The equalization and rationalization of educational resource allocation is of great significance to the coordinated development of education. The study takes the educational resources of 13 districts and counties in Y city in 2023 as an example, and proposes to use the BP neural network-based educational resource allocation evaluation system to analyze it. The results show that only three districts and counties have “very good” and “good” levels of educational resource allocation. Accordingly, this paper constructs a multi-objective optimization model to improve the level of educational resource allocation, reduce the differences between counties, and improve the utilization rate of educational resources. The weights corresponding to the eight indicators of the educational resource allocation evaluation index system are solved by the entropy weight method, after which the preset values of the three objective functions and the weights accounted for by the eight indicators are brought into the model and the artificial raindrop algorithm is used to find the optimal solution. After finding the optimal solution of educational resource allocation, the BP neural network-based educational resource allocation evaluation system is used again to evaluate it, and at this time, the educational resource allocation of a total of 12 districts and counties belongs to the “very good” and “good” grades. The study shows that the optimization method of educational resource allocation designed in this paper can reasonably plan educational resources and realize the coordinated development of education.

With the explosive growth of the variety and quantity of multimedia information in the Internet of Things (IoT) environment, its security problem is becoming more and more prominent. Therefore, this paper constructs APODAC dynamic access control model. The information processing of massive data of IoT is carried out through the fusion technology of multiple media features. Based on the real-time access behavior sequence of IoT, a fuzzy reasoner is used to analyze the degree of risk and assess the network security posture. Based on the degree of risk, IoT access rights are dynamically adjusted. The simulation experiment results show that the fuzzy reasoning method in this paper has a 4.4% higher risk detection rate for IoT network and a 10.5% decrease in false alarm rate compared to the traditional SVM method. In risk behavior oriented dynamic access control, the APODAC model proposed in this paper still outperforms the other 2 models in terms of response time for both higher number of access requests and smaller amount of access request data.

Since the strategic plan for rural revitalization was put forward, the related contents of public facilities have been continuously written into national policies. Promoting the high-quality construction of rural public facilities has become a hot topic of research in China’s rural areas. In this paper, optimization ideas and frameworks are proposed for the layout of rural public service facilities. Using the mixed integer planning model, the optimal solution of facility layout is obtained by calculating the distance between facilities to realize the optimization of rural public facility layout. Moran’s I index in global spatial autocorrelation is used to analyze the degree of spatial autocorrelation of rural public basic facilities accessibility. Construct a multivariate linear model to assess the impact of mixed integer planning applications on rural residents’ sense of social governance effectiveness. Evaluating the efficiency of rural basic public service facility accessibility coverage, the number of rural clinics is much larger than other facilities, with 26 facilities, and the number of middle schools, township general hospitals, and post offices is smaller, all with only two, indicating that there are certain problems in the configuration and spatial layout of public service facilities in a certain rural area nowadays. The application of the mixed integer planning model has a significant impact on rural governance in terms of human development index, public services, social security, public safety and social participation, with regression results of 0.075, 0.068, 0.125, 0.083 and 0.164, respectively.

The power grid, as a unified whole composed of various links of generation, transmission, transformation, distribution and use, needs mutual coordination and unified scheduling in terms of operation characteristics. This paper establishes a power 3D engine based on lightweight 3D engine technology, and builds a panoramic display platform for grid one map generation information on this basis. In order to explore the feasibility of this platform on grid optimization and scheduling, this paper establishes a two-layer optimization and scheduling model of the grid by taking the minimization of the operation cost as the objective function of the upper layer, and combining the supply-side and demand-side balancing objectives of the lower microgrid. The PSO algorithm is improved by introducing the immune mechanism, linear adjustment, and linear combination, and the HPSO algorithm is used to solve the grid two-layer optimal dispatch model. The simulation shows that the economic cost and environmental cost are reduced by 51.78% and 23.07%, respectively, and the total cost is reduced by 8.66*106 yuan after considering the uncontrollable residential electricity load. Relying on the One Grid Map platform can realize the accurate analysis of the peaking capacity and climbing residual capacity of the grid at typical time periods, providing reliable data support to meet the peak shaving and valley filling of the grid. Combined with the lightweight 3D engine technology, the panoramic display platform of production information of one map of power grid helps to obtain real-time grid operation and environmental information, realize monitoring and comprehensive analysis of the system, and make real-time decisions and interactions for optimal dispatching of power grid.

The integration of industry and education refers to the in-depth integration of industry and education, which emphasizes the cultivation of students’ practical ability and vocational quality. The integration of industry and education brings a new development direction for higher vocational education, and at the same time puts forward higher standards and requirements for higher vocational English teachers. In this paper, a new recursive Bayesian network structure algorithm is proposed based on RAI algorithm and CS algorithm, which mainly learns the Bayesian network structure by calling two functions recursively. Then based on the application effect evaluation model of recursive Bayesian network, the index system of classroom teaching evaluation is given based on the characteristics of classroom teaching, and the application effect is evaluated. The experimental results show that the optimization of the Bayesian network model can significantly improve the classification recognition reliability of the classifier, and taking the appearance score as a random effect, it can be found that the teacher’s appearance difference has a significant effect on the teaching evaluation. The results of the study are of great significance to the construction of scientific English classroom construction as well as teaching quality evaluation system.

