
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%).
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.
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.
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.
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.
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.
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.
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.
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%.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Optimizing regional economic resources is a crucial aspect of the Belt and Road initiative. This paper develops a multi-objective optimization model to objectively evaluate the development level of regional economic resource optimization in Belt and Road countries and to identify the key influencing factors. The model maximizes regional economic and social benefits under constraints of resource availability, output capacity, and coordinated regional development, and it incorporates a synergy measure to ensure robust progress. Our findings show that the regional economic benefits index increased from 0.264 in 2017 to 0.575 in 2023 (a growth rate of 117.8%), while social benefits grew by 14.29%. Additionally, panel regression analysis reveals that merchandise trade, foreign direct investment, road traffic mortality, and industrial development all have significant negative impacts on the optimization of economic resources, at the 1% significance level.
Alzheimer’s disease (AD) is a progressive neurodegenerative condition that affects the elderly population. The early detection and diagnosis of AD is critical for achieving effective treatment, as it can greatly improve the patient experience. AD can be viewed through imaging techniques like MRI, PET, and SPECT, providing valuable information about structural and functional changes. These findings are important in understanding this area. However, each imaging modality offers a different perspective. This information can be better collected from several of the other modalities as well as from some others to improve accuracy and reliability in AD detection. By combining information from different imaging modalities, such as MRI, PET, DTI, and fMRI, automated multimodal medical image frameworks aim to create a fused representation that preserves the relevant features from each modality. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), among other deep learning techniques, have been prevalent in these frameworks for learning discriminative and informative features from multi-modal data. In this paper, The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is used for experimental analysis. The proposed work gives 98.94% of accuracy and 1.06% of error which is greater than the existing approaches.
The power of the public key cryptosystem based on Paley graphs is due to several mathematical problems namely quadratic residuosity, local equivalence, and identification of the graphs induced by a sequence of local complementations of the Paley graphs. The classification in terms of degree of these induced graphs can be useful in the cryptanalysis part of the proposed public-key cryptosystem based on these algebraic graphs. This work aims to give the exact value of the minimum and maximum degree by local complementation, then the possible classifications in terms of degree to the graphs induced by a sequence of local complementations of Paley graphs of degree p less than or equal to 13 and some information about the equivalence problem.
Given a graph
Directed strongly regular graphs were introduced by Duval in 1998 as one of the possible generalization of classical strongly regular graphs to the directed case. Duval also provided several construction methods for directed strongly regular graphs. In this paper, an infinite family of directed strongly regular graphs is constructed, as generalized Cayley graphs.
With the need of international dissemination of Chinese culture, the problem of translating traditional Chinese texts gradually emerges. The study embeds a computer semantic model into the English translation of The Analects of Confucius, and constructs a natural language understanding model based on S-LSTM network through semantic representation of natural language processing. In order to explore the performance of the S-LSTM model, it is compared with RNN, LSTM, I-LSTM and other models in terms of training time and accuracy, so as to validate the superiority of the S-LSTM model in this paper. This paper deeply explores the philosophical connotation of the character “body” in The Analects, and studies the structural complexity of the translation of the character “body” through the S-LSTM model. Finally, the English translation strategy of The Analects and other classics is proposed. Among all the comparison models, the S-LSTM model has the fastest training speed and the highest accuracy. The translation of the word “body” in The Analects and the local complexity of the ministry are characterized by complication. The local complexity of the noun and the subject in the source English language, and the overall complexity of the “be-passive” structure have obvious effects on the structure of the translated Chinese character “body”.
Along with the development of the times, online classroom teaching activities have been carried out in different degrees and frequencies in various schools, and the gradual advancement of education informatization has improved the software and hardware environment of online classroom and other forms of teaching. The study designed a 21-item questionnaire related to English online classroom learning and selected all the students who participated in English online classroom teaching in a school for the survey. After collecting the questionnaire data, factor analysis and multiple stepwise regression model were used to conduct multivariate statistical analysis on the English online classroom data. And on this basis, the teaching plan was adjusted according to the actual learning behaviors of the high, medium and low risk level students themselves respectively to achieve personalized teaching. The results show that students’ satisfaction with the English online classroom is high, and that pre-course homework analysis, group learning, formative learning evaluation, students’ independent learning ability and online learning resources are the key positive factors affecting the learning effect of the English online classroom, with the influence coefficients of 0.036, 0.055, 0.048, 0.044, and 0.062, respectively. At the same time, after the optimization of teaching strategies, the students’ logged-in learning behavior, participation rate in interactive test questions and grades were significantly improved, proving the effectiveness of the strategy.
In today’s era, the transformative power of computing is highlighted, and computational thinking has become the core literacy and essential ability of learners, while computer education is an effective carrier for cultivating computational thinking. The article firstly researches the theory related to collaborative filtering and generative adversarial recommender system. Then it combines SeqGAN with traditional CF algorithms, proposes to use sequence generative adversarial network for missing data prediction, and makes appropriate improvements to SeqGAN to make it suitable for generating scoring data, and then further designs a computer teaching system based on this model. The article launches performance testing experiments on Ali’s real dataset UserBehavior, and conducts experiments on the effect of computer education with the students of computer application major in a secondary school as the research object. The results of the study show that in the comparative analysis of the pre-test and post-test of computational thinking of the experimental class, the mean of the total score of computational thinking of the experimental class in the pre-test and post-test is 71.17 and 78.35, respectively, and the post-test is more than 7 points higher than the pre-test. It can be concluded that the teaching model of multilevel computational modeling designed in this paper promotes the development of students’ computational thinking and academic performance, improves students’ learning attitudes, and increases classroom participation.
With economic globalization and the increasing complexity of inter-enterprise business linkages, corporate financial systems have gradually taken on the characteristics of complex networks. This paper firstly gives an overview of the complex network and introduces its basic topological properties, such as clustering coefficient and path length. After that, through the principal component analysis method, the enterprise financial risk early warning indicators are identified, and the key indicators are screened to improve the early warning accuracy. Based on these properties, the financial risk conduction network model of complex enterprises is constructed, the characteristics of the network are analyzed, including network density, centrality distribution, etc., and the effect of financial efficiency enhancement of complex enterprises under the optimization of topology computation is verified in real cases. The results show that most of the financial risk indicators of enterprises have strong correlation, and the degree of centrality of 9 indicators such as “gearing ratio and quick ratio” is more than 50%. In addition, the indicators of “current asset turnover ratio, interest coverage multiple, net profit growth rate” can play the role of intermediary and bridge, and the risk transmission effect among the indicators is high. The threshold value of 0.65 is the watershed of the changes in the financial structure of enterprises, and most of the financial risks in the network have a high degree of similarity in the financial structure when the degree value is 70, and it is negatively correlated with the coefficient of agglomeration, and the coefficient of agglomeration decreases with the increase in the intensity of the points.
In order to explore the deficiencies in the teaching process of marketing majors in higher vocational colleges and further improve the teaching quality of marketing majors in higher vocational colleges. This paper utilizes the improved ID3 algorithm to construct the SLIQ data mining algorithm to improve the teaching quality of teachers of marketing majors in higher vocational colleges and universities. Using ID3 algorithm to build a decision tree to get the portraits of teachers and students, at the same time, in order to reduce the computational complexity of ID3 algorithm and the problem of multi-value bias, the concept of sample structure vector similarity is introduced, and the degree of information gain is optimized to get a more reasonable decision tree. On this basis, based on the improved ID3 data mining algorithm, a teaching quality assessment system for senior marketing majors based on SLIQ algorithm is designed, which identifies important factors affecting teachers’ teaching quality by mining a large amount of data in the teaching process.The AUC value of the SLIQ data mining algorithm is 0.98, which can effectively improve the algorithm’s generalization ability, and it has an excellent performance in the teaching quality assessment task. The performance is excellent. In this paper, we systematically identify “the principles of marketing” and “the degree of seriousness of teachers’ homework correction” as the key factors to improve the teaching quality of marketing teachers. It provides a scientific basis for improving the quality of teachers’ teaching.
Visual communication design requires that feeling information and exchange of information must be conveyed efficiently and accurately. In this paper, we design a robust principal component sub-analysis visual enhancement algorithm based on improved Retinex. The algorithm transforms the image to the logarithmic domain so that it satisfies the decomposition condition of RPCA. After the RPCA decomposition model to get the low-rank component and sparse component, and will use adaptive gamma correction algorithm for the low-rank component for contrast enhancement, the two components are combined and then inverse transformed in the logarithmic domain to get the enhancement results. To avoid color distortion, the input image is converted to HSV color space to separate illumination information from noise. The model uses the inexact augmented Lagrange multiplier method (IALM) to solve the optimization problem, which leads to a significant improvement in the decomposition speed. The performance of the designed algorithm is verified on the dataset, and it is found that after the color equalization process for overexposed images, the gray value distribution is more uniform, and the image shows a better sense of brightness and visual effect after the contrast is increased. The algorithm scores 0.4648 and 0.7577 in UCIQE and UIQM respectively, which are ranked first among all algorithms and have better visual effect and information communication efficiency.
In recent years, China’s research investment in colleges and universities has gradually increased, but not much research and exploration has been done on the construction of the evaluation index system for the integration of industry and education. The state, society, industry and so on have brought rare opportunities for the implementation of in-depth integration of industry and education, which also indicates the imperative of the development of integration of industry and education. Based on the practical significance of educational evaluation, this paper applies the CIPP model to the construction of the quality evaluation system for collaborative education and training in university modern industrial colleges in view of the high degree of fit between the CIPP model and the process of university-industry-industry fusion activities in university modern industrial colleges. The recursive hierarchical structure is established according to the established index system, and the weights of the index system are calculated through the consistency test. The factor loading matrix of the first three principal components is constructed, and the modern industrial colleges are evaluated according to the principal components, and the mean values of the principal components 1, 2, and 3 are 0.27, 0.096, and -0.0186, respectively.In the calculated quality evaluation results of the integration of industry and education in modern industrial colleges, the score of educational and teaching achievements of the modern industrial colleges in Zhejiang Province is relatively low at 85.8439, which indicates that there is a gap in educational and teaching achievements, and there is a need to further improve the education and teaching achievements of modern industrial colleges. In addition, there are differences in the evaluation of the quality of industry-education integration in different modern industrial colleges in Zhejiang Province.The results of this study indicate that it is necessary to further optimize the construction path to meet the actual needs of industry-teaching integration in Zhejiang Province.
Lung cancer is the most common malignant tumor in humans and the leading cause of cancer-related deaths worldwide. In this study, we focused on the immune cells in the microenvironment of lung cancer at the protein expression level by IHC as well as mIHC techniques to explore the spatial distribution characteristics of immune cells within the tumor. To predict the prognosis of NSCLC patients and their potential response to immunotherapy, a machine learning-based immune-related prognostic model for lung cancer was constructed by combining Cox regression analysis, random survival forest and XGBoost algorithm, and the effect of the prognostic model was verified on the relevant dataset. The results showed that there were some differences in the immune cells between lung adenocarcinoma and lung squamous carcinoma in the lung cancer microenvironment, and the spatial distribution heterogeneity of CD3+ T cells and MHC class II antigen-presenting cells was higher in lung adenocarcinoma (P<0.05).The overall survival of high-risk patients was lower than that of the low-risk group in both LUAD and LUSC (P<0.01), and the immuno-associated prognostic model of lung cancer had a stable performance in the AUC value in multiple independent cohorts with stable performance, and the IRS model maintained high accuracy and stable performance in the training set and test set, which indicates that IRS has great potential for clinical application.
Rhythm matching of music and dance is an important research area in cross-modal analysis. In this paper, a music and dance rhythm matching algorithm based on time series analysis is proposed to extract the time series features of music and dance, and a genetic algorithm is used to determine the correspondence between music and dance movements to reflect the degree of correlation between changes in music and dance rhythm movements. In order to improve the matching and smoothing degree between the dance movement time series and the music time series, a constraint-based dynamic programming algorithm is introduced. The experimental results show that the model performs well in the matching degree and matching efficiency enhancement between dance movement time series and music time series, and its matching efficiency is 2-3 times of the traditional method. It shows high practicality in dance choreography and music matching, and can match any music clip with smooth and beautiful dance movements. The research in this paper provides new technical means for dance choreography and music matching, which will further optimize the transition harmony between music time series and dance movement time series.
This study takes the physical properties of high temperature devices as a starting point and the experimental apparatus used to obtain the study samples. The heat transfer process can be categorized into heat conduction, heat convection and heat radiation depending on the mode of contact. Under the theoretical support of the first law of thermodynamics, the nonlinear partial differential equations of the heat transfer characteristics of the high temperature devices are determined, and the above equations are analyzed by numerical simulation with the help of ANSYS software. When the thickness of the device is 1um, 8um and 15um, the heat transfer temperature and the power of the heat source show a monotonically increasing trend, in addition, when the thickness of the device is a fixed value, the spacing of the heat source and the heat transfer temperature show a nonlinear monotonically decreasing, and the present study has an important practical significance for improving the heat transfer performance of high temperature devices.
In order to explore the relationship between multi-source terrain features and lightning activity in Inner Mongolia, monitoring data and digital terrain elevation data of thunderstorm activity in Inner Mongolia from 2014 to 2025 were collected, and the spatio-temporal data mining method of mathematical and statistical analysis was used to analyze the distribution characteristics of lightning activity in Inner Mongolia. Based on the selected terrain feature factors, the machine learning method of multiple regression analysis is used to establish a research model of multi-source terrain features and lightning activity for quantitative analysis. The results show that the frequency of ground flashes in Inner Mongolia is mainly concentrated in May-October, accounting for more than 92% of the whole year, and the seasonal characteristics of its ground flash activities are significant, and the current intensity is mainly concentrated in the range of 20-40 kA. Correlation analysis reveals that multiple features of multi-sourced terrain are positively and negatively correlated with the frequency of lightning ground flashes and the current intensity (p < 0.05), and the prediction error of the constructed regression model for the ground flashes' frequency and the current intensity is 7.31%. The prediction errors of the constructed regression model on ground flash frequency and current intensity are 7.31% and 5.08%, which can provide a reference for lightning disaster prevention and mitigation in Inner Mongolia.
In response to the rapidly developing market demand, this paper proposes the use of genetic algorithms in industrial product design optimization under simulation environment. Design the product base gene coding, use the fitness function to determine the fitness value of different individuals, the genetic operator to support the optimization of industrial product design, by clarifying the optimal individual in the population in order to determine the optimization of industrial product design to meet the conditions. Then build up the industrial product design system based on genetic algorithm, plan the functional modules such as product information collection and coding, genetic generation of product solutions, and formulate the system process and function realization method. Exploring the performance of this paper’s industrial product design model in the simulation environment, this paper’s model in the operation efficiency, convergence speed and other aspects of performance are better than its other comparison model, in the iteration to about 300 times to achieve convergence. In the application practice of this paper’s design system, the values of this paper’s system are close to 1, and the RMSE values of each design parameter are lower than 0.5, and the average product quality score reaches 0.157, which is excellent in real-world applications.
The double bass, as the instrument with the lowest timbre and the largest volume in the string section of a symphony orchestra, is the “mainstay” of the orchestra’s acoustic effect, and grasping the bass performance mode in double bass performance is a problem that all double bass players need to explore in depth. A cluster-weighted multi-view kernel k-means clustering model (CWK2M) is proposed to study the local quality differences of the bass performance score views at the cluster level. The proposed weighted multiview clustering algorithm is then compared with several multiview clustering algorithms on several real multiview data for experiments and analysis of pitch change patterns. The experimental results show that, on the whole, the proposed algorithm in this paper obtains a relatively good clustering effect on each multiview data, especially on the Sens IT dataset of bass performance scores, the performance of each metrics is significantly improved, and the precision, recall, F1 value and NMI metrics are 0.632, 0.653, 0.687, and 0.713, respectively.In addition, the algorithm of this paper is utilized for the three bass playing patterns such as TaS1, Py11 and Mla1 are further analyzed, which further validates the universality and performance effect of the improved weighted clustering algorithm proposed in this paper for the analysis of pitch change patterns in bass playing.
The energy consumption problem of building complexes has become increasingly prominent along with the acceleration of urbanization. In order to achieve efficient energy saving in building complexes, this study proposes a Bayesian network-based uncertainty modeling in decision-making system for energy consumption management. By analyzing the uncertainty factors in the energy consumption data, a Bayesian network model is constructed to predict and analyze the energy consumption. And the uncertainty factors are used as decision variables to construct the energy consumption management decision-making system based on Bayesian network. The experimental results show that the uncertainty model and decision-making system constructed in this paper have more favorable performance compared with other benchmark methods, and exhibit smaller measurement errors in experimental tests. At the same time, the application of this paper’s decision-making system for energy consumption management of building complexes can significantly reduce management costs, and obtain the double benefits of reducing energy consumption and saving costs.
Based on the demand of load balancing in distributed system scenarios, this paper introduces the concept of dynamic priority in the algorithm and designs the dynamic feedback load balancing (DFLB) algorithm for numerical analysis. Through the closed-loop process of collection-feedback-utilization-collection, the overall performance of the system is realized. The Mininet tool and the Floodlight controller are used when building the load balancing system experimental environment to verify the reliability of the algorithm from the response delay, throughput and other indicators. The study shows that the DFLB algorithm reduces the response time of the system by about 20% compared with the static deployment method, and the DFLB algorithm reduces the load variance, saves computational resources, and makes the load of the system more balanced and efficient. The average throughput of the DFLB algorithm is improved by about 10% compared with the PALB algorithm and DALB algorithm, and 6% compared with the PALB algorithm and DALB algorithm, respectively. Starting from 1000 concurrent connections, the DFLB algorithm has a higher access rate. Thus, the algorithm leads to an improvement in the overall performance of the system.
The field of machine translation has made significant progress in recent years, but how to improve translation accuracy and context consistency is still an urgent challenge. In this paper, a context-aware translation accuracy improvement strategy based on deep reinforcement learning is proposed for English translation. Based on CNNs neural machine translation model, the multi-intelligence deterministic deep policy gradient algorithm is utilized to combine the output of the translation model with the human evaluation index (BLEU), and the reward function is constructed to guide the model learning. In addition, in order to enhance the context-awareness of the model, the study introduces a context encoder in the deep reinforcement learning framework to capture sentence-level contextual information and incorporate it into the translation process. The experimental results show that the optimized model has better training performance, with 40 epochs of iterations, the Loss converges to 0.135 up and down, and its English translation F1 value is 94.95%. And as the number of encoder layers rises, the number of semantic high-level features increases. The N-GRR difference between the generated translation and the standard translation of the model in this paper is the smallest, and the over-translation phenomenon is less. The number of out-of-set word interference is more than 6, and the BLEU value of this paper’s model is improved by 17.89% to 55.55% compared with the comparison model. And the algorithm has good translation performance, with METEOR scores of 0.562~0.803 on different topics. The research results fully verify the effectiveness of deep reinforcement learning based on deep reinforcement learning to improve the accuracy of English machine translation.
This paper proposes a risk indicator system for mental health management of college students that takes individual developmental status, social environment, human-computer interaction, and negative emotions as the first-level indicators, and clarifies the path of obtaining mental health management monitoring data, the weights of the indicators, and the safety warning interval of mental health management. Because of the uncertainties in the mental health management of college students, fuzzy logic is introduced to deal with the uncertainties of environmental changes, student behavior and other factors in the mental health management, and to improve the level of mental health management in colleges and universities. A fuzzy logic-based risk warning model for mental health management of college students is designed. The mental health status of students is further refined by the SCL-90 scale, and the mean score level of each factor of the scale is compared with the youth norm and adult norm. Input the fuzzified student mental health data in the fuzzy logic risk early warning model, and output the risk score of the fuzzy logic model for mental health management of college students. When the set threshold is 60, the fuzzy logic risk early warning model can effectively identify the abnormal values of students’ mental health, and the early warning model has practical utility.
How to communicate with users in a timely and effective manner and determine the intentional purpose of customers plays an important role in promoting continuous user interaction and improving service efficiency in the power marketing industry. The article firstly researches on a single-round natural language understanding algorithm based on intent-slot bi-directional interaction, which adopts a bi-directional information flow to realize the bi-directional information interaction between intent and slot. In the intention recognition layer, the interaction attention mechanism is utilized to introduce slot context information. Then the overall design scheme for the construction of an intelligent customer service system for power marketing from dialogue state keeping, multi-round question and answer, model storage to answer visualization is proposed, and the potential functional requirements are analyzed exhaustively. Finally, experiments from various aspects prove the effectiveness of the proposal in this paper. In the comparison experiments on MixATIS with MixSNIPS dataset and DSTC4 dataset, the metrics are improved by 0.3%, 1.5% and 0.5% respectively when comparing GL-GIN model on MixATIS dataset. This leads to the feasibility of the intelligent customer service system for power marketing constructed in this paper.
In recent years, socio-economic development and the process of massification of vocational education have been accelerating. The article surveys the current situation of the articulation between vocational education and undergraduate education through questionnaires. On this basis, in order to better realize the cultivation of employment-oriented talents, it designs a teaching resource acquisition method based on computational optimization, constructs a crawler search method by fusing genetic algorithm and ant colony algorithm, and realizes automatic clustering by using a clustering algorithm based on the combination of K-mean and particle swarm algorithm in random search direction. The results show that only 23.3% of the students think that there is no duplication of content between vocational and undergraduate education, 89.6% of the students want to set the teaching content according to different needs, and the current talent cultivation for the articulation of vocational and undergraduate education suffers from poor wholeness and monotonous tendency. The proposed crawler search method and automatic clustering method show superior performance and can accurately extract teaching resources and process structured information. Finally, the employment-oriented talent cultivation model is proposed to actively explore the path of integrating vocational and undergraduate education and promote the development of vocational education.
Key frame extraction is an important research content for human motion capture data analysis and processing, for this reason, a key frame extraction method for motion capture data based on quantum particle swarm optimization algorithm is proposed, which can either extract a definite number of key frame sequences or extract key frame sequences according to the objective function. In this paper, the spatio-temporal graph convolutional network is selected as the benchmark network for tap dance action recognition, and the dance action recognition is realized by combining adaptive and attention mechanisms. The comprehensive index of tap dance is introduced and used as a constraint, and the golden section algorithm is used to optimize the training path of the dance action to obtain an ergonomic training path. The experimental results of this paper show that the key frame extraction method of motion capture data based on quantum particle swarm optimization algorithm meets the need of real-time compression of motion capture data. By constructing the validation dataset, the accuracy improvement of AAST-GAN algorithm and the effect of gesture extraction are compared and verified, and the recognition accuracy reaches more than 86%, which is a good recognition accuracy for each tap dance action. The dance movement training path proposed in this paper ensures the effectiveness and comfort of tap dance movements.
Dance Anatomy is a basic theory course for university dance majors, which reveals the structure and function of various parts of the human body and their important roles in dance training through an in-depth interpretation of dance anatomy. Using relevant equipment and instruments, we will set up a data acquisition environment for data acquisition and pre-processing. For the problem of coordinating music rhythm and dance movement, a time-series autoregressive model is used to realize music-driven dance synthesis, and the model loss function is clarified. Combining the above model, data, and modeling software, the task of modeling the human dance movement mechanism is completed, and the cosine similarity is adopted to analyze the problem of coordinating music rhythm and dance movement. In both the training and test sets, the music-driven dance sequences and the original sequences fluctuate within a certain range (-8, 13), and the scoreRatio value of this paper’s method (1.505) is much better than that of the other four sets of models, which verifies the efficacy of its model in the application of the task of modeling the mechanism of human dance movement, and also verifies the reliability of cosine similarity method. This will enable better implementation of human movement mechanisms in dance anatomy into practical scenarios, help trainers to better perform dance training and performance, reduce dance injuries and prevent occupational diseases.
Aiming at the many problems in research resource management in private universities, this paper takes the integration of research resources in international business discipline of Xiamen Institute of Technology as an example, proposes a global integration and dynamic allocation model of research resources in distributed computing environment based on mobile agent (DCMA), and designs a dynamic bidirectional matching method of tasks and resources (DBMM) in order to improve the effectiveness of distributed computing. Experiments show that the proposed DBMM algorithm outperforms the LDCP algorithm and the hierarchical node sorting algorithm (SNLDD) in three metrics, namely, scheduling length, acceleration ratio and computational efficiency. Compared with LDCP and SNLDD, the scheduling length of DBMM algorithm is shortened by an average of 19.89% and 11.81%, the acceleration ratio is improved by an average of 19.77% and 9.26%, and the computational efficiency is increased by an average of 10.74% and 3.72%, which further improves the resource utilization rate of distributed computing system. Experiments were conducted using the research resource integration model, which achieved better efficacy in terms of probability value, goodness-of-fit, and stability of research resource integration in international business disciplines compared with the gray correlation analysis method. This paper provides an example reference for distributed computing system to realize research resource integration and efficiency improvement.
Aiming at the dilemma of corpus-based intelligent English translation, the article proposes an English neural machine translation method based on depth-separable convolution, which combines with the dynamic computation method to improve the semantic consistency of the translation system for semantic alignment and fusion. In order to verify the training effect of the proposed convolutional neural network model combined with the dynamic computation method, comparison experiments with one-way and two-way network models and baseline model with different cut-off granularity are conducted respectively. In order to better examine its performance in practical translation applications, online translation, machine translation and systematic methods are utilized for comparison. The BLUE values of this paper’s model for Chinese-English data translation in four different granularities of words, syllables, subwords and characters are 21.41%, 21.91, 29.25% and 20.40%, respectively. In 100,000, 200,000 and 500,000 training English-Chinese bilingual parallel corpus, the training time consumed by the model in this paper is 9.58 h, 15.94 h and 32.69 h. In practical application, the decibel range of the noise reduction of the translation system method designed by the research is distributed in [1.62 ~ 1.89], the average value of coherence is 91.1%, and the average compression rate and the average stability of the BLEU scores are 93.84% and 98.38%, respectively, and the results are better than the comparison methods.
This paper constructs a set of models for monitoring and evaluating the effect of Civics education through the research on the evaluation of Civics education based on educational big data environment. First, based on distributed gray cluster analysis, it analyzes and researches students’ Civics learning behavior, and explores learners’ learning characteristics by mining meaningful behavioral features for cluster analysis. The second is to design the Civics teaching quality evaluation model using principal component analysis, test the effects of population size and convolution kernel number on the performance of the Civics teaching quality evaluation model, and optimize the teaching quality evaluation model by using the dimensionality-reduced evaluation data. Distributed gray cluster analysis gets four clusters according to the characteristics of students’ learning behaviors, which are divided into excellent, diligent, average, and negative students.PCA selection of evaluation indexes found that the cumulative contribution rate of the first 10 principal component indexes to the evaluation of the quality of Civic Teaching in colleges and universities has reached 95.63%, which indicates that these 10 indexes can adequately evaluate the quality of Civic Teaching in colleges and universities. When the number of population size is taken as 31 and the number of optimal convolution kernels is taken as 19 values, the RMSE of the evaluation model is 0.01973, and the test time consumed is 0.0783ms, which is the best performance. The constructed Civics education effect monitoring model can effectively assess students’ learning behavior and efficiently and accurately evaluate the quality of Civics teaching.
This paper proposes knowledge representation based on knowledge graph embedding (TransE model) and based on deep wandering (DeepWalk model) to enhance the level of intelligent recommendation of knowledge points. Synthesize and construct a knowledge graph-based Civic Education model. Analyze the node centrality specifics of the model. Carry out a controlled experiment of model application and investigate student satisfaction on this basis. The three nodes with the highest node centrality are “life view and values”, “morality and law” and “patriotism and nationalism”. The average score of the test questions in the experimental class is 71.25, and the correct rate of the six types of test questions is higher than that of the control class. Most of the students’ satisfaction level with the intelligent teaching mode combined with the model was between 65 and 100 points. 92% of the students found the teaching mode interesting at a level between (75,100]. 90% of the students’ content mastery satisfaction level was between 85 and 100 points. Intelligent teaching using the knowledge graph-based Civics education model can help students improve their interest in learning Civics knowledge and construct Civics knowledge system.
English children’s literature has strong application value in educational content selection. This study takes classic English children’s literature texts as the research object, and constructs a semantic theme mining model based on the implicit Delicacy Distribution (LDA). Through keyword weight analysis and theme probability distribution calculation, multi-dimensional theme clustering and visual characterization of literary works are realized. According to the 2378 English children’s literature collected in the corpus, the LDA model was used to extract five core themes: “Adventure and Fantasy”, “Friendship and Teamwork”, “Growth and Self-Identity”, “Family and Affection”, and “Nature and Animals”.A semester-long controlled experiment was conducted with third-grade students in an elementary school in Guangdong Province, designing graded English teaching content based on the results of topic distribution. Through the questionnaire survey, vocabulary test and reading ability assessment, it was found that students in the experimental group significantly outperformed the control group in terms of active interest in learning (12.42% increase in mean value) and independent learning ability (15.67% increase in test scores) (p<0.05). The study shows that the educational content adaptation method based on the LDA theme model can effectively optimize the selection strategy of teaching resources, and provide a theoretical basis and practical path for the precise matching of literary themes and cognitive development stages in children's English teaching.
In this paper, finite element analysis is applied to the mechanical characterization of the foot. A finite element simulation model of the foot is constructed and its material properties are defined. Finite element analysis is applied to calculate the stresses on various tissues of the foot under different touchdown modes. Set up controlled experiments to verify the advantages of FEA technology in sports. The material property values of each tissue in the simulation model differed greatly, which was in line with the actual situation of biological tissues. In the 2 touchdown modes, the change curves of flexion and extension angles of the supporting foot were generally similar in the latter 75% of the supporting phase, and the differences were concentrated in the first 25%. The movement of the foot on the coronal plane showed a general tendency toward eversion. There were 2 peaks in the vertical ground reaction force variation in the heel-touch mode and only 1 peak in the non-heel-touch mode. The resistance impulse and power impulse ratios varied widely. The time of occurrence of the maximum contact stress on the talo-heel joint surface varied. P<0.05, the experimental group was better than the control group in terms of skill level, learning interest and initiative of the two groups of students after the experiment. The use of finite element analysis to assist physical education teaching can enhance students' enthusiasm and skill level.
The automation system is gradually applied to many fields because of its intelligent and efficient characteristics, and its energy control makes the equipment work in the optimal efficiency zone, however, the actual control effect needs to be further optimized. This paper explains the energy control problem of automation system for its control process, and uses the weighted residual value method to transform the original system into a system dynamics model. On the basis of this model, the optimal control is solved by the variational method, and the energy control algorithm based on the variational method is built by combining Lie algebra. The algorithm of this paper is used to establish the energy optimal control strategy and simulation experiments are carried out as a prerequisite for constructing the driving cycle. In the simulation experiments, the energy optimal control strategy based on this paper’s algorithm saves 4.77% of fuel, which shows that the energy control of the automation system under this paper’s algorithm is better and in line with the environmental protection needs.
