In the context of today’s big data and information age, blended learning has gradually entered people’s vision. At present, the number of business Japanese learners in China is increasing, and the breadth and depth of business Japanese education are expanding in all aspects. Therefore, people gradually pay attention to the practice and exploration mode of Internet, Japanese teaching and mixed learning, and learning evaluation is an important part of them. In this study, the construction of hybrid learning evaluation index is studied in the form of algorithm optimization and experimental verification. It takes blended learning as the evaluation object, aiming to build an evaluation index system of blended learning and help teachers implement high-quality blended learning. Based on the learning theory, this study also proposes the guiding strategies for senior high school students’ business Japanese learning from the perspective of schools, teachers and students themselves. Through empirical research and hypothesis verification, it provides reference for business Japanese education and learning in senior high schools in China. The experimental results show that the Nivre model after our calculation and optimization is based on the new education concept and the era development environment, and constantly improves the mixed learning mode, which improves the quality of business Japanese learning. Through correlation analysis and reliability and validity tests, the accuracy of the model in different test sets exceeded 92\%, which was in line with expected assumptions and empirical tests. The model has certain practical value.

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.

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 high-quality 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 high-quality 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 high-quality 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 e-commerce platforms.

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.

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.

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 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.

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 multi-objective 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.

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.

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.

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.

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 self-calibration 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.

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.

The purpose of this study is to solve the energy efficiency problem of small agricultural base stations, propose an optimal design scheme based on RF power amplification, and verify its effectiveness through simulation experiments. In order to achieve the research purpose, this paper first defines the objectives and principles of energy efficiency optimization design, and puts forward the energy efficiency optimization technology based on RF power amplification. On this basis, a complete set of energy efficiency optimization design scheme for small agricultural base stations is designed. And by building a simulation platform, set the parameters close to reality, and simulate the operation state of the base station in different scenarios. The simulation results show that the stability of the algorithm in this paper is considerable under different loads. Even if the load is large, the stability of this method can reach above 89%. The proposed energy efficiency optimization scheme can significantly reduce the energy consumption of the base station and improve the overall energy efficiency performance under different load and interference conditions. This result proves the effectiveness and superiority of the scheme and provides strong support for practical application.

For building construction enterprises, civil engineering project schedule assurance is the embodiment of project performance ability, and project cost control is the root of project profitability. This paper researches the cost-schedule control method based on BIM and critical path earned value method, and establishes a complete set of dynamic cost-schedule analysis and control method including plan preparation, process evaluation and result correction. This paper takes Project F as an example, integrates project management in the BIM platform and optimizes the plan through construction simulation, so that the construction plan is closer to the actual demand, establishes the Earned Value Method for distinguishing the critical path and embeds it into the BIM platform, reflects the progress with the Earned Value parameters of the critical path, reflects the cost with the Earned Value parameters of the whole project, analyzes the problems of the critical path and the project and proposes cost-schedule corrective measures in a targeted way. The critical path and project problems are analyzed, and cost-schedule corrective measures are proposed, so as to realize the fine management of project cost-schedule. Through the case study, it is proved that based on BIM critical path earned value method can achieve schedule and cost coordination and dynamic control and realize 91.8% cost reduction, good civil engineering project management efficiency and change the status quo of civil engineering project cost management.

Goal-oriented dimension is a new angle to solve the problem of universities’ performance assessment. Firstly, designs an input-output index system, and calculates the Malmquist Index of the performance utilizes the panel data. Then, the non-parametric KDE graph is used in this research for further discussion of the differences of TFP changes. Meanwhile, a non-parametric KDE analysis is carried out respectively for TECHCH, EFFCH, PTEC, and SECH indexes. The Malmquist-KDE index model shows the results as follows: TFP is on a declining curve; the increased range of EFFECH is relatively smaller, while the annual growth of PECH and SECH are slow; the decrease of TFP is caused by the decrease of TECHCH; the general distribution gradually moves leftward, reflecting a fact that the TFP changes are decreasing progressively; the TFP change rate demonstrates obvious a skewed distribution; the patterns in the graph gradually shift from thin and tall ones into short and thick ones. Conversely, the changes of external factors force universities to improve their operations actively.

