Studying the influencing factors of logical reasoning ability can not only help teachers to find out the effective way to cultivate students’ logical reasoning ability, but also provide methodological and theoretical references for the relevant research in the area of artificial intelligence-driven program design education, which is of certain research value. The article firstly introduces the theory of structural equation modeling and the principle of algorithm used in model analysis. Then, taking the students of School S and School T as an example, we designed and distributed relevant collection questionnaires, and analyzed the data using SPSS to understand the overall status of students’ logical reasoning ability and the level of each dimension. Then we make reasonable assumptions about the factors affecting students’ logical reasoning ability, establish a structural equation model of the factors affecting logical reasoning ability, and analyze the effects and paths between the factors and on the logical reasoning ability. Finally, according to the experimental results, we propose targeted teaching reform methods. The results of the study show that: teacher’s activities, learning interest, learning attitude, classroom environment have a positive effect on students’ logical reasoning ability, in which the effect of classroom environment on logical reasoning ability is 0.48. Enhancing the teacher’s power and promoting the diversified development of students is an effective way to improve logical reasoning ability.

Personalized service is a targeted initiative for digital resource libraries to improve the quality of service and better play the function of culture and education. This paper proposes a digital book personalized recommendation algorithm based on artificial intelligence technology. After acquiring the borrowing data and pre-processing, the reader’s portrait is visualized with factor analysis and cluster analysis methods respectively. The traditional Slope one algorithm is weighted and the collaborative filtering algorithm is improved. Combine the user profile with collaborative filtering to realize the personalized recommendation of digital books. User similarity calculates four types of readers such as pragmatic, youthful, recreational and curious. This paper’s algorithm outperforms CFRA and RABC algorithms under each parameter, with the highest recommendation accuracy and novelty, and realizes personalized library services.

There is an increasing demand for assisted training techniques in the sport of sparring. In this paper, a sparring multiple recognition and analysis system is designed and fabricated for the movements of sparring sports and used to recognize and analyze the players’ technical movements using the collected data and the model built using deep neural networks. The CNN-LSTM network is applied to extract the feature classification of the preprocessed sparring inertia data, and then the DTW algorithm is combined with the spatial distance classification method to realize the matching and recognition of sparring behaviors by stretching and compressing transformations of the time axis, effectively eliminating the distortion error in the time domain and obtaining the similar path with the shortest cumulative distance of the effective matches between different sequences. Experiments on the application of this paper’s system were conducted in two groups of sparring players, and after 12 weeks of training intervention, the average confrontation striking speed of the experimental group progressed from 0.36 seconds before the experiment to 0.32 seconds after the experiment, and the average performance of the control group progressed from 0.38 seconds before the experiment to 0.36 seconds after the experiment, which indicates that although the traditional resistance training also has a positive impact on the training effect of sparring training, the training effect of this paper’s system is more obvious The systematic training effect of this paper is more obvious. This paper makes an innovative exploration for the combination of sports programs such as sparring and cutting-edge information technology.

The continuous development of neural network makes the automated style migration technology also rise to a new height. This paper selects digital media art as the research field, constructs Cycle GAN, a cyclic consistent generative adversarial network structure applied to digital media art, on the basic framework of GAN, and optimizes it by adding bilinear interpolation and attention mechanism, so as to build up a style migration model for digital media art. In the style migration simulation experiment, the IS test values of this paper’s model on the photo2vangogh and photo2monet datasets are 5.32 and 6.03, and the FID test values are 97.52 and 75.55, which are better than the other comparative models. Similarly, the optimized performance of FID, SSIM and PSNR values on the dataset is also better than other comparative models, and the style migration performance of the model is verified. Using the model of this paper to design a digital topography with Chinese traditional ink painting as the content, we explore the correlation between the design attributes of the style migration design works in digital media art and the audience’s cognitive evaluation and overall perception. Among the design attributes, “plot relevance” (4.375) and “atmosphere rendering” (3.38) have the highest T-value, which is the most important influence on audience perception.

The implementation of tax incentives is a powerful measure to reduce the burden of enterprises, build a new development pattern, and expand reform and opening-up. Some enterprises in nine provinces from 2010 to 2023 are sampled to verify the role of tax incentives in reducing the tax burden by using the double difference model. The weight coefficients are introduced as learning factors for the population center of mass, and the SWC-PSO algorithm is proposed to improve the shortcomings of PSO, which has low convergence accuracy and is prone to fall into local extremes, and to realize the mathematical planning for minimizing the tax burden of enterprises. After controlling the variables of tax policy and enterprise nature, the regression coefficient reflecting the enterprise tax burden is significantly negative at 1% level, and the tax burden of enterprises receiving tax incentives is significantly reduced, which proves the role of tax incentives in reducing the enterprise tax burden. After using SWC-PSO for planning, the sample units have a total of 1,779,919,000 yuan of tax relief, and the business tax rate of a construction project decreases from 3.35% to 0.42%, which indicates that the improved algorithm in this paper can plan the strategy of minimizing the tax burden of enterprises more efficiently.

With the promulgation of relevant policies, virtual power plant market transactions are facing major adjustments, in order to promote the smooth entry of virtual power plants into market-oriented transactions and improve the economic benefits of virtual power plants, this paper proposes a virtual power plant market transaction model. The traditional virtual power plant resources are mathematically modeled, blockchain technology is introduced to build a decentralized trading framework, and fuzzy neural networks are combined to predict the power load of the virtual power plant. Then the decision-making model of virtual power plant participation in spot market trading is constructed by using two-stage stochastic planning theory with the goal of maximizing expected return. The results show that the prediction effect of the fuzzy logic-based virtual power plant market trading model is 2.925% higher than that of the traditional BP algorithm model, and its accuracy and stability are significantly improved. In addition, the distributed energy storage aggregated by the virtual power plant as well as the dynamic demand response rate is fast, the regulation is flexible, the short-time power throughput capability is strong, and it can accurately track the FM instructions. The cumulative FM capacity and FM mileage provided by the virtual power plant account for 84% and 99% of the total FM capacity demand in the system, respectively, making it highly competitive in the FM market. And under the premise of balancing riskiness and profitability, the bidding scheme of virtual power plant derived in this paper is more effective.

The power optimization of wind farms and the optimal control of wind turbines require high-precision power ultra-short-term prediction for each wind turbine. In order to improve the performance of ultra-short-term prediction of wind power, this paper couples the LSTM model with the Logistic model and combines it with Graph Convolutional Neural Network (GCN) to construct the ultra-short-term prediction model of wind power based on Logistic-LSTM-GCN, and test and analyze the prediction performance of the model. Comparing the LASSO, XGboost, LSTM, GRU and TCN-LSTM models, the MAE and RMSE of this paper’s model are the lowest among all the models, which are 3.34% and 5.89%, respectively, and the R² is the highest, which is 79.76%. And the MAE and RMSE predicted by the model with inputs of four-dimensional spatio-temporal feature matrix are smaller than the model with inputs of one and two dimensions, and the R² value is larger than that of one and two-dimensional model. It indicates that the Logistic-LSTM-GCN model based on spatio-temporal information can extract the spatio-temporal information of wind farms more effectively, which improves the accuracy of wind cluster power prediction. In addition, with the increasing time step, the error indicators MAE, MAPE and RMSE are gradually increasing. Taking a time step of 4s for prediction, the prediction error of the model is minimized when considering multivariate variables such as wind speed, wind speed decomposition component, yaw error, wind direction, and rotor speed. This indicates that the multivariate LSTM, logistic and GCN coupled model can significantly improve the performance of ultrashort-term prediction of wind power.

Some athletes’ lack of basic knowledge of exercise mechanism, mode, method, process and intensity has led to frequent occurrence of athletic risk events such as injury, disease and even sudden death, which seriously affects the physical and mental health of athletes and even threatens their lives. In this study, the data of athletes’ injury and disease risk characteristics were collected, and the feature selection method of Least Absolute Value Convergence and Selection Operator (LASSO) combined with Boruta’s algorithm was used to preprocess the data in order to eliminate redundant features. In terms of model construction, the prediction results of support vector machine, logistic regression, random forest algorithm and deep forest algorithm were integrated by using Stacking algorithm to construct the prediction model of athletes’ injury risk. After the predictive performance of the model is examined, it is used as an intervention for injury rehabilitation to carry out comparative experiments. The results show that the fusion model can effectively extract the feature importance of injury risk factors and predict the risk probability, and the prediction effect is better than that of a single model. Meanwhile, the intervention results show that the model has excellent effects on injury rehabilitation. This study can accurately predict injuries and illnesses, prevent the occurrence of injury and illness risk events in athletes, ensure the successful realization of sports goals, and play a role in assisting injury and illness rehabilitation.