Graph neural networks are widely used in educational research, and have strong application potential in the prediction of students’ comprehensive development and recommendation of personalized educational resources. In this paper, the information and characteristics of students are mined from massive learning data, and the prediction method of multi-topology graph neural network is used to realize the effective prediction of students’ comprehensive development. Through the graph neural network, knowledge graph and cluster search algorithm and other technologies, the personalized learning path planning and optimization are completed, and the personalized learning path is designed. The research shows that the data accuracy of the student development trend prediction model in this paper reaches the qualifying value of 0.1, and the absolute maximum value of the error does not exceed 0.17, so the model constructed in this thesis is effective and robust. It can fulfill the task of student development direction prediction. The usage frequency of generating learning paths are more than 60%, so the learning path generation method proposed in this paper is practical. And the average grade of the users who use this method is 6.17 points higher than the average grade of the users who do not use this method.
Industrial Internet based on distributed computing and cloud computing platform forms a “cloud-edge-end” cooperative system. Facing the problem of computing task offloading for machine-type communication devices in industrial Internet scenarios, this paper transforms the task offloading problem into a Markov decision process problem, proposes an online task offloading algorithm based on deep Q neural network (DQN), and designs an optimal scheduling method based on iterative optimization for industrial Internet resources. Simulation experiments are conducted by comprehensively considering the network environment and server state during the task offloading process, and compared with other resource optimization scheduling strategies. The results show that the DQN algorithm converges in about 9000 steps and has good convergence performance. The offloading strategy based on the DQN algorithm can effectively reduce the delay, energy consumption and total overhead of the computational task offloading system in the economy.
Currently, digital libraries face challenges in piracy and illegal distribution, data and privacy security, digital content identification and traceability. In this paper, we design a blockchain-based copyright protection system for digital libraries to provide true and reliable digital copyright information for libraries and users, and to ensure the security of data information stored in the digital copyright registration system. Firstly, we classify blockchain and analyze in detail the three core technical principles of consensus mechanism, cryptography principle, and hash algorithm. Then design the copyright registration protection system that contains the functions of unique authentication of digital work copyright, IPFS distributed storage, and privacy data encryption. The designed algorithm is tested for performance and the service performance of this paper’s scheme is analyzed in real applications, and it is found that the throughput performance of this paper’s algorithm when the number of nodes ranges from 4 to 20 is on average 36.19% more than that of the PBFT consensus algorithm, and 55.92% more than that of the RBFT consensus algorithm. When there are 5000 digital resource feature vectors in the system database, the time required for similarity retrieval is only 0.523s, which meets the requirements of the system’s non-functional needs for similarity retrieval runtime, and realizes a good balance between the operational efficiency of digital libraries and security. The research has practical reference significance for the application of blockchain technology in the field of digital copyright protection.
Acupuncture has been recognized by more and more experts as a treatment method to relieve various pains in human body, but the association between specific acupuncture treatments and diseases is still unclear, which affects the long-term development of acupuncture treatment. In this paper, we abstract the knowledge of acupuncture points as ontologies in the knowledge graph, and propose a method to improve the RoBERTa-WWM-BiGRU-CRF model to optimize the knowledge extraction of the knowledge graph by combining the SoftLexicon technique and the adversarial training method. Based on the knowledge graph of acupuncture points, the collaborative filtering model is introduced, and the original similarity matrix construction method is replaced by the co-occurrence matrix construction method based on the association characteristics of acupuncture points and diseases, which improves the operational efficiency of the association search and realizes the design of the association search technology of acupuncture points and diseases. The average consultation time in the acupuncture outpatient departments of the experimental and control groups applying this paper’s technology for acupuncture visits was faster than that of the full outpatient clinic by 0.32 min, showing a significant difference (P<0.05). Patients in the experimental group who received acupuncture treatment assisted by the technology of this paper were higher than those in the control group in the dimensions of acupuncture treatment experience, such as physiological reflections, treatment emotions, and treatment effects and treatment feeling dimensions, which were 2.22, 3.57, 2.2, and 1.33, respectively.
In-depth investigation of the combination of innovation and entrepreneurship education and computing technology is of great theoretical and practical significance for the continuous promotion of innovation and entrepreneurship education in colleges and universities. In this paper, after clarifying the three elements of environment, subject and behavior in the design of innovation and entrepreneurship education courses, we design an innovative teaching model for innovation and entrepreneurship education courses based on computing technology and digital learning environment, and adopt similarity metrics and questionnaires to count the frequency of students’ on-line learning behaviors and the level of cultivation of their innovative spirit and ability respectively. The results of teaching practice show that the practice of innovation and entrepreneurship education courses based on computing has a facilitating effect on the cultivation of students’ innovative spirit and ability. The Spearman correlation coefficients of the learning behaviors in the online teaching platform of innovation and entrepreneurship education courses and the dimensions of innovation spirit and ability show medium-high correlation (r>0.3), and its regression model can effectively explain more than 60% of the variance of innovation spirit and ability. The research in this paper provides an effective reference for the innovative development and practice of innovation and entrepreneurship education programs, and lays the foundation for promoting more effective and innovative development of dual innovation education in colleges and universities.
Natural language processing (NLP) is developing very rapidly in the field of artificial intelligence, and has become an important direction in the development of computer science field and artificial intelligence industry. In this paper, in order to realize the efficient construction of natural language processing model in low-dimensional embedding space, firstly, a word vector learning model is constructed based on matrix decomposition for word vectors in natural language processing. On this basis, in order to further realize the efficient construction of natural language processing models, this paper designs the Semantic Discarding Network (SDN) and Semantic Fusion Alignment Method (SFA) for the problem of interfering semantics of the model and the problem of a single way of fusion of local inference results. Finally, the SDF-NN natural language processing model is proposed and a multi-view subspace clustering (DLTE) method based on deep low-rank tensor embedding is proposed. The results of the research experiments show that the average performance index of this paper’s word vector model for each task in three corpora ranges from 71.55 to 89.11, and the performance is stable and the time overhead in the three corpora is 3.93, 7.29, and 13.42 minutes, respectively, and the speed of the model has been significantly improved and the overall performance is better. In addition, the natural language processing model (SDF-NN) constructed in this paper achieves the best performance in the comparison test with strong competitiveness, which further validates the performance of the matrix decomposition-based natural language processing model in this paper, and provides the method and direction for its efficient construction in low-dimensional embedding space.
The risk of financial aspects intuitively reflects the development status and operating results of enterprises, enterprises must control the financial risk of this key link, so that the financial risk of a safe landing, to protect the stability and health of the enterprise. This paper selects the financial data of listed companies, and comprehensively analyzes the level of the company’s financial performance from four aspects, namely, profitability, operating capacity, growth capacity and solvency indicators. Using Benford’s law to test the quality of each data of each financial indicator, the Benford factor is introduced as a new explanatory variable, and combined with the company’s financial risk early warning indicators to establish a random forest early warning model. The results show that profitability and growth capacity are the strengths of listed companies, while operational capacity and solvency are the weaknesses. The results analyzed by K-means clustering algorithm show that the sample companies are divided into 5 categories. And compared with the basic random forest model, the random forest model based on Benford’s law can improve the accuracy of financial risk warning. Finally, the model with the best prediction effect is used to judge the financial status of G listed companies, get the early warning results, verify the accuracy and applicability of the model and put forward corresponding countermeasure suggestions.
The development and utilization of shale gas is the main path to solve the current high carbon dioxide emissions, and this paper proposes to use the LEAP model to explore the role of shale gas development and utilization on carbon emission reduction in all aspects. Under the principle and definition of LEAP model framework, shale gas development and utilization scenarios and parameters are determined to facilitate the research and analysis work, and in order to realize the intelligent monitoring of carbon emission reduction work, the neural network two-layer carbon emission reduction prediction model is constructed. With the support of research data and LEAP model, the relationship between shale gas development and utilization and carbon emission reduction is studied and analyzed, and the carbon pulse analysis and prediction model validation model of LEAR simulation results are also supplemented. Although all three scenarios have different contributions to carbon emission reduction, the green scenario is the most obvious means of carbon dioxide emission reduction, with a total of 52.87 from 2010 to 2050, and the prediction model in this paper is able to satisfy the current demand for carbon dioxide emission reduction work, and provide a guiding reference for urban carbon emission reduction.
The rapid development of artificial intelligence technology has made its application in the field of education increasingly widespread. The purpose of this paper is to design and implement a personalized vocal music teaching system based on artificial intelligence algorithms to solve the problems of single teaching method and lack of personalized guidance that exist in traditional vocal music teaching. The overall architecture of the system is constructed by analyzing the demand for vocal music teaching and combining deep learning and other artificial intelligence technologies. The key algorithms involved in the system are elaborated in detail, including the personalized recommendation algorithm of the learning path fused with the long and short-term memory network (LSTM) and the attention mechanism, and the intelligent evaluation algorithm that includes the evaluation of pitch, rhythm and timbre. Through practical application cases, it is verified that the system in this paper can effectively improve the teaching effect of vocal music and students’ vocal music professionalism, providing an important auxiliary role and key ideas for the innovative development of vocal music teaching.
This study firstly introduces the working principle of deep learning-based neural machine translation model (NMT) and its recurrent neural network translation backbone network, which enhances the semantic characterization capability through Glove word embedding layer. A tree-to-sequence based attention mechanism is innovatively introduced at the encoder side, and a tree-based encoder is appended to the traditional sequence encoder to construct syntax-aware context vectors. On the decoder side, the syntactic tree structure information is integrated into the sequence-to-sequence model (seq2seq), and this model is used to explore the knowledge transfer effect of the English translation teaching process. The results show that the accuracy rates of the neural machine English translation models incorporating syntactic information proposed in this paper are all above 90%. The experiment on the effect of English translation teaching shows that the mean values of students’ scores on the post-test of long sentence translation and composition translation in the reading section of the experimental class increased by 11.022 and 12.5388 points respectively compared with those of the control class, with significant differences between the scores of the two groups of students (p<0.05), and the same significant differences are presented between the scores on the pre-test and post-test of the students' scores on the long sentence translation and composition translation in the experimental class. It can be seen that the application of the model can effectively promote knowledge transfer and help students better understand and utilize translation skills.
In order to realize the intelligent calculation of cost management during the implementation of construction projects, this paper proposes a methodological architecture based on Multi-intelligent Reinforcement Learning (MARL) and Building Information Model BIM. The construction cost management problem of the project is analyzed with examples in order to optimize the construction cost management and construction time management of ZZYH comprehensive business building. The results of the study show that a reasonable rebar path can be found through 40 independent simulation verifications, and the final convergence reaches 100%. Compared to manual savings, the collision-free rebar design using the computational framework of BIM and multi-intelligence saves roughly 90% of the time. In terms of optimizing the construction cost management of civil engineering, installation engineering, cable engineering, and overhead line engineering, the total amount of cost savings of the project amounted to 382,320,000 yuan.
Based on the material demand forecasting model using BP neural network and particle swarm algorithm, the study builds the material whole chain response efficiency calculation model under dynamic multi-objective optimization by comprehensively considering the demand level weights of the affected area, and adopts genetic algorithm to assist the model solution in finding the optimal and decision-making. Taking an earthquake as a case for example analysis, the model in this paper can give the Pareto frontier, and combined with the weight coefficients after the transformation of the model solving results are more scientific and feasible, the demand satisfaction rate of the original model and the transformed model are 73.43% and 74.28% respectively, and the demand satisfaction rate of the affected points is improved by 4.24%, and this paper introduces the material allocation model of the demand level weights to be able to obtain better response efficiency of the whole chain of materials, which can provide important theoretical and practical guidance for the whole chain distribution of materials.
With the rapid development of digital technology, the inheritance and dissemination of folklore sports culture have ushered in new opportunities and challenges. This paper constructs a digital educational resource management platform for China-ASEAN folklore sports culture based on Knowledge Graph. The knowledge system of folklore sports culture is systematically constructed by using Knowledge Graph, the data related to China-ASEAN folklore sports culture are collected and organized, and the construction of the corpus of China-ASEAN folklore sports culture domain is completed. Then we extracted knowledge from the data of folklore sports culture domain and stored the obtained knowledge in Neo4j graph database. The China-ASEAN Folklore Sports Culture Digital Education Resource Management Platform, which includes several modules such as login and registration, courses, personal center, institutions and teachers, and backstage management, was designed. The construction and application of the platform gained 91.2% satisfaction from students, enhanced students’ interest in learning folklore sports culture, helped to protect and pass on the rich China-ASEAN folklore sports cultural heritage, and also promoted in-depth exchanges and communication between the two sides in the field of sports and culture to build a community of human destiny.
In this study, generative adversarial network is used as the basic architecture, and the multi-head attention mechanism is introduced to enhance the model’s ability to perceive and process image features. The image generation process is optimized by bilinear interpolation to further enhance the detail expression of character design. The generation efficiency of the model and the quality of the IP image are improved by the improved network structure. A personalized recommendation model with implicit feedback and explicit feedback is also used to achieve targeted placement of IP image characters for agricultural and sideline products cartoons. The study combines the local characteristics of Jilin Province, taking Jilin rice as an example, and designs two rice brand IP images with regional characteristics, “Rice Xiaoji and Rice Xiaoling”, which have a good migration effect. When the recommended list length is Top=10 and 20, the recommendation effect of internal diversity of Jilin rice brand reaches 83.47% and 89.09% respectively, and the recommendation effect of overall diversity reaches 88.43% and 95.31% respectively. It can be seen that the method of this paper can improve the market competitiveness of agricultural and sideline product brands in Jilin Province, which provides a technical path and practical reference for rural revitalization in Jilin Province.
Supply chain inventory forecasting and control is an integral part of supply chain management system, and it is a focus that industries must pay attention to in their operation and management. In this paper, the supply chain inventory demand forecasting model is constructed from the perspective of supply chain end, combined with the Transformer model in AIGC technology. The DL-Informer model is used to improve the Transformer model, realize the feature fusion of graph convolutional neural network, design and solve the feature graph adjacency matrix and complete the information fusion of each feature subgraph to improve the prediction accuracy. Aiming at the problems faced by supply chain inventory demand forecasting, the traditional algorithm with strong local optimization ability is combined with the genetic algorithm, and the hybrid genetic algorithm (HGA) is proposed to solve the nonlinear optimization problem. In the supply chain inventory forecasting practice, when the forecast length is 12, the MSE, MAE and RMSE index values of this paper’s forecasting model are 0.202, 0.174 and 0.416, respectively, which have more stable long-term forecasting performance compared with other models. And in the nonlinear simulation optimization experiments, the HGA algorithm shows good convergence and outstanding optimization effect in the nonlinear problem of supply chain inventory.
Under the guidance of relevant theories and techniques, this project binarizes and segments red cartoon images, and then extracts their contour features. Neural network classifiers are used to identify and classify the outline features to realize the acquisition of visual symbols of the revolution in the history of Chinese red cartoons in the past 100 years. With the help of Pierce semiotics, the system of revolutionary visual symbols is constructed, and the system is explored in depth. Compared with other models, this paper has a high superiority on the recognition of revolutionary visual symbols in Chinese centuries-old red cartoons, and seven items of revolutionary visual symbols are extracted, specifically, flag, badge, gear, pentagram, wheat ear, hammer and sickle. In addition, the visual symbol system of the revolution has a high degree of recognition, for example, the CMYK value of the flag is 0, 100, 100, 0, and its color is red, which symbolizes the red of “passion and revolution”, which well reflects the “red years” of China’s development and the fruitful results of the revolution and construction. The fruit of construction.
In this paper, first of all, the data preprocessing of ethnic patterns is carried out through image segmentation and grayscaling processing methods, and then the image processing technology is applied to the feature extraction of ethnic dress patterns, and the improved SIFT algorithm is used for the feature extraction of images. The original DCGAN algorithm feature extraction ability is weak generates style picture fuzzy, the effect of the problem of poor, proposed the use of 32-layer deep neural network with residual structure instead of the original 5-layer shallow feature extraction network, significantly enhanced the algorithm’s feature extraction ability, enhance the model of the style migration effect. By introducing the objective evaluation index PA of the improved SIFT algorithm, the algorithm was compared with other algorithms, and the segmentation algorithm experiments were carried out with the local patterns of several images, and the results of pixel accuracy PA were obtained to be greater than 0.95, which confirmed that the improved SIFT algorithm was able to realize the accurate extraction of the contours of local patterns. In terms of pattern quality evaluation dimension, the subjective average scores of the amateur group and the expert group are 4.87 and 4.89 respectively, indicating that the ethnic patterns generated by the algorithm of this paper have reached a high standard in quality.
With the accelerated pace of society and the increasing pressure of competition, the issue of mental health has received increasing attention. Especially in the field of education, students’ mental health status directly affects their student outcomes and overall development. The aim of this study is to design a mental health status monitoring system based on large-scale data streaming computation, to realize dynamic real-time monitoring of individual mental health through multi-source data acquisition and efficient algorithm processing, and to explore its application in educational scenarios. Sliding window algorithm and Hidden Markov Model are used to analyze and process the collected multi-source data such as physiological signals, and the experimental results show that the system is able to significantly test the difference between people with high and low scores on psychological test scales in the monitoring of mental health status, and it can provide educators with valuable decision-making support and help students’ mental health education and intervention.
Solving the health problems of key populations such as people with disabilities is an important way to realize universal health coverage and promote social equity. Sports as the main means of rehabilitation for disabled people at present, this study clarifies the concept of disabled people and sports rehabilitation, and uses empirical investigation to analyze the plight of cruel sports, laying a realistic and theoretical foundation for this paper based on high-dimensional rehabilitation data optimization. In the process of sports rehabilitation exercise for the disabled, it is generated through the modular control of multi-skeletal muscles by the central nervous system as well as the regulation of neural oscillations. And the surface EMG signal is the combined effect of superficial muscle EMG and electrical activity on the nerve trunk on the skin surface, which belongs to the high-dimensional data characteristics. For this reason, this paper constructs a rehabilitation exercise muscle synergy model using matrix decomposition for the rehabilitation of disabled people’s sports. The data were refined in the time domain by adding time windows, and then the data were decomposed into targets based on the non-negative matrix decomposition method to extract the muscle synergy features in each time window, so as to analyze the muscle synergy differences in different exercise processes and different feature frequency bands, and to further obtain the muscle synergy law during exercise and the physiological change mechanism of the nervous system during exercise control. Finally, the experiments were carried out in both non-electrical stimulation and electrical stimulation modes, and the results showed that the number of muscle synergism in wrist flexion and extension was the same in both modes, which was 3. However, the number of synergistic pairs of muscles in the electrical stimulation mode was significantly increased. It also proves the effectiveness of the method of this paper on the analysis of muscle synergy of multi-channel surface EMG signals, which provides a new method for exploring the muscle synergy characteristics and the control mechanism of rehabilitative movement in the process of disabled people’s sports.
In order to cope with the damage of urban electricity and the dilemma of residents’ electricity consumption caused by flooding disaster, we study the dynamic planning of intelligent operation and maintenance equipment scheduling and distribution network restoration under flooding disaster. Consideration is given to both pre-disaster deployment and post-disaster scheduling levels, while dynamic planning is carried out for collaborative repair and energy storage scheduling to construct a scheduling model with multi-source collaboration. Based on this, a multi-resource cooperative post-disaster recovery strategy for distribution networks is further proposed. The usability of this paper’s multi-source cooperative strategy is studied in depth through case analysis. Among the six Cases of the simulation experiment, the total cost in Case 1, which is operated and restored according to the strategy proposed in this paper, is the lowest, which is only 257080.2 RMB. The maximum, minimum, and average values of the solution time of the multi-source cooperative strategy are much faster than those of the comparison methods, and it has obvious advantages in fast decision making. The multi-source synergy model in this paper is able to recover all the loads within 285 min, while the finite synergy model takes 330 min. The multi-source synergy model was able to recover 7,500 kW of load, while the limited synergy model was only able to recover 6,850 kW. The multi-source cooperative model has strong applicability.
Organic light-emitting diodes (OLEDs), as a new generation display and lighting technology, are critical for charge transport and luminescence efficiency enhancement. The study determines the potential, electron and hole concentrations in OLED devices based on Poisson’s equation and current continuity equation, and fits the charge transport process in the devices with the drift-diffusion model. The differential equations are solved by improved Euler’s method and iterative solution method to simulate the operating state of the OLED device. In conjunction with experiments, the enhancement effects of the OLED devices optimized based on the differential equation model in terms of charge transport and luminescence efficiency are analyzed. The optimized device and the comparison device exhibit the same partial pressure and a largely overlapping luminescence curve at 450~460 nm, but the optimized device lifetime and brightness are better than the comparison device. The charge transfer efficiency of the optimized device exceeded 99.99%, while that of the comparison device was lower than 95%. In addition, the light extraction efficiency of the optimized device is more than 20% higher than that of the comparison device, and it has the highest current efficiency, i.e., the optimized device has a better luminescence efficiency. The differential equation model is used in OLED devices to describe the processes of charge transport, optical properties, etc. The model can be used to systematically optimize the material properties and improve the overall efficiency of OLED devices.
This paper proposes to design the power meter based on TMR current sensor, screen the chips that meet the requirements of the power meter, and stipulate the technical specifications and technical parameters of the power meter based on TMR current sensor. Design the system structure of power meter with TMR current sensor including MCU module, storage module, communication module and so on. And design the main and vice system clocks in the single-phase energy meter with TMR current sensor. Analyze the design of signal acquisition module, bias adjustment and temperature compensation module, communication module and circuit protection module in the current monitoring system. According to the characteristics of the TMR sensor, establish the objective function, improve the GWO algorithm, and optimize the design of the multi-stage magnetic ring structure current sensor. The performance parameters of the TMR sensor are analyzed, and the DC current test and AC current test are conducted to verify the performance of the TMR current sensor measurement module. The accuracy, precision and linearity of the current measurement module are tested, and the relative error between the actual current value and the theoretical current value derived from the formulae in the DC current test and the AC current test are controlled within 5% in the TMR current measurement system. The measurement system based on TMR current sensor meets the current measurement requirements.
Artificial intelligence technology can effectively improve the quality and efficiency of industrial design and manufacturing, so the study takes Shuangdun Carved Symbols of cultural products as an example, utilizes the generative adversarial neural network to carry out style migration processing in the design of Shuangdun Carved Symbols and their products, and constructs the DCGAN model to assist the design and generation of Shuangdun Carved Symbols of cultural products. After semantic analysis of the color symbols of Shuangdun Carved Symbols products generated with the aid of DCGAN model in this paper, quantitative and qualitative measurements are carried out. Users of Shuangdun Carved Symbols products rated the products after the style migration significantly higher than before the migration in terms of volumetricity, distance, emotion, character, and texture.CycleGAN and DCGAN models achieved the best overall results in terms of PSNR, SSIM, FID, and KID indicators. The DCGAN model with added spectral normalization and Res2Net outperformed the CycleGAN model in the ablation experiments. The overall user rating of the Shuangdun Carved Symbols product designed by the DCGAN model in this paper is 4.24, and the product has obtained more satisfactory evaluation results.
Under the new situation of continuous and stable development of China’s economy, large products have extremely high requirements on transportation safety due to their high price, complex transportation technical requirements, which determines that large products should be delivered to customers in the safest and most economical way, which poses a difficult problem for decision makers to choose the optimal path. In this paper, we constructed an intelligent approval framework for bulky transportation, made technical and economic analysis of transportation routes, and established a multi-objective optimization mathematical model for path selection of bulky transportation vehicles. A hybrid genetic algorithm incorporating greedy strategy is proposed to solve the problem, which strengthens the ability of the algorithm to jump out of the local extremes and selects the optimal chromosome in the final population as the resulting optimal solution. The results of the approval and optimal route planning for bulky transportation are verified by the method of example experimental analysis. The volume of bulky transportation increases with the increase of years until 2023, and the GDP, value added of tertiary industry, total population, and road mileage are 1015987.54, 553948.15, 140563, and 536.48, respectively. In the instances where the number of orders is 2000 or more, the transportation distance, the maximum number of service bundles of orders on the route, and the maximum service hours of vehicles the mean values are 50, 3.56, and 14.33, respectively. According to the constructed mathematical model, the optimal line for the bulky transportation scheme is 0→2→4→7→8, and the total transportation cost is 670,500,000 yuan, of which the transportation costs are 116,500,000 yuan, 320,000 yuan, 151,000,000 yuan, and 83,000,000 yuan, respectively.
Aiming at the problems of unfixed switching frequency and complicated calculation in the control of permanent magnet synchronous motor, a permanent magnet switch FNN-PID control strategy based on deep learning technology is proposed. Based on the vector control of permanent magnet synchronous motor, the resonant pole inverter is combined with permanent magnet switch control, and then the fuzzy neural network and incremental PID algorithm are used to construct the optimization strategy of permanent magnet synchronous motor switching frequency FNN-PID control. And combined with the finite element simulation software, the permanent magnet switch finite element model is constructed, and the effectiveness of the FNN-PID control strategy is illustrated by verifying the permanent magnet switch control strategy and the temperature rise curve change. When using the FNN-PID control strategy, the electromagnetic torque quickly reaches stabilization near the given torque of 9 N-m after 0.03 s of startup, and the permanent magnet switch frequency of the FNN-PID control strategy is reduced by 24.04%. The difference between the measured maximum winding temperature and the calculated maximum temperature under rated operating conditions is less than 9°C, and the permanent magnet switching loss is reduced by about 35% with the FNN-PID control strategy compared with the traditional MTPA control strategy. Therefore, the combination of deep learning technology and finite element analysis can explore the optimization effect of PM switches from the strategy and application dimensions and provide research ideas for the stable operation of PM switches.
The ring network cabinet of the distribution network is an important part of the urban power system, and its operation state directly affects the stability and reliability of the power system. In this paper, a deep learning algorithm is used to analyze and process the partial discharge signal, and a permanent magnet fast ring main unit partial discharge detection and fault identification model based on improved DBN-LSTM is proposed. By analyzing a large amount of local discharge signal data under normal operation and fault conditions of ring main cabinet, and using these data to train a deep learning-based fault prediction model. The performance of the improved DBN-LSTM model is tested by combining the defect spectrograms of four typical ring network cabinet partial discharge models and compared with other algorithms. The proposed model has good effect on fault identification of ring network cabinet, with a combined identification accuracy of 98.41%, and the overall identification performance is better than both BP neural networks and SVM classifiers. The prediction accuracy of the fault prediction model also reaches 88.52%, and the experimental results of the method in this paper are more satisfactory.
Syntactic analysis is a basic work in the field of natural language processing, which explores the syntactic structures and their interaction relations in sentences. This paper first describes the basic approach of syntactic analysis, and explores the computational method of Chinese syntactic structure classification from large-scale corpus construction. Then, a grid-based large-scale corpus construction and distribution model is constructed. And the word embedding model BERT is used as the pre-trained language model, and the captured semantic features are input into the Bi-LSTM model to extract the contextual bidirectional sequence information, and the results of Chinese syntactic structure classification are obtained by the Conditional Random Field (CRF) processing. Through manual proofreading as well as the calculation of confidence level, the average correct rate of syntactic structure classification of the final Chinese canonical corpus is increased from 94.21% to 99.06%, which is an improvement of 4.85%. The syntactic structure classification accuracy of the BERT-Bi-LSTM-CRF1 and BERT-Bi-LSTM-CRF2 models with “complement structure” and “object structure” were higher than those of the BERT model, the Bi-LSTM-CRF model and the BERT-Bi-LSTM-CRF3 model with all syntactic structures. Meanwhile, the accuracy of the syntactic structure annotation method of BERT-Bi-LSTM-CRF model + manual differs from that of manual annotation by only 0.66%, and the average time spent is reduced by 37.04%, which reduces the workload of the annotators and improves the efficiency of the annotation, which verifies the validity and practicability of this paper’s model in automatic classification of Chinese syntactic structures.
The construction of dual prevention mechanism is a necessary way to solve the problem of “not recognizing, not thinking, not managing well” in the field of enterprise safety production. This paper combines the elements involved in the theoretical framework of the dual prevention mechanism, constructs two evaluation index systems of safety risk classification and the operation effect of the dual prevention mechanism, and then establishes an evaluation model based on the multi-level analysis method and the fuzzy comprehensive evaluation method, to explore the operation effect of the dual prevention mechanism in the enterprise. The evaluation results show that after the dual prevention mechanism of safety risk classification and hidden danger investigation and management strategy is operated in S enterprises with higher safety risk level (1.50 points), the awareness of safety production and the level of intrinsic safety of the enterprises have been significantly improved, and the average value of the evaluation of the operation effect of the dual prevention mechanism in enterprises is 3.91 points, which reaches a good level. The research results of this paper not only have strong guiding significance and practical help for the optimization of risk management of production safety in enterprises, but also can be used by the same type of enterprises and even other enterprises in optimizing the risk management of production safety and the management of hidden danger investigation.
Digital auditing has become the key to the transformation and upgrading of the auditing field. Financial audit data anomaly detection needs to combine multiple aspects of information, and it is of great practical significance to utilize the existing technical means to discover financial anomalies in the limited content. In this paper, based on the limitations of the weighted KNN deep neural network algorithm, a multi-branch deep neural network is proposed and a cost-sensitive loss function is designed. Combining the qualitative and quantitative methods of risk assessment, the enterprise audit risk assessment index system is constructed, the indexes are standardized, and the results of enterprise audit risk assessment are analyzed. The specific application effect of the assessment model is analyzed from the aspects of industry status and key financial performance, and the relevant strategies for corporate audit risk response are proposed. In the 1st risk assessment, 8 of the 20 enterprises are above higher risk, 6 are medium risk, and 6 are below lower risk. The results of the 2nd audit risk assessment have varying degrees of reduction between -0.3663 and -0.0119. From 2017, the overall net profit growth rate of enterprises is decreasing year by year, especially in the period from 2019 to 2020, and the net profit growth rate of the industry in 2020 is -24.87%, which predicts that the future development of the industry is not optimistic.
With the rapid development of blockchain technology, consistency assurance of distributed database has become one of the key issues. In this paper, a blockchain distributed database consistency assurance mechanism based on the practical Byzantine fault tolerance (Rpbft) algorithm and its improved algorithm is studied in depth.The RPBFT algorithm combines the RSA algorithm and the PBFT consensus algorithm, and then performs the signature operation after message encryption in order to increase the system security. Aiming at the shortcomings of the master node selection mechanism of the original algorithm and the RPBFT algorithm, a master node selection mechanism that includes the time factor is proposed, which introduces the role of the recording node, so that the waiting time of the node can be adjusted dynamically. Meanwhile the algorithm changes the conditions of view switching and reduces the system consumption. Through simulation experiments to verify the performance of this paper’s R-PBFT algorithm and OmniLedger and RapidChain two programs in the same network conditions, this paper’s algorithm compared to the comparison algorithm can be more effective in guaranteeing the consistency of the distributed database, when the number of slices is 20, the transaction latency time is 13s, 25s lower than that of RapidChain and OmniLedger, respectively. When the number of shards is 20, the transaction delay time is lower than that of RapidChain and OmniLedger by 13s and 25s respectively, which provides strong support for the application of blockchain technology in the field of distributed database.
