimination method is used to select the features of the mental health status prediction model. The pre-selection-elimination mechanism was used to construct the mental health state prediction model. The prediction models constructed by support vector machine algorithm, decision tree algorithm and random forest algorithm were trained and evaluated respectively. The AUC value and accuracy corresponding to the random forest algorithm are 0.9126 and 86.39%, respectively, which are better than the other two comparison models. Among the 17 mental health characteristic variables selected in this paper, emotional stress and self-acceptance degree have the greatest influence on the prediction model based on the random forest algorithm.

Yangzi Chen1, Sheng Qin2
1Department of Air Transport, Shanghai Civil Aviation College, Shanghai, 200120, China
2Department of Aircraft Flight Test, China Commercial Flying Company Civil Aircraft Flight Test Center, Shanghai, 200120, China
Abstract:

Based on the wide application of collaborative filtering algorithm in the current field of graduate employment, this paper introduces it into the employment recommendation mechanism of senior college students and takes it as one of the auxiliary means to formulate the employment policy for senior college students. By studying the implementation effect of employment policy, so as to explore the adaptability of employment policy. Through the time series prediction method based on neural network, the prediction model of employment policy adaptability of higher vocational tertiary students is constructed. Compare the prediction performance of this paper’s prediction model with other models, predict the employment policy implementation effect through this paper’s model, and finally, construct an evaluation system of employment policy prediction results to evaluate the model prediction results. The prediction fit of the model of this paper is 0.8644, and the average relative prediction error is 0.35%, which is the best performance among all prediction models. In the prediction of the employment of higher vocational college students in province A, the number of employment of higher vocational college graduates is positively correlated with the average annual income level and the market share of graduates, and negatively correlated with the total number of gaps between faculty and students in the institutions and the amount of education expenditure. The overall score of the employment policy implementation effect predicted by the prediction model in this paper is 88.8, which is a good evaluation result.

Wenge Guo 1
1Department of physical education, Luoyang Institute of Science and Technology, Luoyang, Henan, 471023, China
Abstract:

Taijiquan is a kind of sport that can be used as a national ϐitness program, and its effect on the training effect of adolescent physical coordination has important research value. In this paper, particle swarm optimization algorithm is applied to the optimization of taijiquan training program, and independent samples test and analysis of variance (ANOVA) are used to investigate the quantitative impact of taijiquan training on adolescents’ physical coordination. The results show that the particle swarm optimization algorithm can effectively improve the effect of taijiquan training, and the algorithm convergence and other properties have obvious superiority compared with other algorithms. At the same time, after the experiment, all the physical coordination test indexes of the experimental group were signiϐicantly improved compared with the pre-test and the control group, which explains the important role of taijiquan training in the physical coordination training of adolescents.

Changwei Chen1, Kuanbin Zhang1, Xiaowen Song 2
1Basic Department of Qilu Institute of Technology, Jinan, Shandong, 250200, China
2Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014040, China
Abstract:

As the world’s No. 1 sport with wide popularity and high degree of attention, there exists a great application demand and development potential for applying artificial intelligence to soccer sports training. In this paper, Yolov5s-CBAM target detection network is utilized to identify the human body posture of target athletes in soccer sports training, and HRNet network is used to detect the location information of key points of target human skeleton and identify the skill movements of soccer players. Subsequently, the TDS-Fast DTW algorithm is applied to evaluate the skill movements to establish a skill recognition and evaluation system for soccer sports athletes. It is verified that the soccer player skill movement recognition model proposed in this paper outperforms other comparative models, with the checking rate reaching 99.12%, and the evaluation scores of the model on the skill movements of the athletes are not different from those of the manual evaluation scores (P>0.05). It is also found that the application of the system in actual soccer training matches can fully meet the needs of soccer training. The system in this paper can accurately assess the technical movements of soccer sports athletes to meet the needs of scientific training, and at the same time, it can meet the needs of coaches to timely grasp the understanding of the level of technical movements of soccer athletes and improve the quality of training.

Xiao Zhang 1
1College of Movie and Media, Sichuan Normal University, Chengdu, Sichuan, 610066, China
Abstract:

The combination of technology and art in film and television special effects can greatly enhance the visual impact of film and television animation, and improve its commercial and artistic value. The study elaborates on the generation of special effects in 3D modeling technology in film and television production, and based on the application of rigid body special effects, it proposes a highly efficient rigid body crushing mode for optimization in response to the problems such as low real-time performance in rigid body crushing simulation. The model is a particle-based real-time simulation method of object crushing under the impact of external forces, using the discrete unit method to represent the inter-particle force, and proposes an inverse crushing mechanism, which realizes the particle-based DEM simulation on the GPU. Experimental results show that the simulation method of rigid body crushing constructed in this paper can meet the simulation requirements in different scene scales, and the rendering rate in small-scale and large-scale scenes is 90~155FPS and 40~50FPS, respectively, which is not only realistic but also real-time, and can meet the requirements of film and television production.

Hongliang Zhang1, Mei Chen1, Xiangrong Chen1, Na Ma1, Xiang Wei1
1Baoding Cigarette Factory, Hebei Baisha Tobacco Co., Ltd., Wangdu, Hebei, 123456, China
Abstract:

In recent years, hyperspectral imaging technology has a large application prospect in quality inspection in the tobacco industry. The study is based on near infrared spectroscopy technology and partial least squares regression method to establish mathematical analysis model of tobacco adulteration ratio of four components, such as expanded tobacco, stalked tobacco, large threaded tobacco and small threaded tobacco, and carry out internal and external inspection. At the same time, TLBO algorithm is used in the optimization of ELM tobacco purity grade determination model to realize the design of tobacco purity monitoring method, and then build the real-time monitoring system of tobacco blending ratio and purity. Tobacco with different purity grades were selected for experimental testing and model comparison analysis. The results show that the constructed PLS model can accurately predict the adulteration content of the four components in tobacco, and the correlation coefficients between the predicted and actual values are above 0.95 (p < 0.01), and the relative deviation of the prediction is below 3%.The accuracy of the TLBO-ELM model for identifying tobacco with different grades of purity is 88%, and the classification accuracy in the validation set is improved by 9.32% compared with the ELM model, which is within the acceptable range. It shows a better classification effect than PLS-DA in an acceptable range, which proves that the proposed method can be used for discriminating and monitoring the purity of tobacco. The monitoring system in this paper can be used in the analysis of tobacco blending ratio and purity detection.