With the continuous development of deep learning technology and the increasing maturity of rural tourism market, this paper obtains tourism user-generated content data through customized crawler technology, describes the data flow diagram of single-user crawling and the data flow diagram of database batch crawling module. A sentiment index covering multiple dimensions is constructed to mine the deep-seated features of tourist behavior. Fusing effective features in tourism data by using multiple topological maps, using graph convolution network to capture multiple spatial features of scenic spots and recurrent neural network to capture temporal features of traffic, to complete the analysis and prediction of tourists’ behavior. Taking Jiangxi Wuyuan Huangling rural attraction market as an example for empirical analysis, the importance of historical flow and search volume under all time windows is as high as 111 and 117 respectively, proving that these two features have a significant impact on predicting the target variables. The model in this paper is highly fitted to the predicted value of actual passenger flow at 12 time points, especially in the 9th month, the predicted value is 402, which is 401 from the actual value, which is an important reference value for rural tourism management and marketing strategy.

When a manufacturing enterprise adopts lean manufacturing system for multi-species production and processing of products, the workshop production scheduling problem (i.e., production scheduling) is a major factor affecting the production efficiency of products. Aiming at the shortcomings of the standard simulated annealing algorithm, which is easy to fall into the local optimum due to the influence of stochastic factors, this paper designs an improved simulated annealing algorithm with tempering and slow-cooling functions, and an event-driven priority coefficient search for solving the dynamic scheduling optimization model of the production line. At the same time for specific cases of simulation and parameter testing of the algorithm, and respectively with manual scheduling results, the performance of the basic algorithm before the improvement of experimental comparison and analysis, to find the optimization effect of the improved optimization scheduling algorithm. Compared with the manual scheduling method, this paper’s method significantly optimizes the two objectives of total weighted delay time and production energy consumption. Compared with the basic SA algorithm, the accuracy of chromosome encoding of this paper’s method is improved by 233.33% and the computing workload is reduced by 79.51%, which verifies the feasibility and efficiency of this algorithm’s optimization scheme.

Due to its heavy reliance on imports, the futures and spot markets of China’s upstream and downstream soybean products are vulnerable to the impact of the international market. In order to guarantee the security of the soybean industry, China introduced corresponding agricultural subsidy policies in 2008, 2014 and 2018, respectively. In order to test the impact of the subsidy policy on the development of the soybean industry, this paper utilizes an empirical mathematical planning model to evaluate the implementation effect of the subsidy policy for soybean producers ex ante, and explores the defects of the agricultural subsidy policy by simulating the production decisions of farmers. It also measured the efficiency of soybean subsidy, the efficiency of agricultural machinery purchase subsidy and the efficiency of agricultural insurance premium subsidy using a three-stage DEA model. In the empirical research part, the constructed numerical method of soybean producer subsidy policy unfolds the effect assessment. The empirical results show that the implementation of the soybean producer subsidy policy increases the proportion of soybean planting and soybean total factor productivity by 9.47% and 17.43%, respectively, and that the soybean producer subsidy policy has a facilitating effect on the expansion of soybean planting and total factor productivity. Accordingly, five policy recommendations are put forward with a view to promoting the healthy development of the soybean industry.

Virtual teaching and research community is an effective way for teachers to realize communication and cooperation among themselves, to improve their professional level and to promote their career development. Under the framework of teaching and research community community construction, the relevant factors that resound the development of virtual teaching and research community construction were extracted by questionnaire survey method, CRITIC-assigned to them, and the factors with larger weights were taken as the key factors, and the multiple linear regression method was utilized to explore their influence on teachers’ professional development. The analysis found that the key factors with larger weights are teaching and research team building (0.3234) and teaching and research motivation (0.2683), and the regression coefficients of both of them in the regression results of teachers’ professional knowledge and professional skills are 0.18, 0.158, and 0.089, 0.059, respectively, and the significance of all of them is less than 0.05. Therefore, the teaching and research team building and teaching and research motivation are not only crucial to virtual teaching and research community operation, but also have a positive effect on teachers’ professional development.

