模型微调与RAG的爆破领域智能问答系统研究OA

To address the triple demands of accuracy,timeliness,and safety compliance in knowledge question-answering for the blasting domain,this paper proposes the BlastRAG system,which integrates model fine-tuning with safety-enhanced retrieval.The system performs automated knowledge structuring to convert literature,such as the Blasting Safety Regulations,into high-quality datasets and a vector knowledge base.Furthermore,Low-Rank Adaptation(LoRA)technology is employed to inject domain-specific knowledge into the Qwen model through fine-tuning.At the retrieval stage,an enhanced architecture combining hybrid retrieval and a Safety Criticality Re-ranker(SCR)is designed.This mechanism prioritizes official safety regulations by assigning them higher weights,thereby fundamentally ensuring the compliance of generated answers.Experimental results demonstrate that BlastRAG performs effectively in terms of retrieval accuracy and compliance.The safety-enhanced retrieval module achieves an accuracy of 91.8%and an overall F1 score of 89.9%,and the ROUGE metrics of the generated answers are also significantly improved.Examples of model responses and expert evaluations further confirm that the system can generate accurate,professional,and strictly compliant answers,providing a reliable intelligent solution for blasting-related high-risk vertical domains.