煤矿事故大数据驱动的风险治理模式研究综述

doi:10.16265/j.cnki.issn1003-3033.2024.07.0154

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (7): 28-37.doi: 10.16265/j.cnki.issn1003-3033.2024.07.0154

• 安全社会科学与安全管理 • 上一篇下一篇

煤矿事故大数据驱动的风险治理模式研究综述

王启飞¹(), 赵逸涵¹^,²^,^**(), 刘帅¹, 刘昊霖¹, 孙英峰³, 李成武⁴

¹ 北京建筑大学机电与车辆工程学院,北京 102616
² 北京热力集团有限责任公司,北京 100028
³ 北京科技大学大安全科学研究院,北京 100083
⁴ 中国矿业大学(北京) 应急管理与安全工程学院,北京 100083

收稿日期:2024-01-16 修回日期:2024-04-25 出版日期:2024-07-28
通信作者:
^** 赵逸涵(1999—),女,北京人,硕士研究生,研究方向为安全生产大数据分析。E-mail: zhaoyihan142@163.com。

作者简介:

王启飞 (1990—),男,河南信阳人,博士,讲师,主要从事安全生产大数据、城市运行风险管理、韧性城市、建筑和矿山安全管理等方面的研究。E-mail: wangqifei@bucea.edu.cn。

孙英峰副教授；

李成武教授

基金资助:
国家自然科学基金资助(51274206); “十四五”国家重点研发计划项目(2023YFC3009000); 北京市教育委员会科学研究计划项目(KM202410016004)

A review on risk management driven by big data in coal mine accidents

WANG Qifei¹(), ZHAO Yihan¹^,²^,^**(), LIU Shuai¹, LIU Haolin¹, SUN Yingfeng³, LI Chengwu⁴

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

Received:2024-01-16 Revised:2024-04-25 Published:2024-07-28

摘要/Abstract

摘要：

为明确智能化风险治理在煤矿的研究进展,综合分析数据驱动的煤矿安全风险治理模式研究发展现状,评述用于煤矿安全风险评估的不同预测手段和分析模型,首先,明确智能化风险治理的概念,并检索相关文献确定分析范围;然后,从数据驱动分析方法、煤矿安全风险评估模型和煤矿大数据预测预警平台等3方面,综述事故大数据研究现状、存在的问题及发展趋势。结果表明:煤矿安全领域已基本形成数据驱动的风险分析理论和应用框架,但仍不能满足风险评估与应急管理的需求。在预警平台应用方面,已形成统一的、通用的煤矿安全生产大数据分析平台基本框架,但在生产实际中的应用和推广还远远不够。未来应从提高数据质量和融合动静态多源数据入手,构建综合风险评估模型,研判煤炭开采风险,并加强数据驱动分析在生产实际中的应用,以推动煤矿安全风险治理模式由经验主义向数据驱动转变,实现煤矿安全风险治理信息化与智能化。

关键词: 煤矿事故, 数据驱动, 风险治理, 智能化, 风险评估模型

Abstract:

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.

Key words: coal mine accident, data-driven, risk governance, intellectualization, risk assessment models

中图分类号:

X936

王启飞, 赵逸涵, 刘帅, 刘昊霖, 孙英峰, 李成武. 煤矿事故大数据驱动的风险治理模式研究综述[J]. 中国安全科学学报, 2024, 34(7): 28-37.

WANG Qifei, ZHAO Yihan, LIU Shuai, LIU Haolin, SUN Yingfeng, LI Chengwu. A review on risk management driven by big data in coal mine accidents[J]. China Safety Science Journal, 2024, 34(7): 28-37.

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[52]	WANG Dandan, SUI Wanghua, RANVILLE J F. Hazard identification and risk assessment of groundwater inrush from coal mine: a review[J]. Bulletin of Engineering Geology and the Environment, 2022, 81(10) :DOI:10.1007/S10064-022-02925-3.

数据管理平台	所属机构	1	2	3	4	5	6	7	8	9	10	11
煤矿安全风险预测系统^[7]	中国矿业大学(北京) 国家安全生产监督管理总局	√	√	√		√
煤矿安全事故数据库系统^[3]	太原科技大学					√			√	√
煤矿安全生产大数据预警预测平台^[6]	国家安全生产监督管理总局						√	√		√
神东智能矿山大数据平台^[9]	国家能源集团神东煤炭集团				√					√
煤矿事故案例本体知识库^[4]	安徽理工大学										√
事故案例数据库^[5]	中国矿业大学(北京)										√
煤矿井下安全动态信息平台^[11]	中国矿业大学(北京)
煤矿安全生产动态监测预警系统^[8]	中国矿业大学			√		√				√
中国煤矿事故地理参考时空数据库^[2]	浙江大学中国人民大学					√						√
智能矿井安全生产大数据集成分析平台及其应用^[10]	北京大学						√			√

煤矿事故大数据驱动的风险治理模式研究综述

A review on risk management driven by big data in coal mine accidents

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