Study on Key Technologies of Edge Intelligence for Long-Distance, Large-Scale, and Complex Environment Power Inspection

This paper studies the application and key technologies of edge intelligence technology in long-distance, large-scale, and complex environment power inspection. Traditional power inspection relies on cloud computing, but when faced with real-time and low-latency requirements, the cloud computing model has limitations and is difficult to adapt to the inspection needs of complex environments. This study first analyzes the current status and technical challenges of power inspection, and proposes a solution based on edge intelligence. Through a multi-layer architecture design, the collaborative work of data collection, real-time processing, and global analysis is realized between the perception layer, edge computing layer, and cloud layer. On this basis, the key technologies of multi-source data fusion, fault diagnosis algorithm, and system optimization are studied, especially for the specific application of long-distance transmission line and substation equipment inspection scenarios, and performance evaluation is carried out. The results show that the edge intelligence system has significant advantages in reducing data transmission delay, improving fault identification accuracy, and enhancing system responsiveness. This study shows that edge intelligence technology provides a more real-time, stable, and reliable solution for power inspection, and has broad application prospects.