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基于贝叶斯网络的事故多级多米诺效应计算方法研究 被引量:5

Study on calculation method of multi-level domino effect of accident based on Bayesian network
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摘要 为了更合理地分析事故风险,提出了基于贝叶斯网络的多级多米诺效应计算方法及其计算步骤,并从个人风险和社会风险2个角度,定量分析了生产安全事故的多级多米诺效应。同时以某企业的2个汽油罐区为例,运用上述方法对其生产安全事故的多米诺效应进行定量计算,并将计算结果与未考虑多米诺效应、仅考虑一级多米诺效应时的计算结果进行比较。研究结果表明:基于贝叶斯网络的计算方法,同时考虑了多级多米诺效应和事故的协同效应,可以使计算的个人风险和社会风险更接近于实际。 In order to analyze the accident risk more reasonably, a calculation method and its calculation procedure of multi-level domino effect based on Bayesian network were proposed. In addition, the multi-level domino effect of work safety accidents were quantitatively analyzed from the perspectives of individual risk and social risk. Taking two gasoline tank areas of an enterprise as examples, the domino effect of its work safety accidents were quantitatively calculated by using the above method. Furthermore, the calculation results were compared with those without considering the domino effect and only considering the single-level domino effect. The results showed that the multi-level domino effect and the synergistic effect between accidents could be considered at the same time by the calculation method based on Bayesian network. It can make the calculation results of individual risk and social risk closer to the actual situation.
作者 黄海燕 李健 江田汉 吴军 白晓昀 HUANG Haiyan;LI Jian;JIANG Tianhan;WU Jun;BAI Xiaoyun(College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029,China;Beijing Modem Manufacturing Industry Development Research Center, College of Economics and Management, Beijing University of Technology, Beijing 100124,China;China Academy of Safety Science and Technology, Beijing 100012,China;China Huanqiu Contracting & Engineering Co., Ltd. Beijing Branch, Beijing 100029,China)
出处 《中国安全生产科学技术》 CAS CSCD 北大核心 2019年第6期157-161,共5页 Journal of Safety Science and Technology
基金 国家重点研发计划(2016YFC0801500,2017YFC0804907) 国家自然科学基金项目(71571010) 北京市社会科学基金一般项目(17GLB014) 北京市长城学者培养计划(CIT&TCD20180305)
关键词 多米诺效应 个人风险 社会风险 贝叶斯网络 domino effect individual risk social risk Bayesian network
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