工矿自动化2026，Vol.52Issue(7)：9-16,8.DOI:10.13272/j.issn.1671-251x.2026060019
大语言模型驱动的煤矿全链路智能语音调度研究
End-to-end intelligent voice dispatching driven by a large language model for coal mines
杨梁 1任保保 1秦虎豹 1折海生 1刘鑫 1耿一天 2陈泓霖 3季法秀4
作者信息
- 1. 陕西银河煤业开发有限公司,陕西榆林 719000
- 2. 西安理工大学电气工程学院,陕西西安 710048
- 3. 西安建筑科技大学建筑学院,陕西西安 710055
- 4. 西安科技大学通信与信息工程学院,陕西西安 710054
- 折叠
摘要
Abstract
To address response delays and information errors and omissions caused by reliance on manual call answering and paper records in coal mine dispatching,this study investigated end-to-end intelligent voice dispatching driven by a Large Language Model(LLM)for coal mines.An intelligent voice dispatching system integrating domain fine-tuning with Retrieval-Augmented Generation(RAG)was designed.The system adopted a hierarchically decoupled architecture,used a locally deployed LLM as its decision-making core,and integrated speech recognition and synthesis technologies.By introducing an Interactive Voice Response(IVR)virtual agent mechanism,the system established a closed-loop workflow encompassing call routing,automatic work-order generation,and voice-command execution.Low-Rank Adaptation(LoRA)fine-tuning was used to adapt the model to the coal mine domain,and RAG was introduced to fundamentally prevent the generation of noncompliant instructions.A vector knowledge base based on the Coal Mine Safety Regulations was constructed as a compliance constraint,enabling end-to-end intelligent processing from voice-command recognition to execution.Field test results showed that,in coal mine environments with high noise levels and strong dialectal accents,the system's speech recognition word error rate was 0.34-0.42.With the semantic compensation capability of the fine-tuned LLM,intent recognition accuracy remained at or above 93.50％,and the average exact match rate of dispatching instructions reached 94.93％.The system operates stably in a mining area and effectively reduces dispatcher workload and the recording error rate,providing a replicable engineering paradigm for intelligent upgrading of coal mines.关键词
煤矿调度/智能语音调度/大语言模型/领域微调/检索增强生成/虚拟座席机制/低秩适配Key words
coal mine dispatching/intelligent voice dispatching/Large Language Model/domain fine-tuning/Retrieval-Augmented Generation/virtual agent mechanism/Low-Rank Adaptation分类
矿业与冶金引用本文复制引用
杨梁,任保保,秦虎豹,折海生,刘鑫,耿一天,陈泓霖,季法秀..大语言模型驱动的煤矿全链路智能语音调度研究[J].工矿自动化,2026,52(7):9-16,8.