Wei Tang1, Yunpeng Sun 1
1School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an, Shaanxi, 710021, China
Abstract:

The lateral quantitative control of paper machine is the key to the quality control of paper machine. In this paper, the paper machine transverse quantitative control system is introduced, for the transverse quantitative control and the model exists of strong coupling, large time lag, multi-dimensional number of characteristics, combined with the predictive control theory, put forward based on the dynamic matrix control and Gram polynomials of the intelligent prediction method. Meanwhile, the speed chain control device of the paper machine control system is designed based on the self-immunity controller and simulated and analyzed. The simulation results show that the slope of the model size and computation in this paper’s method is 1.97, which is smaller than that of the traditional MPC’s 2.85, and has more computational efficiency without affecting the predictive control effect, which is more suitable for online operation. At the same time, the speed chain control system applying the self-resistant control algorithm is better than the traditional PID control in terms of steady state performance, dynamic performance and anti-disturbance performance. The method proposed in this paper facilitates the predictive control and speed chain anti-disturbance of the lateral dosing control system of paper machine and promotes the improvement of paper quality.

Xiaoyan Li1,2, Wei Chen2, Jingjing Zhang 1
1School of Education, Hefei University, Hefei, Anhui, 230616, China
2School of Foreign Languages, Bengbu University, Bengbu, Anhui, 233030, China
Abstract:

The rapid development of natural language processing technology makes machine translation play an increasingly important role in cross-lingual information exchange. In this paper, we propose an English long text translation paradigm based on the self-attention mechanism and introduce various improvement strategies to enhance the model performance. The model’s ability to process English long text is improved by introducing multi-head attention and hierarchical self-attention modules. The long text translation paradigm is optimized by using techniques such as residual linkage, layer normalization and dynamic memory network. A series of experiments are conducted to verify the effectiveness of the improved model on the English long text translation task. The English long text translation paradigm constructed in this paper outperforms the Transformer model and other related variants on both CPU and GPU. And Transformer outperforms this paper’s model in terms of n-gram accuracy in real translation experiments. The BLEU scores of the improved model on News and other datasets are significantly improved compared with the original baseline model, which verifies the effectiveness of the improvement strategy of this paper and provides a reference for the solution of the problem of English long text translation.

Xiaowei Dai1, Wuying Yang1
1College of Education, Chongqing Industry & Trade Polytechnic, Chongqing, 408000, China
Abstract:

This paper discusses the application of virtual reality technology in enhancing college students’ selfefficacy and proposes an iterative optimization algorithm based on learning experience. By analyzing self-efficacy, the application of virtual reality technology machines in education, and combining relevant theories and empirical studies, the structural equation model of virtual reality technology influencing college students’ self-efficacy is constructed. The original structural equation model is optimized by using algorithms such as stochastic gradient descent method and stochastic average gradient, and the effectiveness of the algorithms is verified through experiments. This paper concludes that virtual reality technology can significantly improve college students’ self-efficacy, and the proposed iterative optimization algorithm can effectively improve the prediction accuracy and fit of the original structural equation model.

Sukai Liu1
1College of Art and Design, Pingdingshan University, Pingdingshan, Henan, 467000, China
Abstract:

The development of digital technology has made the use of machine learning algorithms to protect cultural heritage has become a trend. In this paper, based on the random forest algorithm, the conservation model of tomb mural cultural heritage is recognized. The mural paintings in the tomb of Prince Zhanghuai are used as the data source to construct the tomb mural painting dataset, and the images in the dataset are processed, augmented and labeled. The features such as color, texture and shape in the mural images are extracted as one of the input information of the cultural heritage protection model of the tomb murals. Based on the random forest algorithm, a pattern recognition model for the protection of cultural heritage of tomb frescoes is constructed, and the feature vectors obtained from the feature extraction are used to calculate the split points of the decision tree. The classification results of multiple decision trees are weighted and averaged to obtain the final recognition results. The recognition accuracies of this paper’s model on the training set, test set and validation set are 99.45%, 95.46% and 92.58%, respectively. This is a significant improvement over other existing algorithms. Meanwhile, the algorithm consumes significantly less time than the ResNet18 deep residual network model before and after data enhancement, and is able to efficiently accomplish the task of recognizing the protection of cultural heritage of tomb chamber murals.

Xinyue Yuan 1
1College of Design and Art, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China
Abstract:

In the context of information is mostly trivial, messy and disordered, under the context of information fragmentation, the creation path of new media art is also being affected by it. Based on the color sensual imagery, this paper adopts the gray correlation analysis method to research on the creation of new media art. Through the questionnaire survey, the cluster analysis algorithm is used to filter the color semantics, and the five most representative color imagery semantics are selected as the imagery scale in the quantitative space. Combined with the grey correlation analysis method to construct a new media art creation perceptual evaluation model, the new media art creation works as the object of color design practice, the constructed color design evaluation model well reached the product color scheme with the color screening, confirmation and evaluation of the preferred goals. The design practice based on the evaluation model of new media art creation. The results show that, combined with the gray correlation analysis, the color design evaluation model of new media art creation constructed under the intentional color system can effectively improve the color design efficiency of the work scheme, and give an intuitive and accurate reference standard for the selection of the color scheme of the work.