The field of artificial intelligence provides a new practical path for the inheritance and protection of non-heritage art. This paper proposes an innovative morphological design method for rattan weaving art based on fractal theory, and the grasshopper plug-in is selected to establish a parametric design model. The fractal graphics generated by the iterative function system are used as the input graphics, and the GrabCut algorithm and VGG16 neural network are combined to propose a graphic rendering method based on style migration containing elements of the cultural symbols of the Maonan Flower Bamboo Hat, and to realize the inheritance of the cultural symbols of the Maonan Flower Bamboo Hat. In the high preference survey, the A1 and A4 features of the sun hat in the questionnaire results are consistent with the preference results derived from the fractal design, and the questionnaire results of the handbag and handkerchief are also consistent with the preference results derived from the fractal design. It shows that the product form design method of Maonan flower bamboo hat cultural symbols based on fractal theory and style migration can play a certain role in promoting cultural inheritance.

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.

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.

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.

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.

Machine learning provides new perspectives and methods for company M&A valuation due to its powerful data processing and prediction capabilities. This paper analyzes the prediction steps based on the decision tree algorithm, i.e., decision tree generation, attribute selection, decision tree construction, and accuracy metrics, and obtains the relevant data of AB after merger and acquisition through data mining. The model and SHAP framework are utilized to predict the financial risk, financial performance, and enterprise value of the two post-merger companies. The precision, recall, and F1 scores of this paper’s model range from 91.25 to 93.81, which has a good performance of company M&A valuation. This paper’s model predicts that in 2024, the key indicator of AB’s financial crisis is Gross margin, which has an importance of 0.297, and the possibility of AB’s financial crisis increases when the value of Gross margin is between -0.0279 and -0.0014. The accuracy of the financial performance prediction of this paper’s model is more than 0.97, which can accurately value the company’s performance. The model in this paper predicts the enterprise value of AB in 2024 to be 52.14yuan/share, respectively.

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.

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.

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.

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.

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.

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

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 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.

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.

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.

This paper constructs a multi-agent simulation model to study and prevent juvenile delinquency. A multi-agent reinforcement learning model is constructed according to reinforcement learning theory to simulate the behavioral decision-making process of minors in different social environments. By introducing the NashQ algorithm, it simulates the minors’ strategic choices when facing the temptation of crime. In the simulation experiments, the NashQ algorithm meets the convergence requirements of the model, and only 1/3 of the training times are needed to achieve the stability of the simulated environment. Among them, family factors, school factors and social factors all affect the stability of the prevention effect. Good family environment, high quality teaching conditions and healthy social atmosphere can effectively prevent juvenile delinquency.

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 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.

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.

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.

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

The rapid development of natural language processing technology makes machine translation play an increasingly important role in cross-lingual information exchange. In this paper, we propose an English long text translation paradigm based on the self-attention mechanism and introduce various improvement strategies to enhance the model performance. The model’s ability to process English long text is improved by introducing multi-head attention and hierarchical self-attention modules. The long text translation paradigm is optimized by using techniques such as residual linkage, layer normalization and dynamic memory network. A series of experiments are conducted to verify the effectiveness of the improved model on the English long text translation task. The English long text translation paradigm constructed in this paper outperforms the Transformer model and other related variants on both CPU and GPU. And Transformer outperforms this paper’s model in terms of n-gram accuracy in real translation experiments. The BLEU scores of the improved model on News and other datasets are significantly improved compared with the original baseline model, which verifies the effectiveness of the improvement strategy of this paper and provides a reference for the solution of the problem of English long text translation.

The lateral quantitative control of paper machine is the key to the quality control of paper machine. In this paper, the paper machine transverse quantitative control system is introduced, for the transverse quantitative control and the model exists of strong coupling, large time lag, multi-dimensional number of characteristics, combined with the predictive control theory, put forward based on the dynamic matrix control and Gram polynomials of the intelligent prediction method. Meanwhile, the speed chain control device of the paper machine control system is designed based on the self-immunity controller and simulated and analyzed. The simulation results show that the slope of the model size and computation in this paper’s method is 1.97, which is smaller than that of the traditional MPC’s 2.85, and has more computational efficiency without affecting the predictive control effect, which is more suitable for online operation. At the same time, the speed chain control system applying the self-resistant control algorithm is better than the traditional PID control in terms of steady state performance, dynamic performance and anti-disturbance performance. The method proposed in this paper facilitates the predictive control and speed chain anti-disturbance of the lateral dosing control system of paper machine and promotes the improvement of paper quality.

In recent years, hyperspectral imaging technology has a large application prospect in quality inspection in the tobacco industry. The study is based on near infrared spectroscopy technology and partial least squares regression method to establish mathematical analysis model of tobacco adulteration ratio of four components, such as expanded tobacco, stalked tobacco, large threaded tobacco and small threaded tobacco, and carry out internal and external inspection. At the same time, TLBO algorithm is used in the optimization of ELM tobacco purity grade determination model to realize the design of tobacco purity monitoring method, and then build the real-time monitoring system of tobacco blending ratio and purity. Tobacco with different purity grades were selected for experimental testing and model comparison analysis. The results show that the constructed PLS model can accurately predict the adulteration content of the four components in tobacco, and the correlation coefficients between the predicted and actual values are above 0.95 (p < 0.01), and the relative deviation of the prediction is below 3%.The accuracy of the TLBO-ELM model for identifying tobacco with different grades of purity is 88%, and the classification accuracy in the validation set is improved by 9.32% compared with the ELM model, which is within the acceptable range. It shows a better classification effect than PLS-DA in an acceptable range, which proves that the proposed method can be used for discriminating and monitoring the purity of tobacco. The monitoring system in this paper can be used in the analysis of tobacco blending ratio and purity detection.

The combination of technology and art in film and television special effects can greatly enhance the visual impact of film and television animation, and improve its commercial and artistic value. The study elaborates on the generation of special effects in 3D modeling technology in film and television production, and based on the application of rigid body special effects, it proposes a highly efficient rigid body crushing mode for optimization in response to the problems such as low real-time performance in rigid body crushing simulation. The model is a particle-based real-time simulation method of object crushing under the impact of external forces, using the discrete unit method to represent the inter-particle force, and proposes an inverse crushing mechanism, which realizes the particle-based DEM simulation on the GPU. Experimental results show that the simulation method of rigid body crushing constructed in this paper can meet the simulation requirements in different scene scales, and the rendering rate in small-scale and large-scale scenes is 90~155FPS and 40~50FPS, respectively, which is not only realistic but also real-time, and can meet the requirements of film and television production.

As the world’s No. 1 sport with wide popularity and high degree of attention, there exists a great application demand and development potential for applying artificial intelligence to soccer sports training. In this paper, Yolov5s-CBAM target detection network is utilized to identify the human body posture of target athletes in soccer sports training, and HRNet network is used to detect the location information of key points of target human skeleton and identify the skill movements of soccer players. Subsequently, the TDS-Fast DTW algorithm is applied to evaluate the skill movements to establish a skill recognition and evaluation system for soccer sports athletes. It is verified that the soccer player skill movement recognition model proposed in this paper outperforms other comparative models, with the checking rate reaching 99.12%, and the evaluation scores of the model on the skill movements of the athletes are not different from those of the manual evaluation scores (P>0.05). It is also found that the application of the system in actual soccer training matches can fully meet the needs of soccer training. The system in this paper can accurately assess the technical movements of soccer sports athletes to meet the needs of scientific training, and at the same time, it can meet the needs of coaches to timely grasp the understanding of the level of technical movements of soccer athletes and improve the quality of training.

Taijiquan is a kind of sport that can be used as a national ϐitness program, and its effect on the training effect of adolescent physical coordination has important research value. In this paper, particle swarm optimization algorithm is applied to the optimization of taijiquan training program, and independent samples test and analysis of variance (ANOVA) are used to investigate the quantitative impact of taijiquan training on adolescents’ physical coordination. The results show that the particle swarm optimization algorithm can effectively improve the effect of taijiquan training, and the algorithm convergence and other properties have obvious superiority compared with other algorithms. At the same time, after the experiment, all the physical coordination test indexes of the experimental group were signiϐicantly improved compared with the pre-test and the control group, which explains the important role of taijiquan training in the physical coordination training of adolescents.

Based on the wide application of collaborative filtering algorithm in the current field of graduate employment, this paper introduces it into the employment recommendation mechanism of senior college students and takes it as one of the auxiliary means to formulate the employment policy for senior college students. By studying the implementation effect of employment policy, so as to explore the adaptability of employment policy. Through the time series prediction method based on neural network, the prediction model of employment policy adaptability of higher vocational tertiary students is constructed. Compare the prediction performance of this paper’s prediction model with other models, predict the employment policy implementation effect through this paper’s model, and finally, construct an evaluation system of employment policy prediction results to evaluate the model prediction results. The prediction fit of the model of this paper is 0.8644, and the average relative prediction error is 0.35%, which is the best performance among all prediction models. In the prediction of the employment of higher vocational college students in province A, the number of employment of higher vocational college graduates is positively correlated with the average annual income level and the market share of graduates, and negatively correlated with the total number of gaps between faculty and students in the institutions and the amount of education expenditure. The overall score of the employment policy implementation effect predicted by the prediction model in this paper is 88.8, which is a good evaluation result.

