Improving Power Disaster Prevention Efficiency through Smart Sensors and Automated Monitoring Systems

In this study, we focus on the development of intelligent power disaster prevention monitoring sensors to enhance the stability and reliability of building electrical systems.The proposed system integrates multiple sensing components, including infrared temperature sensors, current sensors, and field-of-view (FOV) camera modules, forming a multi-sensor fusion framework for real-time anomaly detection and fire prevention.By integrating multi-scenario applications, real-time warnings, remote management, and comprehensive sensing technologies, the system provides comprehensive monitoring and automated intervention capabilities, enabling diversified disaster risk management.Each sensor module contributes specific functionsthermal monitoring for early overheating detection, current sensing for overload prevention, and image-based FOV detection for spatial anomaly recognition-enhancing both sensitivity and reliability.The system features high redundancy, low energy consumption, and scalability, making it suitable for industrial, commercial, and residential buildings.In this research, we explore how this disaster prevention solution improves power system resilience and public safety, with outstanding effectiveness particularly in fire prevention.The integration of multitype sensors and the Internet of Things (IoT)-based smart monitoring confirms this study's relevance to sensing systems and intelligent monitoring technologies.