Lichao Zhang1, Yangyi Ou1, Jianmin Zhao1
1School of Soil and Water Conservation, Nanchang Institute of Technology, Nanchang, Jiangxi, 330099, China
Abstract:

The specialty of soil and water conservation and desertification control has gradually become a hot and popular discipline, and the educational practitioners of this specialty must also follow the trend and actively carry out educational reform. This paper utilizes genetic algorithm to conduct in-depth research on the problem of class scheduling, and on the basis of traditional genetic algorithm, an improved adaptive genetic algorithm is proposed to be applied to the class scheduling system. Through the adaptive adjustment of genetic parameters to improve the convergence accuracy of the genetic algorithm and accelerate the convergence speed, and finally after chromosome conflict detection and repeated iterative operations, the final optimal scheduling program is obtained. The improved adaptive genetic algorithm is applied in the course scheduling system of soil and water conservation planning and design in colleges and universities. After experimental verification, the improved new adaptive genetic algorithm, under the setting of different rules of scheduling conditions, under the setting of different rules of scheduling conditions, the fulfillment rate of students’ class selection reaches 100%, and the mean value of the overall rule fulfillment rate reaches 94.1%, and the overall fulfillment rate of the scheduling efficiency is improved to 96% by applying it to the intelligent class scheduling system. Finally, the professional classes were tested on the knowledge of soil and water conservation planning and design, and the remaining eight dimensions of professional knowledge were accompanied by questionnaires, and the achievement data of the test were statistically analyzed using SPSS22.0. The analysis results show that the test scores are quasi-normally distributed, and the actual pass rate of each question in the test paper is roughly close to the preset difficulty, which proves that the test paper is of good quality and the algorithm designed by the institute can basically meet the requirements.

Jun Han1, Ke Liu1, Yutong Liu1, Wenqian Zhang1, Shaofei Wang1
1State Grid Qinghai Electric Power Company Electric Power Science Research Institute, Xining, Qinghai, 810008, China
Abstract:

The existence of a large number of multi-source heterogeneous hosts and application service types in various zones of the power monitoring system leads to difficulties in extracting comprehensive host attack trace data and the problem of fine-grained deep threat detection. This study combines network attack traces extracted from multi-source logs and stores them in attack trace styles. An attack event description model based on key attributes and behavior sequences is constructed. Based on the vulnerability scoring system, an algorithm is designed to map a general attack graph into an absorbing Markov chain attack graph, which provides a computational basis for the analysis of network attacks by calculating the state transfer probability matrix of the attack graph. Finally, the performance of this paper’s method for multi-dimensional data feature extraction is explored in a python experimental simulation environment. The simulation results show that the average mapping time of LSTM model for 7 vulnerabilities is 117ms, while the average mapping time of this paper’s algorithm is improved by 37ms compared to the LSTM model.Meanwhile, the accuracy, stability, average false detection rate and positive and negative recall rate also achieve good results, which verifies the validity of this method in the practice of power monitoring system management.

Rui Hou1, Liang Gao2
1School of Marxism, Shaoguan University, Shaoguan, Guangdong, 512005, China
2Modern Education Technology Center, Shaoguan University, Shaoguan, Guangdong, 512005, China
Abstract:

From World War II to the Cold War (1945-1991), the U.S. military-industrial complex went through a process from its rise to its full expansion, which had a profound impact on the global political and economic landscape. In this paper, computer simulation techniques are used to construct a vector autoregressive model (VAR) to quantitatively analyze the impact of the military-industrial complex on the U.S. economy. Smoothness and cointegration treatment and Granger causality test are done on the collected sample data. After that, the VAR model between three sets of variables, namely, military expenditure as a share of GDP, consumption as a share of GDP, and investment as a share of GDP, is designed. Using impulse response function and variance decomposition to analyze the data, we get that the rise of the U.S. military-industrial complex can effectively promote the growth of the economy in the long term, and the development of the economy can also promote the development of the military-industrial complex, but the promotion effect is not obvious.

Jin Mei1, Yichen Zhou2, Shanxin Zhang1
1Jiangnan University, Wuxi, Jiangsu, 214122, China
2Wuxi Taihu University, Wuxi, Jiangsu, 214064, China
Abstract:

This paper constructs a multi-agent simulation model to study and prevent juvenile delinquency. A multi-agent reinforcement learning model is constructed according to reinforcement learning theory to simulate the behavioral decision-making process of minors in different social environments. By introducing the NashQ algorithm, it simulates the minors’ strategic choices when facing the temptation of crime. In the simulation experiments, the NashQ algorithm meets the convergence requirements of the model, and only 1/3 of the training times are needed to achieve the stability of the simulated environment. Among them, family factors, school factors and social factors all affect the stability of the prevention effect. Good family environment, high quality teaching conditions and healthy social atmosphere can effectively prevent juvenile delinquency.

Xin Yuan 1
1School of Society and Humanities, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, 330013, China
Abstract:

Based on the complexity and nonlinear characteristics of market volatility, this paper proposes a market volatility prediction model that combines MA filtering method, autoregressive moving average (ARMA), and long-short-term memory (LSTM) neural network. And the back-propagation (BP) neural network is utilized to quantitatively solve the problem of corporate strategy formulation, and a corporate strategy formation model is established to determine the corporate strategic choice through the corporate strategic environment and strategic capabilities. In the ablation experiment, the combined model MA-ARMA-LSTM reduces its MSE, RMSE, MAE and MAPE by 0.0007, 0.0131, 0.0074 and 1.57%, respectively, compared to the ARMA model. Compared with common market volatility prediction models, the combined model has the smallest error in each assessment index. The output of BP neural network for corporate strategy selection is consistent with the expert ranking, which is verified to be in line with the actual business situation, indicating that the method in this paper can provide a reasonable corporate strategy.

Shijin Xin1, Kan Feng2, Guojie Hao3, Xiaofeng Wang4, Qing Xu3, Libao Wei3
1Energy Development Research Center, Baiyin Power Supply Company, Baiyin, Gansu, 730900, China
2Party Committee, Baiyin Power Supply Company, Baiyin, Gansu, 730900, China
3Development Planning Department, Baiyin Power Supply Company, Baiyin, Gansu, 730900, China
4Dispatching Center, Baiyin Power Supply Company, Baiyin, Gansu, 730900, China
Abstract:

The article preliminarily studies the structure of flexible interconnection system of MV distribution network, and understands the application scenario and equipment composition of the flexible system. For the purpose of reducing SOP loss, transformer loss and line loss, the operation of the MV flexible interconnected distribution network is optimized, the operation optimization model of the flexible interconnected distribution network is constructed, and the fault enumeration method is adopted as the reliability assessment method of the flexible interconnected system. Through experimental simulation, the stability, reliability and dynamic characteristics of the MV flexible interconnection system are explored respectively, and the system protection control strategy is proposed. For the same constant power load step, the larger the voltage loop proportional parameter is, the more stable the system tends to be, and the larger the voltage loop integral parameter and the station circuit parameter are, the more unstable the system tends to be. The maximum mutation value of the system constant power load gradually decreases when the station load power gradually increases. The reliability of the MV flexible interconnection system increases with the increase of SOP capacity. In the medium voltage flexible distribution interconnection system. The system damping, oscillation frequency and overshoot are significantly reduced and the peak time is increased when the DC voltage sag factor is increased.