The study constructs a prediction model to predict the mental health status of innovative entrepreneurs. The real data of mental health assessment of innovative entrepreneurs in S province in 2023 is chosen as the data source. The recursive random forest feature elimination method is used to select the features of the mental health status prediction model. The pre-selection-elimination mechanism was used to construct the mental health state prediction model. The prediction models constructed by support vector machine algorithm, decision tree algorithm and random forest algorithm were trained and evaluated respectively. The AUC value and accuracy corresponding to the random forest algorithm are 0.9126 and 86.39%, respectively, which are better than the other two comparison models. Among the 17 mental health characteristic variables selected in this paper, emotional stress and self-acceptance degree have the greatest influence on the prediction model based on the random forest algorithm.

Social media as a new type of media has become an important channel for people to obtain information and communicate, which brings new opportunities and challenges for English education. In this study, the Markov chain model is improved by using the weighted ward system clustering method and the fuzzy set theory to improve the prediction performance of the model. Then the ARIMA model and the improved Markov chain model are combined to construct an improved time series prediction model to realize the prediction of the interaction efficiency of social media in English education. The performance of the improved prediction model is superior compared to other comparative models, providing reliability for the subsequent prediction results. The prediction results show that the interactive efficiency of social media interaction data in English education shows an upward trend over time, and the number of readings and playbacks of English courseware resources as well as video resources increases from 18477 and 18147 to 88629 and 84571 in six months. The predicted results of this study indicate that social media has good interactive efficiency in English education, which can be utilized in the future to expand the dimension of education, build an English education platform, expand the teaching space and extend educational thinking, and play a percolating role in English education.

Since the 21st century, the rapid development of artificial intelligence technology, artificial intelligence in many fields have achieved remarkable research results and applications, the integration of AI technology and music has also gradually become an emerging research field. In this paper, first of all, the generation principle of vocal interpretation AI model is studied, in order to realize the digital conversion of vocal interpretation this paper constructs a converter model so as to facilitate the application of artificial intelligence algorithm model. In this paper, in order to match the generated opera vocal music with the given opera performance background, the rhythmic relationship between opera and vocal interpretation is established, and the relationship between motion salience and note intensity is constructed. On this basis, the generator model is changed to a model with a loop structure, and the music theory is mathematically modeled to propose an adversarial network model based on improved multi-track sequence generation. Finally, for the prediction problem in the vocal interpretation AI model, this paper is optimized based on support vector regression. Through empirical analysis, the improved model in this paper has a smaller gap with the real dataset on the metrics of pitch use, pitch shift, note interval and polyphony rate within the track. Meanwhile, the TD distances of this paper’s improved model on the three datasets are 0.655, 0.784, and 0.685, respectively, which is the smallest in the experimental data, and the quality of the improved model’s vocal music generation is excellent. The pitch distribution of this paper’s improved model and the original vocal data basically match, indicating that this paper’s model has better effect on pitch adjustment. In addition, the improved model of this paper generates vocal music with better musicality effect, which has higher musicality while avoiding the generation of more invalid notes. The research work of the paper proves the feasibility of the AI model for opera vocal interpretation and provides a new solution for the current field of vocal music generation.

The application of technologies such as big data, mobile Internet, artificial intelligence and so on has triggered a major change in the field of education and promoted the classroom reform in colleges and universities. Taking deep learning theory as the research perspective, this paper constructs a college English teaching model based on deep learning, and applies the model to actual teaching practice, with a view to promoting students’ English learning level and enhancing their intercultural communication ability. Among them, the K-means algorithm improved by the whale optimization algorithm is also used to cluster and stratify the English proficiency of students in a class to illustrate the specific application of deep learning in English teaching. The results classified the sample students into four categories, A, B, C and D. The English level of students in category A is the highest and the largest, accounting for 35.56%, and teachers can design differentiated teaching based on the results of student stratification. After carrying out the experiment of the teaching model, the practicing students’ English scores improved by 4.01%, and at the same time, they gained 18.87%~28.45% and 18.82%~39.01% of competence in the personal domain and the communicative domain, respectively, which confirms the effect of the constructed English teaching model on the enhancement of the students’ English learning level and cross-cultural communicative competence.

Teaching curriculum design is centered around the three dimensions of affective attitudes and values, processes and methods, and knowledge and skills, which fit with the affective learning model composed of emotion, learning and cognition. This paper brings affective analysis into art curriculum design and proposes a learner affective model for teaching art courses driven by multiple teaching objectives. Through multi-objective optimization, we give an interactive decision-making method based on a hierarchical affective cognitive model to simulate learners’ affective decision-making under multi-objective-driven teaching. Analyze the teaching process of incorporating affective learning strategies in an art course, and examine the interrelationship between affective engagement and learners’ knowledge construction in three rounds of learning activities. To analyze the impact of affective learning strategies on students’ learning outcomes. The experimental group (affective learning strategy group) significantly outperformed the corresponding creativity abilities of students in the control group in the three components of surprise, originality and challenge after the teaching of the art course, and the affective learning strategy succeeded in stimulating students’ creativity. The combination of affective learning model and curriculum design can enhance the effectiveness of art education.

Based on the view that artistic style is mainly reflected in sculpture and painting, the sculpture and painting style of Giacometti is analyzed in depth. Starting from the scope of application of big data technology, the theoretical knowledge based on information theory is proposed to explore the differences in the styles of Giacometti and his contemporaries, and the basic concepts used in the processing are defined, including Shannon entropy, conditional entropy, and interactive information. Redundancy, orderliness, and complexity are set as eigenvalues that can characterize the style of art works, and the eigenvalues of the style of Giacometti and contemporaneous artists are analyzed. The minimum, maximum, and average values of the complexity of Picasso’s works are 207, 991, and 596, respectively, while the values of the three indexes of the complexity of Giacometti’s art works are 446, 990, and 718, respectively, and on the whole, the complexity of Picasso’s works is smaller than that of Giacometti’s works. This paper comprehensively reveals the stylistic differences between Giacometti and his contemporaries through the analysis of quantitative characteristic indexes.

In the context of big data, with the accelerated development of digital technology, enterprises are facing the pressure of digital transformation, and at the same time, big data computing system provides technical support for the digital transformation of enterprises. In this paper, we propose a data analysis system based on iterative computing for the digital transformation of enterprises. In order to avoid the resource consumption caused by unnecessary repeated calculations in iterative computing, this paper proposes optimization based on Spark fault-tolerant mechanism and constructs an enterprise data analysis system based on iterative computing model, which provides technical support for enterprise digital transformation. On this basis, this paper also provides optimization strategies in terms of organizational structure and cultural coordination for enterprise transformation, which provides an effective path for realizing comprehensive digital transformation of enterprises. Through the test of this paper’s iterative computing data analysis system, the speed of Spark optimization based on this paper is increased by nearly 2 times, which illustrates the usefulness of this paper’s optimization based on Sparl fault-tolerant mechanism. Meanwhile, the cache misses of the data analytics system are in the range of 46% to 60%, which provides better performance performance in terms of cache hits and time overhead. In this paper, we provide practical and feasible transformation paths for enterprise digital transformation from three aspects, including digital technology, enterprise organizational structure and culture, and promote the development of enterprise digital transformation.

Product awareness can be spread to a wider audience through advertisements, and the introduction of social media platforms has made it easier for marketers to spread brand advertisements and fully attract consumers to generate corresponding purchasing behaviors. Based on fuzzy set theory, the article establishes a fuzzy evidence theory through evidence-weighted fusion, and calculates the utility value of social media advertisements in order to achieve the optimal evaluation of social media advertisements. Then, it explores the influence of social media advertisements on consumers’ purchasing behavior with OLS-LR model, and combines the VAR model to study the dynamic correlation between different types of social media advertising channels and consumers’ purchasing behavior. Without considering the control variables, the regression coefficient of social media advertising on consumer purchasing behavior is 0.438, which is significant at 1% level. With the fourth-order VAR model, CICs social media advertisements have a significant short-term effect on consumer purchasing behavior, while FICs advertisements show a long-term effect. Based on the fuzzy evidence theory, the utility value of social media advertisements can be calculated, and based on the sorting of the calculation results, the construction of optimization paths of social media advertisements can be realized, which provides a new research basis for improving the efficiency of corporate advertising and marketing.

