Data Collection and Analysis in Sports Practice Teaching Based on Internet of Things Technology

J. Sensors Pub Date : 2022-08-24 DOI:10.1155/2022/2741517
Li Yang
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引用次数: 0

Abstract

Since entering the new century, people’s living standards have been continuously improved, living conditions have been continuously improved, which is the embodiment of the improvement of the country’s comprehensive strength, the increase in national strength has made the development of sports faster and faster, and the annual output value of sports is also increasing year by year. However, there are still many places that do not pay attention to practical content; in view of these problems, we use the Internet technology to collect and analyze the data of sports practice teaching; in this chapter paper, we use the 2SPLM observation matrix method and CS-MDGA algorithm, so that the collected sports practice teaching data is more quick and convenient to be analyzed. This paper also uses a large number of chart data to confirm the correctness and accuracy of their views, making the paper more reliable. The study shows that the teaching of physical education in China is increasing year by year, indicating that the teaching of physical practice in China is developing in a better direction. The mathematical modeling in the abstract section of the article uses computational methods such as constrained nonnegative matrix decomposition and constrained nonnegative matrix decomposition model optimization algorithm that integrates external information for research.
基于物联网技术的体育实践教学数据收集与分析
进入新世纪以来,人民的生活水平不断提高,生活条件不断改善,这是国家综合实力提高的体现,国力的增强使得体育事业的发展越来越快,体育的年产值也在逐年增加。但是,仍然有很多地方不注重实用内容;针对这些问题,运用网络技术对体育实践教学数据进行收集和分析;在本章中,我们采用2SPLM观察矩阵法和CS-MDGA算法,使收集到的体育实践教学数据更加快速方便的进行分析。本文还使用了大量的图表数据来证实他们观点的正确性和准确性,使论文更加可靠。研究表明,中国的体育教学逐年增加,表明中国的体育实践教学正朝着更好的方向发展。本文摘要部分的数学建模采用了约束非负矩阵分解和集成外部信息的约束非负矩阵分解模型优化算法等计算方法进行研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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