基于LLM和GraphRAG的柴油发电机故障维保知识推理方法OA

To enhance the knowledge reasoning performance of diesel generator fault diagnosis,a fault maintenance knowledge reasoning method is proposed based on Large Language Models(LLM)and knowledge graph-enhanced Retrieval-Augmented Generation technology.The research data is extracted from enterprise diesel generator fault maintenance records,processed through LLM for semantic standardization to form an ontological knowledge graph covering fault systems,fault types,fault causes,and solution strategies.Specifically,the CRISPE framework prompt engineering is employed to drive the LLM to automatically extract entities and relationships,thereby build-ing a structured fault diagnosis knowledge graph thet is subsequently integrated into the graph database.By integra-ting semantic embedding technology with graph reasoning mechanisms,a knowledge indexing network is constructed to support functionalities such as natural language Q&A,fault logic tracing,and professional maintenance guid-ance.Experimental results demonstrate that the method achieves an overall answer accuracy rate of 94％,with accuracy rates exceeding 90％for core question types.This indicates that the method significantly outperforms tradi-tional RAG,as well as general LLM such as ChatGPT-4o and DeepSeek in terms of fault attribution precision,solu-tion professionalism,and adaptability to complex fault scenarios,exhibiting higher domain adaptability and reasoning capabilities.