Design and Implementation of Concurrent Optimization Schemes for Sports Health Prediction Platform

Abstract

Exercise simulation application is important to improve on the health and avoid the irreversible damage of human health. The application of sports health prediction software will be the characteristics of multi-users, and the calculation of human sports simulation will spend lots of time. Therefore, the concurrent optimization scheme of the platform is very important to guarantee the efficiency, usability and stability of multi-user platform. In this paper, a sports health prediction platform based on Client/Server architecture is reported, and a concurrent optimization scheme for this platform is designed and implemented. To solve this problem, a cluster on the server side is developed, a front-end server based on Nginx as a user access entry is built. Using the Spring Boot framework, human sports simulation model on back-end application servers is constructed, and Redis method is used to save information as simulation prediction results and users' preference clothing. The comparison results show that our method has better performances for the multi-user system. It is a suitable framework for the sports health simulation prediction application.