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A review on risk management driven by big data in coal mine accidents
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Qifei WANG1, Yihan ZHAO1, 2, **, Shuai LIU1, Haolin LIU1, Yingfeng SUN3, Chengwu LI4
China Safety Science Journal | 2024, 34(7) : 28 - 37
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China Safety Science Journal | 2024, 34(7): 28-37
Safety social science and safety management
A review on risk management driven by big data in coal mine accidents
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Qifei WANG1, Yihan ZHAO1, 2, **, Shuai LIU1, Haolin LIU1, Yingfeng SUN3, Chengwu LI4
Affiliations
  • 1 School of Mechanical-Electronic and Automobile Engineering,Beijing University of Civil Engineering and Architecture,Beijing 102616,China
  • 2 Beijing Thermal Power Group Co.,Ltd.,Beijing 100028,China
  • 3 Safety Science Research Institute,Beijing University of Science and Technology,Beijing 100083,China
  • 4 School of Safety Science and Emergency Management,China University of Mining and Technology,Beijing 100083,China
Published: 2024-07-28 doi: 10.16265/j.cnki.issn1003-3033.2024.07.0154
Outline
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In order to clarify the research progress of intelligent risk management in coal mines,the research status of data-driven coal mine safety risk management models was comprehensively analyzed. The prediction methods and analysis models for coal mine safety risk assessment were also reviewed. Firstly,the intelligent risk management was defined,and the scope of analysis was determined by searching relevant literature. Then,the research status,existing problems and development trend of accident big data were reviewed from three aspects: data-driven analysis method,coal mine safety risk assessment model and coal mine big data prediction and early warning platform. The results show that the theory and application framework of data-driven risk analysis in the field of coal mine safety has been basically formed,but it still cannot meet the needs of risk assessment and emergency management. In the application of early warning platform,a unified and general basic framework of big data analysis platform for coal mine safety production has been formed,but its application and promotion in production practice are far from enough. In the future,it is necessary to construct the comprehensive risk assessment model to study the risk of coal mining,starting from improving data quality and integrating dynamic and static multi-source data. Besides,the application of data-driven analysis in production practice should also be strengthened. These works can promote the transformation of coal mine safety risk management mode from empiricism to data-driven,and realize the informatization and intelligence of coal mine safety risk management.

coal mine accident  /  data-driven  /  risk governance  /  intellectualization  /  risk assessment models
Qifei WANG, Yihan ZHAO, Shuai LIU, Haolin LIU, Yingfeng SUN, Chengwu LI. A review on risk management driven by big data in coal mine accidents[J]. China Safety Science Journal, 2024 , 34 (7) : 28 -37 . DOI: 10.16265/j.cnki.issn1003-3033.2024.07.0154
Year 2024 volume 34 Issue 7
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.07.0154
  • Receive Date:2024-01-16
  • Online Date:2025-07-09
  • Published:2024-07-28
Article Data
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History
  • Received:2024-01-16
  • Revised:2024-04-25
Funding
Affiliations
    1 School of Mechanical-Electronic and Automobile Engineering,Beijing University of Civil Engineering and Architecture,Beijing 102616,China
    2 Beijing Thermal Power Group Co.,Ltd.,Beijing 100028,China
    3 Safety Science Research Institute,Beijing University of Science and Technology,Beijing 100083,China
    4 School of Safety Science and Emergency Management,China University of Mining and Technology,Beijing 100083,China
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表12种不同金属材料的力学参数

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Number of
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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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