Chain-of-Thought-Guided Intelligent Agent for Power Equipment Inspection

This paper proposes a Chain-of-Thought (CoT) reasoning-guided intelligent agent for power equipment operation and maintenance, which integrates multimodal fusion and equipment mechanism-data analysis methods to achieve highprecision equipment status recognition and accurate fault diagnosis prediction. The technology employs step-by-step reasoning to achieve high explainability while organically integrating multi-source heterogeneous data and knowledge models. Experimental results show that the proposed method improves diagnostic accuracy by 6.9 % and prediction accuracy by 9.5 % compared to traditional methods. In practical applications, defect recognition accuracy exceeds 95 %, significantly enhancing equipment operation and maintenance efficiency and quality.