Abstract. In view of the big consumption and low rate of utilization of physical education curriculum resources among colleges and universities, the research uses the collaborative mode and informational platform to implement resources sharing in different colleges and universities physical education teaching. This paper analyze the characteristics of physical education teaching and is based on this collaborative mode of colleges and universities physical education teaching while this mode can help achieve sharing teaching resources. In order to improve the utilization of physical education teaching resources and reduce the conflicts of using the resources among colleges and universities, this paper pay more attention to analyze the internet curriculum and the plan of physical education teaching resources in cross colleges and universities, and researches the resources sharing informational platform. While using the platform to arrange physical education curriculum of different colleges and universities, the research of physical education teaching and education aims to realize the collaborative mode of physical education teaching across colleges and universities.
With the development of sports, competition is becoming more and more confrontational and modern sports are developing in the direction of fast speed, fast rhythm, good skills, and high-altitude combat. And, this development requires good conditions and technology as a guarantee. Sports denote a competitive physical activity, which follows four aspects of competition: technology is the foundation, tactics are the means, the body is the cornerstone, and the psychology is the decisive point. Among these four factors, if the athlete does not have a good body, all techniques and know-how are empty talks. In this paper, two algorithms are introduced to track the athletes’ physical training and tactical training videos and the relevant data in the game are counted to obtain the physical indicators required by the athletes in each position. In a certain game, the sprint distance in the first round was 1979 m, the high-speed running distance was 2426 m, the high-intensity running distance was 4398 m, and the jogging distance was 1267 m.
The progress of social economy has created a better environment for the healthy development of young people, but the heavy schoolwork and life pressure have caused many students to ignore the scientific management of physical health. At this stage, people need a scientific physical health service system to help students understand their own health data, propose targeted exercise methods and health knowledge, and actively encourage and guide students to participate in physical exercise. The purpose of this article is to cultivate students’ good self-exercise awareness and improve their physical fitness and health. To this end, this article has designed a smart health service system for young people. This article introduces the various service functions in the health management service system and explains in detail the entry, induction, and analysis of student physical health data in the system. The essence of the health intelligent service system is to provide students with targeted healthy exercise strategies through data analysis. This paper studies the health intervention plan of the health intelligent service system. From the experimental data, the improved particle swarm algorithm in this paper increases the effectiveness of the system in adolescent health data mining from 80.5% to 92.19%, which undoubtedly optimizes the system. It helps a lot.
With the development of sports and information technology, people use mathematical tools and computer technology to study sports data and mine the intrinsic value of sports data. Statistical methods are the most widely used to achieve this goal. The research purpose of sports effect evaluation research is to understand the impact of sports on physical fitness through mining and analysis of sports data and to provide theoretical guidance for the public to participate in fitness activities scientifically and effectively. At present, in the study of combining individual performance test data, the research on the standardization of physical fitness monitoring data for sports training is relatively scarce. Therefore, based on the background of big data, this paper integrates the existing data standardization work and designs a plan for the standardization of physical fitness monitoring data for sports training. Combined with machine learning, data preprocessing is performed to obtain the data required by the machine model. The comprehensive physical fitness rating model and the recommendation model are established to realize the development of physical fitness monitoring service applications. In the experiment, compared with the three classical methods, the results show that the classification accuracy of this paper is 4% higher than that of other algorithms, which can more intuitively reflect the characteristic samples of sports training. In this paper, the data mining and analysis technology based on feature indicators in the mining and application of sports data has great application value for human fitness guidance and has certain research value and market application prospects.
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