Urban spatial structure and three-dimensional perspective can express personalized city brand image, which is an important feature of city brand form. In this paper, computer graphics technology is applied to design a city 3D modeling algorithm based on point cloud fusion, which transforms city information into city spatial visual symbols, and then carries out the innovation of city brand image morphology. Firstly, on the basis of binocular stereo vision, tilted image generation modeling technology is utilized to realize texture mapping 3D dense point cloud structure network. Aiming at the lack of accuracy of the sparse point cloud and the existence of noise points and mesh voids due to the influence of occlusion and shadows, we design the stereo vision PMVS algorithm based on the faceted slice in order to realize the densification of the point cloud. The algorithm performance is tested on the dataset using standard 3D reconstruction evaluation metrics F-score, chamfer distance (CD), and the application analysis of segmentation and merging execution efficiency for building clusters, optimization effect of rectangle fitting, and height calculation of building clusters, and the study finds that this paper’s algorithm is ahead of the baseline model in 13 categories. When the number of regions reaches 70,000, the traditional RAG method takes 26.9 seconds, while this paper’s algorithm only takes 14.8 seconds. The time consumption reduction reaches more than 40%. The average score of the aesthetic assessment of the city brand design is 83.47 points, and the 10 experts’ evaluation of the spatial aesthetics is above 90 points, and the design is unanimously recognized. The study makes a useful exploration for the innovation of city brand image under the conditions of cutting-edge information technology.
The study of the impact of climate change on permafrost and the response mechanism in the Upper Irtysh River Basin can help to comprehensively understand the impact of climate change and grasp the development of coping strategies. In this paper, the one-dimensional heat conduction equation is used as the core to propose a model for calculating the distribution of permafrost in the upper Irtysh River Basin and the boundary conditions for solving the model, and the model is simulated and solved by using the general form of partial differential equations in the COMSOL Multiphysics finite element analysis software. Subsequently, the simulation results and regression equations are combined to investigate the driving effect of meteorological data changes on permafrost depth distribution changes. The simulation results found that the meteorological factor regression model could explain 30.6% of the variation in maximum permafrost depth, with mean annual relative humidity driving permafrost depth to the greatest extent (Beta = -0.251). This paper finds that the driving effect of meteorological factors on permafrost depth change provides a new perspective for understanding the dynamical mechanism of permafrost change in the upper Irtysh River Basin, and also provides a scientific basis for predicting and responding to the impact of future climate change on permafrost.
In this paper, the basic structure of fuzzy integral-based multi-classifier fusion model is used as a reference to construct Choquet integral vectors, measure the similarity of English sentences, and construct a fast retrieval algorithm for English sentences based on Choquet expectation. Determine the algorithm threshold and compare the running time of similar retrieval algorithms. Deploy the algorithm into the English sentence retrieval model for dataset training and comparison experiments. Verify the model robustness and determine the chosen K value for the model. Further use the test set to compare the retrieval effectiveness of the model with the traditional semantic retrieval model. The algorithm threshold is set to 6 to improve English sentence recall. The running time consumption of the algorithm is 0.827s and 1.941s, which is lower than the other three similar retrieval algorithms. In the dataset comparison experiments, the algorithmic model of this paper scores better than the comparison model in all 5 evaluation metrics. The model has the best robustness when k takes the value of 15. The model check accuracy and check completeness are higher than the semantic retrieval model LM by nearly 8 percentage points. The fast retrieval algorithm for English sentences based on Choquet expectation can improve sentence retrieval timeliness and retrieval accuracy, and reduce retrieval energy consumption.
The development of digital technology provides more possibilities for the inheritance of Chinese excellent traditional handicrafts. This paper takes Chinese movable type printing as the research object, and develops and designs a user-oriented virtual experience system by combining its handicraft characteristics. In order to optimize the rendering of real-time images and video frames of the virtual scene in this system, this paper takes the deep learning oversampling algorithm as the basic framework, and uses two major types of neural network structures, namely convolutional neural network (CNN) and recurrent neural network (RNN), to carry out the rendering reconstruction, and at the same time, it uses the texture enhancement oversampling algorithm to recover the image texture details, improve the edge sharpness of the image, and comprehensively build the DLSS model. The performance of the DLSS model constructed in this paper and the virtual experience system of movable type printing is tested successively. The average score difference between the pre- and post-tests of the virtual experience system of this paper is 34.46, which is much higher than that of the traditional form of knowledge mastery of 20.76, indicating that the virtual experience system supported by this paper’s algorithms can effectively carry out the inheritance of traditional handicrafts.
Phishing has become an increasing threat on online networks with evolving Web, mobile device and social networking technologies. Therefore, there is an urgent need for effective methods and techniques used to detect and prevent phishing attacks. In this paper, a phishing detection model based on decision tree and optimal feature selection is proposed. An optimal feature selection algorithm based on a newly defined feature evaluation metric (f_Value), decision tree and local search is designed to prune out negative and useless features. The overfitting problem in the process of training neural network classifiers is mitigated. The optimal set of sensitive features for feature selection and the optimal structure for training the neural network classifier are constructed by tuning the parameters. Experiments on CART-based phishing detection system and comparative experiments based on different phishing detection models are also conducted. The experimental results show that the model precision, accuracy, and recall of the improved decision tree-based algorithm proposed in the article are 92.7%, 96.5%, and 88.3%, respectively, on the dataset of phishtank, and the three metrics are 98.3%, 99.1%, and 99.5%, respectively, on the datasets of Vrbanˇciˇc-small and show that the proposed CART has a higher performance than the many existing method models.
Image segmentation, as an important direction of computer vision, is gradually being applied to a variety of fields, however, the existing image segmentation methods still need to be improved in terms of segmentation accuracy and effect. In this paper, the variational level set method is used as the level set image segmentation method, and its theoretical basics and solution method (gradient descent flow method) are described in detail. For the problem of insufficient gradient vector flow in the traditional parametric active contour Sanke model, a global gradient vector flow model that can overcome the noise interference is given to obtain a more accurate gradient field, thus combining with the variational level set method to build an image segmentation model based on global gradient vector flow (GGF Snake). In the comparison experiments with three commonly used image segmentation algorithms, the DSC value of this paper’s algorithm reaches more than 96.00%, and the time used is less than 15s, which is better than the remaining three algorithms, and verifies the superiority of this paper’s algorithm.
Jiangnan gardens have become a valuable cultural heritage of China with its elegant garden style. The article proposes a binocular visual recognition system by analyzing the composition of the garden spatial elements and performing feature fusion based on scene-driven coefficients. Ablation experiments are conducted on each part of the constructed data enhancement framework for generating the design of the Jiangnan garden plan, which is applied to generate a set of high-quality datasets and apply the data to image segmentation for generating the design of the Jiangnan garden. The algorithm training is carried out by applying the generated design plan dataset. On this basis, the data from the actual Jiangnan garden research and the spatially quantized feature data are used to do the correlation analysis between the design elements and the aesthetic mood. The data enhancement framework constructed in this paper improves the IOU of ST elements to 0.537, and the average intersection and merger ratio MIOU is 0.389. It shows that the data evaluation framework based on visual recognition is suitable for the study of plan generation of Jiangnan gardens. The correlation coefficients of connection value, spatial control value, average depth value, and integration degree regarding aesthetic context with the data of Jiangnan garden design elements are 0.173, 0.301, -0.278, and 0.325, respectively, which indicate that there is a significant correlation between all of them.
Based on the concept of “user-centered”, this paper designs a product form optimization model based on ant colony algorithm. Through mining the online reviews of the products, we determine the perceptual imagery of users, and categorize the perceptual imagery and determine the weights from the perspective of user satisfaction. Combining the factor analysis of perceptual imagery and the contribution value of morphological features on perceptual imagery, the product morphology optimization fitness function is constructed. Solve the model according to the basic principle of ant colony algorithm, and study the decision-making method to assist product optimization. Take a brand A model forum word-of-mouth data as an example to analyze, obtain users’ perceptual imagery through SO-PMI algorithm, and assign values to perceptual intention weights with the help of cluster analysis. Determine the contribution value of morphological features through the SD investigation of product morphological differences. Genetic algorithm is introduced to carry out comparative experiments to verify the superiority of ant colony algorithm in optimizing model solving. Finally, the application effect of the predictive model solving scheme is analyzed through user satisfaction survey. The results show that the output of the product optimization design model based on ACO algorithm Model A is 8. 23.11% of the users are very satisfied with the optimized Model A, 65.55% of the users are satisfied, and 85.72% of the survey respondents are very willing and ready to buy the optimized Model A.
The evaluation of English course goal attainment is an important basis for colleges and universities to judge whether the goal of cultivating foreign language talents has been achieved. This paper proposes a method for quantitative assessment of course goal attainment according to the OBE concept. Calculating the importance of attributes about classification, the decision tree algorithm based on rough set is proposed, combined with association rules for deep mining of educational data. Collect quantitative educational data and questionnaire data of a university, modeling relying on SPSS Modeler 14.2, and outputting decision tree of influencing factors. Using the evaluation of course goal achievement to analyze the achievement of A4 course goals, and exploring the association rules of influencing factors based on the decision tree. The traditional decision tree algorithm is introduced as a control group to evaluate the performance of the rough set-based decision tree algorithm. The results show that the achievement degree of each sub-objective of A4 course is higher than 0.70, and students who have the achievement degree of A4 course objective greater than 0.7, the nature of their major is foreign language and they have passed the Grade 4 test have a higher possibility of achieving the final foreign language talent cultivation goal of the university. The precision of the assessment method based on rough set decision tree is maintained at about 88%, and the accuracy rate is basically maintained at about 90%.
Frequent outbreaks of cyanobacterial blooms in Lake Taihu are undoubtedly a great threat to the economic development of its neighboring areas and the safety of drinking water of its residents. This paper takes Taihu Lake as the study area and analyzes its geographic location information and development status. Then, based on the remote sensing data from MODIS and Landsat 8 satellites, the normalized vegetation index is improved to identify the blooms, and the dynamic detection method of cyanobacterial blooms is constructed by combining with the remote sensing inversion of water temperature. At the same time, the spectral performance of each band is integrated to excavate the characteristic information of cyanobacterial bloom, and the algorithm in this paper is used to process the satellite remote sensing data of cyanobacterial bloom in Lake Taihu to analyze its spatial and temporal distribution characteristics, which is used as the basis of the dynamic warning model for early warning. Then the LightGBM method is introduced to realize the all-weather spatial and temporal continuous monitoring of cyanobacterial blooms in Lake Tai. Analyzing the monitoring data of this paper’s model on the intraday change process of cyanobacterial bloom in Lake Taihu, it is found that the trend of intraday change in the area of cyanobacterial bloom in Lake Taihu in different seasons is relatively consistent, with the highest area of the bloom in autumn, accounting for 21% of the area of Lake Taihu’s water body. The study pointed out that after entering the fall, extra attention should be paid to the monitoring, prevention and control of cyanobacterial bloom in Lake Taihu.
In this paper, we design and implement a model network for English writing style generation using UNet network as well as ViT for encoding and decoding, and PatchGAN to enhance the identification speed. Based on the CRF-NLG model to identify and extract professional English terms, and design a special loss function to optimize the quality of writing style generation. The F1 value is used to evaluate the model recognition ability, and the writing style generation effect is explored by controlled experiments of the proposed model and three baseline models. The practical application results of the proposed model are visualized from four perspectives: overall evaluation, style strength, content preservation, and fluency, to verify its practical application effect. The results show that the proposed model exhibits the strongest performance in the two levels of content preservation and fluency, which are improved by 12.71% and 39.11%, respectively, compared with the existing GAN-based style generation model. Of the 119 modifications 92 (77.3%) were better, 17 (14.3%) were average, and only 11 (9.2%) were worse.
With the rapid development of science and technology, the traditional mode of teaching is inefficient and difficult to flexibly respond to the needs of knowledge updating, and generating content and applications based on AI has become an important way to solve this problem. According to the form of interaction in the digital exhibition hall, the article proposes SinGAN model and uses the multi-head self-attention mechanism to coordinate the overall features and detailed features in the generated adversarial network image, and to deal with the large range of dependencies in the image. The proposed AI-generated content and SinGAN image processing method are applied in the teaching of practical courses using the course “Digital Electronics Technology and Application” of a university in Guangdong Province, which specializes in electronic information and engineering, as an experimental object. The experiment shows that the percentage of content with a content quality score of 0.6 to 1.0 reaches 75.7%. As the course progresses, the keyword coverage rate reaches 0.996, and AI-generated content is efficiently applied in the course. The student performance of the experimental class with AI-generated content and image processing method teaching mode and the regular class with traditional teaching mode were 80.75 and 67.91 respectively, and the sample t-test for the significance of the student performance of the two classes was P=0.006, which showed a significant difference in the students’ performance between the two teaching modes. Students’ satisfaction with the new teaching mode is high, indicating that the AI-generated content and image processing methods proposed in the article have been well applied in education reform.
With the arrival of the big data era, the demand for massive data storage is growing, and distributed storage systems have become a key technology to solve this problem. The traditional HDFS system has a large storage overhead, this paper in order to improve the storage efficiency of massive data, the introduction of corrective deletion code (RS code) technology, to ensure the reliability of the data at the same time significantly reduce the cost of storage. In order to improve the storage efficiency of massive data, this paper introduces the corrective censoring code (RS code) technology, which ensures the data reliability and significantly reduces the storage cost. In addition, to address the problems of low coding efficiency and high repair overhead in the practical application of RS code, this paper further introduces the local repair code (LRC) technology, which reduces the data repair overhead, and compares and analyzes the application effect of optimization model (RS-LRC-HDFS). The experimental results show that after RS-LRC optimization, the time overhead of the HDFS storage system in the write process and read process is significantly improved by 81.12% and 93.01%, respectively, compared with the pre-optimization period, and the repair time of massive file data is reduced by 87.25%. It can be seen that it provides an efficient and reliable solution for massive data storage.
This paper intends to introduce the multi-intelligence of digital resources in cultural and tourism industry in reinforcement learning. In order to scientifically evaluate digital resource allocation, the index system characterizing resource allocation is constructed using hierarchical analysis. From there, a multi-objective collaborative optimization allocation model of digital resources in cultural and tourism industry based on reinforcement learning and multi-intelligent body system is established. Through empirical analysis, it can be seen that referring to the observation of the development of the comprehensive level of digital resource allocation, there is an imbalance in the development level of N province. The indicator system is refined to consist of 4 guideline level indicators and 26 indicator level indicators. Before and after the multi-objective synergistic optimization, the total amount of digital resource procurement for the cultural and tourism industry in province N was reduced by 460,742 yuan. After optimization, the comprehensive efficiency of resource allocation in area a improves by 0.03136, area b improves by 0.03275, and area h improves by 0.02799. Moreover, all of them tend to be in equilibrium. Therefore, the multi-objective synergistic optimization allocation model in this paper can improve the efficiency of digital resources in cultural tourism industry and reduce the differences between districts and counties.
Based on the Delphi method and relevant definitions, this paper determines the evaluation index system of college students’ employability, adopts the hierarchical analysis algorithm (AHP) to calculate the weights of the evaluation indexes, and for the weights of the evaluation indexes do not satisfy the consistency test, adopts the Adaptive Gradient Algorithm (AdaGrad) to adjust the weight parameters so as to make them satisfy the consistency test, and arrives at the adjusted values of the evaluation indexes weights. The weights of the adjusted evaluation indexes are derived. Using the fuzzy comprehensive evaluation theory, a comprehensive assessment model of college students’ completion ability was constructed, and then the research sample was evaluated and analyzed with the help of this model. It is calculated that the affiliation vector of the evaluation of college students’ employability is (1.9466, 1.2539, 1.1123, 0.9752, 4.714), and the maximum affiliation value is 4.714, which can be inferred that the students of this university have good comprehensive ability of employment and can well face the employment pressure in the current society.
Micro-landscape is a kind of green landscape designed to enhance the local landscape environment of the city along with the renewal of urban green space and the transformation of old city. The article adopts Hadoop technology and utilizes the Hadoop distributed computing framework to preprocess the data, constructs the urban micro-landscape greening evaluation system, and carries out research on four evaluation levels, namely, building façade landscape, multimedia landscape, water landscape, and landscape facilities. At the same time, based on the principal component analysis and factor analysis method for comprehensive evaluation, it is determined that the multimedia interaction factor is the most important factor affecting the effectiveness of micro-landscape greening. Then use SWMM model to design a city urban area, through SWMM model simulation to get the actual average annual runoff control rate of the demonstration area in 2023 is 59%, and the overall long-term goal of urban micro-landscape greening planning in 2020-2030 there is a gap, based on which put forward the urban micro-landscape greening design program.
The basic genetic algorithm suffers from problems such as precocity and low search efficiency when solving multi-objective optimization problems in large-scale computing environments. Aiming at these problems, this paper introduces various improvement strategies such as neighborhood operation, adaptive strategy, chaos optimization and cooling into the classical genetic algorithm, and designs an improved genetic algorithm process that organically combines various improvement strategies. The improved genetic algorithm and other existing large-scale multi-objective optimization algorithms are tested using LSMOP test problems, and the improved genetic algorithm has better convergence and diversity than other algorithms on both two-objective and three-objective LSMOP test problems. The PF curves of the seven algorithms are plotted separately for the two-objective on LSMOP6 and the three-objective on LSMOP5 when the decision variable is 200, and the images show that the improved genetic algorithm has the most uniform population distribution. The experimental results confirm the effectiveness of the improved genetic algorithm in solving large-scale multi-objective optimization problems.
With the development of virtual reality and computer vision technology, the demand for virtual scenes of music performances is becoming more and more prosperous, which brings new development opportunities for music performances and music teaching. In this paper, we use the beam leveling method to determine the camera parameters in the virtual scene, implement the calibration process and parameter solving for the camera, and implement the segmentation process for the virtual scene image through the GrabCut algorithm, formulate the model constraints and objective function, construct a virtual scene for music performance, and design a virtual scene system for music performance. Based on the virtual scene of music performance, the interactive learning model of music is proposed, and the virtual roaming mode is formulated by combining human-computer interaction technology to realize the interactive learning roaming of music learners in the virtual scene of music performance. The PSNR and SSIM values of the music performance virtual scene constructed by this paper’s technology are 25.8291db and 0.9396 respectively, which are higher than those of the virtual scene construction algorithms such as VSRS and JTDI as a comparison. Carrying out music teaching experiments, the experimental class that applies the interactive learning model of this paper for music interactive learning roaming has higher mean values of all dimensions than the control class in both music learning ability and music listening ability, showing significant differences (P<0.05).
Text is the carrier of language, and language is the carrier of cultural soft power, if you want a country’s soft power to be enhanced, it will certainly start from the dissemination of the native language. This paper constructs a complex social network J-SEVIR model for the dissemination of Japanese text information with the help of complex network theory combined with the information dissemination model using graph theory as the technical support. The data about Japanese text information on Sina Weibo is used as the research object, and the data analysis is carried out through the dimensions of model simulation, real data comparison, and information dissemination enhancement strategies. The study shows that the peak number of Japanese text message dissemination nodes is 1.987*107, which is 41.32% and 28.94% higher than the peak number of dissemination nodes in the traditional SEIR model and BCIR model, respectively, and the peak number of disseminators of the Japanese text message dissemination enhancement strategy designed by the J-SEVIR model can be up to 0.62, whereas the number of Japanese text message dissemination counterattackers is only 0.12. Therefore, the number of Japanese text message dissemination counterattackers is only 0.12. Therefore, the establishment of Japanese text information dissemination paths with the help of complex networks based on graph theory can be used to provide new research perspectives for optimizing the effect of Japanese text information dissemination.
The prediction of the scale of big data talent training in colleges and universities belongs to an important content in the field of big data talent research in colleges and universities. The article uses the primary exponential smoothing method in the time series and the gray model prediction method to predict the scale of college big data talent training and talent demand respectively, and then uses the Lorenz curve and the Gini coefficient to study the matching degree of education in the field of big data. There are experimental results can be obtained, the degree of matching between the professional settings of colleges and universities and the trend of the demand for big data-related positions in enterprises needs to be strengthened, in order to adapt to the future demand for big data-related positions in enterprises, and to further output talents that are in line with the enterprises, the article proposes a model of big data talent cultivation civic and political education in colleges and universities based on the KSAO model. Based on the KSAO model, the ideological education mode of big data talent cultivation in colleges and universities can be implemented at six levels: “theory + project” curriculum system, promoting the dual strategy of “on-campus simulation + off-campus practice”, establishing the KSAO multi-dimensional practice assessment system, strengthening the coordination of the industry-teaching cooperation model, building a cloud learning platform with the help of information technology, and implementing the top-down education design.
This study focuses on library data mining scenarios and proposes an optimization method for the deficiencies of existing knowledge discovery algorithms in terms of efficiency, accuracy and interpretability. The method first uses principal component analysis to downscale library high-dimensional data to extract the main features and improve the data mining efficiency. Then, the fuzzy clustering algorithm is used to cluster the dimensionality reduced data to more accurately identify the user groups, resource categories and other implicit knowledge. The clustering results are interpreted and analyzed to provide data support for knowledge discovery in library data mining. The algorithm in this paper demonstrates better performance in data dimensionality reduction at the level of memory usage as well as time consumption, and identifies three major components with cumulative contribution of more than 80%. In addition, the algorithm achieves an average purity of 95.45% for book data clustering and a clustering time consumption of 3.47s with a data stream of 300unit k, both of which are better than the comparison algorithms. The comprehensiveness weight of a university’s book resources is 0.17, which is the highest performance, while the practicality and standardization are the next highest, 0.155 and 0.152, respectively. It can be seen from the clustering that the book category with the highest borrowing rate is science and technology, and the lowest one is literature, which reflects the user’s demand for knowledge of a specific field.
Under the impetus of computer technology, the creation of digital art continues to develop, and computer-assisted creation has gradually become the mainstream of artistic creation. This paper is oriented to digital art innovation, in-depth exploration of computer-assisted art creation and its integration with the development of digital media. Through the in-depth analysis of computer-assisted art creation, this paper constructs an improved CycleGAN art pattern generation model by introducing the self-attention mechanism in the CycleGAN model on the basis of pattern generation. In the generation experiments of the improved CycleGAN model, the SSIM and PSMR values of the improved model in this paper are 0.721 and 17.563, and in the number of in-parameters, the model size, and the running speed are reduced compared with the traditional model, and the overall performance of the improved model is excellent. At the same time, the works based on the computer-aided art creation method of this paper compared with the traditional art creation works of the comprehensive average score increased by 11.40 points, further illustrating the more advantageous in computer-aided art creation. The study concludes by analyzing the path of the combination of computer-aided and digital media, and proposes a path for the integration and development of the two from multiple perspectives, which provides directions and ideas for the research on the integration and development of computer-aided and digital media technologies.
In order to overcome the shortcomings of traditional physical education teaching quality assessment methods, this paper proposes a hybrid online-offline physical education teaching quality assessment method based on the assignment method. The method utilizes the hierarchical analysis method (AHP) to initially assess the quality of hybrid physical education teaching, and introduces the improvement of the pull apart step method (ISD) to improve the assessment accuracy of the hierarchical analysis method. The AHP and ISD methods are weighted to form a comprehensive integrated assignment method to construct a hybrid physical education teaching quality assessment model. Finally, the accuracy of the teaching quality assessment model was tested by the plain Bayesian classifier (NBC). The questionnaire data from teachers and students of an engineering university were collected and applied to the model of this paper, and the final results show that the model of this paper can effectively realize the grade assessment of hybrid physical education teaching quality according to the obtained data. The simple Bayesian classifier used in this paper has obvious performance advantages compared with multiple linear regression (MLR) models. The application of the method in this paper can effectively meet the needs of teachers and students in mixed physical education teaching and learning, and at the same time, it can significantly improve students’ physical education performance, which is highly welcomed by teachers and students in schools.
This paper draws a framework for constructing user demand modal information, uses crawler technology to obtain online review text information, processes the text information, and mines relevant consumer demand information. The LDA topic model is used to extract the topics of consumer concern from the online comments, identify the topics of consumer demand and clarify the concern degree of each demand. The KANO model is proposed to establish a consumer demand classification method based on the KANO model by combining product characteristic attributes and consumer demand information. Examine the theme discrimination performance of the LDA model on the hotel category, footwear category, and food category datasets. Combine the preprocessed user demand data to statistically quantify user demand for quantitative Kano transformation. Classify user demands into Kano categories and calculate the priority order of user demands to get the product optimization strategy. The weighted order of consumers’ demands for automobiles is footrest, cigarette lighter, antenna, window, low beam, key, etc. in order. It can be found that automobile consumers pay more attention to the needs of antenna, cigarette lighter, pedals, and enhancement of accessory functions. As a result, automobile manufacturers should increase the seat comfort, improve power, enhance the flexibility of shifting such aspects of the whole vehicle handling experience, in addition to improving the lights, keys and other car quality related needs.
Answering the spatial relationship between ESG ratings and total factor productivity of enterprises can provide a reference for the high-quality development of macroeconomy and the sustainable and healthy development of enterprises. In this paper, the improved K-means algorithm-PCA-K-means is used to measure the principal component data corresponding to the economic development level of 26 central cities, based on which and cluster analysis is conducted to classify the regions and city types of East, Central and West China. Furthermore, benchmark regression and spatial heterogeneity analyses were conducted using a fixed-effects model. The study shows that ESG ratings have a significant positive relationship on firm-wide factors. Observing the PCA-K-means clustering results, it can be found that there is no significant positive effect between the economic development speed and the ESG ratings of enterprises, which indicates that there is a difference in the impact of ESG ratings on the total factor productivity of enterprises in different regions. Therefore, the spatial heterogeneity analysis shows that the correlation coefficients of ESG rating performance in the central and western regions are 0.0163 and 0.0275, respectively, and ESG rating performance has a greater impact on enterprises in the central and western regions compared with the eastern region. The effect of ESG rating on total factor productivity of enterprises in resource-dependent cities and old industrial bases is not significant.
Due to the development of advanced information technology such as artificial intelligence, the traditional marketing profession is being transformed and upgraded in the direction of intelligent higher vocational marketing, and the requirements of marketing positions on the knowledge, quality and ability of practitioners have changed. The article analyzes students’ cell phone online behavior in different classrooms based on DBSCAN clustering algorithm by collecting students’ campus network usage data, according to which the results can provide an effective basis for school management. By introducing the Interpretive Structural Model (ISM) and analyzing the interrelationships between courses, the article proposes a course cluster division scheme for marketing majors, which provides methodological support for the division of clusters in the construction of course clusters for professional teachers, as well as the selection and organization of the courses within the clusters. Finally, investigate the differential judgment of students from different places of origin about the influence of teaching environment, teacher quality, teaching process, teaching tools and resources on the teaching effect of marketing courses, the data show that the influence factors of marketing course teaching have obvious differences in the influence of the teaching effect of the course, improve the ability of professional teachers to educate people, optimize the teaching process of the marketing course, and deepen the reform of classroom teaching.
In the current fields of quantum information processing and quantum computing, fast and accurate quantum state manipulation and preparation have been of keen interest to researchers, and their potential applications are mainly in quantum measurement, quantum information, quantum communication, and quantum sensing. In this paper, the Hilbert space of a bipartite state system is unfolded by four Bell state entanglement bases and the result is projected to the subsystem to obtain a mixed state. A quantum approximation algorithm is proposed to provide a solution to the combinatorial optimization problem, and based on the workflow of the quantum approximation optimization algorithm, an improvement is proposed to the quantum approximation optimization algorithm to solve the constrained problem using the quadratic unconstrained binary optimization method. Based on the theory of cavity magnetism, the hybrid quantum system model is constructed, and the calculation method of Hamiltonian quantity is proposed. Combined with the quantum entanglement optimal path calculation of UQAOA algorithm, the optimal value of time-microwave entanglement is obtained at r=0.234, so the compression parameter r=0.2 is used in the calculation. Based on the UQAOA algorithm for the analysis of the transmission characteristics of the generated OMA wave in air and the transmission optimization problem, the simulation obtains the reflection coefficient is slightly lower than that of the test, and the maximal error error is controlled at ±7.5dB around, and the two results are basically in agreement.
The organic combination of traditional rule of law culture and Civics education in colleges and universities is a breakthrough to improve the effectiveness of Civics education. Focusing on the Civic and political education that integrates traditional rule of law culture, the article introduces virtual reality technology and differential evolution algorithm to explore the course effect optimization method of Civic and political virtual reality teaching, and obtains the optimal content applied to the corpus through differential evolution algorithm according to the content characteristics of Civic and political education. On this basis, the evaluation index system is constructed to assess the course optimization effect of Civics virtual reality teaching. Example validation shows that the Civics corpus based on differential evolutionary algorithm and the proposed Civics virtual reality teaching method achieve better Civics course optimization effect, with an overall score of 3.833, and have the ability of practical application. Students of different genders and grades show significant differences (P<0.05) in the evaluation results of most of the first-level indicators. The application section of virtual reality technology promotes the teaching effect of traditional rule of law culture into the ideological education of colleges and universities.
The research in this paper mainly focuses on the design of the quality assessment system of Ideological and Political Education to realize the innovation of Ideological and Political Education mode. The principal component analysis algorithm is used as the core algorithm of the assessment system, and combined with the system architecture model of hierarchical design, it realizes the collection, processing, analysis and assessment of the data on the quality of Ideological and Political classes. The research results show that the assessment system based on principal component analysis algorithm in this paper has a higher accuracy rate of education quality assessment compared to the evaluation system based on a single deep learning algorithm such as RBF neural network. At the same time, the system in this paper also has a higher assessment accuracy than the evaluation system using a combination of algorithms, and shows excellent stability performance when assessing the educational quality of 150 teachers. Using this system to assess the quality of Ideological and Political Education of 8 teachers, the comprehensive ranking is more reasonable than the original ranking. The Ideological and Political education quality assessment system designed based on the principal component analysis algorithm in this paper has a far-reaching impact on the innovation and intelligent development of the Ideological and Political Education model in the digital era.