In order to explore and promote the strategy of students’ active health behaviors, this paper designs a personalized scientific guidance system architecture for active health promotion based on a three-tier service architecture model, using students’ sports literacy big data processing technology to construct a sports mobilization effect information system. Second, a sports prescription generation model is designed. The model adopts a multifactor fusion approach to recommend personalized exercise programs based on the different exercise abilities, different physical conditions, and personal exercise preferences of the exercisers. Under the condition of satisfying multiple constraints such as the physical condition, parameter range and exercise ability of the exerciser, the particle swarm optimization algorithm is used to optimize the exercise parameters, and the topological structure is further used to adjust the broadness of the distribution of the solution set in the objective space. The improved particle swarm optimization algorithm is compared, and the experimental results show that the improved TS-PSO algorithm converges faster, the solution accuracy is higher, and the parameter optimization using this algorithm generates a personalized exercise prescription that is more suitable for the exerciser. The exercise prescription generation model studied in this paper provides a new idea for the improvement of the effect of sports mobilization under the perspective of active health.

Information security is the most concerned issue in modern communication, with the continuous development of new computing technologies, classical cryptography has been difficult to effectively guarantee information security, quantum key distribution technology through the theory of quantum mechanics to ensure the absolute security of key distribution. Therefore face recognition system oriented optimization using quantum key distribution, this paper is based on the advantages of OQKD technology such as easy to implement, low overhead, high security, optimization for commercial privacy queries in the system. On the basis of the quantum key distribution regional network of trust relay, a new type of quantum key distribution experimental network structure based on switching nodes which is more flexible, energy-saving and efficient is proposed. Finally, the method of this paper is comprehensively verified through modeling simulation, and the simulation results show that the average call loss is 3.67% when the quantum key generation rate is increased to 20Kbps, which is significantly reduced. Moreover, the network call loss can be reduced to less than 11% when the method of this paper is adopted in the same situation, and the network call loss is even smaller. It shows that the call loss of the network will be greatly reduced when the key generation rate is increased with a fixed amount of voice traffic.

This paper addresses the limitations of the traditional portfolio theory centered on the mean-variance model and expected utility theory, and proposes the establishment of a portfolio model that takes into account the subjective psychological factors of investors, taking into account the fact that investors are susceptible to the influence of various psychological biases, affective biases, and cognitive biases in the actual decision-making process, with respect to the theory of consistency of the assumptions of the investor’s risk attitude. The portfolio model based on fuzzy decision-making is proposed, combined with the development and application of linear programming in portfolio optimization, the return of assets is regarded as a random fuzzy variable, and the stochastic fuzzy portfolio model is constructed to consider the risk characteristics of investors. The portfolio returns under different emotions or different risk preferences are explored separately. Combined with the fund categorization allocation of the sample firms, the fund portfolio C based on the fuzzy portfolio model is proposed and compared with the equal weight allocation fund (fund portfolio A) and the risk coefficient weighted allocation fund (fund portfolio B) based on the risk level of return, respectively. Fund Portfolio C has the highest average return.

This paper combines the demand structure mechanism and the current account mechanism to show that an increase in the share of the elderly population affects the appreciation of the real exchange rate. Based on the formula of the internal real exchange rate and the “Balassa-Samuelson effect”, the transmission mechanism of population aging on the real exchange rate is established. Combining the results of the discussion on the savings rate and investment rate, an analytical model of the impact of population aging on the balance of trade is proposed. Panel models are built with sample data from full sample countries, super-aging countries, deeply aging countries, mildly aging countries and non-aging countries respectively, and heterogeneity analysis is carried out for each type to test the multilevel regression results of population aging on the real exchange rate. The control variables are brought in separately for the benchmark regression of population aging on the size of trade surplus and current account balance. The empirical results of the data regression show that the old age dependency ratio is an important influence on the real effective exchange rate. And if the country is in the stage of super-aging and mild aging, aging leads to the depreciation of the real effective exchange rate. In the stage of deeply aging and non-aging countries, aging leads to real effective exchange rate appreciation. Meanwhile population aging positively affects the trade surplus at the provincial level mainly by affecting the level of savings.

The proposal of “Belt and Road” has helped these countries along the route to stimulate the development vitality and cooperation potential of their respective domains, which fits well with their common needs and opens a new window of opportunity for their complementary advantages and open development. This paper improves the construction of the new regional cooperation mechanism of the Belt and Road from three aspects: power mechanism, coordination mechanism, benefit distribution mechanism and compensation mechanism. The double difference method is utilized to assess the economic benefits generated under the Belt and Road regional economic cooperation mechanism. The assessment results show that the country with the highest import and export trade dependence of China is New Zealand, which reaches 18.5611, and as the dominant country of the Belt and Road, China’s two-way investment in other countries has the highest scale of $124,705.9 million, but the index of investment closeness is -1, which indicates that the capital flow between the two sides is mainly a unidirectional investment from China to other countries.

