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Analysis and Management of Safety Hazards in Intelligent Mines Based on Multidimensional Data Mining
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Ye WANG1, 2, Xulong YAO1, 2, Guangyuan YU1, 2, Yanbo ZHANG1, 2, Zhigang TAO3, 4, Jizhong ZHAO5
Mining Research and Development | 2025, 45(10) : 173 - 181
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Mining Research and Development | 2025, 45(10): 173-181
Analysis and Management of Safety Hazards in Intelligent Mines Based on Multidimensional Data Mining
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Ye WANG1, 2, Xulong YAO1, 2, Guangyuan YU1, 2, Yanbo ZHANG1, 2, Zhigang TAO3, 4, Jizhong ZHAO5
Affiliations
  • 1.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • 2.Hebei Mining Green Intelligent Mining Technology Innovation Center, Tangshan, Hebei 063210, China
  • 3.State Key Laboratory for Tunnel Engineering, Beijing 100083, China
  • 4.School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
  • 5.Shougang Luannan Macheng Mining Co., Ltd., Tangshan, Hebei 063500, China
Published: 2025-10-25
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In order to explore the potential value of a large amount of safety hazard data in the construction process of intelligent mines, taking a mine in Shandong as an example, comprehensive analysis of its historical safety hazard data from 2014 to 2023 was conducted, and a multidimensional analysis model for mine safety management was constructed. Firstly, a Multi-Layer Perceptron (MLP) was used to construct a personnel, equipment, and environmental classification model for identifying hazards and accidents. Using the Latent Dirichlet Allocation (LDA) topic model, equipment hazards were classified into eight topics of lighting, transportation, support, electrical, firefighting, blasting, ventilation, and miscellaneous. Then, based on the principle of Apriori algorithm, key information was extracted from unstructured hazard text, and the relationship between different hazard features and topics was explored and analyzed. Finally, deep analysis of the data mining results was conducted using a combination of multidimensional analysis and data visualization techniques. The results indicate that equipment related hazards are high-risk areas that require special attention in the safety management of the mine. The lack of support for the roof, potholes on sloping road surfaces, and installation of switch grounding are significant hazard topics and associated rules, and the areas such as S16181 and S18165 are gathering areas for this type of hazard. The multidimensional analysis model constructed by the research can provide a basis for the analysis of mining safety hazards.

Intelligent mine  /  Safety hazard management  /  Data mining  /  Multidimensional analysis  /  Associated rules
Ye WANG, Xulong YAO, Guangyuan YU, Yanbo ZHANG, Zhigang TAO, Jizhong ZHAO. Analysis and Management of Safety Hazards in Intelligent Mines Based on Multidimensional Data Mining[J]. Mining Research and Development, 2025 , 45 (10) : 173 -181 .
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Year 2025 volume 45 Issue 10
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  • Receive Date：2024-11-09
  • Online Date：2026-02-06
  • Published：2025-10-25
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  • Received：2024-11-09
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Affiliations
    1.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
    2.Hebei Mining Green Intelligent Mining Technology Innovation Center, Tangshan, Hebei 063210, China
    3.State Key Laboratory for Tunnel Engineering, Beijing 100083, China
    4.School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
    5.Shougang Luannan Macheng Mining Co., Ltd., Tangshan, Hebei 063500, 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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