Yuxuan Li 1
1School of Information, Shanxi University of Finance and Economics, Taiyuan, Shanxi, 030006, China
Abstract:

The article applies recurrent neural networks to multi-intelligent body collaborative autonomous systems and uses optimized RNN algorithms for multi-objective detection and path planning of intelligent bodies. The multi-intelligent body multi-target detection and path planning model optimized based on recurrent neural network is constructed to realize multi-target detection and tracking of intelligent bodies and multi-intelligent collaborative path planning. Simulation experiments are designed with a mobile robot as the research object to analyze the trajectory tracking and path planning effects of the multi-target detection and path planning model in this paper. The error between the actual trajectory and the reference position of the robot trajectory tracking is continuously reduced, and reaches complete coincidence at the 127th reference tracking point. The actual speed and acceleration errors of the robot are infinitely close to 0. The accuracy of this paper’s algorithm in multi-objective path planning is 100%, the average arrival time is 20.02s, and the probability of collision is 0%, which is much better than other algorithms. The algorithm in this paper has the highest path smoothing validity for planning in three environments. In the 30 × 83 warehouse map, the total path length of this paper’s algorithm is shortened by 13.00% and 10.77%, and the total path cost is shortened by 9.71% and 11.52% compared with the Wd-SIPP algorithm for the number of collaborative robots in a single group of three and five, respectively. In 100*100 storage map, the total path length is shortened by 10.32% and 11.67%, and the total path cost is shortened by 7.34% and 12.09%, respectively.

Le Chen1, Yaqi Hao2
1School of Sports and Health Education, Nanjing Normal University Taizhou College, Taizhou, Jiangsu, 225300, China
2Faculty of Education and Science, Yangzhou University, Yangzhou, Jiangsu, 225000, China
Abstract:

The dance teaching method that effectively combines motion capture and posture estimation can effectively differentiate itself from the traditional dance teaching mode, realize the teaching interaction between the 3D virtual world and the real technology, and help to improve the quality of dance movement teaching. In this paper, HRNet network is selected under the framework of human posture estimation for the extraction of key points of human posture, and SPIN algorithm is composed of two parts, namely, regression network and SMPLify, to complete the three-dimensional expansion of human posture information. Design the flow of the dance visual movement tracking decomposition method, and perform feature point labeling and labeling parameter setting for multiple key points and key movement parts in the dance movement. Select the specific parts of the dancer as the motion tracking points, record and record the dancer’s action images, and track and fit the dance action trajectory using the 3D visual motion tracking decomposition method proposed in this paper. Compare the dance trajectory fitting effect of this paper’s method with that of the degree-of-freedom vector method and the tracking differentiator method, and obtain the performance of the three-dimensional visual motion tracking decomposition method. Analyze the students’ physical flexibility, balance ability, and the completion of complex movements after a two-month dance teaching. After the dance teaching utilizing dance movement posture analysis, the students’ body flexibility (shoulder) and balance ability improved by 12.8cm, 18.74s (left), and 22.2s (right), respectively.

Yifeng Lai 1
1China Securities Co., Ltd., Beijng, 100000, China
Abstract:

This paper combines the demand structure mechanism and the current account mechanism to show that an increase in the share of the elderly population affects the appreciation of the real exchange rate. Based on the formula of the internal real exchange rate and the “Balassa-Samuelson effect”, the transmission mechanism of population aging on the real exchange rate is established. Combining the results of the discussion on the savings rate and investment rate, an analytical model of the impact of population aging on the balance of trade is proposed. Panel models are built with sample data from full sample countries, super-aging countries, deeply aging countries, mildly aging countries and nonaging countries respectively, and heterogeneity analysis is carried out for each type to test the multilevel regression results of population aging on the real exchange rate. The control variables are brought in separately for the benchmark regression of population aging on the size of trade surplus and current account balance. The empirical results of the data regression show that the old age dependency ratio is an important influence on the real effective exchange rate. And if the country is in the stage of super-aging and mild aging, aging leads to the depreciation of the real effective exchange rate. In the stage of deeply aging and non-aging countries, aging leads to real effective exchange rate appreciation. Meanwhile population aging positively affects the trade surplus at the provincial level mainly by affecting the level of savings.

Weiwei Luo1, Fang Wang2
1School of Economics and Management, Voronezh National University of Engineering and Technology, Voronezh, Voronezh State, 394036, Russia
2School of Management, Suzhou College, Suzhou, Anhui, 234000, China
Abstract:

The proposal of “Belt and Road” has helped these countries along the route to stimulate the development vitality and cooperation potential of their respective domains, which fits well with their common needs and opens a new window of opportunity for their complementary advantages and open development. This paper improves the construction of the new regional cooperation mechanism of the Belt and Road from three aspects: power mechanism, coordination mechanism, benefit distribution mechanism and compensation mechanism. The double difference method is utilized to assess the economic benefits generated under the Belt and Road regional economic cooperation mechanism. The assessment results show that the country with the highest import and export trade dependence of China is New Zealand, which reaches 18.5611, and as the dominant country of the Belt and Road, China’s two-way investment in other countries has the highest scale of $124,705.9 million, but the index of investment closeness is -1, which indicates that the capital flow between the two sides is mainly a unidirectional investment from China to other countries.

