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Research on College Students’ Physique Testing Platform Based on Big Data Analysis
Author(s) -
Ting Chu
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/4615020
Subject(s) - big data , china , physical education , scalability , medical education , computer science , mathematics education , psychology , data science , engineering , medicine , political science , data mining , database , law
With the progress of the times, people’s living standards have significantly improved, gradually put physical health in the first place, college students as the country’s future talent reserve, its physical health development is particularly important. But for a long time, the physique level of college students has failed to reach the standard, and universities are exploring ways to improve the physique level of college students. In view of the existing deficiencies in the monitoring platform of student’s physique, we fully utilize the advanced technology of computer and Internet development and follow the basic principles of economical practicality, scalability, user-friendliness and real-time information exchange to establish monitoring and service platform for college students’ physique. With the aid of the thinking mode of big data and related technology, build the physical health of college students in China big data analytics platform framework, physical health of large numbers of college students in China are put forward According to the analysis platform construction principle, data source, data handling, data platform and application platform, etc., in order to guide the physical health of college students practice in our country, to promote the reform of physical health education for students in colleges and universities. By using big data analysis technology, 65,535 physical health records of all students in a university from 2017 to 2019 were recorded as data sources, and algorithm based on distance was applied to cluster analysis of two groups of data classified by male and female gender, and a series of data were processed, converted, and modeled.

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