Real-Time Monitoring and Early Warning System of Athlete Health Condition Based on Computer Internet of Things

With the innovative progress of computer Internet of Things technology, the real-time monitoring and early warning system of athletes' health status has become the core issue in the field of sports science and technology. This paper presents a design scheme of athletes' comprehensive health monitoring and early warning system based on evolutionary difference model and improved random forest algorithm. The system integrates cutting-edge wearable sensing devices with intelligent terminal technology to capture athletes' physiological indicators (such as heart rate, blood oxygen saturation, myoelectric activity, etc.), movement dynamics (such as gait analysis, strength output, fatigue index, etc.) and environmental parameters (such as climate conditions, venue type, altitude change, etc.) in real time. These multiple data are synchronized to the cloud data center in real time through efficient data transmission links. At the cloud processing level, the system innovatively introduces the evolutionary difference model to deeply optimize the traditional random forest algorithm. This improvement not only enables the model to integrate a large number of decision trees to reduce the risk of overfitting and maintain a high generalization ability, but also realizes the adaptive optimization of feature selection and weight allocation through the evolutionary computing mechanism, and enhances the prediction accuracy and robustness of the model when processing high-dimensional, non-linear and spatio-temporal correlation athlete health data. The model dynamically evaluates and predicts the trend of key health indicators such as physiological load, fatigue accumulation and potential injury risk of athletes in real time, triggers accurate early warning tips, and helps the coaching team to take preventive measures or adjust training plans in time. Experimental verification shows that the stochastic forest system improved by the evolutionary differential model has excellent performance in real-time, prediction accuracy and reliability, and can accurately monitor athletes' health conditions in complex and changeable sports scenes, providing strong data support for scientific training, tactical decision making in competition and emergency medical intervention.