This paper establishes a solution model for resource scheduling optimization in university laboratories, and sets the corresponding constraints and objective functions. The genetic algorithm under the heuristic algorithm is used to solve the resource scheduling optimization problem. On this basis, the pyramid model is constructed, the population evolution and variant strategy are proposed respectively, the model genes are labeled with scheduling cost adaptation, and the genes are generated in series. The framework of scheduling algorithm is proposed, and the dynamic scheduler is constructed to realize the scheduling of university laboratory resources. Through simulation experiments and algorithm analysis, the effectiveness of the use of the model is verified. The experimental results show that when the number of simulation is 10 times, the fitness of the population is 20, 100 and 200 respectively. After the implementation of scheduling for college laboratory resources, the utilization rate of laboratory equipment is increased by 16.3%, 34.6% and 18.4% respectively.

The aim of this paper is to improve the advertisement display effect and realize accurate placement in the market. Firstly, the convolutional neural network is used to select the advertisement keywords, and optimize the click rate, conversion rate and so on when the number of iterations reaches a certain value. Next, the established hierarchical analysis model is used to conduct a comprehensive evaluation of online advertisement release forms, and select the advertisement form that best suits the needs of the enterprise and the market environment. The weight of the webpage and the similarity between the center of mass of the webpage and the advertisement are used to calculate the final score, and the advertisements are sorted to achieve the improvement of the display effect and placement accuracy of the advertisements. The final analysis found that for short-term user behavior, the weight of text link ad clusters is as high as 0.66, which can improve the accuracy of ad placement. For long-term user behavior, the multi-objective optimization algorithm can accurately identify and assign high weights when users continue to visit specific web pages, for example, the cluster of web banner ads reaches 0.64. Meanwhile, it can be adapted to different application scenarios, and the weight of text link ads cluster is significantly increased from 0.14 to 0.758 when the freshness factor is increased from 0 to 1. The optimal F1 value of the advertisement delivery effect is 97.24, which is the highest F1 value of AIGC. The AIGC ad placement strategy provides a new method for the intelligent development of the advertising industry.

In order to study the role of digital economy on the transformation of regional economic structure, firstly, the mechanism of the role of datatized economy on the change of regional economic form is elaborated, and on the basis of the analysis of theoretical model, the structure of the distribution of capital factors in each industry and the ideal factor are determined. Determine the index system and weights of regional economic structure transformation through the selection of weight indicators, and complete the measurement of the data-based economic situation under the construction of the index system of data-based economic situation. Two hypotheses are proposed that digital industrialization can have an ideal effect on the structural transformation of local development but the shape of the effect is inverted U-shape, and that industrial digitalization can have an ideal effect on the structural transformation of local development. The empirical analysis finds that the Moran’s I index of structural transformation of local development from 2008 to 2020 is prominent in the 1% case, and the FP and UE within, central, eastern, western regions of China and the level of structural transformation of local economy is prominent in the 1% case. It is concluded that there is a prominent spatial isotropic relationship between the datadriven economy on regional economic structural change resilience in the whole region, and the constructed research model has a good robustness.

With the development of Internet of Things (IoT) technology, improving the interactivity of IoT communication teaching has become an important research content. This paper firstly constructs the IOT communication teaching system on the basis of service layer, network layer and teaching layer, through which the teaching information is ensured to be delivered timely and accurately. Secondly, the group intelligence algorithm teaching interactivity is optimized and designed to optimize the teaching environment, network, and teaching layer to get the optimized server resource allocation scheme to achieve the optimization of different levels in the teaching of Internet of Things communication. When the number of iterations reaches 20 and 45, the adaptability of this paper’s algorithm is maintained between 100-10-1, and the optimization of the algorithm improves the student participation, the depth of understanding of knowledge, the accuracy of data, the speed of transmission, the efficiency of management, and the teaching effect by 28.6%, 41.7%, 4%, 100%, 18.8%, and 20%, respectively. In the delay analysis, when the number of terminals is 10, 20, and 30 respectively, the delay of the teaching system in this paper is the lowest among all the compared systems, which is 10ms, 40ms, and 230ms respectively.This study can lay the foundation for improving the quality and effect of IoT communication teaching and promote the cultivation of teaching interactivity between teachers and students.

In order to satisfy consumers’ needs, enterprises must conduct in-depth research on consumers’ purchasing behaviours and design and develop marketing strategies based on the characteristics of consumers’ needs. The article takes 4P marketing theory and SOR model as the guide, and establishes a consumer purchase intention model in combination with the consumer behaviour model. The questionnaire is designed from the product value, price range, channel optimisation, and promotional efforts of the enterprise marketing strategy, and the validity of the questionnaire is tested by principal component analysis. Then meta-analysis method was used to explore the correlation of each variable, and the SEM model was combined to explore the influence path of corporate marketing strategy on consumer purchase intention. The Q-value of the hypothesised relationship of consumer purchase intention ranges from 446.137 to 814.535 and is significant at 1% level, and the correlation coefficients of each variable in the model with consumer purchase intention are more than 0.35. The indicators of model fit, CMIN/DF and RMSEA, are 1.076 and 0.015 respectively, and the path coefficient of the value of the product in the marketing strategy on the purchase intention is the largest at 0.076. The path coefficient of product value on consumers’ purchase intention in marketing strategy is 0.369, and the development of enterprise marketing strategy needs to actively expand marketing channels and design differentiated product and service programmes, so as to enhance consumers’ recognition of the enterprise brand to stimulate their purchase intention.

With the proposal of sustainable development of energy, countries begin to develop from fuel vehicles to new energy vehicle market. Firstly, we construct a consumer purchase behavior recognition model based on XG Boost algorithm, simulate the gradient enhancement process of purchase behavior recognition, obtain the approximation value based on function calculation to become the learning target of the overflow value, and at the same time, give higher learning weight to the samples with unsatisfactory accuracy in the last round, and after continuous iteration, gradually correct the purchase behavior recognition bias. According to the number of purchase behavior features identified correctly, the number of features that do not have purchase behavior features, and the number of features that are not identified, invalid users are eliminated to improve the accuracy of the algorithm. The Cronbach’s alpha coefficients of the four factors are found to be 0.891, 0.895, 0.813, and 0.800, all of which are greater than or equal to 0.800, indicating that the factors are internally consistent. And the relationship values between the factors and purchase intention are 0.439, 0.406, 0.430, 0.387, which are all greater than 0. Therefore, there is a prominent relationship between all four dimensions of consumer purchase behavior factors and consumption impulse, and the identification of purchase behavior patterns has a guiding role in electric energy vehicle marketing strategy.

In order to be able to accurately identify user behavior and emotional tendency, this paper firstly adopts the neural network structure to build the emotion analysis model, and divides the model into four parts to analyze the text and emotion in social media, and obtains the information of semantics and emotion-related content in social media text. Secondly, from the semantic and emotional symbol content of the text in social media, the public emotional tendency model is built, and the sharing content and behavior of a large number of users in social media are analyzed. Finally, the association rule mining algorithm is used to extract the text and emotional symbols in social media, to improve the accuracy of the user’s emotional tendency analysis model, and to be able to accurately derive the user’s behavioral habits. In order to verify the analytical effect of the model, the model was tested, and the training speed of the BLSTM model was fast, and the training time was 1.5 hours in the first iteration of the test with a data set of 1 million. The model is more accurate in analyzing the user’s positive emotions, with accuracy and precision around 85% and 90% respectively, and the results obtained are more accurate, meet the user’s needs, and enhance the user’s experience.

The ontological issues such as the concept, features, and attributes of smart contracts written in code and running on the blockchain have been the focus of research in the academic community. In this paper, we first construct a smart contract illegal behavior determination model based on the C4.5 decision tree algorithm, which realizes accurate prediction and determination of illegal behaviors existing in smart contract transactions by extracting multiple attribute features of smart contract transaction data. Then, the correlation between smart contract features and contract risk is analyzed by Pearson coefficient, and the risk assessment evaluation system of smart contract performance is constructed by using hierarchical analysis. Finally, the fulfillment path of smart contract is proposed by synthesizing all the analysis results. Among the 24 randomly selected samples, the total prediction probability of the illegal behavior determination model based on the C4.5 decision tree algorithm reaches 95.83%, which is able to effectively identify the illegal behavior of smart contracts. The Pearson chi-square value between smart contract features and contract risk is 224.6317, and the Sig.(two-tailed) value is 0.000, indicating that there is a significant correlation between the two. By constructing a smart contract risk assessment index system, this paper designs a dynamic monitoring model of smart contract fulfillment risk level, and proposes a smart contract fulfillment path from the aspects of reasonable allocation of legal responsibility and legal regulation of contract fulfillment.