The development of electronic and electrical architectures towards domain centralization makes it difficult for traditional distributed control architectures to meet the functional needs and performance requirements of increasingly complex intelligent devices. This study utilizes a multi-model adaptive control algorithm to assist the domain controller to adjust the control parameters in real time according to the state of the device and environmental changes, and to realize the optimization of the control of the device. The wi-fi wireless networking communication technology is chosen to transmit the real-time data acquired by the sensors to the web page. The electrical and electronic architecture composed of the two combined with each other is carried to the intelligent control platform to realize the functions of sensing, positioning, planning and decision-making of the equipment platform. The study shows that: the algorithm selected in this paper can reach the target speed of the motor within 0.2s in the process of no-load and loaded operation, and the time required for balancing to the load torque is significantly reduced compared with the comparison algorithm. In this paper, the maximum throughput and CPU occupancy of the domain controller + wireless sensor architecture are lower than that of the traditional distributed architecture. And the platform constructed accordingly has no packet loss when the number of packets sent is less than 10000, and the average communication delay is between 0.65 and 1.2ms, which meets the requirements of vehicle wireless control and communication. Through the domain controller based on adaptive control algorithm to regulate the vehicle speed in real time, to ensure the safety distance between the rear vehicle and the front vehicle.
Learning path optimization aims to generate and optimize a knowledge learning sequence for learners that best meets their knowledge needs. This study focuses on the important role of online learner behavior in personalized path planning. By constructing a knowledge point difficulty model and a learning behavior prediction model based on online learning behavior, together with a user-based collaborative filtering recommendation algorithm, a personalized learning path is proposed comprehensively. The MOOC websites “College English 1” and “Xuedang Online” are selected as sample data to analyze the online learning behavior of English learners and verify the learning effect of the learning path proposed in the article through the change of students’ online time. The personalized teaching model based on the learning path is investigated in practice by taking the college English course in school A as an example. Compared with the traditional teaching mode, the optimized learning path shows a significant difference of 0.01% in the dimensions of learners’ “knowledge and skills”, “process and method” and “affective attitude”. The mean values of the optimized blended teaching mode are 4.12, 4.33 and 4.07 respectively, which are all better than the traditional teaching mode. It shows that the English learning path proposed in this paper is conducive to enhancing students’ personalized learning needs and provides a reference for promoting the effective implementation of personalized learning in the information technology environment.
As the key driving force to promote the development of new quality productivity, the internal logic of the integration of production and education is to provide core support for the development of new quality productivity by training high-quality workers, providing high-quality labor elements and creating an efficient innovation platform. However, at present, the integration of middle and teaching in undergraduate education faces challenges such as “school hot and enterprise cold”, school-enterprise cooperation obstacles, and imperfect mechanism. This paper analyzes the current situation of the integration of production and education in undergraduate education, constructs the corresponding mathematical model. And uses genetic algorithm to solve the optimization objectives of curriculum design and teaching resource allocation under the integration of production and education, include the incorporation of enterprise elements, such as the proportion of enterprise practice courses, enterprise mentors, joint research and development data. Based on the above, the feasibility of GA optimization algorithm is tested from three perspectives: comparison of the same kind, practical application and student satisfaction. In order to effectively enable the development of new quality productivity, it is necessary to optimize the education major setting in accordance with industrial changes, deepen the learning situation and customize practical courses, deepen the school-enterprise cooperation and development platform, strengthen collaborative innovation, and improve the incentive mechanism, so as to form an effective connection between the education chain, the talent chain, the industrial chain and the innovation chain, and jointly promote the high-quality development of undergraduate education.
Teaching and correcting athletes’ techniques by analyzing and referring to the performance of professional tournament players can improve the teaching level and quality of wushu movements. In this paper, the performance of college students in UFC tournaments is taken as the research data, and the multilayer perceptron algorithm is used to process the images and carry out the global modeling of wushu fighting action images. The network coding design is used to improve the data transmission rate of the algorithm, and the activation function is used as the nonlinear expression method of the algorithm. The Tanh_Softsign activation function is improved to counteract the noise interference of the dataset images, in order to construct the multilayer perceptual machine algorithm and develop the learning of martial arts fighting action scores. After optimizing the learning of UFC martial arts action scores by this algorithm, this algorithm shows a high correlation between the performance scores of students and the professional teachers’ scores of an elective class of martial arts in a university with P>0.05, which indicates that the algorithm in this paper can accurately assess the students’ action performance.
Based on the status quo of Sanya Digital Intelligence Tourism Economy, this paper puts forward the strategy of intelligent teaching change under the dual-leader cultivation mode of colleges and universities. Relying on clustering analysis technology to achieve the mining processing of the whole process data of the wisdom teaching platform, to promote the optimization of the process of wisdom teaching change. The catechism data of the basic course of tourism management of a smart teaching platform is collected, and z-score and PCA principal component analysis are utilized to eliminate the quantitative influence of the data. The best cluster values were determined by hierarchical cluster analysis, and the learners were divided into three cluster groups with the help of K-Means clustering algorithm. One-way ANOVA was introduced to compare the achievement data before and after smart teaching of the three groups of students to explore the effect of smart teaching. The results showed that among the paper grades, category 2 students had the greatest change in the mean value of their grades. In practical grades, the mean value of category 2 students’ practical grades was 95.63, which was 20.18 and 26.75 points higher than those of category 0 and category 1 students, respectively. p-value of 1.56951E-17 was less than 0.05, which indicated that the grades of the three categories of students showed significant differences.
This paper combines the necessary functional requirements for teaching system generated by teaching activities in the context of mobile Internet, designs the general framework of the system, users and their rights management, and constructs a set of teaching system. Subsequently, the traditional PSO algorithm is introduced, and the processing scheme of the scheduling problem is defined as particles to form an initial particle swarm, while the particle swarm position in the algorithm is updated by drawing on the crossover idea of the genetic algorithm, so as to optimize and obtain the scheduling algorithm based on DPSO. Then we test the teaching system of this paper from three levels of pressure bearing, response delay and stability performance to ensure the operating environment of the scheduling algorithm of this paper. The courses of three colleges of a university are used as experimental data to analyze the performance of the scheduling algorithm in this paper. In the comparison of course arrangement in different colleges, the adaptability of this paper’s scheduling algorithm is above 0.900, while the highest adaptability of manual scheduling is only 0.8147, which indicates that compared with manual scheduling, this paper’s scheduling algorithm is able to make a more reasonable course arrangement.
Aiming at the potential risks existing in the power market transaction under the new power system, and considering the temporal attributes of the information, this paper proposes to use dynamic Bayesian network to construct the risk monitoring and early warning model of the power market transaction. The dynamic Bayesian network is utilized to calculate the correlation between different risk factors, estimate the risk value of power market transactions, and classify the warning level. Taking the southern regional electricity market as the research object, the relationship between electricity price and transaction volume is explored based on the experimental dataset. A credit grading system is introduced to carry out transaction prediction simulation experiments, relying on the prediction data to determine the link between electricity price and transaction volume. The results show that overall power price and transaction volume show a negative correlation, but in June, when the power price is 0.4370 yuan per kWh, the transaction volume still reaches 19.65 million kWh, and the inverse relationship between the transaction volume and the price is not obvious. The use of dynamic Bayesian network to construct the power market transaction risk monitoring and early warning model can dynamically update and adjust the risk monitoring with the passage of time, making the power market transaction early warning more flexible and real-time.
In this paper, the semantic description framework is used to standardize the extraction of semantic information of non-legacy images. The SIFT algorithm is chosen to calculate the key feature points of non-legacy images. The integrated semantic description framework and SIFT algorithm construct a model to extract the non-heritage image features globally and process them locally, and add the attention feature fusion module to fuse the features that are inconsistent in semantics and scale, so as to realize the accurate extraction of features. Use the algorithmic model of this paper to extract the color features of She ribbons. Develop a website for she color band design and verify its usability. Collect website evaluation data from target users to study the role of digital translation of non-heritage elements. The color feature extraction results are richest and most detailed when the number of She ribbon feature colors extracted is 21. The website usability scale score was 50.31, rating B+, with usability. 65% of the users thought that the website embodied the cultural characteristics of She ribbons. 71% of the users thought that the website was very helpful for understanding the ethnic graphic culture. 88.16% of the users thought that the digital design of She ribbons could effectively promote the dissemination of the ethnic graphic culture.
Increasing urbanization has led to large changes in residents’ consumption behavior, but due to a variety of factors, the overall level of residents’ consumption is low, so that it cannot play its role in promoting economic growth. This paper selects the panel data of China Household Finance Survey from 2011 to 2019, and empirically analyzes the impact and path of changes in residents’ consumption behavior on consumption upgrading by constructing structural equation model and fixed effect model combined with STATA software. The study shows that changes in residents’ consumption behavior and its dimensions can promote residents’ consumption upgrading, and there is regional heterogeneity in the impact of changes in residents’ consumption behavior on residents’ consumption upgrading, and its promotion effect on residents’ consumption upgrading is stronger in the developed regions in the Middle East. Based on the above findings, this paper puts forward feasible suggestions on how to optimize residents’ consumption behavior to better promote residents’ consumption upgrading.
This paper takes the integration of AI technology into piano teaching as the starting point, generates accompaniment rhythms through AI computation, adopts deep learning model to generate accompaniment, and builds a multi-level accompaniment effect generation mechanism. Taking the MuseFlow model as the base model, the generative adversarial network and variational autoencoder are introduced to optimize the structure in a limited arithmetic environment. Quantitative and manual evaluations are used to measure the accompaniment generation effect of the proposed mechanism, and controlled experiments are designed to explore its practical application effect. The results show that the improved MuseFlow model generates accompaniment with an average pitch distance of 0.92, which is 0.15 smaller than that of MMM, and the overall score reaches 4.18. The scores of the experimental group in all six abilities are significantly higher than those of the control group, the degree of students’ positive response to each ability increases to some extent, and the number of students who consider the ability of melodic creation to be at a satisfactory level is 18 more than that of the pre-experiment after the experiment.
Network teaching has become an important way of teaching reform in current higher education and has been applied in the education of various courses. This paper proposes a kind of intelligent auxiliary teaching system based on P2P mode, and researches the realization of the system with the example of Civics course. The construction of “Civics Course Teaching Evaluation System” is systematically discussed by using the fuzzy comprehensive evaluation method, and the weights of the indicators are calculated by entropy weighting method and hierarchical analysis method. Taking the teaching of Civics and Political Science in a university in Guangdong Province as the research object, the intelligent teaching system proposed in this paper is applied to evaluate the interactive effect of teaching with the evaluation system constructed in this paper. The evaluation analysis shows that the school’s evaluation results of all indicators are above 80 points, and the overall teaching rating of its Civics and Political Science course is 86.33, in which social merit, teaching equipment, teaching expression, and professional ethics have the highest scores of 94.37, 92.32, 89.02, and 88.52, respectively. It shows that the intelligent auxiliary teaching system for Civics proposed in the article is well applied in actual teaching.
Aiming at the bridge project in the construction of the development of the status quo of the overdevelopment, maintenance and management level lagging behind, this paper, under the premise of ensuring the safety of the bridge, the bridge surveillance monitoring and risk early warning launched a study to solve the problems of its operation and repair and maintenance. For bridge monitoring and safety monitoring, this paper is based on the vibration acceleration of bridge structure damage identification. On this basis, the damage recognition model constructed by using common neural networks convolutional neural network (CNN), long short-term memory network (LSTM) and deep autoencoder (DAE), and the recognition effect of the three models is compared. This for, for the bridge risk problem, this paper utilizes the Extreme Learning Machine (ELM) and Firefly Algorithm (GSO), constructs the implementation of the GSO-ELM algorithm model for early warning of the bridge safety risk, and the experimental results show that the model proposed in this paper has good effect, which provides support for the future development of the bridge structural safety facilities should be developed in the direction of digitization, automation, and networkization.
A scientific, comprehensive and effective evaluation system of asset management performance of public colleges and universities in the context of high-quality development in the new era contributes to the “asset power” for the construction of high-level and high-quality development of colleges and universities. This paper takes 20 public colleges and universities in Province Y as research samples, and analyzes the asset management performance of public colleges and universities and its influencing factors through the super-efficiency DEA model and SFA model. The results show that the asset management performance of 13 public colleges and universities has reached DEA effective, and the rest of them are DEA ineffective. Human and material inputs have a significant positive effect on the asset management performance of public universities in terms of inputs, and both research income and number of patents have a significant positive effect on the asset management performance of public universities in terms of outputs at the 1% level. Relying on the scientific evaluation index system of asset management performance of public universities, establishing a high-level asset management team and clear budgeting and audit management are effective means to improve the asset management performance of public universities.
With the rapid development of the sports industry, quality traceability and credibility issues have become important issues in the sports industry chain. Traditional quality traceability and credibility assurance methods have shortcomings in efficiency and accuracy, and emerging technologies need to be adopted to solve them. Blockchain technology is considered an important means to address quality traceability and credibility issues in the sports industry chain due to its decentralized, transparent, and tamper resistant characteristics. This article proposed a quality traceability and credibility assurance system based on blockchain technology to address the issues of quality traceability and credibility in the sports industry chain. The system adopted blockchain technology to achieve quality traceability and information credibility assurance in the production, circulation, and consumption processes of sports products. The system adopted distributed ledger technology to record the production, circulation, and consumption records of products, and achieved automated quality inspection and transaction verification through smart contracts. The experiment in this article showed that using this system can improve efficiency and reliability by 80% -95%. The research on methods and systems for enhancing the credibility of quality traceability in the sports industry chain through blockchain can effectively improve the quality traceability ability of the sports industry chain, thereby safeguarding consumer rights and market stability.
As the economy develops, the tourism ecological environment (TEE) has been gradually damaged. The ecological environment is the basis of human life, and the sustainable development of the ecological environment is of great importance to promote the stable development of society. China has rich grassland tourism resources. However, as a result of the rapid development of tourism, some scenic spots have been overdeveloped and commercialised, leading to the destruction of natural landscapes, damage to ecosystems and the gradual sanding of large tracts of grassland. The desert grassland used for tourism development is located between the grassland and the desert, and is the barrier that ensures the entire grassland ecosystem. To carry out environmental management of tourism ecology, it is necessary to construct a statistical monitoring index system for tourism ecology. However, traditional ecological environment monitoring is mainly based on manual sampling survey, which is cumbersome. The monitoring data is not accurate enough. In this paper, remote sensing technology (RST) was used to obtain remote sensing images of desert grassland, and intelligent image processing (IIP) technology was used for feature recognition. Compared with the traditional ecological environment statistical monitoring method, it showed that: In desert grassland A, the average monitoring accuracy of the traditional ecological environment statistical monitoring method and the ecological environment statistical monitoring method based on IIP were 90.12% and 94.56% respectively; in desert grassland B, the average monitoring accuracy of the traditional ecological environment statistical monitoring method and the ecological environment statistical monitoring method based on IIP were 88.20% and 92.60% respectively. Therefore, statistical monitoring of TEE based on IIP can improve the monitoring accuracy of ecological environment indicators.
In order to analyze the reading behavior and its meaning of readers in blockchain online reading platforms, this article conducted research on reading emotion recognition. This article utilized the characteristics of blockchain technology to analyze the reading mode of blockchain internet platforms. By using audio and image bimodal recognition methods, the recognition of readers’ reading emotions can be achieved. After feature extraction of speech and facial images, hidden Markov models (HMM) can be used for speech emotion recognition. Support vector machines (SVM) can be used for facial image emotion recognition, and decision level fusion can be used for bimodal emotion recognition. This article obtained the final emotion recognition results to analyze and predict user reading behavior. Analyzing the psychological state of readers based on emotional recognition results can achieve more intelligent reading information push. Experimental results on the effectiveness of reading bimodal emotion recognition showed that the accuracy of reading bimodal emotion recognition based on decision level fusion was much higher than that of single modal emotion recognition. The bimodal method has an average accuracy rate of over 85% in emotion recognition and has a high effect in emotion recognition. Reading bimodal emotion recognition based on audio and image can accurately identify readers’ emotions, adjust information push content in a timely manner, and achieve the regulation of readers’ emotions, which has high application value.
At present, there are differences in the building of information in various career institutions. The degree of implementation of management, teaching and services is uneven, and educational resources are limited and unevenly distributed. The construction of educational resources includes the overall layout, structure and quantity of resources, information mode, service impact, etc, all of which require systematic planning. Under the above background, this paper conducted research on the topic of building a model of co-construction and sharing of digital ideological and political resources for embedded courses based on artificial intelligence algorithms, and considered the insufficiency of the existing digital ideological and political resources in the allocation efficiency and insufficient system sharing, as well as creatively used artificial intelligence algorithms to improve the previous system. In the algorithm, the texture mapping of the system was carried out, and the duty cycle of each columnar area was specified. In the experiment, the number of resources in the digital resource platform was investigated, and the input of different types of colleges and universities in digital ideological and political resources was collected. The explanation of experimental data: 83% of 985/211 colleges and universities used the database designed in this paper, and 17% of them actively built the database; 57% of the general undergraduate schools used the database designed in this paper, and 20% were under construction, as well as 13% were still preparing. This showed that in general undergraduate schools, a small proportion of the digital ideological and political resource sharing model was used, and the 985/211 colleges and universities had relatively good investment in the construction of digital ideological and political resources.
The rapid expansion of tourism across the world necessitates constant innovation and development in the services offered to visitors in order to assure their comfort and happiness while on the road. Travelers’ experiences may be greatly enhanced by providing them with basic and essential conveniences such as optimal route identification and suggestion technology. In this paper, we use data mining to investigate the effect of scenic site clustering and group emotion on tourist route choosing. It is common for traditional route selection algorithms to just examine the impact of picturesque locations on route design. Many people choose the Chimp optimization algorithm (ChOA) because of its straightforward idea, simple implementation, and high level of resilience. With the goal of solving practical challenges in mind, this study uses real-world geographic data to build a discrete ChOA for the tourism route planning problem, which may be applied in practice. Simulation experiments are done, and outcomes data are studied and assessed. The assessment findings show that the ChOA is suitable for mass tourist data mining. The smart machine’s final best tour routes are directly tied to the requirements, interests, and habits of visitors and are completely connected with geospatial services to ensure accuracy. The ChOA algorithm serves as a good example of how data mining may be used in the field of mass tourism.
Due to the deepening reform of quality education, the requirements for physical education teaching in colleges and universities have become increasingly strict. In this era of rapid renewal and development of multimedia information technology, in order to make the traditional sports basketball teaching keep up with the pace of the trend and to search for the future development direction of college public sports basketball teaching, this paper studied the application of multi information data fusion technology in college public sports basketball teaching. The remote sensing technology and global positioning system in the multi information data fusion technology were used to conduct real-time detection and statistics on the sports effects of students in basketball teaching, and the relevant experimental scheme was designed. The data results recorded by manual recording and multi information data fusion technology were compared. The experimental results showed that when three student representatives and remote sensing technology simultaneously counted the times of passing and touching, the success rate of passing and the scoring rate of throwing for four sports members, the accuracy of remote sensing technology was higher; the Global Positioning System (GPS) system could effectively record the running distance, average speed and heart rate of 4 athletes. The average speed of No. 3 athlete was 9.1 m/s; the passing rate and shooting rate were both 50%, and the average speed of No. 4 athlete was 7.85 m/s. The pass success rate was 50%, and the shooting rate was only 33.3%. These data were conducive to teachers’ timely understanding of students’ personal conditions and basketball level, which could improve the efficiency of college sports basketball teaching and also increase the quality of students’ sports. At the same time, the questionnaire survey method was also used to study the results of the introduction of multi information data fusion technology. The findings shown that multi-information data fusion technology might increase students’ passion for learning basketball courses, hence improving the quality of sports, by altering their interest and attitude. In order to provide guidance for the future development of college public sports basketball instruction, this study offered a reference value for the application of multi-information data fusion.
Artificial intelligence (AI) and multimedia technology (MT) provide a new platform for college physical education (PE), which plays a positive role in promoting college PE. Combined with the actual situation, some discussions are made on the application of multimedia teaching technology in college PE teaching, in order to better serve the MT teaching of college PE teaching. The popularization and wide application of multimedia teaching technology in education and teaching have caused a series of changes in teaching concepts, teaching design, teaching methods, creative teaching, etc., preparing for the development of teaching. Starting from the teaching quality evaluation methods, the existing problems in the evaluation process were analyzed. These problems are reflected in the retrospective evaluation method, which is not scientific enough to summarize the evaluation results, and it is difficult to track and improve the teaching ideas. Teaching evaluation is a complex system that includes classroom teaching, sports facilities, sports activities, classroom teaching, physical health, supervision and management and many other aspects. Modern educational philosophy generally holds that the classroom teaching process should include formulating clear teaching objectives, selecting the most appropriate teaching methods and using scientific evaluation methods to collect information about correct answers. According to the construction of a comprehensive evaluation system of intelligent algorithm and AI technology, the quality of teaching evaluation has been improved by 21.4% after calculation.
Although human motion form capture is widely used in multiple fields, it often requires a significant amount of time and cost to learn how to operate the device during use. Therefore, this article attempted to apply computer vision (CV) technology and image segmentation algorithms to human motion form capture technology, simplifying the operation scheme and improving recognition accuracy and efficiency. This article provided an in-depth analysis of human motion form capture technology. Firstly, it identified several parts of the current human motion form capture technology that can be optimized, and introduced the effects of these optimized parts on human motion form capture in sports training. This article took the form capture of aerobics athletes as a sample and extracted 50 keyframe images containing aerobics scoring actions from 100 aerobics activity videos. The extraction interval for these keyframe images was at least 10 seconds. Next, this article used histogram equalization to enhance the image, while segmenting and recognizing the human motion forms of the five types of actions in the keyframe images, highlighting the level of action standards of athletes in aerobics. Finally, this article selected 6 key frame images containing different movements of aerobics athletes for comparative experimental analysis. In this experiment, both commonly used optical unlabeled capture techniques and motion morphology capture techniques combining CV and image segmentation algorithms were used to capture the human body in the image. The addition of CV technology and image segmentation has improved the overall performance of human motion morphology capture technology by approximately 26.02%. The integration of CV technology and image segmentation algorithms into human motion form capture technology has greatly improved image processing efficiency. At the same time, CV technology and image segmentation algorithms have also enabled better image processing accuracy in human motion form capture.
Image hiding is a technique for transmitting secret information under the cover of a digital image. It usually conceals sensitive information into images for the purpose of encryption. Currently, high embedding capacity and information security remain important research aspects of the image hiding. In this study, a secret image sharing scheme based on a reference matrix is proposed to enhance embedding capacity and verify data integrity. In the proposed scheme, a hill matrix is designed as a reference matrix and a location table is generated. Moreover, a location pair table is generated to ensure the uniqueness of data hiding locations. Then, leveraging the processing of the location pair table, as well as the mapping of the reference matrix and the location table, each pixel pair is exploited to conceal eight secret bits. Furthermore, based on the special construction of the hill matrix, a deception recognition mechanism is designed. This mechanism can detect deceptive behavior and identify tampered images by means of data hiding locations. The experimental results indicate that the proposed scheme achieves a higher embedding capacity and better deception recognition performance than that of most of existing schemes.
Upon the arrival of the sharing consumption model, guaranteeing the authenticity of products and the transparency of transactions has emerged as fundamental challenges hindering the industry’s progression. This paper explores the selection and optimization of blockchain technology implementation methods within the shared supply chain. Through a comparative analysis of non-blockchain, private blockchain, and distributed application models, our findings reveal that distributed application generates higher profits when consumers exhibit high sensitivity to blockchain performance and when such performance adheres to specific standards. Conversely, the private blockchain is more suited to customized requirements. Blockchain technology not only increases prices and transparency but also enhances consumer trust, particularly within the distributed application framework. Performance plays a crucial role in decision-making, with the private blockchain relying on corporate investment for optimization and distributed application being constrained by the limitations of the public chain. Based on these findings, it is recommended that enterprises adopt a flexible approach in selecting the most appropriate mode according to their unique needs. Additionally, they should prioritize technological innovation, strive to improve blockchain performance, consider fostering consumer trust, and promote collaborative development throughout the supply chain. These strategies will collectively contribute to the healthy and sustainable growth of the industry.
“Internet + medical health” service is an important direction of current medical development. The high interactivity between doctors and patients in online medical services and the massive and dynamic nature of recommended information have brought new challenges to the platform’s analysis of patient perceived trust. It is difficult for the trust transfer model to process massive information in real time. Clustering massive recommended trust is an effective solution, but data clustering is difficult to process simultaneously with the perceived recommendation trust tendency, which brings about the problem of perceived recommendation trust clustering. How to measure the trust tendency reflected in the clustering of patient perceived recommendation trust is a difficult problem faced by the trust transfer model in the context of Internet medical health services. This paper proposes a two-stage research idea of ” conversion first, clustering later”. Intuitive fuzzy sets are used to measure the fuzziness of patient perceived recommendation trust, and combined with sentiment dictionary, density clustering method and other methods to cross and penetrate each other, a patient perceived recommendation trust clustering method is constructed in the context of Internet medical health services. Finally, data experiments were conducted using the real data of the top 17 doctors on the Haodafu online platform to verify the effectiveness of the method. This method can reflect the subjectivity and ambiguity of patients’ perceived trust, provide a solution for the processing of massive recommendation information, contribute to the research on the improvement of trust transfer method system, and provide method support for predicting and analyzing the trust measurement of patients in the context of Internet medical health services. The model proposed in this paper can be used as the core of the trust-based recommendation system in Internet medical care, and help Internet medical platforms formulate precise strategies for doctors.
This paper seeks to discuss focused prototype development of self-driving, autonomous, driverless, electric cars with emphasis on subsystem advancement constituting the progress of the technology. The introduction lays special emphasis on the increased role of autonomous technology in transforming transportation by underlining its potential to enhance safety, effectiveness, and sustainability. Some technical background is provided with the definition of what an autonomous car is and its evolution timeline. Electrical vehicle current advancement is also described in detail. At last, comparative analysis of further prototype developments and subsystems with respect to their usefulness and prospects is given. This assessment serves to contribute to the present discourse on self-driving vehicle technology, and the role that these vehicles will play in on-going transport modal shift.
Purpose – This study investigates the impact of career planning education on university students’ entrepreneurial intentions by examining the mediating roles of self-efficacy and perceived behavioral control, as well as the moderating effects of digital competency and risk propensity. Design/methodology/approach – Data were collected from 450 university students through a structured questionnaire. The research model was tested using structural equation modeling with bootstrapping procedures for mediation analysis and hierarchical regression for moderation effects. Findings – The results reveal that career planning education positively influences entrepreneurial intentions both directly ( =0.312, p<0.01) and indirectly through self-efficacy ( =0.178, p<0.01) and perceived behavioral control ( =0.133, p<0.01). Digital competency ( =0.156, p<0.01) and risk propensity ( =0.143, p<0.01) positively moderate these relationships. Practical implications – The findings suggest that higher education institutions should integrate digital skills development into career planning curricula and tailor educational approaches to students' individual characteristics to enhance entrepreneurial intentions effectively. Originality/value – This study extends the theory of planned behavior by incorporating digital competency as a crucial moderating factor and demonstrating the specific mechanisms through which career planning education influences entrepreneurial intentions in the digital era.
As a result of continuous economic development and accelerated urbanization, the agriculture development has had to change from the traditional mode of agricultural production to the modern mode of agricultural production. What kind of method can better help the development of modern agricultural production mode has become one of the current research topics that has attracted much attention. In response to this problem, the field of modern agricultural production models becomes highly relevant for research. With the in-depth study of modern agricultural production, the research on Internet of Things (IoT) technology in rural characteristic ecological agriculture (ECO) is gradually carried out, and its functional advantages are of great significance to promote the development of modern agriculture. This paper aimed to study the application of IoT technology in the development of rural characteristic ECO. The analysis and research of IoT and ECO enables it to be applied to the construction of an ecological farmland information monitoring system to address the problem of enhancing the ECO development with rural characteristics. In this paper, IoT technology, information detection and ECO were analyzed; the performance of the method was experimentally analyzed; the relevant theoretical formulas were utilized for interpretation. The outcomes demonstrated that the incidence of pests and diseases in field A using the IoT-assisted information monitoring system was 31.11% lower than that in field B, and the use of pesticides was reduced by 15.69%. It can be learned that IoT technology can meet the needs of enhancing the development level of rural characteristic ECO, and the level of agricultural development and work efficiency have been greatly improved.
With the rapid development of society, the emergence of society and people’s daily life have put forward higher quality requirements for power supply. The original distribution system cannot monitor and control the circuit condition in real time. The power grid operation efficiency is low, and the loss of electric energy in the transmission process is large, resulting in the unstable power supply to users. With the development of smart grid, distribution automation has become the goal of Power System (PS) development. There are many noise data in the process of medium voltage distribution communication. In this paper, the medium voltage high-speed analog Communication Technology (CT) was applied to distribution automation. By modulating the signal and other operations, automatic power distribution can be realized, which can effectively shorten the maintenance time of fault circuits and quickly share power data resources. This paper compared the traditional medium-voltage distribution with the distribution automation based on the medium-voltage high-speed analog CT. The experimental results showed that the average power supply reliability of the traditional medium-voltage distribution and distribution automation was 88.90% and 95.56% respectively in the 10 kV voltage. In the 20 kV voltage, the average power supply reliability of traditional medium-voltage distribution and distribution automation was 90.24% and 97.04% respectively. Therefore, the application of medium-voltage high-speed analog CT in distribution network to distribution automation can effectively improve the reliability of power supply.
Financial digital management is a new type of financial management method. Through information technology, the financial management process has been digitized, and with the help of technical means such as data analysis and artificial intelligence, financial management automation has been achieved. Traditional financial management methods often require a large amount of manual intervention and processing, which is prone to problems such as cumbersome data processing, time-consuming and labor-intensive, and prone to errors. With the development of computer technology and network technology, digital management has become a new trend in financial management. This article analyzed the application of blockchain and cloud computing technology in financial digital management, and selected 12 enterprises as the research objects. The traditional financial management model and the financial digital management model of blockchain and cloud computing technology were respectively adopted to compare the differences in financial process efficiency, data accuracy, labor cost savings, digital management, and financial risk management between the two models. The experimental results of this article indicated that under the financial digital management mode using blockchain and cloud computing technology, the processing time of the revenue and expenditure process was 4.45 hours in terms of financial process efficiency. In terms of data accuracy, the accuracy rate of accounting was 99.7%. In terms of labor cost savings, the labor cost was 1.505 million yuan/year. In digital management, the data processing efficiency score was 92. In financial risk management, the accuracy score of risk assessment and prediction was 93, which was better than traditional financial management models. The adoption of blockchain and cloud computing technology in financial digital management can significantly improve multiple key indicators such as financial management efficiency, data accuracy, and security. This model has important value and significance for enterprises.
The popularity of the Internet and mobile smart terminals has changed many forms of learning, and the mobile learning model was born in this environment. As a new learning mode, mobile learning has brought certain development opportunities for college English writing teaching. In the current educational environment, many students hold various mobile devices, which also motivates them to have a strong willingness to learn on mobile. It can be said that the application of mobile learning to English writing is quite suitable. At present, the application of mobile learning in college English writing is not mature enough, and there are often a series of problems such as shortage of resources and network freezes, which also reduces students’ enthusiasm for learning. In order to further improve the fluency and maturity of the mobile learning mode, this paper has combined the wireless network to study the new mobile learning mode of college English writing. By building a mobile learning framework based on wireless network, innovating mobile learning writing content and computing learning resource categories, a new mobile learning mode of college English writing has been finally formed. The experimental results have shown that the new model has mobilized students’ enthusiasm for learning and further improved the writing efficiency. Compared with the old model, the efficiency has increased by 6.73%.
