关键词: 垂域大语言模型, 盐穴储氢库, 风险分析, 复合风险指数, 应急处置, 检索增强生成(RAG), 低秩自适应(LoRA)微调

Abstract:

To accurately analyze potential risks in salt cavern hydrogen storage projects and generate scientifically sound and effective response plans, a risk analysis and emergency response method integrating a composite risk index, RAG, and LoRA fine-tuning is proposed. First, a hierarchical monitoring indicator system is established to calculate the composite risk index and determine the corresponding early-warning level. A multimodal knowledge retrieval database incorporating specialized knowledge from geological reports, academic literature, historical cases, and other sources is then constructed using RAG. Subsequently, the base large language model is fine-tuned using LoRA. Finally, emergency response plans are generated based on the identified risk status, retrieved evidence, and structured prompts. Experimental results show that, on an independent test set, the proposed method achieves F₁ scores of 0.91, 0.93, and 0.84 in three core tasks: risk analysis, strategy generation, and scenario generalization, respectively. Ablation experiments show that the RAG-LoRA integrated model achieves an F₁ score of 0.88, representing an absolute improvement of 0.16 over the base model. System latency tests indicate that the average time required to generate an emergency response plan is less than 5 s, satisfying the real-time requirements of engineering emergency response.

Key words: domain-specific large language model, salt cavern hydrogen storage depots, risk analysis, composite risk index, emergency response, retrieval-augmented generation (RAG), low-rank adaptation (LoRA) fine-tuning