This study proposes an optimization scheme for an intelligent monitoring system based on the Internet of Things and big data to address the reliability issues of power plant generator outlet circuit breakers (GCBs). By integrating SF6 gas analysis, temperature and current sensors, and other multidimensional real-time monitoring, a data acquisition and cloud analysis platform is constructed to achieve dynamic characteristic evaluation and fault warning. This system breaks through the limitations of traditional maintenance relying on manual labor, effectively identifies mechanical and electrical hazards, reduces failure rates and maintenance costs, and extends equipment lifespan. Case verification shows that the system significantly improves the accuracy of circuit breaker state evaluation through multi physics field coupling analysis and big data modeling, providing an innovative technological path for intelligent management of power equipment.