Simulation of Artificial Intelligence Algorithm Based on Network Anomaly Detection and Wireless Sensor Network in Sports Cardiopulmonary Monitoring System

Abstract

The impact of network anomaly on data transmission and system operation cannot be ignored, so an effective anomaly detection method is needed to ensure the stability of the system. This study aims to improve the anomaly detection ability of the cardiopulmonary exercise monitoring system by constructing artificial intelligence algorithms based on wireless sensor networks, ensure the accuracy and reliability of real-time data, and provide support for sports health management. In this study, an integrated learning algorithm was adopted, combined with network traffic monitoring and sensor data analysis, and through data preprocessing, feature extraction and anomaly detection model construction, real-time monitoring of cardiopulmonary monitoring data was realized. Simulation platform is used to evaluate the performance of the algorithm in different network environments, especially in wireless networks and mobile networks. The experimental results show that the proposed algorithm can effectively identify abnormal data under abnormal network conditions. Compared with traditional detection methods, the proposed method significantly improves detection efficiency and response speed, and can adapt to complex wireless sensing environment.