Research on Intelligent Management System of Sports Venues Based on Internet of Things

This paper proposes an intelligent management system based on Internet of Things, and designs an innovative intelligent equipment scheduling and energy consumption optimization algorithm (IIMA). The algorithm collects the operation data of venue equipment and user behavior information, and uses the Q-Learning method in reinforcement learning to dynamically schedule the operation status of equipment and optimize energy consumption. The algorithm introduces a multi-objective reward mechanism to reduce energy consumption while maintaining the response speed and service life of the equipment. The experiment was carried out in a simulated venue environment built based on Internet of Things devices. The results showed that the IIMA algorithm reduced the total energy consumption by about 18% in high-usage scenarios and by about 12% in low-usage scenarios. The user interaction response time was shortened by 30% on average, and the user satisfaction score was improved to 4.8/5. In addition, compared with the traditional scheduling method, the convergence speed of the IIMA algorithm was increased by 25%, and the computational efficiency was significantly improved. The study shows that the intelligent management system based on the Internet of Things can significantly improve the energy efficiency and management level of venue equipment, and provide a technical reference for the intelligent application of joint management of multiple venues in the future.