This paper first outlines the theoretical method of parametric modeling of BIM technology in building structural design, and introduces Revit and Dynamo software to ensure the interactivity and sharing of data while parameterizing the influencing factors of the building structure and automating the extraction of data. Multiple linear regression analysis and the least squares method are used to quantitatively analyze the building energy consumption and the enclosure structure, and to construct a calculation model for the overall structural energy consumption of the building. In order to maximize the comfort of the users and minimize the source consumption of Huizhou architecture, NSGA-III algorithm is introduced to design the multi-objective optimization model of Huizhou architecture. Finally, the optimization effect of the model is verified through simulation and emulation tests. The results show that: the proportion of time that the internal temperature of the antechamber of the building is in the thermal comfort zone is the highest throughout the year (38.29%), and the thermal insulation performance of the building is insufficient; the average illuminance of the compartment space does not meet the lighting requirements (52.07 Lux), and there is a lack of diversity in the lighting design; and it is necessary to optimize the thermal insulation performance of the building enclosure structure to ensure the comfort and livability of the indoor environment. In addition, between the optimal solution and the worst solution interval of the annual energy consumption value and the absolute comfort value of Huizhou architecture, the maximum difference between the energy consumption and comfort indexes is 1.051×107kwh and 0.807, respectively, which can be used for the intuitive analysis of the BIM model and the comparison of the solutions.

With the development of the Internet, public safety public opinion events have gradually become an important part of social public opinion and an important content of government response. In order to establish a standard system for evaluating the response effectiveness of the public safety public opinion incident response system, this paper, in accordance with the current status of the government’s public safety public opinion incident response system and the literature, selects four indicators, namely, serviceability, dynamics, timeliness and legitimacy, as the criterion layer of the evaluation system. Hierarchical analysis method and TOPSIS method are used to evaluate the public security public opinion incident response system. Finally, in order to verify the reasonableness of the AHP-TOPSIS method for evaluating the response effect of the public security public opinion event response system to public opinion events, 80 cases were selected, which were clustered and analyzed and the proximity scores between the samples and the positive ideal solutions were calculated, and the clustered samples were ranked to obtain the response effect ratings of the system to different events. The analysis of the data shows that timeliness has the most significant effect on the evaluation of the public security public opinion event response system, and the public security public opinion event response system responds best to government-led policy-oriented major public opinion events. The legal compliance framework can be constructed from three aspects: improving the existing laws and regulations on public security public opinion events, strictly enforcing the existing laws and regulations, and carrying out in-depth legal publicity, so as to lay the foundation for the implementation of the legal review work.

Pre-school education, as a key stage on the path of children’s growth, plays a vital role in their overall development. Based on the independent sample t-test method, this paper explores the gender differences in preschool education. It also takes digital media education methods as an example, and utilizes Pearson correlation coefficient, linear regression model, and systematic clustering algorithm comprehensively to quantitatively assess the impact of education methods. The results of the study showed that there were extremely significant differences (P<0.01) in the five dimensions of language ability, creativity, social interaction ability, critical thinking ability, and independent learning ability between male and female toddlers, indicating that there are significant gender differences in preschool education effectiveness. The correlation coefficients between the frequency and duration of use of digital media education methods and language skills, creativity, social interaction skills, critical thinking skills, and independent learning skills ranged from 0.47 to 0.75, with significant positive correlations, and were associated with higher scores on each of the competencies as well as higher levels of satisfaction. This paper reveals in depth the gender differences in preschool education and the important role of digital media in preschool education, which is of great value for the optimization of teaching methods in preschool education.

With the continuous development of the network environment, the traffic data in the network increasingly presents high-dimensional, huge and complex characteristics, and the network threat is also increasing, the network information security threat prediction and defense mechanism plays an irreplaceable position in network security. Based on the general process of network anomaly detection, combined with deep learning algorithms, the article proposes a network anomaly detection method based on data enhancement to improve the detection accuracy of network anomaly detection model. Self-attention mechanism is embedded in the neural network framework to accomplish the improved SA-GRU network information security threat prediction method. In the performance index comparison experiments of network security posture values predicted using different prediction models, the average absolute error of the training data of the results predicted by this paper’s model is 0.00266, and the average absolute error of the test data is 0.00369, and the prediction accuracy of this paper’s model prediction is significantly higher than that of other deep learning methods. This verifies the effectiveness of the method proposed in this paper. Finally, based on the experimental results, the network information security defense mechanism is proposed from the three levels of data encryption, the use of secret keys and intrusion detection.

In the joint electrical drive system of industrial robots, the optimization and improvement of robot motion control is one of the hotspots of current research, and this paper proposes a method of optimizing the joint electrical drive control of robots using multilevel genetic algorithm. An improved PID control method is used to fuzzify the robot motion, and the robot trajectory fuzzy PID controller is optimized according to the idea of multilevel genetic algorithm. The rise time of each joint of the robot is about 5ms, 55ms, and 75ms, respectively, and the overshooting amount is smaller, and the optimized joint electrical drive system of the industrial robot is more stable in speed control in both the acceleration and deceleration phases, and shows a good dynamic control capability of the motion. It can be seen that the work in this study effectively optimizes the control performance of the industrial robot drive system using multilevel genetic algorithm.