Optimization of regional economic resources is an important part of the construction of “Belt and Road”, and it is of great significance to reasonably and objectively evaluate the development level of optimization of regional economic resources in the countries along the route, and identify the influencing factors of optimization of regional economic resources in the countries along the route, in order to promote the development of the countries along the route. This paper first establishes a multi-objective optimization model and selects regional economic and social benefits as the optimization objectives. Subsequently, the model is constrained by the three parties of resource availability, resource output capacity and regional coordinated development as constraints, and the constraints of each objective and variable are associated to form a complete multi-objective regional economic resource optimization allocation model. Then the degree of synergy is defined to ensure the robust development of the model. According to the synergistic optimization model of regional economic resources, the optimization results of regional economic and social benefits are calculated, and the development of regional economic benefits in the Belt and Road region grows from 0.264 in 2017 to 0.575 in 2023, with a growth rate of 117.8%. Meanwhile, the growth rate of social benefits also reached 14.29%. Finally, the panel regression model was used to analyze the factors affecting the economic resources of the countries along the Belt and Road, and the regression results of the four factors, namely, merchandise trade, foreign direct investment, road traffic mortality, and industrial development, were all less than 0, and all of them had a negative effect at the 1% significant level.

In the operation of storage system, improper scheduling of shuttle and hoist will waste resources and affect the picking efficiency, so it is of great significance to optimize the operation scheduling of storage system. Based on queuing theory, this paper constructs a queuing model of ring RGV system and proposes queuing model assumptions of hoist system to analyze the reasonableness of storage layout. The operation activity scheduling mechanism is designed to execute the warehousing activities strictly in accordance with the established operation order. Agree on the ring track RGV operation rules, calculate the distance between any two points on the track, and ensure the shortest distance of the warehousing operation. Merge the shortest operation path and the shuttle car operation equilibrium rules to construct a dynamic scheduling decision model. Through the storage resources in and out of storage management and scheduling module, improve the measuring equipment intelligent storage system, apply the system to the actual storage operations, analyze the operational efficiency. After the implementation of the strategy proposed in this paper, the optimal scheduling result is 36min, the execution time of different types of work is different, and the operation time of equipment J1-J4 is 15min, 23min, 17min, 34min respectively. The pickup execution efficiency of the strategy used in this paper is improved by 66.38%, and the pickup efficiency is improved by 10% when the number of equipment is less than 300 pieces. The scheduling strategy proposed in this paper has a higher priority when facing a small number of devices.

Based on the complexity and nonlinear characteristics of market volatility, this paper proposes a market volatility prediction model that combines MA filtering method, autoregressive moving average (ARMA), and long-short-term memory (LSTM) neural network. And the back-propagation (BP) neural network is utilized to quantitatively solve the problem of corporate strategy formulation, and a corporate strategy formation model is established to determine the corporate strategic choice through the corporate strategic environment and strategic capabilities. In the ablation experiment, the combined model MA-ARMA-LSTM reduces its MSE, RMSE, MAE and MAPE by 0.0007, 0.0131, 0.0074 and 1.57%, respectively, compared to the ARMA model. Compared with common market volatility prediction models, the combined model has the smallest error in each assessment index. The output of BP neural network for corporate strategy selection is consistent with the expert ranking, which is verified to be in line with the actual business situation, indicating that the method in this paper can provide a reasonable corporate strategy.

The dance teaching method that effectively combines motion capture and posture estimation can effectively differentiate itself from the traditional dance teaching mode, realize the teaching interaction between the 3D virtual world and the real technology, and help to improve the quality of dance movement teaching. In this paper, HRNet network is selected under the framework of human posture estimation for the extraction of key points of human posture, and SPIN algorithm is composed of two parts, namely, regression network and SMPLify, to complete the three-dimensional expansion of human posture information. Design the flow of the dance visual movement tracking decomposition method, and perform feature point labeling and labeling parameter setting for multiple key points and key movement parts in the dance movement. Select the specific parts of the dancer as the motion tracking points, record and record the dancer’s action images, and track and fit the dance action trajectory using the 3D visual motion tracking decomposition method proposed in this paper. Compare the dance trajectory fitting effect of this paper’s method with that of the degree-of-freedom vector method and the tracking differentiator method, and obtain the performance of the three-dimensional visual motion tracking decomposition method. Analyze the students’ physical flexibility, balance ability, and the completion of complex movements after a two-month dance teaching. After the dance teaching utilizing dance movement posture analysis, the students’ body flexibility (shoulder) and balance ability improved by 12.8cm, 18.74s (left), and 22.2s (right), respectively.