This paper introduces the decision tree algorithm into the field of preschool education to categorize the styles of children in preschool education. The learning activities of children with different styles are deeply analyzed by the total number of detections, the task score and the total game time in the small train by counting activity. Decision tree algorithm is utilized to integrate online preschool education resources and used in practice so as to assist teaching. The teaching experiment method is used to test its educational effect. Kindergarten children were categorized into 3 types: extroverted, negative emotional and effortful control children. Effort-control style children performed well in play detection behavior, play task score and total play time. In the teaching experiment, children in the experimental group obtained very significant improvements in small muscle activity, art, music and rhythm, blocks, natural science and mathematical thinking, while the control group also improved, but their changes were not significant. Decision tree algorithm has better results in assisting preschool education.

In today’s society, hospitals are treated with images generated by medical examination equipment for disease diagnosis, and high-resolution images can greatly improve the accuracy of doctors’ disease diagnosis. The study constructs an ultrasound image dataset US-Dataset suitable for the task of super resolution reconstruction of ultrasound images. Based on this ultrasound image dataset, a degradation model is proposed, which in turn constructs ultrasound image matching pairs containing high – low resolution images for training the model proposed in this paper. To improve the perceptual quality of endometrial images, a super-resolution reconstruction model UN-SRGAN based on generative adversarial network is proposed in this paper. The network structure of this model consists of a generator and a discriminator. To validate the effectiveness of the model proposed in this paper, it is evaluated on Accuracy, Precision, Recall, Specificity, and F1-score metrics. The proposed model achieves the lead on PSNR and SSIM metrics and subjective quality evaluation, and the UN-SRGAN model has an accuracy of 0.9721, which is better than the other models, verifying the effectiveness of the model.

Soil microorganisms and mineral ions play a crucial role in the material cycle and energy flow. Different types of sandy, loamy and gravelly soils were selected as experimental sample plots, and the mineral element and microbial diversity of the soils were analyzed by using the curve method with spiked recovery analysis measurement and Illumina high-throughput sequencing technology. Then, principal component analysis and Pearson correlation analysis were applied to extract the factors affecting phosphorus and sulfur cycling by soil mineral ions and microorganisms, and the results showed that the mineral ions in the three different types of soils were mainly Na+, K+, Mg+, and Ca+. The top ten dominant bacterial phyla in relative abundance in different types of soils were Ascomycetes, Actinobacteria, and so on. The eigenvalues of the first four principal components in the principal component analysis of phosphorus-sulfur cycle influencing factors were greater than 1. Therefore, four principal components were selected: soil water content, soil Mg+ content, soil actinomycetes content, and soil Ca+ content.

Soil microorganisms are the main drivers in maintaining soil health. This paper focuses on the process of soil mineral ions and microorganisms involved in regulating the phosphorus-sulfur cycle, and systematically investigates the repair and improvement mechanism of soil microorganisms. Relying on an experimental area of a typical grassland in Inner Mongolia, we set up experiments with different nitrogen addition treatments, and combined with one-way analysis of variance (ANOVA) to investigate the distribution of soil phosphorus and sulfur fractions under various scenarios. Then, structural equation modeling was applied to explore the dynamic role between microbial action and phosphorus-sulfur cycle under N addition. Under different nitrogen addition scenarios, Ca10-P accounted for the largest proportion of inorganic phosphorus fractions, which were all greater than 40%. The percentage of inorganic sulfur in the soil was relatively small, less than 3% of total sulfur, and the response of inorganic and total sulfur to the gradient of nitrogen addition, nitrogen frequency, and different grassland management practices was not obvious. Fungal communities were important drivers of changes in functional genes for interleaf phosphorus and sulfur cycling at different N application levels, i.e., N fertilizer application altered the interleaf fungal communities by affecting soil physicochemical properties, which significantly regulated the interleaf bacterial communities, phosphorus and sulfur cycling functional gene abundance, and pathogenic fungal abundance.

Civil engineering disasters mostly occur in mountainous areas, and it is difficult to comprehensively monitor them using traditional technology, while this drawback can be avoided by utilizing UAV inclined photogrammetry technology. In this paper, with the support of the relevant experimental equipment, we obtain the images of civil engineering disasters with the help of this technology, and in order to avoid the influence of the interference factors in the images on the research results, we propose to use the K-means algorithm to pre-process the images. After completing the image processing, the improved YOLOV4 target detection algorithm is used to complete the design of the intelligent detection model of civil engineering disasters, and the processed images are input into the model for iterative training, so as to realize the intelligent management and early warning of civil engineering disasters. A region in Yunnan Province is taken as an example to explore and analyze the example. As of 2022, it is found that 180 landslides actually appeared in the region, while the model detected 172 landslides, resulting in the model’s civil engineering disaster detection accuracy of 95.56%, which is within the permissible range, proving that the model has a good application efficiency, and can provide certain help and innovative guidance for the relevant units of civil engineering disaster management.