Aiming at the learning path recommendation problem, which is the key in personalized teaching, this paper takes the personalized learning path recommendation model as a guide, and researches and gives a method that combines the learning path recommendation model with the NFSBPSO algorithm. The learning path recommendation model based on the two-dimensional features of learners and learning resources is constructed, the population is initialized using the chaos strategy, and the optimal and worst particles in the iteration are optimized using the particle optimization strategy to obtain the optimal solution of the learning path. In order to verify the effectiveness of the personalized learning path recommendation optimization model in this paper, simulation experiments are carried out, and the teaching prototype system of a higher education institution in F city is seen as the experimental platform, and the model in this paper is applied to carry out personalized learning path recommendation practice. The first group of experimental subjects who learn according to the recommended path of this paper have an average test score of 83.6 and an average learning time of 371.7 minutes, which is better than the second group of experimental subjects who learn according to the default path. Most of the values of the recommended matching degree of personalized learning paths are between 0.64-0.9, and most of the adaptation degrees are between 0.11-0.21, which proves that the learning paths recommended by this paper’s model to the users have a high degree of accuracy and adaptability.

In order to improve the planning efficiency of urban landscape, this paper proposes a combination design method of urban landscape construction based on grid division and a spatial optimization model of urban landscape based on particle swarm algorithm to optimize the spatial and pathway layout of urban landscape that takes both economy and ecology into account. The original landscape image was mapped with 3D remote sensing image to generate a 3D image model, and the gradient decomposition method was used for image sampling. Then the multi-dimensional dynamic feature distribution model of urban landscape was constructed, on which the urban landscape area grid was divided to realize the landscape construction combination design. Using particle position to simulate the meta-space layout results of landscape type raster images, the optimization of landscape pattern space and path is completed. The experiment proves that the algorithm in this paper reduces the influence of multiple types of perturbations on the landscape layout results, and the spatial optimization model of urban landscape pattern based on particle swarm algorithm realizes the organic coupling of quantitative and spatial optimization, which not only improves the utilization rate of the urban land, but also substantially reduces the risk index of the urban landscape, and meets the design expectations.

This paper first describes the basic theoretical knowledge of supply chain inventory control and analyzes the existing supply chain inventory control strategies. For the relationship between safety stock and customer service level and inventory cost, the safety stock factor is used as a decision variable, and a supply chain multilevel inventory control model is established under (t,s,S) inventory replenishment strategy. Secondly, the selection operator, crossover operator and mutation operator of the traditional genetic algorithm are adaptively improved, and an improved multi-objective adaptive genetic algorithm is proposed, and this algorithm is used to solve the inventory optimization with the two objectives of supply chain inventory cost and customer service level. The simulation results of the algorithm show that the improved genetic algorithm has better convergence and the obtained Pareto optimal solution set is closer to the real optimal frontier. When the IGD value is minimized and kept constant, the convergence speed of this paper’s algorithm (34 times) is 38.18% lower than that of the traditional genetic algorithm (55 times), and the model converges faster while its Pareto solution set is more uniformly distributed. Example results also show that using the model in this paper can reduce the inventory of each node in the supply chain system and reduce the transportation cost.

This paper takes 2020-2022 Shanghai main board listed companies as the research object, and empirically examines the relationship between factors such as the establishment of internal audit department and the quality of internal audit empowered by new quality productivity, with the effectiveness of the quality of internal control as the explanatory variable, the degree of separation of two powers and so on as the explanatory variable, and the corporate governance structure as the control variable to carry out a via gradient descent Logistic regression analysis optimized by gradient descent algorithm. On this basis, to address the problem that internal audit is prone to bias or falsehood due to management’s self-interest, the fsQCA method is combined to analyze the influencing factors of the choice of auditing policy (capital item or expense item) for general R&D expenditures. It is found that there is a significant positive relationship between companies with an internal audit department and a higher hierarchical level of affiliation and obtaining a standard audit opinion, and the regression relationship holds at the 0.05 level of significance, with a positive correlation with a regression coefficient of 3.745, and an OR value of 40.099. However, the effect of the company’s twofold separation of powers governance structure on the quality of the audit fails the significance test. Firms with lower profitability levels, higher R&D intensity, higher debt levels, lower tax benefits for R&D additions and deductions and lower external audit quality are more likely to capitalize R&D expenditures. The study uses cutting-edge algorithms to accurately analyze new quality productivity-enabling internal audit quality factors and innovate corporate compliance internal control paths.

At the present stage, the staff of mental health center in colleges and universities have a heavy workload, fatigue work and low work efficiency, and it is urgent to explore new paths to alleviate the severe situation of mental health work in colleges and universities. In this paper, we first start from the students’ mental health assessment data and use data mining technology to analyze the students’ mental health status. Then, students’ behavioral characteristics are digitally represented to construct a prediction model of students’ mental health status based on PDNN neural network. Finally, the design method of psychological intervention system in colleges and universities is proposed. In the collected mental health assessment data, the age distribution is skewed toward the younger population, and nearly 55% of these students show a tendency toward psychological abnormality. And the average accuracy and high group recall of the prediction model of students’ mental health status established using PDNN neural network were 88.95% and 87.44%, respectively, which verified the feasibility of the modeling method in this paper. Using the psychological intervention system designed based on the method of this paper for the intervention experiments, there is no significant difference between the experimental group using the system and the control group not using the system in the factors before the intervention (p>0.05), while after the intervention the experimental group scored significantly lower than the control group in the total mental health score, interpersonal relationship sensitivity, depression and anxiety factor items. This proves the validity of the intervention system design method in this paper, which can be applied in psychological intervention methods in universities.