基于Graph-RAG的BIM知识图谱智能检索系统研究OA

To address the issues of inconsistent naming conventions and high retrieval thresholds in Building Information Modeling(BIM)attributes,this paper designs a graph-enhanced retrieval generation system(Graph-RAG)using the DeepSeek-R1 large language model and the Neo4j graph database.The system employs a regular expression-enhanced natural language to Cypher query(NL2Cypher)mapping technique and a logic self-healing interceptor to enable an automated closed-loop workflow from Industry Foundation Classes(IFC)models to engineering audit reports.Experimental results show that the response time during the mapping phase remains below 812 milliseconds.In a test involving 230 components with missing material attributes,the system achieves a 100％instruction success rate through latent semantic mining,completing the task in 61.151 seconds.The study demonstrates that the system effectively overcomes the reliance on standardized modeling data,significantly improves BIM data governance efficiency,and enhances decision-making support,offering a novel approach to the digitalization of engineering audits.