Research on Construction and Application of Knowledge Graph Based on Large Language Model

Massive amounts of operational and maintenance (O&M) data from nuclear power distributed control system (DCS) contain rich operational experience and expert knowledge. Effectively extracting DCS alarm response information and forming knowledge service is a current hotspot and frontier research area in rapid DCS response. Due to the lack of clear structure and standards in multi-source heterogeneous data of nuclear power DCS, previous knowledge extraction primarily relied on manual annotation and deep learning methods, which require extensive domain knowledge and information processing capabilities and are constrained by the heavy workload of data annotation. Therefore, this study proposes a knowledge extraction method using large language model (LLM) with a step-by-step prompting strategy, constructing a DCS O&M knowledge graph (KG). Based on large language model technology and secondary intent recognition methods, intelligent question and answer (Q&A) and other knowledge services are developed utilizing the knowledge graph. Using O&M data from a nuclear power plant’s DCS as a case study, the research focuses on knowledge extraction, knowledge graph construction, and intelligent Q&A. The results show that the model achieves an overall precision (P) of 91.24%, recall (R) of 85.85%, and F1-score of 88.43%. The proposed method can comprehensively capture key entities and attribute information from multi-source heterogeneous DCS O&M data, guiding domain knowledge Q&A, assisting O&M personnel in timely responding to DCS alarm anomalies, analyzing fault causes and response strategies, and providing guidance for DCS O&M training and maintenance in power plants.