Abstract

In the field of coal mine safety, traditional large language models (LLMs) face issues such as poor knowledge timeliness, lack of traceable evidence, and susceptibility to hallucinations when handling complex knowledge-intensive tasks. To align with the human-centric principles of Industry 5.0 and meet the strict compliance requirements of Industrial Regulatory Technologies (RegTechs), this study aims to enhance the performance of a Retrieval-Augmented Generation (RAG)-based coal mine safety compliance system. A comprehensive coal mine knowledge dataset (CMK) is constructed to serve as a dynamic RegTech knowledge base. Based on the RAG framework, a two-stage retrieval structure is designed and optimized, transforming the generative AI into a safe, constrained reasoning core. Also, a hard-negative ranking dataset (CMK-R) is developed to support the evaluation of the reranking model and mitigate the risk of semantic hallucinations. The proposed approach adopts Dmeta-embedding-base and BGE-rerank-base as backbone models and applies fine-tuning, teacher–student structured distillation, and Matryoshka Representation Learning (MRL) to improve efficiency and reduce energy consumption. Experimental results demonstrate that the proposed CMS-base model achieves FAHR, MRR@10, and mAP scores of 93.48, 95.59, and 95.63, respectively, indicating that the system can retrieve the correct safety regulation or operational clause at the top ranks with high reliability. Furthermore, the lightweight variant reduces inference time by over 60%, and accuracy degradation remains below 1% even when the vector dimension is compressed to 64, significantly facilitating Edge Intelligence deployments. This robust, human-centric framework provides a scalable industrial AI solution for real-time safety compliance and decision support.

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