Jiguang Xue1, Zhibo Feng1, Xiaoze Liu1, Xiaoyi Zhang1, Leiyang Zhao1
1Marketing Service Center, State Grid Liaoning Electric Power Company Limited, Shenyang, Liaoning, 110168, China
Abstract:

In the operation of storage system, improper scheduling of shuttle and hoist will waste resources and affect the picking efficiency, so it is of great significance to optimize the operation scheduling of storage system. Based on queuing theory, this paper constructs a queuing model of ring RGV system and proposes queuing model assumptions of hoist system to analyze the reasonableness of storage layout. The operation activity scheduling mechanism is designed to execute the warehousing activities strictly in accordance with the established operation order. Agree on the ring track RGV operation rules, calculate the distance between any two points on the track, and ensure the shortest distance of the warehousing operation. Merge the shortest operation path and the shuttle car operation equilibrium rules to construct a dynamic scheduling decision model. Through the storage resources in and out of storage management and scheduling module, improve the measuring equipment intelligent storage system, apply the system to the actual storage operations, analyze the operational efficiency. After the implementation of the strategy proposed in this paper, the optimal scheduling result is 36min, the execution time of different types of work is different, and the operation time of equipment J1-J4 is 15min, 23min, 17min, 34min respectively. The pickup execution efficiency of the strategy used in this paper is improved by 66.38%, and the pickup efficiency is improved by 10% when the number of equipment is less than 300 pieces. The scheduling strategy proposed in this paper has a higher priority when facing a small number of devices.

Zhibo Feng1, Jiguang Xue1, Sitong Dong1, Ye Tang1, Xuliang Zhao1
1Marketing Service Center, State Grid Liaoning Electric Power Company Limited, Shenyang, Liaoning, 110168, China
Abstract:

When a manufacturing enterprise adopts lean manufacturing system for multi-species production and processing of products, the workshop production scheduling problem (i.e., production scheduling) is a major factor affecting the production efficiency of products. Aiming at the shortcomings of the standard simulated annealing algorithm, which is easy to fall into the local optimum due to the influence of stochastic factors, this paper designs an improved simulated annealing algorithm with tempering and slow-cooling functions, and an event-driven priority coefficient search for solving the dynamic scheduling optimization model of the production line. At the same time for specific cases of simulation and parameter testing of the algorithm, and respectively with manual scheduling results, the performance of the basic algorithm before the improvement of experimental comparison and analysis, to find the optimization effect of the improved optimization scheduling algorithm. Compared with the manual scheduling method, this paper’s method significantly optimizes the two objectives of total weighted delay time and production energy consumption. Compared with the basic SA algorithm, the accuracy of chromosome encoding of this paper’s method is improved by 233.33% and the computing workload is reduced by 79.51%, which verifies the feasibility and efficiency of this algorithm’s optimization scheme.

Bushuo Guo1, Liqiu Xin1
1School of Economics and Management, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China
Abstract:

Due to its heavy reliance on imports, the futures and spot markets of China’s upstream and downstream soybean products are vulnerable to the impact of the international market. In order to guarantee the security of the soybean industry, China introduced corresponding agricultural subsidy policies in 2008, 2014 and 2018, respectively. In order to test the impact of the subsidy policy on the development of the soybean industry, this paper utilizes an empirical mathematical planning model to evaluate the implementation effect of the subsidy policy for soybean producers ex ante, and explores the defects of the agricultural subsidy policy by simulating the production decisions of farmers. It also measured the efficiency of soybean subsidy, the efficiency of agricultural machinery purchase subsidy and the efficiency of agricultural insurance premium subsidy using a three-stage DEA model. In the empirical research part, the constructed numerical method of soybean producer subsidy policy unfolds the effect assessment. The empirical results show that the implementation of the soybean producer subsidy policy increases the proportion of soybean planting and soybean total factor productivity by 9.47% and 17.43%, respectively, and that the soybean producer subsidy policy has a facilitating effect on the expansion of soybean planting and total factor productivity. Accordingly, five policy recommendations are put forward with a view to promoting the healthy development of the soybean industry.

Na Zhao1, Shun Yao2, Jie Zhao3
1 School of Journalism, Communication University of China, Beijing, 100000, China
2School of Economics and Management, Communication University of China, Beijing, 100000, China
3School of Economics and Management, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, 010000, China
Abstract:

In order to explore and promote the strategy of students’ active health behaviors, this paper designs a personalized scientific guidance system architecture for active health promotion based on a three-tier service architecture model, using students’ sports literacy big data processing technology to construct a sports mobilization effect information system. Second, a sports prescription generation model is designed. The model adopts a multifactor fusion approach to recommend personalized exercise programs based on the different exercise abilities, different physical conditions, and personal exercise preferences of the exercisers. Under the condition of satisfying multiple constraints such as the physical condition, parameter range and exercise ability of the exerciser, the particle swarm optimization algorithm is used to optimize the exercise parameters, and the topological structure is further used to adjust the broadness of the distribution of the solution set in the objective space. The improved particle swarm optimization algorithm is compared, and the experimental results show that the improved TS-PSO algorithm converges faster, the solution accuracy is higher, and the parameter optimization using this algorithm generates a personalized exercise prescription that is more suitable for the exerciser. The exercise prescription generation model studied in this paper provides a new idea for the improvement of the effect of sports mobilization under the perspective of active health.

Jiao Xue1,2, Hongxing Dong3, Qiubo Zhong3
1Basic Courses Department, Zhejiang Police College, Hangzhou, Zhejiang, 310053, China
2Office of Academic Affairs, Hangzhou Polytechnic, Hangzhou, Zhejiang, 310000, China
3Office of Academic Affairs, Ningbo Institute of Technology, Ningbo, Zhejiang, 315000, China
Abstract:

Virtual teaching and research community is an effective way for teachers to realize communication and cooperation among themselves, to improve their professional level and to promote their career development. Under the framework of teaching and research community community construction, the relevant factors that resound the development of virtual teaching and research community construction were extracted by questionnaire survey method, CRITIC-assigned to them, and the factors with larger weights were taken as the key factors, and the multiple linear regression method was utilized to explore their influence on teachers’ professional development. The analysis found that the key factors with larger weights are teaching and research team building (0.3234) and teaching and research motivation (0.2683), and the regression coefficients of both of them in the regression results of teachers’ professional knowledge and professional skills are 0.18, 0.158, and 0.089, 0.059, respectively, and the significance of all of them is less than 0.05. Therefore, the teaching and research team building and teaching and research motivation are not only crucial to virtual teaching and research community operation, but also have a positive effect on teachers’ professional development.