In order to be able to study in-depth image recognition technology for the detection of emergencies, this paper firstly adopts the image processing technology image processing, removes the noise in the image, improves the clarity of the image, and reduces the distortion of the image. Secondly, the signal in the image is extracted, and the network transmission algorithm is used to detect the signal in the image and calculate the corresponding transmission energy value. Finally, a standard threshold is set according to the calculation results, and once the transmission energy exceeds the threshold, it is an abnormal event. The analysis of the emergency event detection model based on image recognition technology shows that the image contrast effect is good, around 8.5 points, indicating that the image quality obtained based on image recognition technology is good. For the third emergency detection, the value based on image recognition technology is 93.3%, the detection results are more accurate, the response speed is faster, the fastest can reach about 1.1s, can real-time feedback on the results of the detection of the emergency situation in a timely manner to deal with the emergency situation, to reduce the loss of personnel, and to improve the efficiency of the management of the smart community emergencies of public health events.

The integration of modern information technology and civil litigation promotes the electronic civil litigation, online litigation as a kind of litigation behavior, promoting the development of traditional trial mode. This paper starts from analyzing the relationship and conflict between civil e-litigation and traditional civil litigation, and organizes the relationship between online trial mode and traditional court trial, and the relationship between civil online trial mode and traditional trial mode respectively. Based on the influencing factors of civil trial, the time proof consensus algorithm and data security transmission algorithm are respectively proposed to combine the network nature of online litigation and blockchain storage data information to optimize the online litigation electronic evidence storage. Summarizing the litigation efficiency of online trial and traditional trial under different control variables, from the point of view of the complexity of the case, the litigation efficiency of online trial mode for more complex cases is significantly higher than that of traditional trial mode. For non-complex cases, the efficiency increases but the difference is not significant. The online litigation mode is a part of the civil online trial mode and serves the traditional civil trial mode.

In order to accurately assess the financial status of a company and identify potential anomalies, this paper first implements unsupervised classification of financial transaction data based on Support Vector Machines, which automatically classifies the data into normal and abnormal categories. Histograms are introduced in combination with LightGBM to quickly fuse data from multiple sources. The most suitable first layer is selected by different algorithms, and the outputs of these algorithms are combined with industry-wide common abnormal features as inputs for LightGBM’s second layer identification. With this two-layer structure, the model not only takes into account the industry characteristics, but also the common anomaly features. Empirical results show that in the accuracy of smart financial statement generation, the sensitivity of this paper’s model iterates to 99.99% at 41.25% specificity, and the accuracy of this paper’s model is as high as 0.98 when dealing with financial private information, macroeconomic, and market information.In the identification of financial transaction anomalies, the number of anomalous weeks is identified to be 24, 29, 34, and 36, and the fusion of multi-source data effectively identifies the large amount of financial transactions, fluctuating transactions and other suspicious abnormal transactions.

Tunnel gas and shallow natural gas overflow have been a major problem plaguing the safe construction of tunnels and one of the main types of common diseases in tunnel engineering. The article chooses the tunnel construction of Funci Highway as the research object, and collects the rock and gas data in the research area on the basis of analyzing the distribution characteristics of shallow natural gas. Based on the AVO analysis technique, the PP wave reflection coefficient is approximated as a linear combination of longitudinal wave velocity, transverse wave velocity, density and other elastic constants to construct a pre-stack AVO inversion model to analyze the shallow natural gas distribution in the Funci Highway Tunnel construction. The porosity of the rock layer in the tunnel construction area ranges from 4.5% to 12%, with an average porosity of 8.93% and a maximum permeability of 0.004 μm². The longitudinal wave impedance distribution of the non-reservoir surrounding rock ranges from 1.48 to 2.01, and the error between the longitudinal wave velocity and density obtained by the inversion and the original logging curves is up to only 2.04%. Combined with the logging data, it can realize the comprehensive evaluation of the oil and gas geological environment of Funci Highway tunnel construction, and provide data support for ensuring the safety of Funci Highway tunnel construction.

This paper examines the differences and convergence of regional real estate markets based on panel statistics of 28 provinces, autonomous regions and municipalities directly under the central government in China from 2010 to 2023. Relevant variables such as urban construction land area, population and economic growth are set and the data are processed. The data show that the degree of industrial convergence and circulation costs have a positive spatial correlation and an upward trend from 2015 to 2021. From the viewpoint of regional real estate market divergence, the proportion of the real estate industry in GDP has remained above 5% since 2015, and this proportion is larger in the eastern region, for example, it was 8.74% in Beijing in 2015, but it has slightly decreased in some provinces and cities. The proportion in central and western provinces and cities has been rising faster year by year. The extreme deviation and standard deviation coefficient of the eastern region are relatively large, with the extreme deviation of the eastern region being 4.35% and the standard deviation coefficient being 1.45529 in 2021, indicating that the internal development is not balanced. From the analysis of convergence, the rate of convergence in the absolute convergence test is 3.66%, and the rate of convergence in the conditional convergence test is 2.89%, with a half-life of about 23.8 years. It indicates that the regional real estate market differences are shrinking, showing a trend of convergence, but the convergence process is relatively slow, which provides an important basis for an in-depth understanding of the characteristics of the regional real estate market.

With the increasing complexity of the financial market, corporate financial fraud events occur frequently, posing a serious challenge to investors and market regulators. Aiming at the limitations of traditional financial fraud recognition methods, this paper constructs a financial fraud recognition model MCN based on the topological data analysis method. The model consists of two parts: the Mapper algorithm and one-dimensional convolutional neural network (1DCNN), which combines the global topology extracted by the Mapper algorithm with the local features of the IDCNN to realize the effective identification of financial fraud samples. In order to evaluate the recognition performance of the model, this paper controls the topological feature extraction method unchanged and the classifier unchanged respectively, and compares the performance of the MCN model with other financial fraud recognition models. The results show that the Acc and F1-score of the MCN-based financial fraud recognition model in this paper are 98.69% and 97.64%, respectively, which are better than other models in both perspectives, proving the superiority of the financial fraud recognition model based on topological data analysis constructed in this paper, and thus providing powerful technical support for the regulation of the financial market and the risk management of enterprises.

This paper applies smart technologies to urban rain garden design and analyzes the hydrological effects based on urban smart rain garden technologies. The SWMM model is used to simulate runoff water quantity and quality under the environment of long-term and continuous rainfall events in urban areas. Building area A is selected as the case study object of this paper, and its geographic location and precipitation data are analyzed to preliminarily explain the hydrological conditions of the case study area. Based on the SWMM model, the model pipe network generalization and other operations are carried out to establish the SWMM model of the study area. The SWMM model is calibrated in terms of the calibration of the model’s parameters and the feasibility of the structured network SWMM model to verify the validity of the SWMM model of the study area and its catchment delineation method. Based on the urban smart rain garden technology, the LID module is added to the SWMM model of the study area and the hydrological effects under different scenarios such as combined LID are analyzed. Each LID measure can have a certain reduction effect on the combined runoff coefficient and total runoff. The combined LID measures in this paper have the best reduction effect, with the reduction rate of the integrated runoff coefficient over 35% and the total runoff over 50% in the 2h rainfall event. The combined LID scheme has the best reduction effect on the flood peak, and the reduction rate can reach more than 40% in both 2h rainfall events.

In this paper, the image parameters are preprocessed by the gray scale histogram statistical image parameters, which reflect the gray scale distribution information of the plant images, using the zero-mean normalization formula. According to different lighting conditions, the plant image is segmented, and the texture feature information in the plant image is extracted by using the improved grayscale covariance matrix. The hyperspectral linear mixing model is constructed, and the MVSA algorithm meta-decomposes the mixing model to solve the solution optimization problem. Using the natural gravity embossing method, produce plant embossed flowers and analyze the features and spectral curves of different parts of the embossed flowers to evaluate the comprehensive use of the embossing method proposed in this paper. The ROI images of 1200 embossed pattern petals were calculated to obtain the sample spectral matrix of embossed petals, in which the reflectance of the central petal was the highest among the three parts at a wavelength of 450 nm, with a reflectance of 0.46487, and then decreased, and then gradually increased to one place after the wavelength was equal to 694, with a reflectance of 0.8. The reflectance of the Shaanxi Weixiang (Weixia), the single side-embossed Yuanbaosi (Yuanbao maple), the hammered elm (fruits), and the pachypodium (Green) obtained a full score of 35 in the comprehensive evaluation after drying, which is a perfect embossed plant material, and all the plant materials embossed using the method proposed in this paper averaged above 30, and the comprehensive effect of plant embossing was good.