Traditional power load forecasting (PLF) usually uses statistical models or time series analysis methods, but they often only consider historical load data and ignore the impact of meteorological, temperature, humidity and other factors on load, resulting in inaccurate load forecasting. Moreover, traditional methods have limited real-time performance in power load data transmission and cannot respond to changing load demands in a timely manner, which limits the real-time and accuracy of PLF. Wireless networks (WN) and intelligent sensing technology (IST) were used to obtain real-time charge data, and these data were intelligently analyzed to improve prediction performance. WN and IST were used to improve the transmission efficiency and prediction accuracy of PLF. This article studied the transmission delay and integration delay of power load data in WN, and conducted experimental tests on the root mean square error (RMSE) of CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets using an intelligent sensing algorithm based on sensors to study their predictive effect on power load. As the number of users continues to increase, the transmission delay and integration delay of power load data were also increasing. During the process of increasing the number of users from 0 to 500, the transmission delay increased from 389ms to 735ms; the integration delay increased from 568ms to 1086ms. The power load prediction algorithm based on intelligent perception technology had average prediction RMSEs of 0.2885, 0.2716, and 0.2618 for CER Electricity Data, REFIT Power Data, and Umass Smart Data Set datasets, respectively. In WN, the transmission delay and integration delay of power load data are relatively small, and with the increase of the number of users, the impact of this delay is relatively small, which can have the effect of supporting the transmission and integration of power data for a large number of users. The power load prediction algorithm based on intelligent perception technology has good prediction results for different datasets and can accurately predict power loads.
This research presents an innovative machine learning framework for predicting library space utilization patterns through the integration of multi-modal deep learning architectures and ensemble methodologies. The proposed system combines Long Short-Term Memory (LSTM) networks with attention mechanisms and sophisticated feature engineering techniques to achieve superior prediction accuracy while maintaining computational efficiency. The methodology encompasses three primary contributions: (1) development of a comprehensive feature extraction pipeline incorporating spatial, temporal, and environmental data streams; (2) implementation of a novel LSTM-Attention hybrid architecture with adaptive learning rate optimization; and (3) integration of ensemble learning techniques for robust prediction performance. The framework demonstrates significant improvements over existing approaches, achieving 96.8% prediction accuracy across diverse operational scenarios. Experimental validation, conducted using an extensive dataset comprising 2.1M samples collected over 33 months from multiple library facilities, demonstrates the framework’s effectiveness. The proposed model achieves a Mean Absolute Error (MAE) of 0.142 and Root Mean Square Error (RMSE) of 0.186, representing a 39.8% reduction in prediction error compared to baseline approaches. The system’s computational efficiency is evidenced by an average processing time of 45.3ms per prediction, with a memory footprint of 512MB. The research contributes to the field of intelligent library management systems by establishing a theoretically grounded and practically implementable solution for space utilization prediction. The framework’s superior performance in capturing complex spatial-temporal patterns, combined with its computational efficiency, makes it suitable for real-time applications in resource-constrained environments. These advances provide a foundation for enhanced space management strategies in modern library systems.
Amidst the digital economy and ESG policy frameworks, digital transformation emerges as the prime strategy for high-tech companies to enhance their corporate performance. The research investigates the impact of high-tech organizations’ digital transformation on their performance, utilizing data from A-share listed tech firms in Shanghai and Shenzhen spanning 2018 to 2022.The research indicates that digital transformation enhances the performance of high-tech firms in the context of ESG. The modulating mechanism shows that executive compensation will weaken the impact of digital transformation on enterprise performance. The intermediary mechanism demonstrates that internal control and cost effect contribute to the mediating influence on the relationship between enterprise performance and digital transformation. Each of them has successfully cleared multiple tests for robustness. At the same time, there is a certain heterogeneity in the influence of high-tech enterprises on firm performance, and the improvement effect on firm performance is significant in the east and the growth and maturity period. The research presents new empirical evidence and acts as a benchmark for understanding how digital transformation affects high-tech companies’ performance.
Purpose – This study aimed to explore the internal structure of sustainable employability of liberal arts college students in China and develop a comprehensive scale to facilitate research on this topic and establish a theoretical framework for cultivating sustainable employability of liberal arts college students in China. Design/methodology/approach – Through theoretical derivation and open questionnaire and the Delphi method, the main dimensions of sustainable employability of liberal arts college students are explored. The components elments of each dimension are explored through a text analysis of 189 job advertisements. Through 392 questionnaires and statistical analysis techniques, a scale is developed for measuring the sustainable employability of liberal arts college students. Findings – This study found three dimensions characterizing the sustainable employability of liberal arts students in China: attribute characteristics, general ability of employment, and innovation-driven ability. Additionally, the attribute characteristics encompassed five attribute elements, the general employment ability included six, and the innovation-driven ability included four. This study also developed a 34-item scale for measuring the sustainable employability of liberal arts students that demonstrated good reliability and validity. Originality/value – This study was among the first to investigate the internal structure of sustainable employability of liberal arts students in China.
This study introduces a new methodology for the configuration and optimization of algorithm-driven strategies in the digital economy. It puts forward a hybrid optimization algorithm for the efficient handling of complex resource allocation problems. The proposed approach combines adaptive learning mechanisms with traditional optimization methods, showing significant improvement in convergence speed, solution accuracy, and stability of the system. Through extensive experimental validation conducted on a range of benchmark functions and real-world contexts, this algorithm proves to be outstanding at a 48.7% reduction in convergence time, as well as a solution quality enhancement by 66.4% compared with the traditional methods. Robustness analysis confirms consistent effectiveness under all diverse noise conditions and retains high success rates, even in demanding environments. This result greatly contributes to advancing algorithmic optimization approaches for digital economic systems and paves the way toward concrete applicative implementations.
With the rapid development of artificial intelligence technology, the education sector is undergoing unprecedented changes. Personalized learning has become a key method to enhance teaching quality and learning outcomes. This paper aims to explore the application of artificial intelligence technology in personalized learning resource recommendation for students, by constructing user profiles, multidimensional models, and personalized recommendation algorithms, in order to provide precise learning resource recommendations for students. This paper proposes a personalized learning resource recommendation algorithm based on a one-dimensional convolutional neural network (1D-CNN). The algorithm first extracts local features of the sequence through convolutional operations, then uses pooling operations to extract long-term features of the sequence, and combines the two features through weighted addition to obtain the user feature information, which allows for the comprehensive extraction of both local and long-term features. Subsequently, the user feature information is multiplied by the linearly transformed sequence information to introduce temporal information. Additionally, student learning records, class performance, and incorrect question records are collected and integrated as user feature information. These user features are passed through a feedforward network to achieve nonlinear transformation and cross-dimensional interaction enhancement. Finally, the user feature vector and item feature vector are computed to obtain their relevance, which is then used for recommendations. Experimental evaluations validate the effectiveness and feasibility of the proposed method, with the aim of providing valuable insights for educational reform and development.
The rapid development of digital technology and artificial intelligence has made the improvement and optimization of intelligent warehousing and automated distribution systems important topics for research in modern logistics management. With this as the background, the current study uses a systematic approach to explore critical factors, innovative ways, and implementation strategies related to these factors and their role in improving the effectiveness of intelligent warehousing systems. The study adopts a mixed-methodological approach, establishing a comprehensive evaluation index system including operational efficiency, technical performance, and economic benefits, and simultaneously verifying the implementation of the system through empirical analysis. According to the findings, the intelligent warehousing system increased the efficiency of operations in relation to order processing time and had reduced it by 71.7%, and enhanced the accuracy of picking to 99.8%. The intelligent warehouse system by use of machine learning and meta-heuristic algorithms had greatly improved the efficiency in resources utilization and energy as storage utilization increased by 19.3% while energy consumption dropped by 31.4%. A cost-benefit analysis shows that, despite the significant up-front financial investment, the system achieved a 186% return on investment over three years. This research deepens the theoretical understanding of intelligent warehousing and, at the same time, provides optimization strategies applicable to industry practice. Future research directions should focus on exploring the applications of multi-agent digital twin technology and researching how intelligent warehousing systems contribute to supply chain resilience and sustainability.
Power metering system is directly related to the production and operation level and benefit of power supply enterprises, and even has a close relationship with the national economic development and people’s life. Numerous scholars have applied deep learning to the field of fault diagnosis. Accordingly, this paper proposes a fault diagnosis method for power metering system based on stacked autoencoder (SAE) algorithm. The deep learning data samples are formed by comprehensively collecting the historical operation data of the system and the feature data provided by the third-party manufacturers. And the fault diagnosis model is designed with the SAE algorithm, and the training and optimization fine-tuning of the algorithm model is realized by BP neural network. Finally, the model is trained using explicit test data samples, and the BP neural network can reach the set accuracy after 3804 training sessions with the output error. Compared to Elman neural network iterations are less and converge faster. Using the trained fault detection model of power metering system for fault diagnosis, the model can successfully classify the faults and achieve the expected diagnostic effect.
This paper presents an AdaBoost-DNN (Adaptive Boosting-Deep Neural Network) model for the detection of anomalous electricity consumption in power grid users. Initially, the k-means SMOTE (Synthetic Minority Oversampling Technique) technique is employed to enhance the sample set of the original anomalous consumption data to address the issue of data imbalance. Subsequently, an ensemble learning model based on AdaBoost-DNN is designed for the detection of anomalous consumption. To validate the effectiveness and superiority of the proposed AdaBoost-DNN model, comparative experiments are conducted with three traditional algorithms.
This study aims to construct a corporate demand model of brand design for financial central enterprises based on grounded theory, providing a systematic theoretical framework to help financial central enterprises effectively meet their brand design needs. The method of grounded theory is used to extract relevant information from public information and in-depth interviews, and to demonstrate the rationality of the corporate demand model by combining quantitative research. Through an in-depth analysis of the subsystems of demand motivation generation, demand factor analysis and demand design realisation, a feasible theoretical framework is provided for financial central enterprises to achieve effective satisfaction of brand design corporate demands. The problem of accurately grasping requirements in the practice of brand design for financial central enterprises is solved. Useful reference and support are provided for the brand design of financial central enterprises. The innovative practice of brand design is promoted for financial central enterprises.
This study investigates the application of artificial intelligence techniques in coal mine gas monitoring and prediction, aiming to construct more efficient and accurate gas concentration prediction models to reduce the risk of gas explosion in coal mine production. Due to the limited performance of traditional prediction methods in dealing with high-dimensional and dynamic three-dimensional mining environments, this study employs a fusion model based on temporal convolutional network (TCN) and temporal generative adversarial network (TimeGAN), TCN-TimeGAN, to predict the gas concentration. The model combines the interval sampling advantage of TCN and the time series characteristics of TimeGAN, and through four processes of embedding, recovering, by generating and discriminating gas concentration time sequences, the time-dependent features of gas concentration data can be effectively captured, thus improving the prediction accuracy and timeliness. In this study, gas concentration data from September 2020 through December 2021 were used as the basis, through data cleaning and outlier processing, it is found that the gas concentration data has obvious time-dependence, which is suitable for using time series modeling. Embedding and Recovery Networks via TCN-TimeGAN Modeling, the gas concentration data are mapped to a low-dimensional feature space, a generative network is then used to generate new time series data from random noise, and the model parameters are optimized by combining the discriminative network in order to improve the quality and consistency of the generated data. In particular, to cope with the problem of gradient instability of generative adversarial networks during training, In this paper, Wasserstein distance is introduced to optimize the loss function and a gradient penalty term is added during the training process to improve the stability of model training and the realism in the samples generated. In addition, this study also explores the prediction performance of combining LSTM networks for gas concentration. The standard recurrent neural network (RNN) faces the problem of gradient vanishing in the processing of long time-dependent data, whereas the improved LSTM overcomes this problem through memory cells and gating mechanism for real-time prediction task of gas concentration. In this study, the LSTM is further extended to three-dimensional spatial input data, experiments demonstrate the prediction accuracy of the improved LSTM. To verify the validity of the model, this paper adopts a hierarchical K-fold cross-validation method, which divides the data into a training set and a validation set to ensure that the model can be generalized. Experimental results indicate that TCN-TimeGAN and improved LSTM significantly outperform traditional methods in gas concentration prediction. By analyzing the training and validation accuracies, the models showed high prediction accuracy (89.1% to 93.8%) after 20 epochs, verifying the stability and applicability of the models.
In conclusion, this study shows that the gas prediction model based on TCN-TimeGAN and improved LSTM can more accurately predict the gas concentration in coal mines, improve the intelligence level of coal mine gas monitoring, and provide technical support for safe production in coal mines. Meanwhile, the methods and models in this study also provide new thoughts and methods for time series data prediction in other fields.
This paper presents an innovative optimization framework aimed at data mining in social networks, guaranteeing solutions for some of the basic challenges of computational efficiency, scalability, and accuracy. This work presents a precise approach that integrates state-of-the-art algorithmic enhancements with dynamic resource management techniques. Extensive experimental validation using real and synthetic datasets has marked the significant performance gains achieved within the framework. These results point to a 70.2% reduction in processing time and a 71.2% saving in memory consumption, all while maintaining accuracy rates above 95%. This optimization framework is very stable under different operation conditions, since its responses have always remained below 85 ms under peak loads of up to 245,000 requests per second. The empirical evaluation of the framework across diverse social networking platforms bears testimony to the fact of practical efficacy and has emerged strongly while dealing with dynamic network architecture with extensive data processing needs. The application results in significant improvement in resource utilization efficiency, providing sub-linear increase in memory consumption for maintaining consistent performance under fluctuating load scenarios. The present study extends the scope of social network analysis by proposing a scalable, efficient, and reliable optimization framework that might be of vital importance in both research and practical implementation contexts.
With the unprecedented growth of technological advancement, effective technological transfer has become increasingly important in all dimensions of human lives. Technological transfer is a multi-level and complex ecosystem network with complicated inter-relational elements and effective fac-tors. This complexity raises the question of how to rearrange the elements of the technology transfer to improve its positive performance. To address this issue, this study aims to compare the perfor-mance and gaps of the three modes of technology transfer, which are technology entrepreneurship, technology licensing, and technology shareholding, by evaluating the three participants, which are universities/research institutes, corporations, and intermediary agencies, using related attributes. This study applies a hybrid multiple attribute decision-making (HMADM) model including the DE-MATEL for constructing the INRM, DANP for computing influence weights, modified VIKOR for evalu-ating the performances and gaps among the three technology transfer modes so that to develop sus-tainable and systemic improvement strategies. At the macro level, the results show that, the technol-ogy transfers modes receive an overall positive effect, especially universities/research institutions. At the micro level, the technology licensing has not only the highest performance but also the largest gap. According to this finding, technology licensing is the most feasible way to cater to technology transfer at the macro level from the micro level. The findings suggest decision makers pay attention to the role of universities/research institutes as the main factor influencing technology transfer ef-fectiveness. Also, they should focus on influential attributes such as researcher participation and technical collaboration ability for reducing the gap.
With the reforms in competition rules and equipment by the International Table Tennis Federation (ITTF), the number of rounds in table tennis matches has increased, placing higher demands on athletes’ abilities to transition between technical and tactical offensive and defensive strategies, as well as on their physical and psychological qualities. Therefore, this study employs methods such as the strength difference evaluation, competition performance (CP), and multiple regression. Using 48 international important matches in which Player W (anonymous) participated as case studies, the study evaluates and predicts the competitive performance of W when facing athletes of different world rankings. The results indicate that in matches against athletes with different skill rankings, the phases where the technical strength difference significantly affects competition performance (CP) are the attack-after-serve phase and attack-after-receive phase, followed by the rally phase, and finally, the rally phase Ⅱ. The competitive level in serve rounds is superior to that in receive rounds. The Kruskal-Wallis test results reveal significant fluctuations in the competition performance (CP) during the rally phase Ⅱ, demonstrating highly significant differences (P < 0.01). In matches, Player W has a very low probability of winning when not holding an absolute advantage in key techniques (the first four strokes) – particularly pronounced when facing athletes ranked in the world top 20. The multiple regression model for the technical strength difference in table tennis matches plays a certain role in predicting the performance of athletes in terms of technical and tactical indicators during matches, offering a clear reflection of the effectiveness of these indicators.
The presented article develops the detailed analysis of battery performance degradation profiles for EVs, based on operational data collected in real-world use. Based on data points gathered for 150 vehicles over 24 months, we have developed and then validated an integrated degradation prediction model incorporating several degradation mechanisms. Our study applies a novel hybrid approach that will combine physics-based principles with data-driven methods for outlining the battery aging profile. The model proposed in this paper realizes a better prediction performance of 94.3% under different operational conditions and thus proves to be considerably superior to the existing techniques. Indeed, the change of temperature and charging behavior becomes the main influence factor with the correlation coefficient of 0.85 and 0.78, respectively. After applying the proposed model to a fleet management system, there are 32.4% maintenance cost reduction and 15.8% increasing of the cycle life for batteries. It represents in detail the continuous degradation assessment and predictive maintenance framework, validated on different vehicle platforms under varying operational conditions. These findings provide valuable inputs related to the improvement of battery management strategies and life extension of a battery in electric vehicle applications, hence benefiting theoretical understanding and practical application in electric vehicle battery management.
It has identified and presented a unified machine-learning-based malware defense system that can handle dynamic features in cyber-security challenges. This approach will leverage recent deep learning models, ensembles, and automatic generation of defense strategies to construct an effective and adaptive framework for malware detection and mitigation. These results tend to indicate significant gains compared with traditional signature-based approaches, whereby known malware detection rates reached 99.2%, and zero-day vulnerabilities reached 87.5%. The system also recorded an extra 68% reduction in false positives after one month of operations due to the adaptive learning component, while real-time detection features yielded less than a one-second response time for 95% of the threatened records. The generated defense strategy module can demonstrate a 92% success rate in the automated mitigation or containment of identified threats. The paper further presents that even with such advances, much potential still exists for optimizing resource use, enhancing model interpretability, and building more robust defenses against adversarial attacks. It enhances the area of cybersecurity and adds a new dimension by showing the capability of AI-enabled methodology to create much more efficient, agile, and flexible malware protection systems-thereby paving the way for more advanced cybersecurity innovations.
This paper presents a hardware encryption system based on FPGA (Field-Programmable Gate Array) implementing the elliptic curve cryptography algorithm. Using FPGA as the core control unit, IoT (Internet of Things) data transmission terminals are connected to FPGA-specific external interfaces via USB/SPI interfaces. Data collected into the FPGA undergoes encryption and decryption using the FPGA’s internal hardware resources. The encrypted data is then converted into TCP/IP protocol packets and transmitted to a cloud server through the FPGA’s internal Ethernet interface circuit module. A detailed analysis and design of the hardware implementation of the elliptic curve encryption algorithm are provided. Simulation validation of the point multiplication algorithm was conducted on a computer platform with a quad-core 3.2GHz processor and 8GB of memory, using the Xilinx 5vlx20tff323 chip. The simulation results indicate that the maximum execution frequency reached 372.686 MHz, with a single point multiplication operation completed in 3328 . This significantly enhances the processing speed of the algorithm, bearing significant theoretical value and practical implications for advancing the security of the IoT ecosystem.
Aiming at the current problems of low level of intelligent development and backward infrastructure in the countryside, this paper proposes a multi-objective optimization model for rural construction. According to the overall principle of optimization and the current situation of rural infrastructure construction, model assumptions, objective functions and constraints are determined. Facing the problem of calculating the optimal values of the four objective functions, NSGA-II method is chosen to solve and analyze the problem. NSGA-II algorithm is calculated in 100 iterations, and the optimal solutions of the four objective functions are 0.813, 0.943, 0.852, and 0.886, which are better than NSGA and GA algorithms in terms of performance. In order to improve the intelligent development of the countryside, two targeted development proposals are put forward.
With the progress of modern technology, smart wearable devices have been gradually applied in the field of sports. This paper focuses on the experiments of motion recognition of the main joints realized by convolutional neural network-assisted smart wearable devices. Using smart wearable devices to feature extraction of a variety of sports signals, using GAF algorithm for sports signal image coding, and using convolutional neural network and gated recurrent unit, a CNN-GRU-based motion recognition method is proposed. Through the training and evaluation experiments of the model, it is found that the average accuracy of the CNN-GRU model training and testing is higher than 96%, and the loss value is lower than 1.5%, and the performance of sports recognition is better than that of CNN and CNN-LSTM models. Meanwhile, it presents excellent performance in the recognition of sports with different classifications and different signal durations, reaching 97.02% and 92.63% accuracy in the recognition of three and four types of sports, respectively, and the distribution of the values of human body indexes in different sports in the case study presents a certain degree of regularity, which verifies the effectiveness and feasibility of the CNN-GRU model in different application scenarios. It also shows that the method has great development potential in the field of intelligent sports.
This paper constructs a heterogeneous network adjacency matrix containing multiple user relationships from the connotation of professional organizations and other guides to individual behaviors covered by the take-read mechanism. The GAT algorithm is used to learn the embedding of its heterogeneous network in order to obtain the embedding vectors of user nodes, which serves as the basis for the analysis of the spreading influence of group behavior. An event recognition method based on word embedding and hierarchical cohesive clustering is proposed to analyze the recognition and evolution of social media essay-carrying behavioral events (group behavioral events) for complex networks. We point out that the distribution of group behavior affects the dynamics of information dissemination, set the adoption threshold parameter of the group, and analyze the dissemination pattern of individuals’ (individual information) participation in essay-reading behaviors. Analyze the emergence and evolution of thesis-reading behavior in social media, and explore the influence of individual’s own attributes and the attitude of neighboring nodes on the evolution of group behavioral events in complex networks. The spreading degree analysis is conducted for different relational social media bandwagon behaviors. When =0.6 and =0.8, the individual’s decision is supported by the neighbor’s viewpoints, and the users who have already participated in the paper band-reading activities have a strong attraction to the individual. When the strong degree increases to a certain value, the individual decides to participate in the dissertation banding activity, at which point the individual is no longer influenced by the external environment. The degree of the initial node for the propagation of thesis banding behavior in random networks and small-world networks is linearly and negatively correlated with the percentage of the information audience.
This paper constructs an improved Changsha city brand image communication model on the basis of the traditional contagion model, and studies the communication effect of Changsha in the process of city brand image transformation from “online star city” to “long-term famous city”. By summarizing and analyzing the current situation of Changsha’s city brand image communication, the evaluation index system of Changsha’s city brand image communication effectiveness is constructed, and the collected evaluation index data are downscaled using principal component analysis. The support vector regression machine combined with differential evolution algorithm is used to quantitatively analyze the communication benefits of Changsha city brand image. The improved city brand image communication model in this paper has a higher accuracy compared with the traditional contagion model, and can accurately grasp the communication effect of Changsha city brand image. The average relative error of the support vector regression machine model in the quantitative analysis of communication benefits for the test samples from 2020 to 2023 is only 1.53%, which is 27.86% lower than that of the BP neural network model. It strongly demonstrates the effectiveness of the regression model selected based on the communication big data in this paper, and provides a useful reference for accurately measuring the communication benefits of Changsha’s city brand image.
Under the background of carbon peak carbon neutrality, the competition among ports is not only the competition among terminal scale, throughput, and service level, but also the competition of low energy consumption and low pollution, and with the development of China’s carbon trading mechanism, the cost of carbon emission has become more and more a part of the enterprise that cannot be ignored. In this paper, the berths and shore bridges of the port are taken as the target variables, and the fuel consumption in the process of ships traveling to the port is inferred according to the assumed conditions, and the BAP model under the carbon peak carbon neutrality is deduced, and the relevant constraints are proposed. The initial population is randomly generated, and the first generation of offspring population is obtained through the selection, crossover and mutation operations of multi-objective genetic algorithm, which then continues until the end conditions of the program are satisfied. Through the empirical method, comparing the effect of carbon cost optimization scheme generated by multi-objective genetic algorithm and traditional method, the value of the objective function under the multi-objective genetic algorithm model decreased by 10.48%, the operation cost of the port decreased by 4.54%, the cost of the ship’s in-port time decreased by 24.9%, and the ship’s average in-port time decreased by 11.01%, as compared with the traditional allocation scheme. The multi-objective genetic optimization model of berth shore bridge considering carbon cost can shorten the ship’s time in port, which reduces the carbon emission from the side and achieves the promotion purpose of green port. In the model sensitivity analysis, with the increase of carbon trading price, the four indicators F, F1, F2 and T also showed linear growth, with the growth rate of 17.24%, 18.44%, 14.37% and 18.02%, respectively, and the model sensitivity is good.
Participatory culture, as one of the characteristics of audience performance in the current communication environment, provides imaginative space for stimulating the power of audience participation in the communication of non-heritage culture, and at the same time provides new thinking direction and inspiration for the current communication of non-heritage culture. In this paper, we mainly apply recurrent neural networks to model sequence data, and control the flow of information by adding special gating structures, so as to be able to effectively memorize and process long sequence data. Self-attention is constructed so that the network can better focus on the important parts of the sequence while ignoring the irrelevant information in the sequence. Identify non-heritage communication behaviors based on time-series data, and model non-heritage cultural communication behaviors based on the length of time the behaviors occur under the framework of situational awareness. The research experimental model is designed, relevant hypotheses are proposed, and examined through empirical evidence. The number of borrowings by visitors under 18 years old, which is the main group of visitors, declined from 737 in 2016 to 357 in 2022, with an overall decline of 51.56%, and the overall visiting behavior also showed a declining trend. In order to test the mediating role of perceived value in the relationship between interactive behavior and the communication effect of intangible cultural heritage, the benchmark model M3 model was constructed with the communication effect as the dependent variable and gender and whether the only child was the controlling variable, and the independent variables “interactive behavior” and “perceived value” were added on this basis, and the perceived value had a significant positive impact on the communication effect, β=0.485, p<0.001. The influence of interactive behavior on communication effect remains significant, at this time the β-value is 0.487 and p<0.001, the mediating role of perceived value between interactive behavior and non-heritage culture communication effect.
Science and technology innovation talents are the center of gravity of the national strategic power, which is crucial for promoting social development and scientific and technological progress. The purpose of this paper is to study the scientific and technological innovation talents of power grid enterprises, build the evaluation index system of scientific and technological innovation talents with reference to the CIPP model, select a power grid enterprise to analyze the examples, and use the fuzzy AHP model to evaluate its scientific and technological innovation talents training. Then build the role mechanism model of science and technology innovation talent cultivation, conduct regression analysis of the influence factors of science and technology innovation talent cultivation, and verify the research hypothesis. The evaluation results of the STI talents of the sample grid enterprises range from 3.6 to 4.0 points, and the evaluation grades are all good, confirming the practicality of the proposed STI talent evaluation method. Except for years of education, high focus in research field and teamwork, the selected personal factors, organizational factors and environmental factors have positive and significant effects on the quality of STI talents training. It is suggested that power grid enterprises improve and promote the development of the training system of scientific and technological innovation talents by building a training and development channel, developing a layered training model, innovating training methods as well as building a research platform.
Artificial intelligence digital tools are widely used in teaching scenarios. This study designs a digital learning tool capable of personalized learning resource recommendation and applies it to tourism English education to improve teaching quality. The study first establishes a set of nearest-neighbor user selection scheme based on clustering algorithm and analyzes the overall user behavior in a collaborative filtering way, so as to provide the target users with learning materials pushing service with high accuracy. Then a personalized teaching model for tourism English education is designed based on this system. Finally, the model is applied to actual teaching, and the application effect of this AI digital tool in tourism English education is verified through teaching practice. The students’ performance in tourism English teaching using the personalized learning resources recommendation system increased by 13.59 points compared with that before using the system, which is a significant difference. It shows that the personalized learning resources recommendation system has value in tourism English education.
The electric power industry is an important basic industry of the country, and among all the electric power equipment, the distribution lines are directly facing the end-users, which is an important infrastructure to serve the people’s livelihood. In this study, we first transformed the distribution line engineering quality defect acceptance problem into a sequential decision-making problem, and constructed an improved reinforcement learning network model DDQN based on it, and introduced a reward function into the model to improve the intelligent adjustment ability of the intelligent bodies in the model to the data related to the distribution line, so as to improve the detection performance of the DDQN model in the distribution line engineering quality defect acceptance. The results show that the improved DDQN model is highly feasible and effective in the detection of quality defects in distribution line engineering compared with other comparative models. The simulation test of distribution line engineering quality defects found that the accuracy of the DDQN model-based distribution line engineering quality defects acceptance technique in detecting line quality defects is 95%. It is verified that the accurate and reliable distribution network line engineering quality defect acceptance technology based on the improved DDQN model is conducive to guaranteeing the safe and stable operation of the power grid system.
In this paper, OpenCV technology is used to produce the distribution network defects dataset, which can be used as a training set, validation set, and test set in the ratio of 6:2:2. Combining the dataset and the Transformer framework, the S-Transformer based distribution network key quality defect identification model is constructed together. At this level, the degree of equipment deterioration is fitted, the distribution network intelligent operation and maintenance optimization strategy is formulated, and the experimental method is applied to evaluate the distribution defect identification and intelligent operation and maintenance. The identification rate of S-Transformer network for the six collected distribution network equipment defects is 0.9~0.95, which accurately controls the potential dangers, and is conducive to the subsequent intelligent equipment operation and maintenance of the distribution grid and its management and control, compared to the Compared with the traditional operation and maintenance program, the operation and maintenance program in this paper can reduce the operation and maintenance time by 52 hours per month, which greatly provides the efficiency of operation and maintenance labor.
Financial sharing has become an important trend in the process of enterprise development in the era of big data. This topic centers on the research of the application of cloud computing technology in financial shared services, and introduces machine learning algorithms into financial risk early warning. Financial and non-financial indicators are selected to construct the financial analysis index system, K-tuning and mean value algorithm is used to realize the risk level division, SVM algorithm is used to construct the financial risk early warning model, the parameters are continuously adjusted according to the model accuracy rate, and the model is applied to the benefit analysis. Dividing the samples into four financial risk levels of none, low, medium and high can more accurately reflect the specific situation of enterprise finance. It is proved through experiments that the financial risk prediction performance of SVM model in this paper far exceeds the logistic regression model and Gaussian plain Bayesian model, the accuracy rate is improved by 9.7% and 18.6% respectively, and the average accuracy rate in the test set reaches more than 93%. Therefore, it is feasible as well as of great research value to apply cloud computing technology in artificial intelligence to the research field of risk warning of financial shared services.
