The article takes the defect detection and recognition of railroad track as the main research point, and extracts, preprocesses and corrects the railroad track surface image by introducing image segmentation algorithm. Gabor function, K-means clustering method and conditional iterative pattern algorithm are embedded in the original Markov random field model to construct the improved two layer graph model for railroad track defect segmentation. The recall, precision, mean average precision, and loss function of the improved Markov defect segmentation model are significantly better than those of the original model, and the mean average precision of the defect segmentation model is increased to 95.7% after the Gabor function, K-means clustering method, and conditional iterative pattern algorithm are applied. The improved Markov defect segmentation model fused with clustering features in this paper can better meet the classification and identification of railroad track defects.

In recent years, with the rapid development of artificial intelligence, big data, machine learning and other technologies, human society is entering a more and more intelligent society, and the interaction between humans and machines becomes more and more common. In this paper, image processing operations are added on the basis of Kinect’s original acquisition of gong dance images, which reduces the influence of external light, background and other factors, and makes the human capture efficiency increase dramatically, and a spatio-temporal graph is constructed on the basis of the continuous human posture key point data, which describes the distribution of the human posture key points in different dataset types. Aiming at the problems existing in the traditional spatio-temporal map convolutional network, a multi-dimensional attention mechanism is designed to guide the model to reasonably allocate the weight resources in three dimensions: space, time and channel, respectively. Experiments are conducted on NTU-RGB+D, Kinect skeleton and Taiji datasets, respectively, which show that the AGCN-STC proposed in this paper has better recognition performance on all three datasets, and the recognition accuracy is improved by 0.9 percentage points compared with AM-GCN. Two actors are used as samples for visual measurement and quantitative analysis to compare the differences between the performance gestures of the two ornaments. Finally, based on the results of the study, we propose a transmission path for the Guanzhong gong dance, which is a reference for the cultural transmission of the Guanzhong gong dance.

In the process of social development of Tang Dynasty, literary works behind the depth of interpretation and expression, systematized spiritual concepts. In this paper, the text data of Tang Dynasty literary works are processed by word division and de-discontinued words, and it is intended to use Transformer model to realize the word vector transformation of text data, and put the word vector into Text-CNN network for iterative training to realize the text feature extraction. By means of text feature screening, the cultural value assessment system of Tang Dynasty literary works is formed, and a comprehensive evaluation model of cultural value is designed under the role of convolutional neural network and text features, and using the model of this paper, the cultural value of Tang Dynasty literary works is assessed. The accuracy rate of cultural value classes “Ⅱ”, “Ⅲ” and “Ⅴ” is 1, while the accuracy rate of cultural value classes “I” and “Ⅳ” have accuracy rates of 0.98 and 0.96, indicating that the model in this paper can accurately assess the cultural value in Tang Dynasty literary works.

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.