Wei Chang1, Tingting Zhang2
1School of Finance, Shanghai Lixin University of Finance and Economics, Shanghai, 201209, China
2School of Business, East China University of Science and Technology, Shanghai, 200237, China
Abstract:

Machine learning provides new perspectives and methods for company M&A valuation due to its powerful data processing and prediction capabilities. This paper analyzes the prediction steps based on the decision tree algorithm, i.e., decision tree generation, attribute selection, decision tree construction, and accuracy metrics, and obtains the relevant data of AB after merger and acquisition through data mining. The model and SHAP framework are utilized to predict the financial risk, financial performance, and enterprise value of the two post-merger companies. The precision, recall, and F1 scores of this paper’s model range from 91.25 to 93.81, which has a good performance of company M&A valuation. This paper’s model predicts that in 2024, the key indicator of AB’s financial crisis is Gross margin, which has an importance of 0.297, and the possibility of AB’s financial crisis increases when the value of Gross margin is between -0.0279 and -0.0014. The accuracy of the financial performance prediction of this paper’s model is more than 0.97, which can accurately value the company’s performance. The model in this paper predicts the enterprise value of AB in 2024 to be 52.14yuan/share, respectively.

Hai Huang1
1School of Economics and Business Administration, Chongqing University, Chongqing, 400044, China
Abstract:

Information security is the most concerned issue in modern communication, with the continuous development of new computing technologies, classical cryptography has been difficult to effectively guarantee information security, quantum key distribution technology through the theory of quantum mechanics to ensure the absolute security of key distribution. Therefore face recognition system oriented optimization using quantum key distribution, this paper is based on the advantages of OQKD technology such as easy to implement, low overhead, high security, optimization for commercial privacy queries in the system. On the basis of the quantum key distribution regional network of trust relay, a new type of quantum key distribution experimental network structure based on switching nodes which is more flexible, energy-saving and efficient is proposed. Finally, the method of this paper is comprehensively verified through modeling simulation, and the simulation results show that the average call loss is 3.67% when the quantum key generation rate is increased to 20Kbps, which is significantly reduced. Moreover, the network call loss can be reduced to less than 11% when the method of this paper is adopted in the same situation, and the network call loss is even smaller. It shows that the call loss of the network will be greatly reduced when the key generation rate is increased with a fixed amount of voice traffic.

Yongming Zhou1
1Guangxi Transportation Industry Co., LTD., Nanning, Guangxi, 530000, China
Abstract:

For building construction enterprises, civil engineering project schedule assurance is the embodiment of project performance ability, and project cost control is the root of project profitability. This paper researches the cost-schedule control method based on BIM and critical path earned value method, and establishes a complete set of dynamic cost-schedule analysis and control method including plan preparation, process evaluation and result correction. This paper takes Project F as an example, integrates project management in the BIM platform and optimizes the plan through construction simulation, so that the construction plan is closer to the actual demand, establishes the Earned Value Method for distinguishing the critical path and embeds it into the BIM platform, reflects the progress with the Earned Value parameters of the critical path, reflects the cost with the Earned Value parameters of the whole project, analyzes the problems of the critical path and the project and proposes cost-schedule corrective measures in a targeted way. The critical path and project problems are analyzed, and cost-schedule corrective measures are proposed, so as to realize the fine management of project cost-schedule. Through the case study, it is proved that based on BIM critical path earned value method can achieve schedule and cost coordination and dynamic control and realize 91.8% cost reduction, good civil engineering project management efficiency and change the status quo of civil engineering project cost management.

Junfei Yang1, Zhiqun Cheng2, Song Qian1
1Information Engineering School, Xinjiang Institute of Technology, Aksu, Xinjiang, 843100, China
2Electronic Information College, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
Abstract:

The purpose of this study is to solve the energy efficiency problem of small agricultural base stations, propose an optimal design scheme based on RF power amplification, and verify its effectiveness through simulation experiments. In order to achieve the research purpose, this paper first defines the objectives and principles of energy efficiency optimization design, and puts forward the energy efficiency optimization technology based on RF power amplification. On this basis, a complete set of energy efficiency optimization design scheme for small agricultural base stations is designed. And by building a simulation platform, set the parameters close to reality, and simulate the operation state of the base station in different scenarios. The simulation results show that the stability of the algorithm in this paper is considerable under different loads. Even if the load is large, the stability of this method can reach above 89%. The proposed energy efficiency optimization scheme can significantly reduce the energy consumption of the base station and improve the overall energy efficiency performance under different load and interference conditions. This result proves the effectiveness and superiority of the scheme and provides strong support for practical application.

Yuanlei Tao1, Yijie Qin2, Ying Li2
1Management Big Data Research Center of Anhui University, Huaibei Normal University, Huaibei, Anhui, 235000, China
2School of Economics and Management, Huaibei Normal University, Huaibei, Anhui, 235000, China
Abstract:

Goal-oriented dimension is a new angle to solve the problem of universities’ performance assessment. Firstly, designs an input-output index system, and calculates the Malmquist Index of the performance utilizes the panel data. Then, the non-parametric KDE graph is used in this research for further discussion of the differences of TFP changes. Meanwhile, a non-parametric KDE analysis is carried out respectively for TECHCH, EFFCH, PTEC, and SECH indexes. The Malmquist-KDE index model shows the results as follows: TFP is on a declining curve; the increased range of EFFECH is relatively smaller, while the annual growth of PECH and SECH are slow; the decrease of TFP is caused by the decrease of TECHCH; the general distribution gradually moves leftward, reflecting a fact that the TFP changes are decreasing progressively; the TFP change rate demonstrates obvious a skewed distribution; the patterns in the graph gradually shift from thin and tall ones into short and thick ones. Conversely, the changes of external factors force universities to improve their operations actively.