The construction of ecological civilization is a fundamental plan related to the sustainable development of economy and society, and the dispute settlement mechanism of environmental damages is its innovative and important content. Starting from the environmental legal dispute resolution mechanism, the article analyzes the legal basis of environmental dispute mediation and the process related to pre-litigation mediation. Considering environmental legal dispute resolution as a kind of multi-objective decision-making optimization problem, a multi-objective decision-making optimization model for environmental legal disputes is constructed with the objective functions of legal effectiveness, legal applicability and subject interest rate. Then adaptive inertia weights and dynamic image Pareto solution set updating strategy are introduced to improve the multi-objective particle swarm algorithm, and combined with information entropy-based TOPSIS decision-making to realize the optimal solution selection for environmental legal dispute resolution. In the multi-objective decision-making optimization model, the improved multi-objective particle swarm algorithm achieves the optimum for a total of 15 data, and the simulation time in solving the optimal solution of the 10*10*5 case problem is only 2.314s, and the optimal solution of environmental legal dispute resolution can be obtained based on different objective functions. Environmental legal dispute resolution needs to aim at effectiveness, applicability and subject’s interests, introduce appropriate punitive damages, realize the effective connection between administrative law and criminal law, and promote the high efficiency of environmental legal dispute resolution.
This paper improves the deep residual network, proposes 3DResNet network and carries out particle swarm optimization, constitutes the PSO-3DResNet model, and designs the coal mill fault diagnosis model based on PSO-3DResNet model. The technical parameters, common fault types and fault characteristics of the coal mill are analyzed, and the relationship between the input and output parameters of the coal mill is decomposed by the residual-based condition monitoring method. Combining the numerical simulation model of coal mill and historical operation data, the typical fault condition monitoring of coal mill is constructed. Compare the classification accuracy of each model on the working state of blast furnace wind mouth, and get the anomaly detection performance of each model. The PSO-3DResNet model is analyzed to monitor the normal operating state of the coal mill, and the model is tested using the historical current and outlet wind temperature anomaly data of the coal mill. When the coal mill is in an abnormal state, the estimated residuals of the current abnormal condition fluctuate within [-16,3] with a small range, and the weighted average residuals of the current abnormal condition index remain within [-4,1].
Artificial Intelligence Generated Content (AIGC), as a computer technology mainly characterized by intelligent content generation, has caused significant changes in film and television performances and creations, and has greatly broadened the creation and development space of film and television performances. In this paper, we use motion capture technology to obtain the character movement data in film and television performances, and combine it with the skeletal motion data generation algorithm to realize the mapping of skeletal motion data. Using ResNet-122 as the backbone network, a 3D action pose estimation model is constructed by combining multi-view and multi-feature fusion networks. Based on the 3D action pose estimation sequence, the character animation generation model is constructed by combining GAN and action detail attention mechanism, and the action detail feature loss function is designed to improve the generalization ability of the animation generation model. In order to verify the effectiveness of the above method, data analysis is carried out through simulation verification. The average value of PCP3D index of the 3D action pose estimation model is 98.37, which is 0.28 percentage points higher than the sub-optimal model, and the average joint position error is only 16.07 mm. The animation generation model combining GAN and the action detail attention mechanism has the values of animation generation diversity and richness index of 5.104 and 3.997, respectively, and the animation generation diversity and richness indexes of the animation generation model combining GAN and the action detail attention mechanism are 5.104 and 3.997, respectively. 3Ds MAX software can map the generated animation sequences into the virtual space, providing assistance for optimizing the motion design of film and television performances.
The emotional curve of a story is the core embodiment of the reading value of a novel, and good novels tend to have similar patterns of emotional changes, which are explored in novels by combining artificial intelligence technology. After collecting modern Chinese novel texts, Chinese word segmentation and de-duplication are performed to complete the novel text preprocessing. In view of the limitations of convolutional neural network (CNN) and recurrent neural network (RNN) in text feature extraction, this paper proposes a multi-channel convolutional and bi-directionally gated recurrent unit (BiGRU) deep learning model, Pt-MCBGA, to mine the emotional polarity in the text and analyze the emotional trend of modern Chinese novels. After a series of comparison experiments, it is demonstrated that the model performance achieves a relatively excellent performance, and the recall rate on the two datasets is improved to 83.53% and 83.69%, respectively. According to the Pt-MCBGA model, the sentiment analysis of the modern Chinese novel The Legend of the Eagle Shooting Heroes finds that the novel is dominated by positive sentiment, with both positive and negative sentiment values being relatively high, and that the characters are rich in emotions and have great emotional ups and downs.
In wireless sensor networks in industrial control systems, wireless communication security is challenged due to the broadcast nature of the wireless channel, where information is more easily eavesdropped by illegal nodes on the network. The article establishes a secure communication system based on ZigBee wireless communication technology applied to wireless sensor networks in industrial control systems. In order to improve the secure communication performance of wireless sensor networks, this paper combines the Merkle tree with the μTesla protocol to establish a key management scheme for wireless communication. Then from the node trust degree, the node two-way authentication mechanism for data transmission is constructed by combining the digital signature algorithm. For the effectiveness of the secure communication mechanism of wireless sensor networks, this paper carries out data analysis through performance testing. The key management scheme takes about 17.37 μs and 3.24 μs to add and revoke a key, respectively, and the local optimal value of user time consumption is 7.26 s when the connectivity frequency is 12 min and the revocation threshold is 60. The average value of the node bidirectional authentication mechanism can reach 96.17% for the accuracy of identifying the malicious nodes in the wireless sensor network, and the bit error rate is lower than 0.5 % for the communication transmission with the mesh topology. The bit error rate is less than 0.1%. The introduction of Merkle tree and digital signature algorithms into the construction of secure communication mechanisms in wireless sensor networks can significantly improve the data transmission security performance of industrial control systems.
Tang poetry, as a treasure of ancient Chinese literature, contains a wealth of natural imagery, which not only add to the picture sense of Tang poetry, but are also important carriers of the poet’s emotions and thoughts. The study outlines the nature imagery from the perspective of Tang poetry, as well as the key elements and intrinsic connections among them, and borrows k-means clustering to categorize the nature imagery groups. In addition, the study improves the principal component model by using index homogenization, homogenization, and entropy weighting, so that it achieves the best dimensionality reduction effect while guaranteeing the integrity of the data of Tang poetry text.The F1 value of SVM and KNN classifiers for classifying the natural imagery and emotional expression of Tang poetry text is more than 0.9 after dimensionality reduction of the method in this paper, which is a good classification performance. Cluster analysis divides the natural imagery of Tang poetry into astronomical imagery, landscape imagery, and animal imagery, which account for 38%, 53%, and 9%, respectively. “Old times – bright moon”, “Thinking – slanting sun”, “Looking back – west wind”, “the end of the world – west wind” natural discourse is more likely to form word clusters in the natural imagery of Tang poetry. The analysis of principal component model shows that poets are more willing to express their emotions through natural imagery, and the proportion of neutral emotional expression is 5.17% to 7.43%.
This paper proposes a vocal music teaching system architecture integrating multimedia technology, aiming to enhance the intuitiveness, interactivity and personalization of vocal music teaching through technical means. The system is equipped with virtual reality and voice interaction technologies to realize the digital presentation of the functional modules of the architecture. In addition, in order to evaluate the teaching effectiveness of the system, a number of evaluation indicators are designed. The fuzzy comprehensive evaluation algorithm is used as the main method, supplemented by hierarchical analysis method, to comprehensively evaluate the teaching effectiveness. Multimedia technology can improve students’ vocal ability and mastery of theoretical knowledge, in which the vocal ability is improved by 5.98% to 10.48% compared with the control class, and at the same time, there is a promotion effect on students’ positive interest in vocal learning. The students’ recognition of the system in terms of technology application, learning interaction experience, learning content and process, and teaching effect ranged from 4.077 to 4.608, with a high degree of recognition. The experts’ comprehensive evaluation of the classroom effectiveness of vocal music teaching under the system of this paper is 93.437, which is highly satisfactory. This study not only provides new technical support for vocal music teaching, but also provides a scientific assessment method for teaching evaluation, which is of great significance to improve the level of vocal music teaching.
With the progress of the times, the scientific and reasonable planning of physical education infrastructure and resources is an important way to realize the fair development of education. Firstly, a physical education resource input-output evaluation index system and a multi-objective optimization model of resource allocation to improve the utilization rate of physical education resources are constructed for the integration of physical education resources in Wuhan private colleges. In order to achieve the effect of enhanced spatial traversal ability, the collision range of raindrops is expanded by adding the hybrid collision strategy and introducing the adaptive collision factor, and the artificial raindrop algorithm with the introduction of hybrid collision and stretching is proposed on the basis of the original artificial raindrop algorithm. The improved artificial raindrop algorithm is compared with different optimization algorithms for simulation comparison experiments and model solving. The results show that the improved artificial raindrop algorithm converges faster and with higher accuracy, while the multi-objective optimization model proposed in this paper achieves the balanced development goal of physical education resources integration and allocation in Wuhan private colleges and universities.
This study aims to construct an effective pathway for students’ career planning and innovative industry education by integrating support vector machine algorithm with big data analysis technology. By effectively integrating multi-source data and combining the improved genetic algorithm for feature selection and extraction of student data, the support vector machine algorithm is used to conduct in-depth analysis of the data related to students’ career planning and innovation and entrepreneurship education, to provide students with accurate and personalized career and entrepreneurship guidance, and based on which, the career planning and innovation and entrepreneurship education path is constructed. Experimental analysis of the classification prediction performance of the support vector machine algorithm and comparison with other classification prediction algorithms show that the support vector machine algorithm used in this paper has the highest classification accuracy in the assessment of students’ career planning and innovation and entrepreneurship ability, and the model performance is the most stable. The results of the educational experiment show that after using the educational path proposed in this paper, the students’ satisfaction with career planning and the mean value of the assessment score of innovation and entrepreneurship ability increase by 70.89% and 170.73%, respectively. The above results fully demonstrate the effectiveness of the educational path constructed in this paper, which provides a useful reference for efficient education and teaching reform.
This paper measures the international trade efficiency of developing countries based on the data envelopment analysis (DEA) model, and explores the impact of digital transformation on trade efficiency differentiation using regression analysis. Relevant data of 19 developing countries, including China, are selected, and the trade efficiency at each stage is calculated separately using the three-stage DEA model in this paper. The regression model is constructed to quantitatively analyze the impact of digital transformation in the differentiation of trade efficiency of developing countries. From 2011 to 2020, the trade efficiency of each developing country shows a wave-like upward trend, and the average value of the comprehensive average efficiency in the third stage is 0.728, but only China, Peru and Colombia have a higher than average level of trade efficiency, which intuitively demonstrates the trade efficiency differentiation of developing countries. Differentiation. The overall regression results show that the elasticity coefficient of digital transformation on the international trade efficiency gap is -0.274, indicating that digital transformation has a greater effect on narrowing the trade efficiency gap than widening it. And in the subregional regression, the elasticity coefficient of digital transformation in Asia is 1.398, and the elasticity coefficients in Africa and Latin America regions are -0.953 and -0.603 respectively, and the digital transformation has significantly different impacts on trade efficiency differentiation in different regions.
The continuous improvement of judicial construction has led to the emergence of a large amount of judicial data on the Internet, and how to make full use of judicial data to promote judicial openness, fairness and efficiency has become an important issue in the construction of judicial informatization. In the article, the word vector generation technique is used to obtain the annotation sequence of legal text, and then the BiLSTM model is combined with the CRF model to realize the recognition of legal text entities, and the Adam algorithm is used to optimize the training of the model, so as to improve the recognition effect of the model on legal text entities. The GCN model in the graph representation learning algorithm is introduced, and the legal text entity recognition results are used as inputs for the construction of sequential and semantic relationships, and the GCN-BiLSTM model for legal text entity relationship extraction is constructed by combining the graph representation attention network and the BiLSTM model. Based on the self-constructed legal text dataset, the validation analysis of the above model is carried out through simulation experiments.The accuracy of the BILSTM-CRF model in legal text entity recognition is 85.67%, which is 7.35% higher than that of the single LSTM-CRF model. The GCN-BiLSTM model improves its accuracy by 2.14 percentage points compared with the CasRel model in extracting the entity relationships of legal texts with multi-entity overlapping. Combined with the legal text entity relationship extraction results, the knowledge map of legal cases can be constructed to provide accurate knowledge relationship support for sorting out the veins of legal cases.
Under the accelerated process of economic globalization and the booming development of Internet technology, cross-border e-commerce, as a new mode of international trade, is becoming a new driving force for the transformation and upgrading of foreign trade with its high efficiency and convenience, low cost and high benefit. This study uses data cleaning and missing value filling methods to preprocess user behavior data and merchandise sales marketing data in cross-border e-commerce Wish platform, and discretizes user behavior data using rough set method. Then, we select the merchandise sales and user behavior as the dependent and independent variables to construct a multiple nonlinear regression model in order to analyze the influence of user data on sales in cross-border e-commerce Wish platform. The results of the multivariate nonlinear regression model show that user behavior in cross-border e-commerce Wish platform has a significant effect on merchandise sales (P=0.005243). It is also found that the sales strategy adjusted according to the regression results can improve the sales and promotion effect of enterprises in cross-border e-commerce platform. The research results of this paper enrich the theoretical and practical research on the optimization and adjustment of cross-border e-commerce enterprises’ sales strategies, provide theoretical basis and decision-making reference for the subsequent adjustment of cross-border e-commerce enterprises’ sales strategies, and help cross-border e-commerce enterprises to go global.
In this paper, based on the knowledge graph, word vectors and other personalized path generation related technologies, based on the graph convolutional neural network to complete the construction of the English knowledge graph model, to generate a personalized English knowledge graph, drawing on the data structure in the graph, to generate a personalized learning path, in order to make the generation of personalized learning path is more reasonable, in accordance with the difficulty value of the exercises for the exercises to be sorted. Simulation experiments are designed to evaluate the difficulty level of the generated exercises. The difficulty level of most of the English exercises generated by the personalized recommendation path is concentrated in the easy and general levels, and there are a total of 2,229 questions in these two difficulty levels, so the difficulty level of the generated questions is moderate. After a period of personalized path-generated English learning, six teaching activities were carried out, and the average score of the first post-test of the experimental group was higher than that of the control group, and the Sig values were all less than 0.05, indicating that the difference in the scores of the two groups of students was significant, which side by side reflected the accuracy of personalized path-generated English teaching.
In the era of digital economy, the digital transformation of enterprise financial management has become an important topic that needs to be studied and solved at present. In this paper, based on analyzing the internal and external drivers on the digital transformation of enterprise financial management, the financial data of 3,498 Shanghai and Shenzhen A-share listed enterprises were obtained using Python technology. Then a fixed effect model was constructed by combining the multiple linear regression model to analyze the degree of influence of internal and external drivers on the level of digital transformation of enterprise financial management. Policy support, digital technology environment, leadership support, team awareness, and digital technology investment all have a significant effect at the 1% level on the level of digital transformation of enterprise financial management. Among them, the influence of digital technology investment is the largest, that is, every 1 percentage point increase in the enterprise’s digital technology investment in financial management, the level of digital transformation of enterprise financial management will increase by 0.204 percentage points. And there is significant regional and equity heterogeneity in the level of digital transformation of enterprise financial management, and the effect of digital transformation of financial management is stronger in the eastern region and state-owned enterprises. Therefore, in the era of digital economy, enterprises need to build a digital financial management system, strengthen cross-departmental collaboration and communication, and combine composite talents to realize the digital transformation of financial management.
In this paper, the financial structure is defined as two parts, asset structure and capital structure, with respect to the mechanism of enterprise financial management on the economic performance of enterprises. The multivariate regression model of asset structure and business performance is constructed with the dimensions of asset turnover efficiency and asset structure ratio. In order to represent the operating performance, total return on assets and return on net assets are chosen as the measures of operating performance and as the explanatory variables. It is proposed that there is a linear correlation between capital structure and corporate profitability, and the linear model between capital structure and corporate operating profitability is constructed. Combined with empirical tests to verify the relationship between asset structure or capital structure on business operations. The curve estimation method of the regression model is used to analyze the effects of inventory ratio, money fund ratio and fixed asset ratio in asset structure and capital structure on the total return on assets and return on net assets. The coefficients of fixed asset turnover on performance are 0.033 and 0.025 respectively, i.e., for every increase of 1 in fixed assets, total return on assets and return on net assets increase by 0.033 and 0.025. Similarly, the fixed asset turnover, inventory turnover, and the ratio of long term financial assets are positively correlated with the performance of the enterprise. The correlation coefficients of equity ratio and state-owned ratio of enterprise capital structure are positive, which bring positive impact on enterprise operating profitability.
Six historical building clusters in the main city of Changchun, namely People’s Street, Xinmin Street, the Palace of the Forged Manchus, the South Square, the First Automobile Manufacturing Plant, and the Kuanchengzi Station of the Middle East Railway, with a total of 2,501 historical building sites, are taken as the research objects. Using ArcGIS software, the morphology and spatial distribution pattern of the historic building clusters in the main city are discussed based on the perspective of spatial layout by invoking spatial measurement methods such as kernel density, standard deviation ellipse, algebraic geometry, and spatial correlation, etc. The results are summarized in the following table. The results show that the spatial distribution of historic buildings in the main city of Changchun is dominated by a “single center (People’s Square)” agglomeration, with a maximum kernel density of 0.9950. At the same time, the periphery also appeared to diffuse re-agglomeration, hierarchically showing a “two-axis” diffusion pattern. Among them, the main axis resides in the center of the city and extends infinitely from north to south. The secondary axis is the administrative office and center of the pseudo-Manchukuo State, which is the pseudo-Manchu Imperial Palace and Xinmin Street respectively. Finally, from the perspective of planning and design, it tries to put forward the strategy of protection and utilization, including environment, function, and culture, etc., to provide methods and bases for the holistic protection and utilization of Changchun’s historical buildings.
Students’ mental health problems are increasingly becoming an important part of the educational and teaching process in colleges and universities. In this paper, we collect students’ psychological data through the students’ mental health early warning system and preprocess the data through data cleaning and other data. The features of the processed mental health data are extracted using Global Chaos Bat Based Algorithm (GCBA). Construct a mental health early warning system for college students and build a decision tree model into the system for categorizing students’ mental health status. The performance of the decision tree model in this paper is verified by evaluating the finger with other models and comparing the actual classification prediction results, constructing the decision tree model with the psychological condition of interpersonal relationship of college students as an example, and conducting the visualization analysis of the decision tree. Independent sample t-test is conducted on three measures such as using the mental health early warning system constructed in this paper, and according to the results, the application of the system in this paper highlights the role of the enhancement of the level of students’ mental health and the significant improvement of depression and other psychological conditions.
This paper points out that dance movements can be regarded as the carrier of the fusion of traditional cultural elements and styles, and ethnic folk dance movements are used as the dynamic expression of inheriting traditional cultural elements and styles. Analyze the characteristics of non-negative matrix decomposition algorithm, and use the non-negative matrix decomposition algorithm to reduce the dimensionality of dance action images. In order to optimize the classification effect of the classifier on the data after dimensionality reduction, SVM algorithm is selected to form a dance movement recognition method based on matrix decomposition technology and SVM classifier. By adjusting the values of penalty factor and kernel parameter , the effectiveness of matrix decomposition algorithm for image dimensionality reduction is verified. Analyze the feasibility of the dance movement recognition method based on matrix decomposition technique and SVM classifier by selecting different data sets. Establish the dance movement evaluation model based on matrix decomposition technology, compare the evaluation model scores with the dance expert scores, and test the effect of matrix decomposition technology on the classification of dance movement styles. The Spearman’s correlation coefficient between the expert’s score and the model’s score remains above 90% in the evaluation of different dance movements. Combined with the evaluation guidance of dance experts, the dance style movement evaluation model proposed in this paper can effectively evaluate and analyze dance movement styles.
AIGC-driven development and innovation of regional education has become an important issue, and in the context of the era when AIGC technology has triggered profound changes in education, the traditional education model is experiencing a paradigm shift from the transmission of knowledge to the cultivation of innovation ability. Based on this, we first construct a model of influencing factors in the application of AIGC in course management based on the rooting theory, and verify the proposed hypotheses to provide a theoretical basis for the construction of course management optimization and multi-level decision-making model. Then we optimize the course management of foreign language teachers in colleges and universities by relying on the all-round and multi-level innovation of AIGC in the field of education, and construct a multi-level decision-making model. In the teaching application practice, the scores of the experimental class on learning interest, learning attitude and learning motivation are all higher than 75 points after practice, and the average score is 8.87 points higher than that of the control class, and the P is less than 0.05. The learning achievement of the experimental class is increased from 73.95 to 80.95 (P < 0.05), and the optimized multilevel decision-making model of this paper has a significant effect on improving students' learning interest, learning attitude, learning motivation and learning achievement, learning attitude, learning motivation as well as learning achievement, which further validates the application effectiveness of the multilevel decision-making model and provides case references for researchers of AIGC-based instructional decision-making.
The research selects the documents related to the legal regulation of civil abuse of rights of action as the research object, crawls the central and local legal regulation database through Python, and uses the social network analysis method to quantitatively analyze the dimensions of the subject of legal regulation from the composition of the subject of legal regulation, the density of the network, the centrality, and the cohesive subgroups, etc. The data preprocessing is carried out on the valid data obtained. Secondly, we pre-processed the acquired valid data, extracted high-frequency words using the improved TF-IDF algorithm, and obtained the probability distribution of the subject strength of “document-subject” and “subject-phrase-item” by calculating the degree of perplexity and utilizing the LDA subject model, and obtained the probability distribution of the subject strength at different stages of civil abuse litigation. In order to obtain the themes and evolution characteristics of the legal regulation of civil abuse of rights of action at different stages, the research results are combined with the results of the study from multiple dimensions. Finally, the research results are combined to design the strategy of legal regulation of civil abuse of rights of action from multiple dimensions.
With the deepening of education modernization, improving teachers’ digital literacy has become the key to promoting the digital transformation of education. The growing demand for professionals in modern society has made the digital literacy of physical education teachers in vocational undergraduate colleges more and more important. This paper defines digital literacy and the digital literacy of vocational undergraduate teachers in turn, explores the four connotations of digital literacy, and proposes strategies to improve the digital literacy of physical education teachers in vocational undergraduate colleges. The entropy value method was used to measure the digital literacy level of physical education teachers in vocational undergraduate colleges, determine the weight of teachers’ digital literacy evaluation indexes, and select and analyze the influencing factors of teachers’ digital literacy. Pearson correlation analysis was conducted on teachers’ digital literacy and influencing factors, as well as various dimensions and influencing factors, and multiple linear regression models were constructed to analyze the improvement path. The measurement results show that in the dimension of digital awareness, the mean values of digital willingness, digital cognition, and digital will are 4.4269, 4.3484, and 4.3748, respectively, indicating that the subject vocational undergraduate physical education teachers are highly willing to learn and use digital technology resources. The correlation coefficients between the dimensions and influencing factors of digital literacy were roughly in the range of 0.4~0.7, and the P values were all < 0.01, indicating that there was a significant positive correlation between them. The path coefficients of "TS→DA", "TE→DA" and "TM→DA" were 0.0533, 0.0796 and 0.0789, which did not reach the significance level, while the other paths reached the significance level (P<0.05), indicating that there was a significant positive impact.
The application of big data in modern enterprise finance is becoming more and more common, and the research adopts the random forest algorithm to explore the enterprise financial risk status, so as to make personalized financial decisions. Construct the enterprise financial risk early warning model based on random forest and construct the financial risk early warning index system. The performance of the random forest model is tested by comparing the financial risk early warning effect of the random forest model with other models. Taking M company as an example, by analyzing its financial risk situation from 2019 to 2023, it puts forward targeted financial decision-making suggestions. The random forest model performs best in the financial risk early warning performance experiment, far outperforming other models. The financial risk status of Company M in 2019-2023 is dangerous, sub-safe, general, dangerous, and general. Although it has been improved in general, it is still in a fluctuating state and the development status is unstable. For the specific financial risk status of Company M, financial decision-making suggestions are proposed for the three aspects of solvency, operating capacity and development capacity.
This paper studies the 3D target modeling method under multi-view video based on deep convolutional network. Through the detailed exposition of the basic theory of 3D target modeling technology and the complete derivation of non-uniform rational B spline curve, this paper establishes technical support such as camera coordinate system for the generation of 3D target model. According to the basic structure of Deep Convolutional Network (DCNN), a DCNN network model suitable for the research scenario of this paper is established, and the model is utilized for feature extraction of images in multi-view videos. The softargmin algorithm is used to generate the parallax map for parallax estimation in the parallax calculation stage. According to the parallax map, voxel-based 3D reconstruction of the target in the multiview video is performed, and the surface reconstruction of the voxel model is performed using the Marching Cubes algorithm, and after obtaining the surface model of the target object, texture mapping is performed to enhance the realism of the model. The deep convolutional network based 3D building method in this paper can effectively realize the feature extraction of target objects in multi-view video. In 3D target modeling, the model in this paper achieves good results on both public and measured datasets, and has obvious performance superiority and generalization ability compared with other methods.
According to the principle, characteristics and use of CCD, this paper designs a laser beam quality measurement program using CCD as a beacon light capture detector and proposes a laser spot detection method based on CCD. The experimental steps and calculation steps for laser beam width measurement and laser power measurement by CCD camera are proposed respectively. The beacon light is used as a light source, and the spot image is processed according to the principle of gray-scale image thresholding to capture the beacon light and present it in the form of a spot on the CCD image sensor. Then, through binarization processing, the spot of the beacon light is distinguished from the background, so as to realize the spot position detection of the beacon light beam. The image data are collected to experimentally detect the laser spot position detection algorithm based on CCD image sensor proposed in this paper, respectively. In the fine-tracking spot position detection, the spot is adjusted in the range of ±9.25mrad, and the solution value is set to be determined every 0.78mrad. The spot center is kept in the range of ±9.05mrad, and centering is carried out every 0.003mrad according to the fine-centering algorithm. The experimental results show that the spots after fine centering are all within the range of ±0.78mrad, and the change trend is consistent with the simulation results, so the laser spot position detection algorithm proposed in this paper is feasible in fine tracking spot position detection.
Driven by the core qualities of the Civics discipline, the requirements of curriculum reform and the needs of teaching practice, the optimization of teaching strategies has become particularly urgent in the field of Civics education. The article introduces the Markov decision-making process and basic elements of reinforcement learning, combines the Q learning algorithm with neural networks, and constructs a deep reinforcement learning model (IDQN) for multiple intelligences with collaborative scheduling. Based on this, a numerical simulation experiment of deep reinforcement learning strategy in Civics teaching was designed and implemented. Through experimental analysis: when the recommended path is 30, the IDQN model has the best learning path recommendation effect, with an IKL of 0.477. The model also has excellent performance in the allocation of teaching resources, with the accuracy, recall and F1 value of 5 tests above 90%. After the numerical simulation of Civic Education teaching, the learning interest, attitude, and motivation of students in the experimental group increased by 27.52% to 34.49%. Under this influence, combined with the learning path and resource allocation provided by the IDQN model, students in the experimental group showed a significant improvement in their learning effect, and the average score of Civic Education Theory was 6.06 points higher than that of the control group.
The continuous development of digital informatization has opened the era of intelligent education in the field of education. Higher education has accumulated a huge amount of data, but it is not fully utilized, and in-depth mining and analysis of these data can reveal the students’ learning and life status and provide powerful support for teaching management. Therefore, the research of using clustering algorithm to build a hierarchical management model for English teaching is very necessary. Clustering algorithm provides an effective way for the analysis of students’ learning behavior, and for the research needs of English teaching, this paper proposes a multi-factor improved K-means clustering algorithm and compares and verifies its clustering effect. For the problem of stratified division of student groups, firstly, the clustering index system of students’ book borrowing behavior and English course learning behavior constructed is used. Then, the improved K-Means clustering algorithm is used to cluster and mine the data of each student’s behavior to discover the student groups under different behaviors, so as to realize the hierarchical clustering of students in hierarchical management. Finally, for English teaching, a student stratification management model is established from three aspects: student stratification, teaching goal stratification and teaching process stratification, which provides important decision support for student stratification determination in English teaching and provides a more rationalized management model for student management workers.
Image alignment is a fundamental problem in the field of computer vision and an important prerequisite for carrying out many other tasks. Firstly, the theoretical basis and realization method of image alignment as well as the process and the method of alignment are introduced to provide alignment ideas. Subsequently, an image alignment method based on the union of multi-scale features is proposed, and a new loss term is introduced to the small-scale features therein, which further improves the distinguishability of the small-scale feature descriptors while guaranteeing the invariance of the large-scale feature descriptor matching therein. Three common alignment algorithms (RIFT algorithm, HAPCG algorithm, and SAR-SIFT algorithm) are selected for stability assessment and quantitative evaluation on the dataset, and an image enhancement algorithm with histogram equalization is used to enhance the dataset. The results show that the feature stability of this paper’s method is described as 99.1%, which is better than other algorithms. Meanwhile the desired effect is achieved on the dataset.
At present, the evaluation of spoken English in domestic universities is affected by the evaluation teachers’ personal cognition, preference, time, energy and other factors, and it is difficult to unify the standard of oral evaluation in the implementation, and the evaluation frequency and timeliness are insufficient to meet the students’ willingness to improve their oral language. In this paper, multimodal speech recognition technology is utilized to firstly collect students’ speech signals through microphone arrays, secondly extract acoustic and linguistic features of speech, and construct multimodal feature vectors by combining visual information such as students’ lip movements and facial expressions. Subsequently, the feature vectors are input into a deep neural network model for training and recognition, fusing LSTM network with attention mechanism to analyze the speech emotion and capture the emotional changes in speech. Meanwhile, the interaction behavior in speech is analyzed by combining temporal convolutional network. Construct a deep reinforcement learning model, introduce a user item interaction layer, design a user interaction simulator, and obtain user feedback on the smart English classroom. Using multimodal speech recognition technology, the temporal waveform of classroom speech is analyzed for sound pressure value, and the normalized sound pressure value range fluctuates around [-1.5,1.5].The average recognition rate of the six emotions rises to 67.86% with the joint effect of LSTM and attention mechanism. By comparing the experiment, analyzing the difference between the experimental class and the control class before and after the reading aloud ability, the average score of the experimental class is 23.945, and the average score of the control class is 21.464, at the same time, the post-test of reading aloud ability corresponding to the experimental class and the control class P=0.005<0.05. It can be seen that the intelligent interactive classroom of English language constructed in this paper has a facilitating effect in the process of teaching reading aloud in the aspect of reading aloud ability of students The classroom can be seen that the intelligent English interactive classroom constructed in this paper has a promoting effect in the process of teaching reading aloud in terms of students' reading ability.