Since the financial crisis, the economies of all countries have been affected by the recession triggered by global events, and the uncertainty brought by the changes in economic policies has also become a risky shock, and the uncertainty of economic policies has been climbing worldwide. This paper firstly briefly analyzes the mechanism of economic policy and financial market, in order to comprehensively study the changes of market economic liquidity, this paper starts from the return of the market economy, and adopts the symbolic time series analysis method to analyze the prediction of the financial market by taking the stock market as an example. Then construct the regression model, and then study the impact of economic policy uncertainty on market liquidity. The regression coefficient of economic policy uncertainty is 0.064, which is significant at 1% level. Secondly, when GDP growth rate and inflation level are added as control variables, the regression coefficient of economic policy uncertainty obtained is 0.108, which is still significant at 1% level, implying that a rise in economic policy uncertainty brings about a decline in market liquidity. This study provides an effective analytical tool for the impact of economic policies on market liquidity. It also provides a basis for the government to improve market liquidity and enhance market vitality.

The study adopts a detection followed by tracking paradigm. In the detection stage, the BiFormer dynamic sparse attention module is embedded in the YOLOv8 network model, while the original nearest neighbor interpolation upsampling is improved by replacing it with the lightweight upsampling operator CARAFE. In the target tracking stage, a multi-vehicle steering trajectory tracking algorithm based on particle filtering is proposed, and the particle filtering algorithm is improved by combining the target motion direction weighted resampling algorithm. The two improved algorithms are combined for multi-vehicle detection and tracking in tunnel scenarios, and the average tracking accuracy can reach 97.3%. Compared with the traditional YOLOv8 combined with particle filtering algorithm for tracking, the method in this paper is more advantageous.

Curriculum Civics refers to the integration of Civics elements into the teaching of professional courses, so that courses other than Civics courses can also play the role of Civics teaching. In this paper, we study a knowledge mapping-based content generation technology for teaching course Civics and Politics, so that the knowledge of Civics and Politics courses can be integrated and visualized. The knowledge points, concepts, definitions and other information of the course Civics and Politics are extracted in the form of Civics and Politics knowledge triples. Through the extraction of the knowledge entity of curriculum Civics and politics, the relationship between semi-structured data and unstructured data is extracted to realize the integration of knowledge and content generation. After achieving content generation, the generated content is personalized through a deep reinforcement learning recommendation algorithm based on diversity optimization. Taking the two courses of Engineering Cost Management and Engineering Economics in the engineering management specialty as an example, it is found that the proposed knowledge graph construction method has an accuracy rate of 96.2%, which is able to effectively establish the knowledge association between the civic elements and the elements of professional knowledge, and realize the mining and generation of the civic elements. Meanwhile, the DDRL-Base recommendation algorithm achieves the optimum in accuracy, recall and F1 value indexes, and optimizes the problems such as cold start and sparse data in resource matrix, which improves the effect of recommending the Civics and Politics teaching content of the course.

The technical analysis of conventional tennis sports basically focuses on individual studies, with less research on the basic theory of tennis, and the theoretical analysis of tennis trajectory is even rarer. In this study, based on the calculation equations of the main forces during tennis movement, the dynamics analysis of tennis serve movement is carried out, and the three-dimensional trajectory equations of tennis serve are established. Then, based on the ODE dynamics engine technology, the simulation platform of tennis serve is built to realize the simulation and visualization analysis of tennis trajectory. Since the simulation system beat frequency is 1000Hz, the time difference between tennis simulation and actual movement is the smallest, so the frequency of 1000Hz is chosen for the simulation study of tennis serve trajectory. The simulation results show that under the same hitting height and ball angle, the larger the initial velocity of the tennis ball is, the farther the X-axis landing point is from the center line. In addition, under the consideration of air resistance and Malnus force, the difference between the Y-axis landing point of tennis ball when the initial serve angle is 30° and 60° is 1.81098 m. The present study provides a certain reference for the in-depth study of the serving strategy of tennis ball, and at the same time, it also provides a certain theoretical basis for the improvement of the tennis players’ training method and technical playing style.

Flipped classroom teaching puts forward new requirements on the enthusiasm of students’ independent learning, however, the traditional independent learning lacks scientific aids and cannot meet the individual needs of students in the process of self-study. Therefore, this paper exploits the neural network technology in intelligent computing technology to extract the deep implicit semantic representation, combines the implicit semantic indexing (LSI) to improve the traditional collaborative filtering algorithm, and explores an optimized implementation path of the flipped classroom teaching mode. The improved ICF algorithm outperforms the comparison algorithm in terms of recommendation accuracy, average recall, and average coverage in the three datasets. The computational time consumed is reduced by 44.85%, 57.34%, and 73.68%, respectively, compared with UCF. Incorporating the learning resource recommendation model constructed in this paper in a traditional flipped classroom, it is found that the post-test scores of the experimental class in Moral Education are significantly higher than those of the control class (p<0.01), and its post-test scores are significantly higher than its pre-test scores (p<0.01). The collaborative filtering algorithm optimized by intelligent computing technology facilitates students' personalized independent learning, innovates the general flipped classroom teaching mode, and receives the expected results.

The study applies machine vision technology to the production and operation process of energy enterprises, and constructs a fire detection model based on improved YOLOv4 from the real-time monitoring of fire emergency safety scenarios. Based on the original YOLOv4 algorithm, the model lightens the feature extraction and feature fusion networks, and introduces CA attention mechanism in the bottom layer of the feature extraction network to improve the accuracy of target detection. An intelligent fire alarm system is built on this basis as a response method for emergency security scenarios. Comparison with the basic YOLOv4 algorithm reveals that the improved YOLOv4 algorithm reduces the parameter amount by 45.97%, improves the FPS by 27.75, and improves the mAP value by 14.10%, which achieves a better detection accuracy on the basis of greatly reducing the amount of computation and parameter count, and also achieves a better Loss value and mAP in the comparison with other detection methods. Intelligent Fire Alarm The system integrates intelligent detection, intelligent alarm, intelligent alarm receiving and intelligent alarm dispatching, and can complete the fire alarm process within 6s. In summary, it shows that the method proposed in this paper can be used in real-time monitoring of emergency security scenarios and can provide timely warning at the early stage of security hazards.

In recent years, study travel has become a popular way to expand teaching outside the classroom. Based on the trajectory of the development of study travel, the article conducts an in-depth study of the current development of study travel in the context of the new era, and explores the 4.0 model of regional study travel development. Introducing big data and new technologies into study travel and designing a digital platform for study travel. Construct the evaluation index system of study travel, and evaluate the study travel 4.0 mode through questionnaires. Detect the study effect of the study travel 4.0 mode by comparing the impact of the study travel 4.0 mode and the traditional study travel mode on students’ disciplinary literacy. The comprehensive score of the evaluation of the study trip was 4.17, and the study trip 4.0 mode achieved excellent evaluation results. The experimental group and the control group did not show significant differences before the experiment, and significant differences were produced after the experiment. The experimental group’s scores on each dimension of geographic literacy increased by 6.35, 5.56, 7.57, 5.01, 7.89, 5.75, and 38.13 points after the experiment, showing significant differences (p<0.05), while none of the control group's scores increased by more than 1.5 points, with p-values of greater than 0.05. The research and study trip 4.0 model has a significant positive effect on improving students' disciplinary literacy. At the same time, under the background of regional study tours, the cultural innovation strategy is put forward.

The rapid development of the economy in recent years has brought convenience to enterprises, but also made the competition between enterprises more intense, enterprises want to stand firm in the fierce competition not only to improve financial performance, but also from a multi-dimensional integrated perspective. For this reason, this paper launched a multidimensional financial comprehensive evaluation research for enterprises. Based on the Harvard analytical framework, the study firstly emphasizes the financial performance of enterprises and at the same time combines the social responsibility perspective to screen the indicators. Then the quantitative evaluation method of this paper is proposed, i.e. the entropy weight method and gray correlation method are combined to analyze the development status of multidimensional financial performance from an objective point of view. Then the entropy weight method and gray correlation method model are introduced respectively, and the modeling method of combining the two applied in this paper is explained. Finally, by analyzing and evaluating the results of the sample company M, it can be obtained that (1) the results of the correlation degree of company M from 2017 to 2022 are 0.722, 0.473, 0.398, 0.389, 0.426, and 0.496 respectively, and the results of the multidimensional financial synthesis evaluation of company M during these six years are optimal in 2017. (2) The overall performance of the financial capital status of Company M from 2017 to 2022 is gradually deteriorating. (3) Overall, the performance of Company M’s responsibility to its employees is evolving from 2017 to 2022. (4) The company’s performance of responsibility to consumers and government during the six years from 2017 to 2022 is good, but ecological responsibility is at a medium level and has some room for development. This paper provides a multidimensional and comprehensive evaluation of the financial indicators of the company from a scientific point of view, which provides some reference for investors and business managers.

