Construction of a physical health monitoring platform for adolescents driven by big data

In order to solve the problems of scattered data, poor real-time performance and insufficient analysis depth in the current physical health monitoring of adolescents, a physical health monitoring platform based on big data technology was constructed. The platform adopts a distributed architecture, integrates multi-source heterogeneous data, and uses machine learning algorithms to establish a health evaluation model. Through the construction of a multi-dimensional index system, the dynamic monitoring and analysis of 15 physical fitness indicators such as height, weight, and cardiopulmonary function were realized. The results show that the application of the platform in five pilot schools has improved the data processing efficiency by 85%, the early warning accuracy rate has reached 92.3%, and the real-time health status monitoring of more than 15,000 students has been realized. The platform has functions such as intelligent analysis, risk early warning, and personalized intervention suggestions, which provides data support for the education management department to formulate adolescent physical health policies, and provides a convenient channel for parents and schools to understand the physical health of students in a timely manner.