Intent Recognition Enhanced by RAG and Knowledge Graphs for Regulation Execution and O&M Support in Power Engineering Secondary Systems

INTRODUCTION: Secondary systems in power engineering involve complex regulatory documents, operational procedures, dispatching requirements, and maintenance knowledge. Natural-language queries with implicit domain semantics create difficulties for conventional intent recognition in regulation matching and operation-support scenarios. OBJECTIVES: This study aims to enhance regulatory semantic understanding and intent-recognition accuracy, thereby supporting dispatching, maintenance, regulation execution, and operation and maintenance of power engineering secondary systems. METHODS: A knowledge-enhanced intent-recognition method combining Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) is proposed. A seven-label regulatory corpus is constructed, and structured semantic triples together with retrieved regulatory contexts are used as composite inputs. RESULTS: Experimental results show that the proposed method outperforms traditional techniques in terms of Accuracy and Macro-F1 score, improving the recognition of regulation-related, operation-guidance, maintenance-related, and procedure-support intents. CONCLUSION: The proposed approach strengthens the semantic alignment between technical queries and regulatory knowledge, providing an effective foundation for dispatching support, maintenance guidance, regulation execution, and intelligent operation and maintenance in the power industry.