Research on Spillover Effect of Urbanization on Rural Land Transfer Based on the SDM Model of Intelligent Computing

In order to explore the spillover effect of urbanization on rural land transfer, this paper uses the panel data of various regions and cities in Xinjiang from 2008 to 2018. Moran's I method is used to test and analyze the spatial correlation between urbanization and farmland transfer. Intelligent computing SDM is used to analyze the spillover effect of urbanization on farmland transfer. The results show that there is spatial correlation between farmland transfers in Xinjiang. There is spatial heterogeneity in the spatial agglomeration of urbanization and farmland transfer in northern and southern Xinjiang. The content of this paper can provide some reference and ideas for follow-up research.

Evaluation Index of School Sports Resources Based on Artificial Intelligence and Edge Computing

As the demand for education continues to increase, the relative lack of physical resources has become a bottleneck hindering the development of school physical education to a certain extent. This research mainly discusses the evaluation index system of school sports resources based on artificial intelligence and edge computing. Human resources, financial resources, and material resources in school sports resources are the three major resources in resource science. University sports stadium information publicity uses Internet technology to establish a sports information management platform and mobile Internet terminals to optimize university sports resources and stadium information management services. It uses artificial intelligence technology to improve venue information management. It establishes a comprehensive platform for venue management information, collects multidimensional information, provides information resources and accurate information push, and links venue information with public fitness needs. Using edge computing to realize nearby cloud processing of video data, reduce the phenomenon of black screen jams during live broadcast, improve data computing capabilities, and reduce users’ dependence on the performance of terminal devices, build a smart sports resource platform, combine artificial intelligence (AI) to create smart communities, smart venues, and realize intelligent operations such as event service operations and safety prevention and control in important event venues. During the live broadcast of the student sports league, the nearby cloud processing of video data is realized in the form of edge computing, which improves the data computing ability and reduces the performance dependence on the user terminal equipment itself. In the academic survey of college physical education teachers, undergraduates accounted for 26.99%, masters accounted for 60.3%, and doctoral degrees accounted for 12.8%. This research will help the reasonable allocation of school sports resources.

Application of Deep Neural Network Algorithm in the Analysis of Legal Precedent Citation Basis

In order to improve the retrieval efficiency of civil litigation cases, the research introduces the fuzzy neural network algorithm and constructs a targeted retrieval algorithm system. In the simulation verification, it is found that, in the artificial subjective evaluation results of the expert group, the comprehensive score of reference cases given by the retrieval scheme exceeds the level of reference cases in the cases promoted and studied by the Supreme Court. The use of this scheme can effectively save the preparation time of prelitigation documents and help to improve the fairness and justice of the court trial process. It is proved that the retrieval scheme has certain popularization value.

A Disembarking Notification Application for Public Transportation Using Inaudible Frequency

Currently, many people enjoy videos and music content through their smart devices while using public transportation. However, because passengers focus so much on content on their smart devices, they sometimes forget to disembark and miss their destination stations. Therefore, in this paper, we propose an application that can notify users via smart devices when they approach the drop-off point in public transportation using an inaudible high frequency. Inaudible frequency signals are generated with announcements from speakers installed on subways and city buses. Smart devices receive and analyze the signals through their built-in microphones and notify users when they reach the drop-off point. We tested destination notifications with the proposed system and 10 smart devices to evaluate its performance. According to the test results, the proposed system showed 99.4% accuracy on subways and 99.2% accuracy on city buses. Moreover, we compared these results to those using only subway app in subways, and our proposed system achieved far better outcomes. Thus, the proposed system could be a useful technology for notifying smart device users when to get off public transport, and it will become an innovative technology for global public transportation by informing users of their desired stations using speakers.