The article is based on Cite Space software for bibliometric analysis of the impact of artificial intelligence on economic development. Literature information comes from CNKI Knowledge Network database, identifying the hotspots and characteristics of the research related to artificial intelligence and economic development from the perspective of the number of articles issued, core authors, keywords, etc., and comprehensively analyzing 3,340 pieces of literature during the period from 2013 to 2023. The study shows that the number of published articles on the research on the impact of artificial intelligence on economic development increases year by year, and by 2021, the number of published articles is more than 600. Most authors publish related articles in the range of 3-7 articles, and there are fewer collaborations between authors. There are 16 keywords that appear more than 30 times in the field of the impact of AI on the economy between 2013~2023, which is statistically accounted for the total of 15.41%. The keyword clustering is divided into 7 cluster classes, and the clustering module Q=0.781, S=0.877, which has a high feasibility degree. The keyword with the highest intensity of emergence (3.91) in the field of research on the impact of artificial intelligence on economic development after 2018 is “research and development applications”.

Rapid and accurate assessment of power network loss in the power system has become a key research topic for the vast and diverse dataset of power grid operation. This study integrates data mining techniques with typical scenario modeling concepts and innovatively designs a distribution area power network loss rate multi population parallel spectral clustering evaluation strategy that incorporates distribution characteristics. Firstly, clustering attributes are determined for power network loss evaluation, and a power network loss evaluation framework based on clustering algorithms is proposed. Based on power flow calculation, the distribution characteristics and indicator system of each node’s output are analyzed; Secondly, in order to improve the clustering accuracy of power network loss evaluation, spectral clustering algorithm is introduced, and automatic algorithm design is carried out to address the issue of manually setting the initial number of clusters and cluster centers. Then, multi cluster partitioning and parallel computing methods are used to significantly improve the computational efficiency of spectral clustering algorithm; Finally, to verify the practicality of this method, a provincial power grid was selected as a case study. The results showed that this method not only has high accuracy in evaluating power network loss, but also has excellent computational efficiency, demonstrating good feasibility in practical engineering applications.

Transportation electromechanical engineering has an important role in the process of transportation engineering construction. After studying the basic situation and problems of the current transportation electromechanical engineering, the author selects the Q city subway line 1 as a test of the effect of quality control of its transportation electromechanical engineering construction, so as to assess its performance. Ten monitoring points are randomly selected, and four groups of control quantities between the monitoring points are detected, and their qualification rate is judged by the gap between the detection results and the standard value. Then, optimize the transportation electromechanical engineering by using big data and emerging detection technology, etc. Construct the performance evaluation index system of traffic electromechanical engineering to evaluate the performance of the optimized traffic electromechanical engineering scheme. The pass rate of the preoptimized traffic electromechanical engineering in the four groups of control volume testing is 100%, 22%, 80% and 70%, respectively, and the construction performance is poor. The scores of the equipment layer indicators of the optimized traffic engineering scheme were all above 80 points, and 19 indicators scored more than 90 points. The subsystem index scores are between 87.1 and 96.2 points, and the comprehensive score of traffic E&E engineering performance is 92.2 points, which shows that the optimized traffic E&E engineering has achieved more excellent performance evaluation results.

In the face of the requirements of the financial management system, enterprises need to accelerate the digital transformation of finance and realize the “data-driven” management decision-making operation mechanism. The article constructs a new quality productivity-based finance-driven enterprise digital transformation path, and makes it clear that enterprises need to play a new type of labor objects, labor materials and laborers to achieve digital transformation. Based on this transformation framework, a system dynamics approach is used to construct an enterprise financial dynamic planning system, which consists of five parts: a financial analysis subsystem, a target gap adjustment subsystem, an income statement subsystem, a balance sheet subsystem, and a production and operation subsystem, and analyzes the driving factors that affect value growth. The feasibility of the model is determined through the methods of structure test and sensitivity test on the dynamic financial planning model. Taking Group A as a case study object, the financial data for the five-year period from 2019-2023 are analyzed, and the operation of the enterprise is reflected through the financial indicators of each system, which proves the validity of the model and promotes the realization of the digital transformation of the enterprise, which contributes to the management of enterprise value.

Encouraged by the strategy of rural revitalization, rural areas in many places are exploring the development path of characteristic industries. The article embeds the multi-objective optimization model into the development of rural characteristic industries to optimize the current rural industrial development path. The multi-objective optimization model of rural characteristic industry development is constructed, and the ACO-PSO algorithm is used to solve the model, in order to realize the organic unity of economic, social and ecological benefits of rural industry development. The multi objective optimization model is used to optimize the industrial development of village S. The total regional output value of village S in 2035 is 2.08 times of that in 2025. The proportion of output value of primary industry and secondary industry decreases by 20.29% and 18.50% respectively. The proportion of tertiary industry output value increases by 38.79%, and the industrial structure becomes more and more reasonable. After the multi-objective optimization, Village S changes the development mode at the expense of resources and the environment, and maintains the survival of the ecological environment by appropriately slowing down the economic development. After the multi objective optimization, the total output value of the primary industry and the per capita income of farmers in Village S increased by 17,412 and 205.76 yuan respectively. The total output of tourism in the tertiary industry is 465,222,000 yuan, which is 126% higher than that before optimization.

As the most popular mode of out-of-school education in recent years, study tour plays an important role in the comprehensive ability improvement and overall development of students. Based on the path planning problem of study tour, this paper proposes a travel route optimization model with time optimization as the goal orientation, aiming to plan the time-optimal path for students in the study tour process. The particle swarm algorithm is used to improve the genetic algorithm for solving the travel route optimization model. The effectiveness of the optimization model and the hybrid algorithm is verified through the analysis of an actual case of a study tour, and the experimental results are substantially optimized compared with the traditional planning path, reducing the time spent by 2.2 days. Then we use qualitative comparative analysis method to explore the efficiency improvement of the curriculum of study tours, and obtain four grouping paths, which can cover more than 85% of the cases. The research in this paper not only helps to enrich the academic research of cross disciplines in the form of “travel + education”, but also provides theoretical basis and practical reference for the development of study tours to a certain extent.

Particle swarm algorithm, as a kind of population intelligent optimization algorithm, shows great potential in solving multivariate and nonlinear optimization problems due to its simple and efficient characteristics. The article constructs a concrete ratio optimization model in construction engineering technology, which is supported by particle swarm algorithm as the main technology. The model also integrates the least squares support vector regression algorithm, which makes it not only simple ratio optimization, but also has the function of concrete performance prediction. The relative error of the model in predicting the physical properties of concrete is small, less than 5%, which improves the reliability of concrete proportioning. The concrete samples generated by the model with five different ratios have better physical properties for daily needs. In the durability test, the concrete sample with proportion 4 showed the best performance in terms of mass loss rate and impermeability, which were 3.52% (after 400 cycles) and 156.44C (after 56d), respectively. And all the concrete samples used were in the range of proportional qualification and the cost was 5.99% to 28.61% lower than the comparison method.

Blockchain theory and its key technologies are developing rapidly, and the industrial internet combined with blockchain technology is driving the realization of safe and reliable comprehensive connectivity in multiple fields. In this paper, we propose a resource optimization allocation method for industrial internet that integrates edge computing and blockchain to reduce the task computing energy consumption and computational overhead of the system while improving the efficiency of the consensus process. This optimization problem is constructed as a Markov decision process, and a deep reinforcement learning algorithm is used to solve the optimal resource scheduling strategy under a single edge node. The effectiveness of the proposed resource optimization allocation method for industrial internet fusing edge computing and blockchain is verified by simulation validation. The method is able to obtain better and smoother convergence under the premise of harvesting high total rewards, effectively reduces the computational energy consumption and computational overhead of the device, and at the same time effectively improves the consensus efficiency of the blockchain.

This paper firstly constructs a coupled evaluation index system based on three primary indicators, seven secondary indicators and 29 tertiary indicators for agriculture, culture and tourism. Then the entropy weight-TOPSIS method and the coupling coordination degree model are selected to measure the development of agriculture, culture and tourism industry and the coupling level in Yijun County respectively. Finally, 23 spatially related villages in Yijun county area are selected to reveal the reasons for their spatial differences with spatial measurement model, and analyze the factors affecting the coupled and coordinated development of agricultural, cultural and tourism industries in Yijun county area. From the comprehensive evaluation results, the trend of the development level of agriculture, culture and tourism in 2017-2023 was generally upward, in which the agriculture industry had the highest growth between 2022 and 2023, with an increase of 0.0445. After analyzing the factors influencing the development of the coupled agriculture, culture and tourism industries in the Yijun county region by applying the spatial Durbin model, it was found that the general budget expenditures, human capital, infrastructure construction, fixed asset investment and education investment in the region at a significant level of 0.01 correlation of 0.211, 0.03, 0.082 and 0.085, and education investment in the region at a significant level of 0.1 correlation of 0.211. These five factors significantly affect the Yijun County region agriculture, culture and tourism industry, and deepen the development of integration of tourism industry.

