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Constructing Sports Multi-Index Data Analysis Based on 5G IoT Technology

机译:Constructing Sports Multi-Index Data Analysis Based on 5G IoT Technology

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摘要

The arrival of the new era and the development of 5G Internet of Things (IoT) technology have made our lives and work easier and more convenient. The vigorous development of the IoT has been applied in many fields, among which, especially the data mining technology of the IoT ushered in the spring of this era of information explosion. Full application of data mining technology can provide real data well. Application analysis provides value and decision support. In order to apply 5G Internet of Things technology to the sports industry to help study the multi-index data of various sports activities so as to better help modern people have a healthy sports concept, Nemo builds relevant data analysis based on 5G Internet of Things technology. This article analyzes the research on the construction of sports multi-index data based on 5G IoT, makes full use of the IoT to mine sports-related data, and launches a multi-index discussion on it. First, the literature data method is adopted to learn the theoretical knowledge of IoT, artificial neural network, deep learning, etc., and establish a sports multi-index data analysis research model based on machine learning and massive data processing technology. Finally, for modern people, sports hobbies, types, exercise duration, exercise heart rate, and other aspects are analyzed. The results show that modern people prefer aerobic exercise, especially jogging and cycling, accounting for 47% and 41%, and the proportion of people who spend more than 60 minutes in the gym is as high as 48%. This shows that even though most people are busy at work, they still realize the importance of physical exercise and are willing to do sports.

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  • 作者

    Wang Hui; Zhao Ben;

  • 作者单位

    East China Normal Univ, Coll Phys Educ & Hlth, Shanghai 200241, Peoples R China|Xinjiang Normal Univ, Coll Phys Educ, Urumqi 830054, Xinjiang, Peoples R China;

    East China Normal Univ, Coll Phys Educ & Hlth, Shanghai 200241, Peoples R China;

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