Research on key technologies of power substation inspection based on large model

This study proposes an intelligent solution based on large models for the application of key technologies in power substation inspections. By constructing a multi-layer neural network model and combining multi-task learning and adaptive learning rate mechanisms, the application of large model technology in equipment fault detection, environmental monitoring and operating status analysis was studied and explored. Experimental results show that the system significantly improves inspection efficiency, reduces equipment failure rates, and effectively saves operation and maintenance costs. At the same time, actual case analysis verified the effectiveness and practicability of the system and proved the broad application prospects of large model technology in power substations. This research provides an important reference for the intelligent development of power systems and lays the foundation for intelligent applications in other industrial scenarios.