In recent years, intelligent control has realized rapid development in the field of electrical engineering, the article initially studied the principle of electrical intelligent control, accordingly built the electrical intelligent control system, and designed the system hardware, the system module is divided into the main control module, the expansion module, the digital input and output module and the mounting rail. Based on the working principle of fuzzy control, design the software of the electrical intelligent control system, and optimize the traditional fuzzy controller by using fuzzy adaptive hybrid genetic algorithm, so as to improve the fuzzy control accuracy of the electrical intelligent control system in this paper. The electrical control system of this paper is applied to greenhouse greenhouse temperature and humidity control, substation air conditioning energy consumption control and subway station illumination control, and the control effect of the electrical intelligent control system of this paper is known through three experimental data. The system of this paper can effectively deal with the dissimilar data in the greenhouse temperature control experiment. Under the steady state environment, the temperature deviation of this paper’s fuzzy control method and conventional single structure fuzzy control is within 0.1℃ and 1℃ respectively, and the humidity deviation is within 5%RH and 10%RH respectively. Obviously, the fuzzy control method in this paper has higher control accuracy. In the substation air conditioning energy consumption experiment, the annual power consumption of this paper’s electrical intelligent control system and the traditional ventilation and air conditioning system are 32,660 degrees and 45,620 degrees, respectively. The electrical intelligent control system in this paper can save 22,000 yuan per year. The output illuminance of the subway station of the fuzzy control system in this paper increases with the comfort of the light environment and the density of the crowd, which achieves the expected effect.

In today’s rapid development of information technology, the big data industry has ushered in explosive growth, and big data analysis has become an important research topic in the cross-cutting field of computing. This study constructs a big data prediction base model based on deep learning, and uses the improved butterfly optimization algorithm with OGRU model to realize feature selection and classification processing of big data. Then the Adam algorithm is used to optimize the parameters of the model, and finally the classification and prediction model of big data based on deep learning is constructed. Simulation and empirical analysis results show that the model proposed in this paper has excellent classification and prediction performance, and can meet the efficiency requirements of big data classification and prediction. The prediction errors of distribution network load data and smart charging pile operation data are lower than 9% and 16%, respectively, which have high practical application value. This study is of great significance to the research related to big data classification and prediction in different fields, and provides an effective method for data prediction in complex scenarios such as industrial as well as power grid scheduling.

Big data is an important foundation in social economy, science and technology, life and other fields, which also becomes a strategic emerging industry and has a crucial impact on the development of enterprises. As a new business model, its development is greatly limited due to the huge amount of data and difficult management. At present, there are many problems in power trading enterprises, such as backward management and low efficiency. The development of big data and blockchain technology would provide new management models for power trading enterprises and eliminate data inconsistency. It can improve data quality and help improve work efficiency, so as to reduce operating costs. Therefore, this paper introduced big data and blockchain based on fuzzy algorithm into the research of digital transformation of enterprise management. Blockchain technology provided technical support for enterprise data management. By starting from the concept of big data and blockchain, this paper would study and analyze how to promote the digital transformation of enterprise management. The research results showed that big data and blockchain based on fuzzy algorithm could promote the digital transformation of power enterprise management and improve the digital transformation process of power enterprises. This was about 11% higher than the digitalization process of traditional enterprises, and the satisfaction score was about 14.7% higher. Through data governance, the speed of digital transformation of power enterprise management was improved.

With the rapid expansion of high-speed railway network, the real-time monitoring of trackside equipment becomes particularly important. To detect trackside equipment information more accurately, a YOLO-R algorithm grounded on the improved You Only Look Once v3 (YOLOv3) algorithm is proposed, and the trackside equipment identification and detection model is constructed. By introducing feature pyramid network and adaptive Bessel curve network, the new model can effectively identify and locate different types of trackside equipment such as switch machine, derailer, and shaft counter. The experiment findings denote that the new model is superior to the existing technology in all aspects of on-orbit equipment recognition and detection, the computer resource occupancy rate is only 22%, the image recognition accuracy rate is more than 98%, and the processing speed is up to 200 images/second. This research not only raises the automation level of trackside equipment monitoring, but also provides a powerful technology for railway safety operation.

Generative artificial intelligence represented by ChatGPT has attracted wide attention in the field of education because of its powerful generative ability, both personalized learning, understanding the learner’s motivation, and providing personalized tutoring and feedback for education. With the advent of the Education 2.0 era, smart classroom has become a strategic choice for the construction of education modernization, and is widely used in higher education and vocational education. Generative AI enlightens students’ engineering thinking, computational thinking, design thinking and systems thinking, which not only helps students to master their professional courses, understand what they have learned, and improve their academic performance, but also assists teachers in updating their course content, keeping abreast of students’ learning trends, improving their teaching efficiency, and simplifying their work. However, generative AI is faced with expertise gaps and uncertainty about the existence of generated content in its application, as well as ethical issues, and this study proposes that the needs and values of education should be respected, with the aim of efficient and convenient services, and that data-driven and ethical ethics should be emphasized in future development. Smart classroom and enlightened thinking with the application of generative AI is a new way of thinking about educational change, which can help teachers and students to effectively carry out multiple interactions, enable teachers to better understand students, play the role of human beings in education, and truly allow technology to be used for teaching and promote classroom teaching reform.

Ultra-low energy buildings for building energy efficiency development, compared with traditional buildings have obvious advantages. This paper simulates ultra-low-energy residential buildings in severely cold regions through Software PHES, and calculates the energy-saving results of ultra-lowenergy residential buildings. The carbon emission factor method is analyzed, and the carbon emission factor is calculated at different stages in the life cycle of the building. Select ultra-low-energy residential buildings in cold regions for modeling, input meteorological parameters, indoor environmental parameters and internal disturbance settings, building envelope, and combine with heat recovery system to simulate the operation of ultra-low-energy residential buildings in cold regions. Analyze the indoor and outdoor temperature and humidity values of traditional houses and compare them with those of ultra-low-energy-consumption houses to verify the advantages of ultralow-energy-consumption residential buildings. Calculate the energy-saving efficiency of ultra-lowenergy residential buildings. Using the 9# residential building of Ruihu·Yunshanfu in Datong as a practical verification case, this ultra-low energy residential building has a total life-cycle carbon emission of 171.078 tCO₂/a, with a unit area carbon emission of 16.415 kgCO₂/m²·a. Compared to the energy-saving design standards implemented in 2016, the carbon emission intensity is reduced by 60.02%, fully confirming the carbon reduction benefits of ultra-low energy residential buildings in severe cold regions.

In recent years, the development of study activities is in full swing. In order to study the eco-education effect in national park study activities, this paper introduces Bayesian network and constructs an ecoeducation effect assessment model based on Bayesian inference. In the comparison of the absolute error of the assessment value with other assessment models, the assessment accuracy of the Bayesian inference assessment model in this paper is obtained. After constructing the ecological education effect assessment index system and completing the assignment, the level of ecological education that should be achieved in the national park study activities is obtained through Bayesian inference diagnosis. Finally, according to the results of education effect assessment, the probability of each indicator being in various states is obtained by simulation using Monte Carlo method. The mean absolute error of the Bayesian assessment model is 0.26 points, which is smaller than other comparative assessment models and has the highest assessment accuracy. The model’s ecosystem principles, anthropogenic intervention impacts, ecological disasters and ecological protection measures should be guaranteed to reach 75.6, 64.8, 67.9 and 69.4. The ecological operation rules (59.4→79.8), climate change (50.6→70.2), biodiversity reduction (52.2→69.8), and pollution prevention and control (56.4→78.3) have the highest accuracy for the ecosystem principle, anthropogenic intervention impacts, ecological disasters and ecological protection measures, respectively. , anthropogenic intervention effects, ecological disasters and ecological conservation measures, and ecological education effects had the greatest impact. The overall score of ecological education effect was 84.1, and the scores of ecosystem principle, human intervention impact, ecological disaster and ecological protection measures were 83.8, 85.2, 83.0 and 84.2.

For enterprises, development is ultimately reflected in the task completion performance of employees, and in order for employees to create higher task performance, it is necessary to consider not only their education and knowledge level, but also their emotional management ability. This study first collects data related to employees’ emotion management ability and task completion efficiency improvement through questionnaires, and then analyzes the statistical data by using the potential impact identification model designed based on Bayesian neural network model to obtain the potential impact probability of each dimension of emotion management ability on task completion efficiency improvement. The analysis of the forward and reverse inference probabilities of the Bayesian network model indicated that the most important potential influence factor leading to the improvement of task completion efficiency was the emotion expression ability, with a forward and reverse inference probability of 36.2% and 59.4%, respectively, followed by the emotion regulation ability and emotion acceptance ability. The results of this study reveal the important potential influence of emotion management ability on task completion efficiency enhancement, and the formulation of task completion efficiency enhancement strategies based on the perspective of emotion management ability can effectively enhance employee task performance, which in turn promotes the overall development and competitive advantage of enterprises.