基于RAG与DeepSeek的HAZOP智能辅助系统优化OA

To address the issues of low efficiency,poor consistency,and heavy reliance on expert experi-ence in traditional Hazard and Operability(HAZOP)analysis in chemical enterprises,an intelligent auxiliary optimization method based on Retrieval-Augmented Generation(RAG)technology and the DeepSeek large lan-guage model was proposed.Specifically,this method constructed a structured vector database integrating multi-source heterogeneous safety knowledge and utilized the domain-knowledge-enhanced model to achieve the auto-mated review and in-depth supplementation of HAZOP analysis results.A case study demonstrated that the sys-tem effectively identified the omissions of typical hazards and insufficient cause identification within the traditional analysis,subsequently generating targeted optimization recommendations.These findings offer an innovative and intelligent tool for enhancing the completeness and reliability of HAZOP analysis in the chemical industry.