Intellectualization of agricultural machinery can effectively improve the efficiency and quality of operations, and has an important role in promoting agricultural development. Based on AR technology, this paper introduces the key technology to build the interactive control system of agricultural machinery, uses NURBS to realize virtual agricultural machinery modeling, uses VRML technology to design a prototype of the scene environment of interactive farmland virtual reality, and details the methods of virtual modeling, virtual roaming, interactive control and collision detection in the process of system development. A four-degree-of-freedom simulation test bed is established to realize the simulation of the tractor’s attitude when walking in the field. The position information of the crop rows is extracted from the virtual scene, and the control signals are given according to this information to carry out the speed, direction and balance control of the traveling of the agricultural machine, so that the tractor travels along the crop rows. The maximum deviations of the roll angle, pitch angle and yaw angle are within 0.36°, and the maximum deviations of the elevation and traveling speed are 2.11 mm and 0.14 km/h. The simulation analysis and the physical test show the feasibility of the interactive control system of the farm machine.
In this paper, we use a large language model for business English translation and context analysis, and propose an adaptive parameter unfreezing method based on the quantization difference between adjacent layers within the decoder to fine-tune the layers of the language model related to the translation task, and to understand the behavior of the model in the relevant layers. Then the method of combining different encoders is proposed as a dual encoding-decoding framework on top of the traditional encoding-decoding framework, which is applied to the task of context analysis in business English translation. The fine-tuning method in this paper significantly improves the text translation quality of the language model, especially in the English-X tri-lingualization, which improves the COMET and BLEU metrics by 3.22 and 2.58 points respectively. In addition, the dual encoding-decoding model proposed in this paper is applicable to the task of contextual analysis in business English translation, which significantly improves the performance of contextual analysis in business English, and the F1 value on the HIT-CDTB dataset is improved by 11.60% compared with that of Rutherford’s model. The experiment proves that the proposed method of text has made progress in the research of the task of analyzing textual contextual relations in business English.
In this paper, the 3D reconstruction of the finite element model of the knee joint is completed by first generating and editing the 3D images of the martial arts movements through Mimics software. After that, Hypermesh and Abaqus software are used to pre- and post-process the properties of materials in the knee joint biomechanical finite element model. Visual 3D software and low-pass filter smoothing technique were used to obtain and process the kinematic and kinetic data of the martial arts maneuvers, and the processed data were used as boundary and loading conditions to import the data of the three martial arts maneuvers, namely, horse stance, lunge stance, and servant stance into the finite element model for calculating and comparing the biomechanical responses of the articular cartilage and meniscus. The results showed that the movement pattern of horse stance has a larger knee range of motion and a smaller peak ground reaction force compared to the lunge and servant stance movements in the martial arts maneuvers. Finite element simulations showed that the straddling knee stance produced smaller peak contact stresses on the knee cartilage and meniscus, and the peak stress area changed more during the movement. Three-dimensional finite element simulation analysis obtained four characteristic moments, namely: the first peak ground reaction force moment, the maximum external rotation-external rotation moment, the maximum dorsiflexion moment, and the second peak ground reaction force moment, which corresponded to a greater difference in ground reaction force values. Therefore, it is recommended to wear protective equipment in advance for the injury-prone areas to reduce the risk of injury before the wushu performance.
Under the background of big data era, big data mining technology is widely used, through data mining technology, deeper exploration of data, discovering the relevance of data, can provide decision support for decision makers. This paper analyzes the Internet big data of college students’ employment decision-making based on big data mining technology, uses Apriori algorithm to mine the influencing factors of college students’ vocational skills generation, meanwhile applies ID3 decision tree algorithm to analyze the college students’ tendency of vocational choice, and explores the relevant factors affecting college students’ employment through correlation analysis and clustering analysis. The results of the study show that students’ personal, family and school have strong correlation with students’ vocational skills generation, which affects the improvement of students’ personal job-seeking ability. Meanwhile, the ID3 decision tree algorithm is applied to the employment consulting service for graduates to construct a career decision tree for individual college students, which visualizes their career choice paths under the influence of career values and helps them make more appropriate career choices. In addition, qualification certificates, social practice experience, academic performance, expected salary, ideal employment unit and other factors will affect the employment choice of college students, and there are individual differences among different students.
With the continuous promotion of the integration of industry and education, constructing a quality evaluation system for the integration of industry and education in vocational education has become a key issue to improve the level of vocational colleges and universities’ curricula. Based on the CIPP model, the article builds a quality evaluation system of vocational education industry-teaching integration that includes 4 first-level indicators, 12 second-level indicators and 34 third-level indicators, and empirically analyzes the quality of industry-teaching integration in three higher vocational colleges, H1, H2 and H3, using the fuzzy comprehensive evaluation method through the questionnaire survey from the viewpoint of empirical application. According to the results of the fuzzy comprehensive evaluation, the quality of industry-teaching integration in H1 and H2 higher vocational colleges and universities belongs to the good level, and its comprehensive judgment value is 78.2 and 78.395 respectively.The comprehensive judgment value of the quality of industry-teaching integration in H3 higher vocational colleges and universities is 82.037, which belongs to the excellent level. The three sample higher vocational colleges have achieved outstanding results in the integration of industry and education, providing an example for the development of integration of industry and education for higher vocational colleges in the region.
In the digital campus network security construction, the existence of potential security vulnerabilities can easily cause serious threats to campus information security, resulting in significant losses. In order to prevent and mitigate the risk, the article designs a security vulnerability identification system. Firstly, the URL similarity is compared by machine learning in order to scan the vulnerability information. The SeCF embedding layer is utilized to improve the input speed and the discard layer is designed to improve the overfitting problem during the training process. Finally, TextACBL security vulnerability identification model is proposed by combining CA, 1D-CNN and BiLSTM techniques and analyzed numerically. The average recognition rate of this paper’s method is as high as 80% for 10 common security vulnerabilities, which achieves better security vulnerability recognition results compared with existing methods such as cppcheck, deepbugs, flawfinder and vuldeepecker. The experimental results verify the effectiveness and feasibility of the method in this paper, which provides ideas for safeguarding campus network security during the construction of digital campus.
This paper focuses on the characteristics of multilevel information extraction, based on the convolutional neural network model (CNN), introduces the multi-scale feature fusion and multilevel feature fusion strategy to study the multilevel information extraction method, and proposes the full convolutional neural network based on the attention mechanism and residual connection to form the multilevel information extraction model. Aiming at the gradient disappearance and saddle point problem of convolutional neural network, an activation gradient (AG) algorithm is proposed to optimize its training, which is improved to a class of activation gradient convolutional neural network (AG-CNN). The practical application effect of the multilevel information extraction model in this paper is verified by the information extraction work of net-pen culture in river-type reservoirs. Compared with the classical models such as UNet and ResUNet, the intersection and integration ratio (IoU), recall rate, precision rate, and F1 score of this paper’s model reach the highest 80.28%, 91.02%, 87.18%, and 89.03% among all the models, which possesses a stronger extraction capability. And in the multilevel information extraction experiments on Cifar100 and Caltech256 datasets, when the number of batch training data is greater than 100, the accuracy rate and performance of the experimental group basically remain stable.
Scientific and efficient curriculum design and teaching activity plan is the key to the quality of teaching in higher vocational colleges and universities. Based on the principle of SPOC segmented teaching, this paper proposes a “two-line hybrid” language teaching model. Combined with the implementation process of blended teaching, a blended teaching quality evaluation index system for higher vocational colleges is constructed, which includes the dimensions of rule of law and ethics, professionalism, learning ability, skills and technology. Using the standardization principle of hierarchical analysis, the judgment matrix was constructed by comparing two by two to achieve the empowerment of the indicator system. Introducing cloud model comprehensive evaluation, combining the weights of indicators from the forward cloud generator to get the cloud diagram, and derive the evaluation results. The initial matrix is constructed according to the scores of experts, and all the items passed the consistency test, which verifies that the index system has high reliability and validity. The obtained cloud diagram shows that the cloud model parameter Ex = 5.462, in which the A rule of law ethical dimension Ex is about 5.58, closest to the medium level. This paper makes a useful exploration for actively promoting the teaching reform of higher vocational discipline courses.
This paper first outlines the theoretical method of parametric modeling of BIM technology in building structural design, and introduces Revit and Dynamo software to ensure the interactivity and sharing of data while parameterizing the influencing factors of the building structure and automating the extraction of data. Multiple linear regression analysis and the least squares method are used to quantitatively analyze the building energy consumption and the enclosure structure, and to construct a calculation model for the overall structural energy consumption of the building. In order to maximize the comfort of the users and minimize the source consumption of Huizhou architecture, NSGA-III algorithm is introduced to design the multi-objective optimization model of Huizhou architecture. Finally, the optimization effect of the model is verified through simulation and emulation tests. The results show that: the proportion of time that the internal temperature of the antechamber of the building is in the thermal comfort zone is the highest throughout the year (38.29%), and the thermal insulation performance of the building is insufficient; the average illuminance of the compartment space does not meet the lighting requirements (52.07 Lux), and there is a lack of diversity in the lighting design; and it is necessary to optimize the thermal insulation performance of the building enclosure structure to ensure the comfort and livability of the indoor environment. In addition, between the optimal solution and the worst solution interval of the annual energy consumption value and the absolute comfort value of Huizhou architecture, the maximum difference between the energy consumption and comfort indexes is 1.051×107kwh and 0.807, respectively, which can be used for the intuitive analysis of the BIM model and the comparison of the solutions.
With the development of the Internet, public safety public opinion events have gradually become an important part of social public opinion and an important content of government response. In order to establish a standard system for evaluating the response effectiveness of the public safety public opinion incident response system, this paper, in accordance with the current status of the government’s public safety public opinion incident response system and the literature, selects four indicators, namely, serviceability, dynamics, timeliness and legitimacy, as the criterion layer of the evaluation system. Hierarchical analysis method and TOPSIS method are used to evaluate the public security public opinion incident response system. Finally, in order to verify the reasonableness of the AHP-TOPSIS method for evaluating the response effect of the public security public opinion event response system to public opinion events, 80 cases were selected, which were clustered and analyzed and the proximity scores between the samples and the positive ideal solutions were calculated, and the clustered samples were ranked to obtain the response effect ratings of the system to different events. The analysis of the data shows that timeliness has the most significant effect on the evaluation of the public security public opinion event response system, and the public security public opinion event response system responds best to government-led policy-oriented major public opinion events. The legal compliance framework can be constructed from three aspects: improving the existing laws and regulations on public security public opinion events, strictly enforcing the existing laws and regulations, and carrying out in-depth legal publicity, so as to lay the foundation for the implementation of the legal review work.
Pre-school education, as a key stage on the path of children’s growth, plays a vital role in their overall development. Based on the independent sample t-test method, this paper explores the gender differences in preschool education. It also takes digital media education methods as an example, and utilizes Pearson correlation coefficient, linear regression model, and systematic clustering algorithm comprehensively to quantitatively assess the impact of education methods. The results of the study showed that there were extremely significant differences (P<0.01) in the five dimensions of language ability, creativity, social interaction ability, critical thinking ability, and independent learning ability between male and female toddlers, indicating that there are significant gender differences in preschool education effectiveness. The correlation coefficients between the frequency and duration of use of digital media education methods and language skills, creativity, social interaction skills, critical thinking skills, and independent learning skills ranged from 0.47 to 0.75, with significant positive correlations, and were associated with higher scores on each of the competencies as well as higher levels of satisfaction. This paper reveals in depth the gender differences in preschool education and the important role of digital media in preschool education, which is of great value for the optimization of teaching methods in preschool education.
With the continuous development of the network environment, the traffic data in the network increasingly presents high-dimensional, huge and complex characteristics, and the network threat is also increasing, the network information security threat prediction and defense mechanism plays an irreplaceable position in network security. Based on the general process of network anomaly detection, combined with deep learning algorithms, the article proposes a network anomaly detection method based on data enhancement to improve the detection accuracy of network anomaly detection model. Self-attention mechanism is embedded in the neural network framework to accomplish the improved SA-GRU network information security threat prediction method. In the performance index comparison experiments of network security posture values predicted using different prediction models, the average absolute error of the training data of the results predicted by this paper’s model is 0.00266, and the average absolute error of the test data is 0.00369, and the prediction accuracy of this paper’s model prediction is significantly higher than that of other deep learning methods. This verifies the effectiveness of the method proposed in this paper. Finally, based on the experimental results, the network information security defense mechanism is proposed from the three levels of data encryption, the use of secret keys and intrusion detection.
In the joint electrical drive system of industrial robots, the optimization and improvement of robot motion control is one of the hotspots of current research, and this paper proposes a method of optimizing the joint electrical drive control of robots using multilevel genetic algorithm. An improved PID control method is used to fuzzify the robot motion, and the robot trajectory fuzzy PID controller is optimized according to the idea of multilevel genetic algorithm. The rise time of each joint of the robot is about 5ms, 55ms, and 75ms, respectively, and the overshooting amount is smaller, and the optimized joint electrical drive system of the industrial robot is more stable in speed control in both the acceleration and deceleration phases, and shows a good dynamic control capability of the motion. It can be seen that the work in this study effectively optimizes the control performance of the industrial robot drive system using multilevel genetic algorithm.
Aiming at the learning path recommendation problem, which is the key in personalized teaching, this paper takes the personalized learning path recommendation model as a guide, and researches and gives a method that combines the learning path recommendation model with the NFSBPSO algorithm. The learning path recommendation model based on the two-dimensional features of learners and learning resources is constructed, the population is initialized using the chaos strategy, and the optimal and worst particles in the iteration are optimized using the particle optimization strategy to obtain the optimal solution of the learning path. In order to verify the effectiveness of the personalized learning path recommendation optimization model in this paper, simulation experiments are carried out, and the teaching prototype system of a higher education institution in F city is seen as the experimental platform, and the model in this paper is applied to carry out personalized learning path recommendation practice. The first group of experimental subjects who learn according to the recommended path of this paper have an average test score of 83.6 and an average learning time of 371.7 minutes, which is better than the second group of experimental subjects who learn according to the default path. Most of the values of the recommended matching degree of personalized learning paths are between 0.64-0.9, and most of the adaptation degrees are between 0.11-0.21, which proves that the learning paths recommended by this paper’s model to the users have a high degree of accuracy and adaptability.
In order to improve the planning efficiency of urban landscape, this paper proposes a combination design method of urban landscape construction based on grid division and a spatial optimization model of urban landscape based on particle swarm algorithm to optimize the spatial and pathway layout of urban landscape that takes both economy and ecology into account. The original landscape image was mapped with 3D remote sensing image to generate a 3D image model, and the gradient decomposition method was used for image sampling. Then the multi-dimensional dynamic feature distribution model of urban landscape was constructed, on which the urban landscape area grid was divided to realize the landscape construction combination design. Using particle position to simulate the meta-space layout results of landscape type raster images, the optimization of landscape pattern space and path is completed. The experiment proves that the algorithm in this paper reduces the influence of multiple types of perturbations on the landscape layout results, and the spatial optimization model of urban landscape pattern based on particle swarm algorithm realizes the organic coupling of quantitative and spatial optimization, which not only improves the utilization rate of the urban land, but also substantially reduces the risk index of the urban landscape, and meets the design expectations.
This paper first describes the basic theoretical knowledge of supply chain inventory control and analyzes the existing supply chain inventory control strategies. For the relationship between safety stock and customer service level and inventory cost, the safety stock factor is used as a decision variable, and a supply chain multilevel inventory control model is established under (t,s,S) inventory replenishment strategy. Secondly, the selection operator, crossover operator and mutation operator of the traditional genetic algorithm are adaptively improved, and an improved multi-objective adaptive genetic algorithm is proposed, and this algorithm is used to solve the inventory optimization with the two objectives of supply chain inventory cost and customer service level. The simulation results of the algorithm show that the improved genetic algorithm has better convergence and the obtained Pareto optimal solution set is closer to the real optimal frontier. When the IGD value is minimized and kept constant, the convergence speed of this paper’s algorithm (34 times) is 38.18% lower than that of the traditional genetic algorithm (55 times), and the model converges faster while its Pareto solution set is more uniformly distributed. Example results also show that using the model in this paper can reduce the inventory of each node in the supply chain system and reduce the transportation cost.
This paper takes 2020-2022 Shanghai main board listed companies as the research object, and empirically examines the relationship between factors such as the establishment of internal audit department and the quality of internal audit empowered by new quality productivity, with the effectiveness of the quality of internal control as the explanatory variable, the degree of separation of two powers and so on as the explanatory variable, and the corporate governance structure as the control variable to carry out a via gradient descent Logistic regression analysis optimized by gradient descent algorithm. On this basis, to address the problem that internal audit is prone to bias or falsehood due to management’s self-interest, the fsQCA method is combined to analyze the influencing factors of the choice of auditing policy (capital item or expense item) for general R&D expenditures. It is found that there is a significant positive relationship between companies with an internal audit department and a higher hierarchical level of affiliation and obtaining a standard audit opinion, and the regression relationship holds at the 0.05 level of significance, with a positive correlation with a regression coefficient of 3.745, and an OR value of 40.099. However, the effect of the company’s twofold separation of powers governance structure on the quality of the audit fails the significance test. Firms with lower profitability levels, higher R&D intensity, higher debt levels, lower tax benefits for R&D additions and deductions and lower external audit quality are more likely to capitalize R&D expenditures. The study uses cutting-edge algorithms to accurately analyze new quality productivity-enabling internal audit quality factors and innovate corporate compliance internal control paths.
At the present stage, the staff of mental health center in colleges and universities have a heavy workload, fatigue work and low work efficiency, and it is urgent to explore new paths to alleviate the severe situation of mental health work in colleges and universities. In this paper, we first start from the students’ mental health assessment data and use data mining technology to analyze the students’ mental health status. Then, students’ behavioral characteristics are digitally represented to construct a prediction model of students’ mental health status based on PDNN neural network. Finally, the design method of psychological intervention system in colleges and universities is proposed. In the collected mental health assessment data, the age distribution is skewed toward the younger population, and nearly 55% of these students show a tendency toward psychological abnormality. And the average accuracy and high group recall of the prediction model of students’ mental health status established using PDNN neural network were 88.95% and 87.44%, respectively, which verified the feasibility of the modeling method in this paper. Using the psychological intervention system designed based on the method of this paper for the intervention experiments, there is no significant difference between the experimental group using the system and the control group not using the system in the factors before the intervention (p>0.05), while after the intervention the experimental group scored significantly lower than the control group in the total mental health score, interpersonal relationship sensitivity, depression and anxiety factor items. This proves the validity of the intervention system design method in this paper, which can be applied in psychological intervention methods in universities.
The article firstly outlines the concept of parametric design and modeling techniques and processes, then expresses the relationship between customer needs and functional design parameters of smart home products with discrete sensitivity matrix, and introduces the fuzzy pairwise comparison method to calculate the importance of customer needs. The correlations in the dataset are mined on the Rough Set (RS) tool. AGO and IAGO are used to predict the customer demand importance and design parameter importance in the future cycle, and the parametric product family optimization model is solved by combining the non-occupancy sorting genetic algorithm with congestion distance. In this paper, the optimization ranking and core parts of the functional modules of the smart flowerpot are obtained through the parametric smart home design method, and the functional rankings of the modules are automatic irrigation function, intelligent light replenishment function, monitoring function, and human-computer interaction function; the core parts include temperature and humidity sensors, light sensors, water tanks, and single-chip microcomputer parts, and so on. In the intelligent flowerpot product family design, this paper finds that the efficiency of this paper’s optimization method increases significantly (4.23%-9.12%) and the weight of the product decreases significantly (0.1141kg-0.617kg), both in the known platform mode and in the unknown platform mode. The results of this paper are extremely important for the development and design of parametric product families based on platforms.
This paper realizes the detection of changes in physical fitness of track and field athletes in different training cycles by monitoring their sports training functions. The method used is the time series model ARMA. The athletic training function time series data were preprocessed to fit the ARMA (p,q) model, and the optimal time series fitting model was selected by examining the coefficient of determination, AIC criterion, and SC criterion. Four biochemical indexes, hemoglobin, urea, creatine kinase, and testosterone, were selected as the content of training monitoring for track and field athletes, and the ARMA(1,1) model was selected to analyze the changes in physical fitness of track and field athletes in different training cycles. Taking the hemoglobin index (HB) as an example, through the numerical simulation of the time series of HB levels of 16 track and field athletes preparing for the 15th National Games in Guangdong Province, it can be learned that the change trends of male and female track and field athletes are basically the same throughout the whole year training cycle. From the first cycle, the athletes’ Hb levels began to decrease, fell to the lowest level in the third cycle, and rebounded in the fourth cycle, reaching the highest Hb level in the winter training period.
This paper designs a multimodal data mining and learning behavior analysis model for civic education, uses improved clustering and association rule algorithms to analyze the multimodal data obtained from students, mines the basic consumption, learning and life behavior characteristics, and carries out analysis of the students’ civic situation in order to take targeted civic education measures. Aiming at the problem that traditional clustering results are greatly affected by the selection of initial clustering centers, Gaussian density function is used to determine the initial clustering centers, and Euclidean distance is replaced by density-sensitive distance to avoid sensitivity to noise and anomalies, which improves the accuracy of the clustering results of students’ behaviors. Then we use the FP-Growth association rule algorithm to improve the Apriori construction, recursively and iteratively construct the frequent pattern tree and get the final frequent item set, which improves the efficiency of student behavior data mining. After analyzing the processed student data of a university, it is found that most of the students have low interest in borrowing books, 38.22% of the students borrowed only 2.19 books on average, and the total number of times of book borrowing is only 5.4 times, and the average number of days of single borrowing is 62.3 days, and the school library needs to increase the promotion of students’ reading, which can be done through the way of offline book fairs and e-recommendations to improve students’ interest in reading books. Reading interest. The study makes a useful exploration for the informatization and intelligentization of ideological education in colleges and universities.
Medical image segmentation is the basis for realizing intelligent medical treatment, and plays a very important clinical significance in the localization and identification of lesion areas and the formulation of surgical plans. In this paper, we investigate the image segmentation techniques based on algebraic topology methods in computer vision, and propose an image segmentation network model based on asymmetric topology preservation (ATSNet), with a view to applying it to clinical practice.The ATSNet model adopts the parallel branching structure of CNN and Transformer in the coding part, and proposes a hybrid feature aggregation strategy (HFAS) to achieve image segmentation with high efficiency. Comparison experiments on three benchmark datasets and one clinical dataset prove that the ATSNet model proposed in this paper achieves better results on different datasets, and the statistical analysis results obtained by the model are consistent with those of clinical experts (P>0.05). Meanwhile, ablation experiments demonstrate the effectiveness of the hybrid feature aggregation strategy used in this paper in improving the image segmentation performance of the model. In addition, the proposed method in the Transformer branch when the number of network layers is 3 when the overall accuracy of the largest, and the use of bilateral filtering can be better edge retention, improve the effect of image segmentation. This paper provides a technical path for the practical application of image segmentation technology.
This paper puts forward countermeasures to maximize the ecological benefits of agroforestry resources from the perspective of sustainable development of urban agroforestry resources. Taking the maximization of ecological benefits as the goal, the optimal allocation of agricultural and forestry resources is carried out. Based on the results of the optimal allocation of water resources, the planting structure of crops in the irrigation area is adjusted with the water allocation of irrigated crops as the constraint. The optimization model under the constraint of eco-efficiency objective was constructed based on the variational method and optimal control model, and the model was solved by the method of Pontryagin’s great value. After the model adjustment in this paper, the planting structure of crops in the irrigation area of city A was obviously optimized, and the planting area of potatoes accounted for the largest share of the planting area of all the crops in the irrigation area, which was about 40.61%, and the ecological benefits of potato crops were higher, which got the priority of the model, and at the same time, the model also reduced the planting area of the crops with low ecological benefits, and this reasonable allocation adjustment method satisfied the goal of maximizing ecological benefits.
This paper combines the project response theory to dynamically adjust and update the resources according to the learning effect and learning feedback in the process of Civic Education, so as to achieve the goal of matching the learners with the learning resources and realize efficient learning. The differential artificial raindrop algorithm based on perturbation mechanism is designed to realize the solution of multi-objective combinatorial optimization of learning resource allocation. Performance experiments show that the convergence curve of the resource allocation algorithm in this paper is gradually flattened, and the algorithm still has the evolutionary ability, the convergence curve is still decreasing, and the final characteristic difference value is also better than other BPSOR and GAR algorithms. In the case of the number of learning resources of 10, 20, 30, 50, 100, the time consumed is 207ms, 1602ms, 20506ms, 68430ms, 354687, all of which are the lowest, and the success rate is also the highest in the model. The optimal learning path is applied to an experimental class in a university for a 6-week teaching experiment, and the experimental class scores 87.2 points in the Civics test, which is much higher than the control class. This paper realizes the accurate capture of students’ Civics learning problems and the recommendation of targeted teaching resources, which can improve the quality and effect of Civics teaching.
Due to differences in lifestyle, cultural capital and social support, foreign immigrants often have difficulty integrating into the ecology of their native communities and are limited in their space for development. To solve this difficulty, this paper applies the principle of regularization to obtain a logistic regression model by categorizing the factors affecting the social integration of foreign immigrants. The algorithms of log-likelihood function and negative Hessian matrix are used to optimize the parameters of the model, construct the multivariate logistic regression model based on the social integration of foreign immigrants, and analyze the regression results among various factors. The success rate of foreign immigrants’ local integration is higher when the immigration-related system is more perfect, the foreign immigrants’ cultural identification with the local area is higher, the cognitive deviation between foreign immigrants and locals is smaller, and the community integration structure is more appropriate. The highest correlation between the factors affecting the social integration of foreign immigrants is the formation of ethnic networks that are not embedded in the community by foreign immigrants who “embrace the group”, and the cognitive bias of local residents towards foreign immigrants, with a correlation coefficient of 0.9214, and the correlation coefficients of the rest of the indicators are less than 0.9. This paper classifies the migrants into “migrants of work nature” and “migrants of employment nature” in accordance with the purpose of their migratory activities. In this paper, according to the purpose of migration, migrants are classified into four categories: “work migration, study migration, investment migration and shelter migration”, and the results of the multivariate logistic regression analysis are credible.
New biofuels, as a sustainable energy alternative to traditional fossil fuels, are attracting global attention. With the increasing awareness of environmental protection and the continuous growth of energy demand, biofuels offer the possibility of reducing greenhouse gas emissions and decreasing dependence on fossil fuels. In this paper, by introducing the Wasserstein distance, which is used to describe the objective function of the GAN model, the self-attention mechanism is applied to improve the discriminator structure of the traditional WGAN-GP to achieve more efficient generation of high-quality data samples. The WGAN-GP model is used to design a new biofuel combustion scenario, and based on the combustion data, the new biofuel is prepared in the scenario. The final data generation results of the model are evaluated based on relevant evaluation indexes. It can be seen that the trend of the generated data set is consistent with the trend of the actual output value of the power station, and the interval range formed by the generated 50 sets of data can include the real data in a more complete way, with a high data coverage, and the error between the generated value and the real value is in the range of ±250-±300. The new biofuel output scenarios generated by the WGAN-GP model were utilized for EMF synthesis experiments. PTFE@ACMS-SO3H samples showed strong absorption peaks at 759cm-1 and 54cm-1 , indicating that the acidic groups-SO3H were successfully loaded on the surface of the material and the preparation of the novel biofuel was successful.
This study utilizes the Apriori algorithm for association rule mining, aiming to deeply explore the intrinsic connection between college students’ physical health and sports performance. The relevant definitions of association rule mining and the application process of Apriori algorithm in this study are elaborated in detail, including data preprocessing, frequent item set generation, and association rule extraction. Through empirical analysis, various combinations of physical fitness factors affecting college students’ athletic performance and the corresponding association rules are revealed. For example, under the condition of support degree of 0.598 and confidence level of 0.709, when male students’ “stiffness upward grade” is passing, their athletic performance is also passing. By mining the correlation rules between college students’ sports performance and physical health, it provides scientific basis and targeted suggestions for physical education and students’ health management in colleges and universities.
With the rapid development of virtual reality technology, its application in the field of art and design is attracting increasing attention. Based on the perspective of user demand, the article combines the Kano model to analyze user satisfaction with virtual reality technology used in modern Chinese image culture design, and finds that its landing point is the desired attribute in the first quadrant, with the Better and Worse coefficients of 0.531 and -0.141, respectively, which indicates that users expect the application of virtual reality technology in the design of image art and culture. Then the evaluation index system of VR image art and culture design is constructed, and the principal component analysis method is used to assign weights and establish the quantitative model of VR image art and culture design. The analysis shows that the weight of the sensory level is the largest 0.3780, and users attach great importance to the aesthetic experience (0.3780) and emotional experience (0.2710) of VR image art and culture design. The application of virtual reality can draw on the results of the quantitative model to design optimization strategies, combine traditional and modern elements, use the interactivity of VR to enhance artistic expression, create an immersive experience, and create more in-depth and original works of video art and culture.
In recent years, the construction of education informatization has been comprehensively promoted, and the personalized learning recommendation model has brought a new direction for the development of intelligent learning platform for college English vocabulary. This study constructs the KCPE-SR model based on collaborative filtering algorithm and knowledge graph, generates and optimizes the suitable personalized learning paths for learners through the interaction between learners of college English vocabulary and resources, and develops a personalized college English vocabulary learning system based on this model. The analysis of the application effect of the system reveals that the experimental class students’ English vocabulary learning performance has been significantly improved with the help of the personalized learning system, and the students’ English vocabulary knowledge mastery (20.00 points) and vocabulary comprehensive application ability (20.49 points) have also increased. The personalized college English vocabulary learning path generation and optimization system proposed in this paper is able to achieve accurate personalized recommendation of learning resources and can meet the needs of college English vocabulary learning.
In recent years, the scale of the electric vehicle industry and social ownership are gradually growing, in the case that the charging facilities are not yet able to meet the demand for electric vehicle charging. Aiming at the situation described above, the research of charging station siting supported by variable neighborhood genetic algorithm is proposed. Based on the principle of charging station siting, the objective function and constraints are set, and the design of charging station siting model is realized. It is found that the traditional genetic algorithm, which has the problem of poor search ability, adopts the variable neighborhood genetic algorithm to solve the model. Calculated, this paper’s algorithm in the charging demand peak period scenario, to determine the optimal charging station site selection there are four, the two objective function value of 0.94, 0.98, both in the charging peak period or the low peak period, this paper’s method compared to the traditional genetic algorithm has a higher superiority.
The rapid growth in the scale of cross-border data flow has pushed the protection of personal information to become an important issue of global concern. This paper drafts a legal adjustment mechanism for the protection of personal information under cross-border data, and builds a data sovereignty practice system from the aspects of comprehensive strength construction and cross-border flow pilot. It utilizes civil law, criminal law and administrative law to protect personal information in cross-border data flow. Based on the numerical analysis method, the legal protection mechanism of personal information in cross-border data flow is discussed in depth. The numerical analysis results show that the probability of personal information exposure increases to about 0.35 when the ratio of malicious nodes under the legal mechanism of this paper is 0.5. The estimated accuracy of personal information protection effect increases by 65.16% to 80.52% when the enforcement strength of this paper’s mechanism is 0.7 and the sample size of companies is 300. Fixing the initial ratio of cross-border data information disclosure, the smaller the initial ratio of personal information protection, the faster the speed of personal information leakage under the legal mechanism. The investigators’ scores on the personal information risk indicators of a cross-border e-commerce platform are uniformly distributed between 1 and 2, and the sum of the overall scores is less than 10, demonstrating the effectiveness of the legal mechanism constructed in this paper on the protection of personal information.