The article preliminarily studies the structure of flexible interconnection system of MV distribution network, and understands the application scenario and equipment composition of the flexible system. For the purpose of reducing SOP loss, transformer loss and line loss, the operation of the MV flexible interconnected distribution network is optimized, the operation optimization model of the flexible interconnected distribution network is constructed, and the fault enumeration method is adopted as the reliability assessment method of the flexible interconnected system. Through experimental simulation, the stability, reliability and dynamic characteristics of the MV flexible interconnection system are explored respectively, and the system protection control strategy is proposed. For the same constant power load step, the larger the voltage loop proportional parameter is, the more stable the system tends to be, and the larger the voltage loop integral parameter and the station circuit parameter are, the more unstable the system tends to be. The maximum mutation value of the system constant power load gradually decreases when the station load power gradually increases. The reliability of the MV flexible interconnection system increases with the increase of SOP capacity. In the medium voltage flexible distribution interconnection system. The system damping, oscillation frequency and overshoot are significantly reduced and the peak time is increased when the DC voltage sag factor is increased.

The article applies recurrent neural networks to multi-intelligent body collaborative autonomous systems and uses optimized RNN algorithms for multi-objective detection and path planning of intelligent bodies. The multi-intelligent body multi-target detection and path planning model optimized based on recurrent neural network is constructed to realize multi-target detection and tracking of intelligent bodies and multi-intelligent collaborative path planning. Simulation experiments are designed with a mobile robot as the research object to analyze the trajectory tracking and path planning effects of the multi-target detection and path planning model in this paper. The error between the actual trajectory and the reference position of the robot trajectory tracking is continuously reduced, and reaches complete coincidence at the 127th reference tracking point. The actual speed and acceleration errors of the robot are infinitely close to 0. The accuracy of this paper’s algorithm in multi-objective path planning is 100%, the average arrival time is 20.02s, and the probability of collision is 0%, which is much better than other algorithms. The algorithm in this paper has the highest path smoothing validity for planning in three environments. In the 30 × 83 warehouse map, the total path length of this paper’s algorithm is shortened by 13.00% and 10.77%, and the total path cost is shortened by 9.71% and 11.52% compared with the Wd-SIPP algorithm for the number of collaborative robots in a single group of three and five, respectively. In 100*100 storage map, the total path length is shortened by 10.32% and 11.67%, and the total path cost is shortened by 7.34% and 12.09%, respectively.

From World War II to the Cold War (1945-1991), the U.S. military-industrial complex went through a process from its rise to its full expansion, which had a profound impact on the global political and economic landscape. In this paper, computer simulation techniques are used to construct a vector autoregressive model (VAR) to quantitatively analyze the impact of the military-industrial complex on the U.S. economy. Smoothness and cointegration treatment and Granger causality test are done on the collected sample data. After that, the VAR model between three sets of variables, namely, military expenditure as a share of GDP, consumption as a share of GDP, and investment as a share of GDP, is designed. Using impulse response function and variance decomposition to analyze the data, we get that the rise of the U.S. military-industrial complex can effectively promote the growth of the economy in the long term, and the development of the economy can also promote the development of the military-industrial complex, but the promotion effect is not obvious.

The development of digital technology has made the use of machine learning algorithms to protect cultural heritage has become a trend. In this paper, based on the random forest algorithm, the conservation model of tomb mural cultural heritage is recognized. The mural paintings in the tomb of Prince Zhanghuai are used as the data source to construct the tomb mural painting dataset, and the images in the dataset are processed, augmented and labeled. The features such as color, texture and shape in the mural images are extracted as one of the input information of the cultural heritage protection model of the tomb murals. Based on the random forest algorithm, a pattern recognition model for the protection of cultural heritage of tomb frescoes is constructed, and the feature vectors obtained from the feature extraction are used to calculate the split points of the decision tree. The classification results of multiple decision trees are weighted and averaged to obtain the final recognition results. The recognition accuracies of this paper’s model on the training set, test set and validation set are 99.45%, 95.46% and 92.58%, respectively. This is a significant improvement over other existing algorithms. Meanwhile, the algorithm consumes significantly less time than the ResNet18 deep residual network model before and after data enhancement, and is able to efficiently accomplish the task of recognizing the protection of cultural heritage of tomb chamber murals.

