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Technological Evolution of Intelligent Mine Large Models and Layered Collaborative Architecture for Safety Governance
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Xiangyu Kou1, 2, Huihui Guo3, Cao Gao1, 2, Chen Chen1, 4, Shuang Huang1, 4
Mining Research and Development | 2026, 46(9) : 235 - 243
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Mining Research and Development | 2026, 46(9): 235-243
Technological Evolution of Intelligent Mine Large Models and Layered Collaborative Architecture for Safety Governance
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Xiangyu Kou1, 2, Huihui Guo3, Cao Gao1, 2, Chen Chen1, 4, Shuang Huang1, 4
Affiliations
  • 1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
  • 2.State Key Laboratory of Safety Technology of Metal Mines, Changsha, Hunan 410012, China
  • 3.Minmetals National Innovation Center Technology (Hunan) Co., Ltd., Changsha, Hunan 410012, China
  • 4.National Engineering Research Center for Metal Mining, Changsha, Hunan 410012, China
Published: 2026-09-25
Outline
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The engineering basis for introducing large models in mines is established through the long-term accumulation of digitalization, automation, and specialized models. However, the integrated understanding of complex risks by safety systems is still hindered by fragmented data, isolated knowledge, and the disconnection between early warning and response. Based on the technological evolution of intelligent mines and engineering practices, the prior foundations, including platform interfaces, specialized tools, multi-source safety evidence, and domain knowledge, were examined, and the differentiated requirements of high-risk scenarios such as goaf, tailings pond, and hoisting system were analyzed. The study suggests that mine safety large models should not replace control systems and on-site responsibility chains, but rather serve as a "cognition-and-proposal layer" to participate in multimodal understanding, risk assessment, mechanism interpretation, and solution generation. Its outputs should be checked by data evidence, professional rules, mechanistic models, protection interlocks, and human review, and routes for rejection and safe fallback shall be retained. Data governance and knowledge base, cognition and proposal, constraint checking, on-site response, and outcome feedback are incorporated into the same accountability chain by the resulting layered collaborative architecture. The engineering value of mine safety large models should possess interpretability, traceability, verifiability, and fallback capability.

Intelligent mine  /  Mine safety  /  Large model  /  Layered collaboration
寇向宇, 郭晖晖, 高操, 陈谌, 黄爽. 智能矿山大模型技术演进及安全治理分层协同架构. 矿业研究与开发, 2026 , 46 (9) : 235 -243 .
Xiangyu Kou, Huihui Guo, Cao Gao, Chen Chen, Shuang Huang. Technological Evolution of Intelligent Mine Large Models and Layered Collaborative Architecture for Safety Governance[J]. Mining Research and Development, 2026 , 46 (9) : 235 -243 .
Year 2026 volume 46 Issue 9
42
11
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Article Info
  • Receive Date:2026-03-04
  • Online Date:2026-10-27
  • Published:2026-09-25
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  • Received:2026-03-04
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Affiliations
    1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
    2.State Key Laboratory of Safety Technology of Metal Mines, Changsha, Hunan 410012, China
    3.Minmetals National Innovation Center Technology (Hunan) Co., Ltd., Changsha, Hunan 410012, China
    4.National Engineering Research Center for Metal Mining, Changsha, Hunan 410012, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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