People’s pursuit of public health continues to improve with the rapid economic development. Physical activity is an important way to achieve public health. Excessive physical activity intensity and uncomfortable forms of physical activity can affect people’s physical and mental health. Reasonable physical activity intensity and reasonable physical activity form will be beneficial to public health. People need to choose the corresponding sports mode according to physical function parameters and mental health parameters. However, it is difficult to understand the relationship between physical activity patterns and public health-related parameters, which limits people to establish reasonable exercise patterns. This research uses big data technology to design an intelligent sports-oriented public health data analysis scheme. It mainly uses MLCNN method and LSTM method to extract physical function parameter features, mental health parameter features, and sports parameter features. The research results show that the MLCNN method and LSTM can accurately extract and predict the parametric features related to sports and public health. The largest relative mean error is only 2.52%, which is the predicted value of the physical performance parameter characteristics. The smallest prediction error is also 2.27%, and this part of the relative error comes from the prediction of sports parameters.
The Internet of Things(IoT) is one of the products of rapid development in the 5G era, and the cloud computing processing capabilities of edge computing are also very powerful. How to apply the IoT and edge computing to the forecast of sports industry trends is a problem. Therefore, this article studies the prediction of the development trend of sports industry from the perspective of the IoT and edge models. This paper proposes a multi-user QoE fairness bandwidth allocation modeling and model training service deployment algorithm to build a sports industry development prediction model based on the IoT and edge computing. And this paper designs an algorithm simulation experiment, and the experimental results show that its success rate is as high as 99%. So this article is based on the 2011-2017 Chinese sports industry trends to make predictions and comparisons, and the comparison results show that the similarity rate is as high as 90%.
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