Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
To address the issues of low efficiency in manual processing and lack of accuracy in judgment within traditional mine gas safety inspections, this paper designs and implements the Intelligent Mine Gas State Decision-Making System based on large language models (LLMs) and a multi-agent system. The sy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/968 |
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| author | Yi Sun Xinke Liu |
| author_facet | Yi Sun Xinke Liu |
| author_sort | Yi Sun |
| collection | DOAJ |
| description | To address the issues of low efficiency in manual processing and lack of accuracy in judgment within traditional mine gas safety inspections, this paper designs and implements the Intelligent Mine Gas State Decision-Making System based on large language models (LLMs) and a multi-agent system. The system aims to enhance the accuracy of gas over-limit alarms and improve the efficiency of generating judgment reports. The system integrates the reasoning capabilities of LLMs and optimizes task allocation and execution efficiency of agents through the study of the hybrid multi-agent orchestration algorithm. Furthermore, the system establishes a comprehensive gas risk assessment knowledge base, encompassing historical alarm data, real-time monitoring data, alarm judgment criteria, treatment methods, and relevant policies and regulations. Additionally, the system incorporates several technologies, including retrieval-augmented generation based on human feedback mechanisms, tool management, prompt engineering, and asynchronous processing, which further enhance the application performance of the LLM in the gas status judgment system. Experimental results indicate that the system effectively improves the efficiency of gas alarm processing and the quality of judgment reports in coal mines, providing solid technical support for accident prevention and management in mining operations. |
| format | Article |
| id | doaj-art-b9e5b77c2ffa43688829c2d68f640680 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b9e5b77c2ffa43688829c2d68f6406802025-01-24T13:21:31ZengMDPI AGApplied Sciences2076-34172025-01-0115296810.3390/app15020968Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making SystemYi Sun0Xinke Liu1College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaTo address the issues of low efficiency in manual processing and lack of accuracy in judgment within traditional mine gas safety inspections, this paper designs and implements the Intelligent Mine Gas State Decision-Making System based on large language models (LLMs) and a multi-agent system. The system aims to enhance the accuracy of gas over-limit alarms and improve the efficiency of generating judgment reports. The system integrates the reasoning capabilities of LLMs and optimizes task allocation and execution efficiency of agents through the study of the hybrid multi-agent orchestration algorithm. Furthermore, the system establishes a comprehensive gas risk assessment knowledge base, encompassing historical alarm data, real-time monitoring data, alarm judgment criteria, treatment methods, and relevant policies and regulations. Additionally, the system incorporates several technologies, including retrieval-augmented generation based on human feedback mechanisms, tool management, prompt engineering, and asynchronous processing, which further enhance the application performance of the LLM in the gas status judgment system. Experimental results indicate that the system effectively improves the efficiency of gas alarm processing and the quality of judgment reports in coal mines, providing solid technical support for accident prevention and management in mining operations.https://www.mdpi.com/2076-3417/15/2/968large language modelsmulti-agent orchestration algorithmgas risk assessment knowledge basehuman feedback mechanismretrieval-augmented generation |
| spellingShingle | Yi Sun Xinke Liu Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System Applied Sciences large language models multi-agent orchestration algorithm gas risk assessment knowledge base human feedback mechanism retrieval-augmented generation |
| title | Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System |
| title_full | Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System |
| title_fullStr | Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System |
| title_full_unstemmed | Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System |
| title_short | Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System |
| title_sort | research and application of a multi agent based intelligent mine gas state decision making system |
| topic | large language models multi-agent orchestration algorithm gas risk assessment knowledge base human feedback mechanism retrieval-augmented generation |
| url | https://www.mdpi.com/2076-3417/15/2/968 |
| work_keys_str_mv | AT yisun researchandapplicationofamultiagentbasedintelligentminegasstatedecisionmakingsystem AT xinkeliu researchandapplicationofamultiagentbasedintelligentminegasstatedecisionmakingsystem |