Siyang Wang 1
1College of Vocational and Technical, Guangxi Normal University, Guilin, Guangxi, 541000, China
Abstract:

With the continuous development of deep learning technology and the increasing maturity of rural tourism market, this paper obtains tourism user-generated content data through customized crawler technology, describes the data flow diagram of single-user crawling and the data flow diagram of database batch crawling module. A sentiment index covering multiple dimensions is constructed to mine the deep-seated features of tourist behavior. Fusing effective features in tourism data by using multiple topological maps, using graph convolution network to capture multiple spatial features of scenic spots and recurrent neural network to capture temporal features of traffic, to complete the analysis and prediction of tourists’ behavior. Taking Jiangxi Wuyuan Huangling rural attraction market as an example for empirical analysis, the importance of historical flow and search volume under all time windows is as high as 111 and 117 respectively, proving that these two features have a significant impact on predicting the target variables. The model in this paper is highly fitted to the predicted value of actual passenger flow at 12 time points, especially in the 9th month, the predicted value is 402, which is 401 from the actual value, which is an important reference value for rural tourism management and marketing strategy.

Mingxing Xu1, Xiongjun Tao1, Jingyu Liu1, Liqi Pan1, Zishen Huang2
1College of Architectural Arts, Guangxi Arts University, Nanning, Guangxi, 530000, China
2College of Film, Television & Media, Guangxi Arts University, Nanning, Guangxi, 530000, China
Abstract:

The field of artificial intelligence provides a new practical path for the inheritance and protection of non-heritage art. This paper proposes an innovative morphological design method for rattan weaving art based on fractal theory, and the grasshopper plug-in is selected to establish a parametric design model. The fractal graphics generated by the iterative function system are used as the input graphics, and the GrabCut algorithm and VGG16 neural network are combined to propose a graphic rendering method based on style migration containing elements of the cultural symbols of the Maonan Flower Bamboo Hat, and to realize the inheritance of the cultural symbols of the Maonan Flower Bamboo Hat. In the high preference survey, the A1 and A4 features of the sun hat in the questionnaire results are consistent with the preference results derived from the fractal design, and the questionnaire results of the handbag and handkerchief are also consistent with the preference results derived from the fractal design. It shows that the product form design method of Maonan flower bamboo hat cultural symbols based on fractal theory and style migration can play a certain role in promoting cultural inheritance.

Jiayue Yang 1
1 Jilin Police College, Changchun, Jilin, 130000, China
Abstract:

Some athletes’ lack of basic knowledge of exercise mechanism, mode, method, process and intensity has led to frequent occurrence of athletic risk events such as injury, disease and even sudden death, which seriously affects the physical and mental health of athletes and even threatens their lives. In this study, the data of athletes’ injury and disease risk characteristics were collected, and the feature selection method of Least Absolute Value Convergence and Selection Operator (LASSO) combined with Boruta’s algorithm was used to preprocess the data in order to eliminate redundant features. In terms of model construction, the prediction results of support vector machine, logistic regression, random forest algorithm and deep forest algorithm were integrated by using Stacking algorithm to construct the prediction model of athletes’ injury risk. After the predictive performance of the model is examined, it is used as an intervention for injury rehabilitation to carry out comparative experiments. The results show that the fusion model can effectively extract the feature importance of injury risk factors and predict the risk probability, and the prediction effect is better than that of a single model. Meanwhile, the intervention results show that the model has excellent effects on injury rehabilitation. This study can accurately predict injuries and illnesses, prevent the occurrence of injury and illness risk events in athletes, ensure the successful realization of sports goals, and play a role in assisting injury and illness rehabilitation.

Zhihao Pan1, Zhenyu Fu1, Guiquan Lin1, Tao Wu1, Zhifeng Yang1, Xiao Teng2
1Zhanjiang Power Supply Bureau of Guangdong Power Grid Co., Ltd., Zhanjiang, Guangdong, 524000, China
2College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu, 211100, China
Abstract:

The power optimization of wind farms and the optimal control of wind turbines require high-precision power ultra-short-term prediction for each wind turbine. In order to improve the performance of ultra-short-term prediction of wind power, this paper couples the LSTM model with the Logistic model and combines it with Graph Convolutional Neural Network (GCN) to construct the ultra-short-term prediction model of wind power based on Logistic-LSTM-GCN, and test and analyze the prediction performance of the model. Comparing the LASSO, XGboost, LSTM, GRU and TCN-LSTM models, the MAE and RMSE of this paper’s model are the lowest among all the models, which are 3.34% and 5.89%, respectively, and the R² is the highest, which is 79.76%. And the MAE and RMSE predicted by the model with inputs of four-dimensional spatio-temporal feature matrix are smaller than the model with inputs of one and two dimensions, and the R² value is larger than that of one and two-dimensional model. It indicates that the Logistic-LSTM-GCN model based on spatio-temporal information can extract the spatio-temporal information of wind farms more effectively, which improves the accuracy of wind cluster power prediction. In addition, with the increasing time step, the error indicators MAE, MAPE and RMSE are gradually increasing. Taking a time step of 4s for prediction, the prediction error of the model is minimized when considering multivariate variables such as wind speed, wind speed decomposition component, yaw error, wind direction, and rotor speed. This indicates that the multivariate LSTM, logistic and GCN coupled model can significantly improve the performance of ultrashort-term prediction of wind power.

Jinqing Luo1, Shaohua Lin1, Feng Qian1, Kang’an Shu1, Liu Yang1, Qing Chen 1
1Guangdong Power Exchange Center Co., Ltd., Guangzhou, Guangdong, 510600, China
Abstract:

With the promulgation of relevant policies, virtual power plant market transactions are facing major adjustments, in order to promote the smooth entry of virtual power plants into market-oriented transactions and improve the economic benefits of virtual power plants, this paper proposes a virtual power plant market transaction model. The traditional virtual power plant resources are mathematically modeled, blockchain technology is introduced to build a decentralized trading framework, and fuzzy neural networks are combined to predict the power load of the virtual power plant. Then the decision-making model of virtual power plant participation in spot market trading is constructed by using two-stage stochastic planning theory with the goal of maximizing expected return. The results show that the prediction effect of the fuzzy logic-based virtual power plant market trading model is 2.925% higher than that of the traditional BP algorithm model, and its accuracy and stability are significantly improved. In addition, the distributed energy storage aggregated by the virtual power plant as well as the dynamic demand response rate is fast, the regulation is flexible, the short-time power throughput capability is strong, and it can accurately track the FM instructions. The cumulative FM capacity and FM mileage provided by the virtual power plant account for 84% and 99% of the total FM capacity demand in the system, respectively, making it highly competitive in the FM market. And under the premise of balancing riskiness and profitability, the bidding scheme of virtual power plant derived in this paper is more effective.