The specialty of soil and water conservation and desertification control has gradually become a hot and popular discipline, and the educational practitioners of this specialty must also follow the trend and actively carry out educational reform. This paper utilizes genetic algorithm to conduct in-depth research on the problem of class scheduling, and on the basis of traditional genetic algorithm, an improved adaptive genetic algorithm is proposed to be applied to the class scheduling system. Through the adaptive adjustment of genetic parameters to improve the convergence accuracy of the genetic algorithm and accelerate the convergence speed, and finally after chromosome conflict detection and repeated iterative operations, the final optimal scheduling program is obtained. The improved adaptive genetic algorithm is applied in the course scheduling system of soil and water conservation planning and design in colleges and universities. After experimental verification, the improved new adaptive genetic algorithm, under the setting of different rules of scheduling conditions, under the setting of different rules of scheduling conditions, the fulfillment rate of students’ class selection reaches 100%, and the mean value of the overall rule fulfillment rate reaches 94.1%, and the overall fulfillment rate of the scheduling efficiency is improved to 96% by applying it to the intelligent class scheduling system. Finally, the professional classes were tested on the knowledge of soil and water conservation planning and design, and the remaining eight dimensions of professional knowledge were accompanied by questionnaires, and the achievement data of the test were statistically analyzed using SPSS22.0. The analysis results show that the test scores are quasi-normally distributed, and the actual pass rate of each question in the test paper is roughly close to the preset difficulty, which proves that the test paper is of good quality and the algorithm designed by the institute can basically meet the requirements.

In the context of information is mostly trivial, messy and disordered, under the context of information fragmentation, the creation path of new media art is also being affected by it. Based on the color sensual imagery, this paper adopts the gray correlation analysis method to research on the creation of new media art. Through the questionnaire survey, the cluster analysis algorithm is used to filter the color semantics, and the five most representative color imagery semantics are selected as the imagery scale in the quantitative space. Combined with the grey correlation analysis method to construct a new media art creation perceptual evaluation model, the new media art creation works as the object of color design practice, the constructed color design evaluation model well reached the product color scheme with the color screening, confirmation and evaluation of the preferred goals. The design practice based on the evaluation model of new media art creation. The results show that, combined with the gray correlation analysis, the color design evaluation model of new media art creation constructed under the intentional color system can effectively improve the color design efficiency of the work scheme, and give an intuitive and accurate reference standard for the selection of the color scheme of the work.

This paper discusses the application of virtual reality technology in enhancing college students’ self-efficacy and proposes an iterative optimization algorithm based on learning experience. By analyzing self-efficacy, the application of virtual reality technology machines in education, and combining relevant theories and empirical studies, the structural equation model of virtual reality technology influencing college students’ self-efficacy is constructed. The original structural equation model is optimized by using algorithms such as stochastic gradient descent method and stochastic average gradient, and the effectiveness of the algorithms is verified through experiments. This paper concludes that virtual reality technology can significantly improve college students’ self-efficacy, and the proposed iterative optimization algorithm can effectively improve the prediction accuracy and fit of the original structural equation model.

The existence of a large number of multi-source heterogeneous hosts and application service types in various zones of the power monitoring system leads to difficulties in extracting comprehensive host attack trace data and the problem of fine-grained deep threat detection. This study combines network attack traces extracted from multi-source logs and stores them in attack trace styles. An attack event description model based on key attributes and behavior sequences is constructed. Based on the vulnerability scoring system, an algorithm is designed to map a general attack graph into an absorbing Markov chain attack graph, which provides a computational basis for the analysis of network attacks by calculating the state transfer probability matrix of the attack graph. Finally, the performance of this paper’s method for multi-dimensional data feature extraction is explored in a python experimental simulation environment. The simulation results show that the average mapping time of LSTM model for 7 vulnerabilities is 117ms, while the average mapping time of this paper’s algorithm is improved by 37ms compared to the LSTM model.Meanwhile, the accuracy, stability, average false detection rate and positive and negative recall rate also achieve good results, which verifies the validity of this method in the practice of power monitoring system management.