Curriculum Civics reform in physical education should keep pace with the times and actively explore modern technical means. This study addresses the problem of regulating the elements of Civic and political education in physical education, and establishes a mathematical model of multi-objective optimization and regulation by comprehensively considering the various factors and constraints involved in the problem. In order to further optimize the regulation results, an improved two-population genetic algorithm is used to solve the model. Taking the physical education course of a university as an example to analyze, the design algorithm of this paper is compared with the experiments, and the improved two-population genetic algorithm completes the convergence in 300 iterations, and the degree of adaptability is improved by 2.04%, which has the characteristics of strong global search ability and fast convergence speed, which proves that the improved two-population genetic algorithm has a certain degree of superiority and validity. The utilization rate of the elements of ideology and politics education in the experimental solution results reaches 0.87, and other factors meet the actual needs of sports teaching, and the method of this paper can realize the intelligent regulation of the elements of ideology and politics education in sports teaching.

In the process of increasing the service capacity of digital infrastructure, the complex data generated by data terminals grows rapidly, which puts forward higher requirements for complex data task scheduling preprocessing. In this paper, based on particle swarm algorithm and improved artificial fish swarm algorithm, a hybrid particle swarm multi-objective optimization scheduling algorithm applicable to task scheduling and processing of complex data sets is designed. Then we design a reasonable expression method for the particle position and adaptation value algorithm in the multiobjective optimization algorithm, and put forward the pre-search strategy of the particle swarm algorithm to improve the search performance of the particles in the algorithm. Finally, the algorithm is equipped to construct a task scheduling and processing model for complex data sets. The results show that the hybrid particle swarm optimization algorithm established in this paper outperforms the comparison model in terms of load balancing and processing time, and is able to keep the system CPU utilization between 0.350-0.491 in the simulation experimental environment. It is also found that the application of the task scheduling and processing model in this paper can increase the power of photovoltaic and wind power generation in the grid system and reduce the operating cost of the grid system. This study provides an effective reference method for the processing of data and task scheduling in various types of complex systems, and brings new ideas and directions for research in related fields.

This project defines and generalizes the groundwater flow and soil deformation in geotechnical engineering by combining the hydrogeological conceptual model. Based on the fluid-solid coupling theory, a coupled model of groundwater flow and soil deformation is constructed, and the SUB program package in MODFLOW simulation software is selected to numerically simulate and analyze the relationship between groundwater flow and soil deformation in the study area. In layer2 and layer3, the trend of groundwater level decline and soil compression is shown, and the other layers4~layer9 also show the same situation, due to the over-exploitation of groundwater, resulting in serious decline of the soil in the study area, which reveals the causal relationship between groundwater flow and soil deformation at present.

Random forest algorithm is a kind of integrated learning algorithm with strong universality, high prediction accuracy and not easy to overfitting, and strong stability in stock index prediction application. This study constructs a stock index prediction model based on the random forest algorithm, and predicts the stock index futures price state according to the iteration of the decision tree in the random forest algorithm. Then we propose to use the regular term and ARMA-GARCH time series forecasting model to optimize the overfitting and large forecasting errors in the Random Forest model to achieve the construction of stock index forecasting optimization model. It is verified that the average absolute error of the random forest optimization model proposed in this paper is only 0.0316 in stock index forecasting, and the robustness in stock index forecasting is excellent. The empirical application results of stock index forecasting show that the accuracy of this paper’s model for CSI 300 and CSI 500 indexes is above 90%, and the total return of the strategy during the backtesting period is relatively high. The practical application of the stock index forecasting model proposed in this study has the value of further research, which can provide reference and guidance for investors.

Financial risk has a greater impact on the operation and development of enterprises, and accurate prediction of financial risk has become an industry demand, so as to better help enterprises avoid possible financial risk. The article establishes an enterprise financial risk prediction model based on the random forest algorithm, and fills in the oversampling of financial data through the SMOTENC algorithm, and realizes the downsizing of financial data by combining with the KPCA algorithm. Based on the enterprise financial risk characterization index system, the financial data of 358 listed enterprises were selected to carry out model validation and application analysis. The accuracy of corporate financial risk prediction based on Random Forest can reach up to 94.17%, and the average value of the overall time efficiency of the model is 0.68%, which is faster than the comparison algorithm in terms of financial data processing capability. Based on the results of financial risk prediction, the changes in corporate profitability, operating ability, solvency and development ability can be analyzed in depth, providing data support for enterprises to formulate preventive measures for corporate financial risk.

