摘要
针对我国煤矿安全风险评价基础薄弱、手段单一、客观性不强等问题,结合云模型理论和基于层次分析法主观赋权、灰关联分析客观赋权的组合赋权方法,构建了一种煤矿安全风险评价模型。建立了包含人员、设备、环境、管理和历史5个一级评价指标,29个二级评价指标的煤矿安全风险评价指标体系;确立了煤矿安全风险评价指标的云模型标尺;采用安全风险评价云模型计算煤矿安全风险评价指标体系中指标层、准则层、目标层的数字特征,得到煤矿安全风险评价云。将该模型应用于红庆梁煤矿安全风险评价,结果表明:该煤矿现阶段安全风险等级为中等风险,影响安全生产的主要因素为作业人员违章操作率、煤层顶底板稳定性等。该模型为煤矿安全管理及风险防控工作提供了一定参考。
For problems of weak foundation,single means and weak objectivity of coal mine safety risk assessment in China,a coal mine safety risk assessment model was constructed based on cloud model theory and combination weighting method of subjective weighting by AHP and objective weighting by grey correlation analysis.Firstly,a coal mine safety risk assessment index system is established,which includes 5 first-level assessment indexes of personnel,equipment,environment,management and history,and 29 second-level assessment indexes.Then,cloud model scale of the coal mine safety risk assessment indexes is established.Finally,digital characteristics of index layer,criterion layer and target layer in the coal mine safety risk assessment index system are calculated by use of safety risk assessment cloud model to get coal mine safety risk assessment cloud.The model has been applied to safety risk assessment of Hongqingliang Coal Mine.The results show that safety risk level of the coal mine is medium at present,and the main factors affecting safety production are operation rate of operators against rules,stability of coal-seam roof and floor and other indexes,which provides a certain reference for safety management and risk prevention and control of coal mine.
作者
郜彤
张瑞新
郜赛超
何谦
刘贇
GAO Tong;ZHANG Ruixin;GAO Saichao;HE Qian;LIU Yun(School of Energy and Mining Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;North China Institute of Science and Technology,Langfang 065201,China)
出处
《工矿自动化》
北大核心
2019年第12期23-28,共6页
Journal Of Mine Automation
关键词
煤矿安全管理
煤矿安全风险评价
风险评价指标
评价指标体系
云模型
组合赋权
coal mine safety management
coal mine safety risk assessment
risk assessment index
assessment index system
cloud model
combination weighting