County economic development directly affects the national economy, and the county economy of Henan Province has become the economic pillar of the province. The purpose of this paper is to analyze the county-level economic development of Henan Province and its economic influencing factors by using the quantitative evaluation method. From the time series, the level of economic development of 105 county units in Henan Province from 2000-2023 is analyzed from two perspectives, absolute difference and relative difference, using the indicator of GDP per capita. Screening of factors affecting the level of economic development of counties in Henan Province is carried out from the aspects of population, resources, policies, etc., and a four-aspect indicator system is constructed, namely, human capital, government regulation, industrial level, and economic vitality. A multiple linear regression model is established, and the regression model is fitted by the regression coefficients of each influencing factor, and the fit of the regression model is examined. Each county in Henan Province is divided into three development gradients: developed, generally developed and less developed counties. Panel data regression analyses were conducted on the overall county economy of Henan Province and the influencing factors of developed, generally developed and less developed counties respectively. In the overall economic development of counties in Henan Province, the degree of influence of physical capital investment and the structure of secondary and tertiary industries on the overall differences in county economies is particularly significant. It is manifested in the fact that for every 1% increase in the investment in fixed capital of the whole society, the output of GDP per capita increases by 0.09112% accordingly. Therefore, in order to improve the differences in the economic development of counties in Henan Province, local governments and enterprises should make efforts to improve the market and investment environment and adjust the structure of secondary and tertiary industries.
The construction of harmonious labor relations is of great significance in improving the quality of public services and promoting social harmony and stability. The study uses multi-period DID algorithm to construct a mathematical model of artificial intelligence application and labor dispute resolution, and conducts research on the influence relationship between the two. Aiming at the lack of preventive mechanisms for labor dispute resolution at present, principal component analysis and artificial neural network are used to establish a labor relations early warning model. The results show that artificial intelligence application has a significant positive impact on labor dispute resolution at the 5% level, and there is regional heterogeneity.The prediction accuracy of PCA-ANN model on labor relations in the training set and test set is 81.25% and 85.71%, respectively, which presents a good effect of early warning of labor relations, and it can be used to improve the mechanism of labor dispute resolution. Finally, based on artificial intelligence technology, the online labor dispute resolution mechanism is proposed to prevent the escalation of labor disputes and improve the effectiveness of labor dispute resolution by focusing on prevention, secondary control and subsequent resolution.
Data empowers educational evaluation, and blockchain technology aids in the governance of educational evaluation data. The union of big data and blockchain technology has prompted the development of educational evaluation toward digitalization and precision of educational evaluation. This paper combines the multifaceted governance utility of blockchain technology for educational evaluation data and proposes to improve the consensus mechanism in educational evaluation information sharing. The PBFT consensus algorithm is updated with node contribution reward and punishment mechanism, the consensus nodes are selected by Fibonacci function characteristics, and the consistency protocol is optimized, so as to design a practical Byzantine fault-tolerant algorithm NCG-PBFT based on node contribution grouping, and analyze the credit value, throughput, normal block out delay, and the number of communications of NCG-PBFT consensus algorithm. Build a comprehensive education quality evaluation platform and bring in the improved PBFT consensus algorithm to test the operation performance of the comprehensive education quality evaluation platform. When the request frequency tends to be stable, the education comprehensive evaluation system of NCG-PBFT consensus algorithm is able to improve the system throughput by 74.54% compared with the PBFT algorithm, which is able to meet the performance and stability requirements of the education comprehensive quality evaluation system.
The development of Artificial Intelligence has renewed the direction of art history, making the relationship between technology and art a matter of great interest once again. The application of artificial intelligence in the field of fashion design brings new tools to the designers’ way of designing and displaying. This paper researches artificial intelligence technology and analyzes the application of artificial intelligence as an auxiliary means in the field of art and design, and deeply researches the way of applying artificial intelligence in fashion design as well as its advantages. It also researches the intelligent image generation problem under the fashion big data environment, adopts the method of fusing the external features of fashion images and decoupling the internal features, and provides theoretical methods and bases for the controllable generation of fashion images based on the architecture of generative adversarial network. A multiconditional information fusion generative adversarial network architecture (MCF-GAN) is proposed, and the experimental results show that the image generation performance of the model in this paper is excellent, and better performance is obtained compared with other comparative methods. And it is applied to the actual fashion design for evaluation, the designer’s evaluation in all dimensions are more than 10 points, indicating that the method in this paper has a better application value in fashion design, and provides an effective path for fashion design optimization.
Aiming at the traditional pavement construction, there are problems such as poor construction conditions, limited quality inspection methods, backward control mode and incomplete management means. In this environment, the project in this paper (Gansu Road and Bridge Anlin Pavement Second Standard Project) uses multi-objective particle swarm optimization algorithm to establish a multi-objective machine group optimization configuration model based on quality constraints under the schedule – cost, and the first time to quote asphalt pavement to carry out the intelligent construction of unmanned machine group in Gansu Province. Analyze the intelligent unmanned machine group composed of auto-pilot paving technology and roller auto-pilot technology. Design the optimal configuration model of highway construction machine group, and use multi-objective particle swarm algorithm to design the cooperative operation of unmanned machine group. Combined with the optimal configuration of highway construction fleet problem itself, the standard particle swarm algorithm and fleet configuration model are also modified and improved. Simulate the highway pavement construction process, emphasizing the preparation of construction personnel, machinery, and management platform. The parameters of particle swarm algorithm are designed to solve the optimal construction machine fleet optimization configuration under quality constraints of duration-cost. The machine utilization and duration of scheme 2 are 15.23% and 10.96%, respectively. With the priority of duration, scheme 2 is selected as the machine fleet configuration scheme. Option 4 has the lowest machinery cost of 9.41%. With the priority to ensure the maximum profit, option 4 can be chosen as the machine swarm configuration scheme.
This paper combines the development situation of blue carbon industry to formulate the multi-dimensional optimization model construction of blue carbon industry cluster path. First set the model decision variables and objective function, and divide the constraints. Select the genetic algorithm to solve the optimization model. Determine the research data sources and genetic algorithm parameters, and analyze the multidimensional optimization model. The sensitivity coefficients of each decision variable to the optimization model are 0.2~0.1, and its sensitivity level is III, which means that the selected decision variables meet the research requirements. Compared with the other three algorithms, this paper’s genetic algorithm has superiority in four performance indicators, indicating that the genetic algorithm is more suitable for optimization model solving, and finally, the optimization model of this paper is put into the actual blue carbon industry, and it is found that there is a significant difference in the effect of carbon reduction, economic gain, green environmental protection, and satisfaction before and after the optimization (P<0.05), which verifies the effectiveness of this paper's optimization for practical application, and finally, according to the optimization results, the Finally, according to the optimization results, the corresponding optimization path is proposed.
Although China’s research on English is not as early as that of the western countries, researchers, combining the basic national conditions of China and the actual situation of the nationals’ learning of English, have been making continuous efforts in the research on the construction and application of English corpus, and have already achieved satisfactory results. In this paper, we first analyze the related contents of English corpus, and construct English corpus corpus from phonological and semantic aspects by analyzing the correlation characteristics between English corpus and semantics, according to the basic principles of corpus selection. Combining two word vector similarity measures, Jaccard similarity and edit distance, finally constitutes the final similarity calculation algorithm for English sentences. The MECNC model is constructed by integrating the joint representation and co-representation learning methods, and using edge probability to abstract the connection between two nodes. Experimentally analyze the word vector similarity of English corpus with the results of English corpus recommendation based on multilayer network representation. The correlation scores of Jaccard similarity metric in WS-SIM, WS-REL, MEN, Mtruk-771, and Simverb-3500 are 0.8069, 0.6668, 0.7389, 0.7125, respectively, 0.2769, which achieves the best results, so Jaccard captures more of the correlation between words. Experiments on link prediction task were conducted on five corpora using 3, 5, 8, and 10-fold cross-validation methods, and on the corpus CKM [245,1550], MECNC model OM3 has a maximum AUC value close to 0.94 at a cross-validation number of 8, which shows that MECNC, which is used as a guiding information for intra-layer wandering, shows a better performance.
The international development of the railroad industry puts forward higher requirements for the English application ability of senior railroad students, and reinforcement learning provides new ideas for the optimization of their teaching strategies. Based on reinforcement learning, the article constructs an adaptive learning path recommendation model (RL4ALPR). The model achieves application learning of multi-scenario knowledge of English in the railroad industry through railroad English knowledge level modeling, candidate learning item screening, recommender modeling, and reward calculation. The recommended effective value of the model in this paper is 0.581 at a learning path length of 60, which is 7.79% to 13.70% higher than the control model. The model realizes accurate recommendation of English exercises for the railroad industry based on the answers to the exercises. The evaluation scores of the students in the experimental class under the intervention of the model in this paper are improved to 24.26, 17.50, and 19.64 for speaking, reading comprehension, and translation of English in the railroad, respectively. Under the model of this paper, English teaching in the higher vocational railroad industry is highly recognized by students in terms of “content setting”, “teaching quality” and “teaching effect”. And the experimental class is better than the control class in terms of the level of knowledge about English for the railroad industry, the application of English for the railroad industry in multiple scenarios, and the comprehensive ability evaluation scores of 4-5 points more than the control class.
Based on the scheme of multi-objective planning, this paper conducts an in-depth investigation on the design path of interdisciplinary teaching aids for STEAM project-based learning in the context of science education. A multi-objective planning model is constructed, which includes the integration of subject knowledge, the cultivation of students’ ability and cost control, and a multi-objective genetic algorithm is introduced to solve the model. The feasibility of the design path of this paper and the enhancement of students in project-based learning are verified through real cases. Compared with the other three schemes, the interdisciplinary teaching aids production using the mathematics and electricity fusion scheme can maximize the Pareto optimality, i.e., the integration of disciplinary knowledge and the cultivation of students’ abilities are maximized, as well as the goal of minimizing the production cost. The use of this paper’s scheme to produce teaching aids and apply them in course practice can effectively enhance students’ interest in learning and course performance.
With the deterioration of the global economic situation and the stagnation or regression of the development of enterprises, the problem of college students’ employment and entrepreneurship has been particularly prominent in recent years, and it is also one of the key points that can not be ignored in carrying out economic construction. The article realizes the prediction of college students’ entrepreneurship and employment market trends based on ARIMA-LSTM by designing the ARIMA algorithm model and combining it with the LSTM model architecture, taking the college students’ entrepreneurship and employment data from 2010 to 2022 as the research data, and using two evaluation indexes, namely, the mean absolute percentage error (MAPE) and the root mean square error (RMSE), to predict the results. Evaluation. From the analysis results ARIMA model prediction fit is high. Comparing the prediction results of the combined model with those of the LSTM model and the ARIMA model, the comparison results show that the combined model constructed in this paper can effectively fit the linear and nonlinear intertwined and superimposed trends of the time series compared with a single model, and the relative error of prediction is smaller at 33.78, which makes the results more accurate. The combined model can help the management department related to college students’ employment and entrepreneurship make reasonable decisions and improve efficiency.
This paper attempts to conduct a systematic study on the constructions of quantity phrases in modern Chinese on the basis of relevant research results, drawing on the theory of constructive grammar, in order to demonstrate the mechanism of constructions of quantity phrases in modern Chinese. The study firstly researches and analyzes the matching and distribution of quantity phrases as well as the Chinese construct grammar. Then, the study is based on random forests to investigate the constructions of quantifiers. By extracting and labeling six modern Chinese corpora, the analysis is carried out using random forests. On this basis, in order to further analyze the role of the relationship between the constructions of quantifiers, this paper also invokes a multinomial logit regression model for the study. It is found that the construct variant, regional variant, verb immediately following at the end of the sentence, structure initiation, and verb prototypicality are important factors affecting the number word constructions. In addition, the probability of quantifiers was higher when the construction variants were A and D, and sentence initiation while more inclined to co-occur with quantifiers. These findings reveal constraints on quantifier constructions and demonstrate the advantages of combining machine learning methods to analyze Chinese constructions.
Under the support of education digitalization strategy, in order to adapt to the development needs of education modernization, it is necessary to strengthen the research on the application of artificial intelligence technology in the main education of Marxism. Based on this, this paper closely follows the background of the artificial intelligence era, takes Marxist theory as a guide, and builds an intelligent communication platform for Marxist education based on the deep reinforcement learning model and the new media platform, which serves as a key link in the precise communication path of Marxist education. Relying on the state representation model and decision-making model in the deep reinforcement learning algorithm, the platform realizes the intelligent recommendation and dissemination of Marxist education content. The results of the precise communication path show that the intelligent communication platform has good application recognition and perceived satisfaction, and the audience students have a strong sense of belonging and responsibility for Marxist education in the communication, and the average score of the survey on the cultivation of values such as life ideals and political attitudes is above 4.50 points. The precise communication path of Marxist education proposed in this study, as an implementable countermeasure in the new media environment, can help the audience students to establish a correct worldview, life view and values.
The rapid development of the information age has prompted the exchange and sharing of information resources more and more frequently. Aiming at the problem of propagating information data in the center of data network, which is easy to cause congestion and delay, this paper uses deep neural network to research on the optimal path selection method for propagating information. A network traffic prediction model is designed based on multi-task learning and LSTM, and a dynamic multipath load balancing algorithm (FNN-LB) based on feed-forward neural network is proposed to solve the problem of scheduling and allocation of network traffic. The traffic prediction accuracy and generalization ability of the MT-LSTM model are verified, and the prediction mean square error is only 0.573%. Analyzed from several performance metrics, the FNN-LB algorithm improves the network throughput by 2.34% to 10.35% relative to other algorithms, effectively reduces the number of idle and overloaded links, as well as the average network delay and packet loss rate of the rat flow, while the first packet round-trip delay of the rat flow is reduced by more than 12.58%. Therefore, the proposed method in this paper can ensure the transmission quality of communication information data and improve the efficiency of data flow of communication information.
The integration of artificial intelligence and tourism culture industry requires that it is consumer-centered, and everything is based on the fundamental starting point of improving service quality and providing better tourism products. The article explores the impact of AI application on the cultural cognition level of tourists based on the role mechanism of AI and innovative inheritance methods in tourism culture inheritance. The level of tourists’ cultural cognition is quantified through the degree of understanding of tourism culture, the willingness to accept and disseminate tourism culture, the degree of preference and internalization of tourism culture, and the willingness to practice tourism culture, and the relevant research data are obtained through questionnaires. Then the benchmark regression model was constructed by combining the multiple linear regression model with the level of cultural cognition of tourists and the level of AI application as the explanatory variables and core explanatory variables. For every 1 percentage point increase in the level of AI application in tourism cultural heritage, the level of cultural cognition of tourists will increase by 0.419 percentage points. The application of artificial intelligence in tourism culture inheritance can expand the way of tourism culture inheritance and enhance the cultural cognition level of tourists through intelligent transmittable knowledge base.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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”.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Green construction is becoming a mainstream model of the transformation and upgrading of the construction industry, which has the advantages of energy saving, environmental protection and ecology, which can effectively reduce energy deficiency and improve environmental quality, which is the need for high quality sustainable development. This study is based on BIM software and the intelligent construction technology to propose the green architectural design party case. Building energy-saving efficiency evaluation system, using fuzzy Borda method and the CRITIC method of evaluation, the objective of the index, and the example of a community, the use of the object meta-effect model. The evaluation scores of the energy saving efficiency of the building of green energy saving and renovation are in the 90.11-99.28 points, and the high energy demand in the process of running the use of the building is excellent in the heating, refrigeration and other aspects of the building. This paper shows that the goal of the green transformation project is basic, which is effective and the efficiency of energy efficiency is generated. This study can provide guidance for the work of the green building energy saving and renovation work, and further promote the energy saving and transformation of China.
The supply chain applies large number according to the technology, can reduce the cost of each link, optimize the resource allocation, increase the enterprise benefit. In this paper, the supply chain cost control program based on large number according to the previous forecast, the control of the event and the analysis of the three levels of the analysis of the supply chain. The combination time series model and the multivariate regression model, the joint CPFR concept, the establishment of the CPFR sales combination demand prediction model, the design form according to the sales prediction system, the resource optimization plan of the supply chain inventory in real time. Analyze the prediction effect of the combination prediction model, predict the product sales in the week, calculate the product safety inventory and the remaining inventory. The analysis is based on the cost control effect of the enterprise supply chain according to the sales forecast. The cost of purchasing the supply chain costs less than the operating income, which fell to 0.5107in 2023. The gross margin of gross profit was 0.53666 in 2023, which was controlled by the gross margin, and the gross profit was improved. It is said that the enterprise is using large number according to the technology to the supply chain resource optimization in the supply chain cost control, the cost control effect is better.
The research of modular and personalized balance strategy in assembly building design can improve the efficiency of construction and meet the demand of design diversification. Based on bim technology, an assembly building modular design method is proposed to determine the required space module, to determine the required space module, to strengthen the module structure, to set up the layout of the building, to formulate the modular panel and the assembly frame platform, and through the revit implementation of the three-dimensional visual design of modularity and personalization. The design of the 9 building of this article, in the collaborative function, spatial adaptability and the design diversity score in turn for 10th 10 “10” 9 points (full score 10). This article is designed to meet the demand of the building in daylighting and ventilation, the average daylighting coefficient is 6.440%, and the minimum value of the floor area of the building room is 18.25cent. Modular and personalized assembly frame structures have a better seismic resistance, and their limit cumulative energy consumption is 2.38 times the traditional way. Experts have the highest social benefit satisfaction in this article strategy, and the satisfaction score is 92.05.
Based on the definition of volatility and conditional value risk (CVaR), this paper introduces the implied volatility into CVaR model, and further analyzes the partial differential equation of stock portfolio optimization in the form of BS model. In the process of multi-stage investment, in order to reasonably control the investment risk of each stage, the CvaR model based on implied volatility is constructed by using the scenario tree method. With the data of 1166 trading days as the data, 4 stock assets as the data set of this study, the optimization model is applied to the calculation and analysis. The numerical simulation shows that the stock price fluctuation of the four multi-cycle stocks ranges from -23.45% to 41.97%, showing a clustering phenomenon. Among them, the volatility of stocks A and C is more obvious than that of stocks B and D, and the probability density tails of stocks are longer in the cycle, and they all show thick tail characteristics, indicating that the introduction of implied volatility of CVaR model makes the risk control of actual equity asset investment more reasonable.
In this paper, the characteristics and distribution of the spatial clustering diffusion characteristics and distribution of the spatial accumulation of rural areas are quantified by using the GIS space analysis method, the analysis method of the nuclear density estimation, the hotspot analysis, the spatial self-correlation, and the large number of the rural areas of Chongqing. Compared with the difference of the amount of the education facility in Chongqing, the difference between the amount of the education facility was compared, and the development gap of the education facility was assessed. The study showed that in 2023, the imbalance coefficient of the school of compulsory education in Chongqing was reduced from 0.3637 in 2013 to 0.02433 in 2023, and the primary school stage was reduced from 0.3582 to 0.1952. This paper shows that the imbalance coefficient of education resource layout in Chongqing is decreasing year by year, and the spatial equilibrium of resource space increases. This study provides the effective thinking and method for the adjustment of the education resource space layout structure in Chongqing, and provides the scientific decision basis for the calibration of the existing planning and the formulation of future planning.
This paper discusses the appreciation of the elderly to influence the actual exchange rate by using the requirements structure and the current account mechanism. Using the internal actual exchange rate formula and the Balassa-Samuelson effect, the propagation mechanism of the aging of the population was established. This paper discusses the influence of aging on trade balance, and sets up the panel model of countries of different age categories. Through heterogeneity analysis and multivariate regression test assessment. The study of mathematical methods found that the rate of pension care significantly affected the actual effective exchange rate. In countries where aging and moderate aging lead to depreciation, aging and non-ageing countries can rise.
In this paper, the basic Wiener filter structure and adaptive algorithm module are used to optimize the parameter adjustment and data noise processing in the adaptive filter algorithm. Based on the LMS criterion, the algorithm is further refined by quantization error and affine projection optimization, which improves the accuracy and speed of vortex and circulation data analysis. The optimized algorithm reduces noise and covariance error, and achieves excellent performance in filtering evaluation (SRTAE:
The stretched Littlewood-Richardson coefficient
In this work, we study type B set partitions for a given specific positive integer
Suppose that
Let
Using generating functions, we are proposing a unified approach to produce explicit formulas, which count the number of nodes in Smolyak grids based on various univariate quadrature or interpolation rules. Our approach yields, for instance, a new formula for the cardinality of a Smolyak grid, which is based on Chebyshev nodes of the first kind and it allows to recover certain counting-formulas previously found by Bungartz-Griebel, Kaarnioja, Müller-Gronbach, Novak-Ritter and Ullrich.
Topological indices have become an essential tool to investigate theoretical and practical problems in various scientific areas. In chemical graph theory, a significant research work, which is associated with the topological indices, is to deduce the ideal bounds and relationships between known topological indices. Mathematical development of the novel topological index is valid only if the topological index shows a good correlation with the physico-chemical properties of chemical compounds. In this article, the chemical applicability of the novel GQ and QG indices is calibrated over physico-chemical properties of 22 benzenoid hydrocarbons. The GQ and QG indices predict the physico-chemical properties of benzenoid hydrocarbons, significantly. Additionally, this work establishes some mathematical relationships between each of the GQ and QG indices and each of the graph invariants: size, degree sequences, maximum and minimum degrees, and some well-known degree-based topological indices of the graph.
Some methods of decomposing
Cultural heritage represents the historical and cultural achievements of a nation, playing a vital role in studying human civilization and preserving national languages and scripts. This study utilizes virtual simulation technology to design a virtual pavilion for Chinese language and writing, employing image and text feature extraction algorithms for feature fusion and 3D modeling. The effectiveness of Chinese character extraction is validated through feature point matching, while the virtual exhibition’s impact is assessed via user experience scores. Results indicate that the proposed algorithm achieves accurate extraction with no misrecognition. User interest rankings highlight text images as the most influential factor, followed by visual imagery, pavilion experience, scene art, and language culture. Analysis of user feedback shows an average experience score exceeding 60 points, confirming the pavilion’s effectiveness in preserving and promoting Chinese language and writing culture.
In recent years, due to the adjustment of economic structure, the people’s living standard and the increase of leisure time, the sports industry has become a new economic growth point. This paper studies and analyzes the characteristics of the industry background and business background of the sports industry, explores the factors and internal driving force affecting the design of its business model, and fully analyzes the mechanism, functional role, and logical relationship of the elements for constructing the business model of the sports industry, and then explores the characteristics of the business style of the sports industry. From the perspective of knowledge state, using the reinforcement learning mechanism, the evolution process of the sports industry business model from the first stage to the fourth stage is described. Taking Company H as a research case, the process and economic effect of the transformation and upgrading of its business model through the reinforcement learning mechanism is analyzed and it is found that as of 2023 the company’s operating income has increased by 2.4 times through transformation and upgrading, and its net profit has increased by 125.57 percentage points compared to 2016. It further understands the role that the enhanced learning mechanism brings to the development of the sports industry, and expects to be able to provide a reference for the sports industry to carry out business model transformation in the future.
We initiate a study of the toughness of directed graphs by considering the natural generalization of that for ordinary graphs. After providing some general results, computations are completed for a few natural examples. Maximum possible toughness is also considered. Some open problems are posed.
Let
This study applies Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms to classify five types of basketball footwork. SVM maps the training data into a high-dimensional space using nonlinear transformation and classifies it with support vectors and a hyperplane. Experimental analysis showed minimal differences in peak and trough values of footwork movements; therefore, only mean and standard deviation features were retained, resulting in 12 effective features. KNN experiments demonstrated that recognition accuracy varies with different K values. The highest accuracy (80.7%) was achieved when K = 5 with the selected features. The study also examined the physical characteristics of basketball players, analyzing height, weight, and other indicators. Statistical results showed no significant body shape differences between experimental and control groups (P > 0.05). A T-test on dribbling, shooting, and layup performance also revealed no significant differences between the groups (P > 0.05).
This study explores the employment competitiveness of computer science majors by integrating combinatorial mathematics into the evaluation process. Utilizing the Analytic Hierarchy Process (AHP) and the improved FKCM clustering algorithm, we construct a hierarchical model to assess the impact of entrepreneurial education, learning motivation, and investment on job competitiveness. Data from 314 participants were analyzed using combinatorial techniques to derive optimal weightings for each factor, ensuring the evaluation model’s robustness. The results highlight significant gender differences in practical and feedback-based entrepreneurship education, with males outperforming females. However, no notable differences were observed in job interest, learning motivation, or overall employment competitiveness.
An (unrooted) binary tree is a tree in which every internal vertex has degree
This paper proposes an optimized Backpropagation (BP) neural network for improving intelligent elderly care talent training. To address BP’s limitations, including noise sensitivity and slow convergence, we introduce Particle Swarm Optimization (PSO) to refine network weights and thresholds. The model integrates course quality, teacher effectiveness, platform support, and market demand, aiming to optimize elderly care service talent cultivation. Experimental results demonstrate a significant improvement in prediction accuracy, with average error reduced from 9.94% to 6.3%. This enhanced model offers a more efficient and accurate solution for aligning educational outcomes with industry needs.
Amnesty international is recognized as a key force in promoting social development, with higher education also facing the need for innovation. This paper explores new opportunities in educational theory and policy proposed in a recent initiative. The proposal emphasizes filtering ideology, political education, and public opinion to enhance the accuracy of ideological and political teaching. By incorporating personal suggestions through interviews, the model recommends learning materials tailored to student characteristics. System implementation and testing demonstrate its potential as a core tool for ideological education in colleges, supporting the integration of knowledge, politics, and technology to meet students’ educational needs.
Networks with smaller strong diameters generally have better fault tolerance because they enable closer connections between vertices, leading to shorter information paths. This allows the network to maintain communication and functionality more effectively during attacks or failures. In contrast, larger strong diameters mean vertices are connected over longer distances, increasing vulnerability to disruptions. Thus, the strong diameter is a key metric for assessing and optimizing network fault tolerance. This paper determines the optimal orientations for the Cartesian and strong products of even cycles, provides the minimum strong diameters and their bounds under specific conditions, and establishes a lower bound for the maximum strong diameter. A conjecture about the exact value of the maximum strong diameter is also proposed.
For a graph
The development of artificial intelligence enables computers to not only simulate human artistic creations, but also synthesize fine art works with deeper meanings based on natural images. This study digitally parses the fusion of fine art and philosophy visual expressions, and develops a visual expression system based on the fusion of fine art and philosophy by utilizing a variety of key big data algorithms for visual expressions such as adversarial networks. Research on pattern recognition of this system in art creation is carried out through model training, recommendation performance evaluation, pattern recognition strategy application and regression analysis. The model in this paper works best when the number of nearest neighbors k=15, and the recommendation model in this paper can provide a personalized list of artwork recommendations for different people. The recognition of the system in this paper in the five dimensions of “spiritual level”, “value level”, “philosophical level”, “aesthetic level” and “technical level” is distributed between 4.24
With development of Internet of Things, big data and artificial intelligence, cell phone signaling data, point-of-interest data and machine learning methods have been widely used in research of various fields of transportation. The use of big data processing techniques and machine learning methods to mine intercity travel data collected by various types of traffic detectors provides a new way of thinking to study travel mode selection behavior. In this paper, we pre-processed cell phone signaling data, geospatial data and interest point data around three aspects: personal attributes, travel attributes and travel mode attributes, and designed intercity travel target group extraction, travel chain extraction, travel mode extraction and travel purpose extraction algorithms, which provide basis for travel feature analysis and travel mode choice behavior prediction modeling.
Conventional techniques to electric power network (EPN) design and management are insufficient to handle extreme weather events like hurricanes due to the growing complexity and fragility of power systems. As a sophisticated simulation and optimization tool, digital twin (DT) technology may offer real-time power infrastructure monitoring and prediction. This study aims to investigate the possible application of digital twin technology in enhancing power system resilience and streamlining the design process, as well as to use it for the 3D design of the full substation engineering infrastructure process. A digital twin-based EPN model that incorporates all of the main components of the power system—power plants, substations, transmission and distribution networks, and customers—is proposed in this paper. Every component of the power system undergoes vulnerability analysis, and the chance of the system failing is calculated using a Bayesian network (BN) model and a parametric vulnerability function. According to modeling projections, Hurricane Ike will cause the majority of consumers’ power supplies to be interrupted. The model predicts that power consumption for residential, commercial, and industrial buildings will be 96.4%, 96.0%, and 94.2%, respectively, depending on the kind of building.
Radar ranging and speed measurement are common applications in daily life, with performance largely dependent on the radar signal processor. However, existing civilian radar signal processors struggle with weak signal reception and low analysis efficiency. This study designs a high-speed radar signal processor based on FPGA architecture, incorporating a fusion processing algorithm to integrate different radar signal bands, enhancing processing efficiency and accuracy. The design includes data feature analysis, storage, and fusion modules. Tests showed that the processor achieved real-time performance with a processing time under 1ms, a ranging error below 1m, and speed measurement accuracy within 5m/s, meeting practical requirements.
Intriguing symmetries are uncovered regarding all magic squares of orders 3, 4, and 5, with 1, 880, and 275,305,224 distinct configurations, respectively. In analogy with the travelling salesman problem, the distributions of the total topological distances of the paths travelled by passing through all the vertices (matrix elements) only once and spanning all elements of the matrix are analyzed. Symmetries are found to characterize the distributions of the total topological distances in these instances. These results raise open questions about the symmetries found in higher-order magic squares and the formulation of their minimum and maximum total path lengths.
In this paper, we introduce the concept of vertex-edge locating Roman dominating functions in graphs. A vertex-edge locating Roman dominating
Traditional personnel recruitment methods are often inefficient and struggle to find candidates who meet job requirements. In this paper, we first develop a comprehensive personnel management system for colleges and universities that streamlines the recruitment process and information management. Next, recruitment data from the system is analyzed using the fuzzy C-means algorithm to cluster applicant profiles and extract position-specific user characteristics. Finally, a joint embedded neural network is employed to match applicant profiles with job positions by optimizing an objective function. Experimental results demonstrate a high job matching rate (up to 98.1%), a significantly reduced recruitment cycle (from job posting to candidate onboarding in 25 days), and a system response time as low as 0.5 seconds. These findings highlight the effectiveness of big data technology in providing timely feedback, reducing recruitment costs and staff workload, and promoting the intelligent development of talent recruitment.
1970-2025 CP (Manitoba, Canada) unless otherwise stated.