The article uses web crawling to obtain public opinion data after the Sichuan Luding MS6.8 earthquake and preprocesses this data. Aiming at the limitations of the traditional LDA topic model, an improved topic model based on LDA, TT-LDA, is proposed. the BERT model is used to encode the public opinion data, and on the basis of the BERT embedding, the BiLSTM model is used for contextualized word representation for deep feature extraction to complete the modeling of public opinion sentiment evolution. Combining the crawled data and the model, we analyze the public opinion after the Sichuan Luding MS6.8 earthquake. Three days after the earthquake, positive sentiment, neutral sentiment, and negative sentiment increase to 488498, 466832, and 516560, respectively, a total of 1471890 sentiment data, and after time evolution, the sentiment polarity intensity increases from -0.178 to – 0.886, indicating that when the official announcement of the number of casualties of the accident is made, the netizens’ negative sentiment fully erupts to show the post-earthquake public opinion sentiment evolution process.

E-commerce classroom teaching is an important means to improve the quality and teaching effect of e-commerce teaching, and effective interaction in teaching is an important carrier of e-commerce teaching classroom activities. This study combines pan-reinforcement learning and reinforcement Q learning algorithms to recognize and analyze speech data in e-commerce teaching classroom, and uses head posture estimation algorithm to recognize interactive behaviors in e-commerce teaching classroom video, and combines the video and speech interaction data to get the e-commerce teaching interactive behavior recognition model. The model is then equipped with web application technology to design a visual analysis system for e-commerce teaching interaction, and the optimization strategy of e-commerce teaching interaction is realized with the assistance of this system. The results of the study show that the interactive behavior recognition model proposed in this paper can accurately identify the interactive behavior of teachers and students in each course of e-commerce teaching. It is also found that after the implementation of interaction optimization strategy in college e-commerce teaching classroom, the frequency of effective interaction behaviors of teachers and students increases from 351 to 391 times, and the meaningless classroom silence time is reduced. And the learners’ cognition of knowledge is also improved under the influence of the improvement of the effect of interactive behavior. The visual analysis system of teaching interaction proposed in this paper based on reinforcement learning algorithm is of great significance for optimizing the effective interactive behaviors of teachers and students in e-commerce teaching and improving the degree of students’ knowledge cognition.

Green economy is an important factor to measure the quality of economic development. In order to explore the current green economy resource allocation, this paper embeds methods such as DEAMalmquist model and Tobit regression model into the study of green economy resource allocation, explores the green economy resource allocation efficiency of 30 provinces in China by constructing a multilevel model of green economy resource allocation, and analyzes China’s green economy during the period of 2021-2023 through the results of the measurement of the Static, dynamic and level changes of resource allocation efficiency. Tobit regression analysis of the influencing factors of green economy resource allocation efficiency is carried out to optimize the current resource allocation based on the influencing factors. The green economy resource allocation efficiency increases year by year in 2021-2023, and the resource allocation effect improves continuously, with the mean value of the comprehensive efficiency of 0.712, 0.762, and 0.809, respectively. The green economy resource allocation efficiency in Beijing, Shanghai, Jiangsu, and Zhejiang is the highest, and the allocation structure is the most reasonable. Chongqing, Gansu, Qinghai, Ningxia and Xinjiang are less efficient in green economy resource allocation. The per capita GDP and the ratio of education expenditure to GDP have a positive impact on the effect of green economy resource allocation, with an impact of 1.246 and 0.489, respectively.

In recent years, with the rapid development of information technology, the traditional single-threaded processing method can no longer meet the rapid growth of digital media data volume. In this paper, based on the digital media data processing system based on BS structure, the GPGPU parallel processing architecture is used for optimization. The access efficiency of massive parallel multithreading is ensured by executing a multilevel storage architecture composed of behavior decision unit, branch merge unit and branch recovery stack. The study designs the computational resource pool as well as the storage resource pool to form an infrastructure solution to the data processing problem. The query performance of the digital media data processing system using the GPGPU microarchitecture with multithreaded parallel processing is improved by about 81% and 69% or so compared to the Ocelot and prototype systems, respectively. And the average execution time for performing dynamic data allocation is 5.17s less than that of the original system. It shows that the optimized digital media data processing system has better data processing efficiency.

The problems of debt value and optimal capital structure of enterprises are the main issues in corporate finance research. Under the ESG rating mechanism, the article first utilizes the real option theory to study the optimal capital structure and investment and financing decision-making methods of enterprises. Then it puts forward model assumptions and combines the jump diffusion model for the construction of enterprise project investment and financing decision-making model and the dynamic planning adjustment of capital structure. Finally, through specific numerical experiments, the influence process of each variable in the model on the enterprise investment and financing decision is analyzed, and the agency problem is analyzed.Through the experiment, it can be obtained that when the residual value after stopping production, the risk-free interest rate, the variable production cost and the tax rate are set to γ = 1, r = 0.1, ξ = 0.1, θ = 0.2, respectively, with the increase of the frequency of the jump, the investment price of the positive-jump model gradually decreases, and the investment price of the negative-jump model gradually increases, which can be obtained that the reasonable simulation estimation of the relevant parameters has an important impact on the enterprise’s investment strategy, so the enterprise should make a more accurate assessment of the parameters, otherwise they will lose part of the benefits or lose good investment opportunities.