Yanfeng Jiang1, Yanfang Jiang2
1School of Accounting, Guangdong University of Finance, Guangzhou, Guangdong, 510521, China
2School of Finance and Investment, Guangdong University of Finance, Guangzhou, Guangdong, 510521, China
Abstract:

The implementation of tax incentives is a powerful measure to reduce the burden of enterprises, build a new development pattern, and expand reform and opening-up. Some enterprises in nine provinces from 2010 to 2023 are sampled to verify the role of tax incentives in reducing the tax burden by using the double difference model. The weight coefficients are introduced as learning factors for the population center of mass, and the SWC-PSO algorithm is proposed to improve the shortcomings of PSO, which has low convergence accuracy and is prone to fall into local extremes, and to realize the mathematical planning for minimizing the tax burden of enterprises. After controlling the variables of tax policy and enterprise nature, the regression coefficient reflecting the enterprise tax burden is significantly negative at 1% level, and the tax burden of enterprises receiving tax incentives is significantly reduced, which proves the role of tax incentives in reducing the enterprise tax burden. After using SWC-PSO for planning, the sample units have a total of 1,779,919,000 yuan of tax relief, and the business tax rate of a construction project decreases from 3.35% to 0.42%, which indicates that the improved algorithm in this paper can plan the strategy of minimizing the tax burden of enterprises more efficiently.

Jiayi Yu1
1The Shanghai Conservatory of Music, The Institute of Digital Media Art, Shanghai, 200031, China
Abstract:

The continuous development of neural network makes the automated style migration technology also rise to a new height. This paper selects digital media art as the research field, constructs Cycle GAN, a cyclic consistent generative adversarial network structure applied to digital media art, on the basic framework of GAN, and optimizes it by adding bilinear interpolation and attention mechanism, so as to build up a style migration model for digital media art. In the style migration simulation experiment, the IS test values of this paper’s model on the photo2vangogh and photo2monet datasets are 5.32 and 6.03, and the FID test values are 97.52 and 75.55, which are better than the other comparative models. Similarly, the optimized performance of FID, SSIM and PSNR values on the dataset is also better than other comparative models, and the style migration performance of the model is verified. Using the model of this paper to design a digital topography with Chinese traditional ink painting as the content, we explore the correlation between the design attributes of the style migration design works in digital media art and the audience’s cognitive evaluation and overall perception. Among the design attributes, “plot relevance” (4.375) and “atmosphere rendering” (3.38) have the highest T-value, which is the most important influence on audience perception.

Ranfeng Fan 1
1Police Combat Teaching and Research Department, Guangxi Police College, Nanning, Guangxi, 530000, China
Abstract:

There is an increasing demand for assisted training techniques in the sport of sparring. In this paper, a sparring multiple recognition and analysis system is designed and fabricated for the movements of sparring sports and used to recognize and analyze the players’ technical movements using the collected data and the model built using deep neural networks. The CNN-LSTM network is applied to extract the feature classification of the preprocessed sparring inertia data, and then the DTW algorithm is combined with the spatial distance classification method to realize the matching and recognition of sparring behaviors by stretching and compressing transformations of the time axis, effectively eliminating the distortion error in the time domain and obtaining the similar path with the shortest cumulative distance of the effective matches between different sequences. Experiments on the application of this paper’s system were conducted in two groups of sparring players, and after 12 weeks of training intervention, the average confrontation striking speed of the experimental group progressed from 0.36 seconds before the experiment to 0.32 seconds after the experiment, and the average performance of the control group progressed from 0.38 seconds before the experiment to 0.36 seconds after the experiment, which indicates that although the traditional resistance training also has a positive impact on the training effect of sparring training, the training effect of this paper’s system is more obvious The systematic training effect of this paper is more obvious. This paper makes an innovative exploration for the combination of sports programs such as sparring and cutting-edge information technology.

Zhenyi An 1
1Library of Guizhou Minzu University, Guiyang, Guizhou, 550025, China
Abstract:

Personalized service is a targeted initiative for digital resource libraries to improve the quality of service and better play the function of culture and education. This paper proposes a digital book personalized recommendation algorithm based on artificial intelligence technology. After acquiring the borrowing data and pre-processing, the reader’s portrait is visualized with factor analysis and cluster analysis methods respectively. The traditional Slope one algorithm is weighted and the collaborative filtering algorithm is improved. Combine the user profile with collaborative filtering to realize the personalized recommendation of digital books. User similarity calculates four types of readers such as pragmatic, youthful, recreational and curious. This paper’s algorithm outperforms CFRA and RABC algorithms under each parameter, with the highest recommendation accuracy and novelty, and realizes personalized library services.

Yuanyuan Xiao1, Yingxin Zhang1, Jianzhi Sun1
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China
Abstract:

Studying the influencing factors of logical reasoning ability can not only help teachers to find out the effective way to cultivate students’ logical reasoning ability, but also provide methodological and theoretical references for the relevant research in the area of artificial intelligence-driven program design education, which is of certain research value. The article firstly introduces the theory of structural equation modeling and the principle of algorithm used in model analysis. Then, taking the students of School S and School T as an example, we designed and distributed relevant collection questionnaires, and analyzed the data using SPSS to understand the overall status of students’ logical reasoning ability and the level of each dimension. Then we make reasonable assumptions about the factors affecting students’ logical reasoning ability, establish a structural equation model of the factors affecting logical reasoning ability, and analyze the effects and paths between the factors and on the logical reasoning ability. Finally, according to the experimental results, we propose targeted teaching reform methods. The results of the study show that: teacher’s activities, learning interest, learning attitude, classroom environment have a positive effect on students’ logical reasoning ability, in which the effect of classroom environment on logical reasoning ability is 0.48. Enhancing the teacher’s power and promoting the diversified development of students is an effective way to improve logical reasoning ability.

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