Assessment of Power Plant Based on Unsafe Behavior of Workers through Backpropagation Neural Network Model

Safety is an essential topic for electric power plants. In recent years, accidents caused by unsafe behaviors of electric power plant employees are frequent. To promote the sustainable development and safety of electric power plants, studies on the assessment of unsafe behavior are becoming increasingly important and urgent. In this study, accident statistical analysis, literature review, and expert survey are adopted to select more comprehensive and accurate assessment indicators of unsafe behavior of the workers in electric power plants. Data about indicator and unsafe behavior were obtained through a questionnaire survey, and 27 indicators were used as inputs, and the unsafe behavior was taken as the output of a backpropagation (BP) neural network based unsafe behavior assessment model. An assessment indicator system about power plant workers’ unsafe behavior composed of 4 first-level indicators and 27 second-level indicators was established and the weights of the assessment indicators were determined. A three-layer feedforward BP neural network assessment model of “27-13-1” layers was found to be a suitable model. The proposed model can demonstrate the nonlinear complex relationship between the assessment indicator and the unsafe behavior of power plant workers. The model can be helpful to evaluate, predict, and monitor the safety performance of electric power plants.

Virtualized High Throughput Satellite Gateway with a Global Bandwidth Management Method

With the development of new satellite payload technology, in order to improve the utilization of system resources, research is based on software-defined network (SDN) and network function virtualization (NFV) gateway architecture. Based on this architecture, the system realizes global resource management and overall data distribution, which can solve the problem of resource allocation and maximum/minimum rate guarantee between different VNO terminals under different beams, different gateways, and different satellites. For this, a global bandwidth management method can be used which is mainly a process of management to control the traffic on a communication link. The proposed global resource management and control method can be based on the rate guarantee value of the VNO/terminal configured in the system as the basic limiting condition and reallocate the rate guarantee value limiting parameter according to the resource application status of the online terminal. The method can maximize the resource utilization of the entire satellite communication system and satisfy the resource request of the user terminal as much as possible.

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.

Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence

Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.

The Influence of Football Training Based on Big Data on Physical Function and Football Skills

In order to actively respond to the government’s call to scientifically create campus football culture, combine the characteristics of football sports, and improve people’s understanding of the mental and intellectual functions of football, this article focuses on the impact of football training on physical function and football technology. Based on the understanding of related theories, the experiment on the impact of football training on physical function and football technology was carried out. The experimental results showed that the weight, height, and BMI increased significantly during the period of football training ( P < 0.05 ). The independent sample T test showed that there were no significant differences in height, weight, and BMI between the two groups before and after training; the standing long jump performance of the control group after training showed an upward trend, but the significance level was not statistically significant. Three months later, the time for the experimental team to complete the eight-character dribble test in football training was reduced from 20.51 seconds to 15.57 seconds. The independent sample T test found that there was no significant difference in the physical fitness of the two groups before training and the changes in football skills of the subjects before and after training. Then, the clustering algorithm in the big data was used to analyze the data of the experimental group. The standing long jump has the highest performance; the second category belongs to the third level, and the third category belongs to the second level.

A Novel Online Education Reform Model Based on Risky Decision-Making under the Situation of Internet Plus

In the new situation of Internet plus, information technology has been widely applied in education, and hence online education has attracted wide attention from all walks of life. Today’s society is a risk society, and risk is everywhere. Online education reform is also risky, which is determined by many reasons. Some risks will cause certain losses to the online education reform, so based on risky decision-making, it is necessary to carry out online education reform under the new situation of Internet plus. At first, the risky decision-making in online education reform is analyzed, which is the risk of online education reform in risk society and the allocation logic of online education reform. Then, taking interval type-2 fuzzy logic (IT2FL) as the information environment, this study proposes the optimal risky decision-making method based on IT2FL utility functions, IT2FL entropy, and risk preference factor of online education reform to solve the multipath risky decision-making problem of online education reform. Finally, the experimental results show that, in the risky decision-making model, the decision-maker’s risk preference has an impact on the path weight and the ranking of the scheme, and the idea has a certain reference role for risky decision-making. Compared with the three benchmarks, the proposed method has the fewest ranking time with the same ranking results.