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Article(id=1149735805746328195, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149735800964825832, articleNumber=1003-3033(2024)11-0026-09, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.11.0321, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1715875200000, receivedDateStr=2024-05-17, revisedDate=1724083200000, revisedDateStr=2024-08-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1752047977148, onlineDateStr=2025-07-09, pubDate=1732723200000, pubDateStr=2024-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752047977148, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752047977148, creator=13701087609, updateTime=1752047977148, updator=13701087609, issue=Issue{id=1149735800964825832, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='11', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752047976008, creator=13701087609, updateTime=1756361988347, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1167830080236565470, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149735800964825832, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1167830080236565471, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149735800964825832, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=26, endPage=34, ext={EN=ArticleExt(id=1149735805989597829, articleId=1149735805746328195, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Study on risk factors coupling of personal injury and death accidents in electricity production, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To quantitatively analyze the coupling relationship between risk factors of personal injury and death accidents in Chinese electricity production and identify key risk factors，these risk factors were categorized into four dimensions: human，machine，environment and management based on the 4M accident causation theory. Utilizing grounded theory，32 secondary risk factors were identified，and the coupling methods were categorized into three types: multi-factor，double-factor and single-factor risk coupling. Drawing from investigation reports of 196 personal injury and death accidents in Chinese electricity production from 2016 to 2022，the N-K model was used to measure the coupling level of risk factors，while DEMATEL method was used to pinpoint key risk factors. Finally，the countermeasures and suggestions for risk prevention and control were put forward. This study shows that the frequency of accidents is closely related to the coupling level of risk factors，and the coupling level of risk factors is proportional to the number of participating factors. The risk coupling value of the four types of risk factors is 0.162 8，indicating the highest coupling level. The risk value of human factors participating in the risk coupling is relatively high，followed by the risk values of management and environmental factors. Among the three-factor risk couplings，the human-machine-environment risk coupling has the highest risk value at 0.104 7. For the double-factor risk couplings，the human-environment risk coupling has the highest risk value at 0.05. Both risk couplings involve human risk factors. The failure to implement safety production investment and main responsibility，illegal operations and illegal command and decision-making are the key factors in the coupling effect of risk factors of personal injury accidents in electricity production，and priority should be given to prevention and control.

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为定量分析我国电力生产人身伤亡事故风险因素间的耦合关系，并识别关键风险因素，首先，依据4M事故致因理论将风险因素划分为人、机、环、管4类一级风险因素，采用扎根理论辨识出32个二级风险因素，将风险因素耦合方式划分为多因素、双因素和单因素耦合3个类别；其次，基于2016—2022年我国发生的196起电力生产人身伤亡事故调查报告，使用N-K模型测度风险因素耦合水平；然后，采用决策试验和评价实验法(DEMATEL)辨识关键风险因素；最后，提出风险防控对策建议。结果表明：事故发生频率与风险因素耦合水平密切相关，而因素耦合水平与参与耦合因素数量成正比，4类风险因素的风险耦合值为0.162 8，耦合水平最高；人员风险因素参与耦合的风险值较大，管理风险因素和环境风险因素参与耦合的风险值次之，三因素风险耦合中人-机-环的风险耦合值最大，数值为0.104 7，双因素风险耦合中人-环的风险耦合值最大，数值为0.05，均含有人员风险因素；安全生产投入和主体责任未落实、违规违章作业、违规违章指挥决策等人员风险因素和管理风险因素是电力生产人身伤亡事故风险因素耦合作用中的关键因素，应予以重点防控。

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庞敏 (1982—)，女，山东德州人，硕士，教授，博士生导师，主要从事工程项目管理、安全与风险管理等方面的研究。E-mail：。

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庞敏 (1982—)，女，山东德州人，硕士，教授，博士生导师，主要从事工程项目管理、安全与风险管理等方面的研究。E-mail：。

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庞敏 (1982—)，女，山东德州人，硕士，教授，博士生导师，主要从事工程项目管理、安全与风险管理等方面的研究。E-mail：。

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figureFileBig=GLGJrK0ScnRpHkjlqXYt1Q==, tableContent=null), ArticleFig(id=1167815849051173824, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=图2, caption=电力生产人身伤亡事故风险因素耦合过程, figureFileSmall=hBxiKqZlnUK67EyguosZGA==, figureFileBig=GLGJrK0ScnRpHkjlqXYt1Q==, tableContent=null), ArticleFig(id=1167815849135059905, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=EN, label=Fig.3, caption=Risk factors for personal injury and death accidents in electricity production, figureFileSmall=YhHctB2oEHHOFiE9iYIU0A==, figureFileBig=Yf8JEbxm1VrjiUXupd7qBw==, tableContent=null), ArticleFig(id=1167815849214751682, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=图3, caption=电力生产人身伤亡事故风险因素, figureFileSmall=YhHctB2oEHHOFiE9iYIU0A==, figureFileBig=Yf8JEbxm1VrjiUXupd7qBw==, tableContent=null), ArticleFig(id=1167815849290249155, 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ArticleFig(id=1167815849583850438, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=图5, caption=电力生产人身伤亡事故风险因素权重值雷达图, figureFileSmall=KlLFg4cW4PoGgrECDrX1PA==, figureFileBig=uxxBklCGq1QZvhFlw9e6qw==, tableContent=null), ArticleFig(id=1167815849638376391, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=EN, label=Table 1, caption=

Coupling frequency and probability of risk factors for personal injury and death accidents in electricity production (2016-2022)

, figureFileSmall=null, figureFileBig=null, tableContent=
耦合方式 事件 频数 概率 耦合方式 事件 频数 概率 耦合方式 事件 频数 概率
单因素 0000 0 0 双因素 1100 6 0.030 6 多因素 1110 1 0.005 1
1000 13 0.066 3 1010 0 0.000 0 1101 48 0.244 9
0100 1 0.005 1 1001 79 0.403 1 1011 11 0.056 1
0010 3 0.015 3 0110 3 0.015 3 0111 2 0.010 2
0001 1 0.005 1 0101 6 0.030 6 1111 19 0.096 9
— — — 0011 3 0.015 3 — — —
), ArticleFig(id=1167815849755816904, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=表1, caption=

电力生产人身伤亡事故风险因素耦合的频数与概率(2016—2022年)

, figureFileSmall=null, figureFileBig=null, tableContent=
耦合方式 事件 频数 概率 耦合方式 事件 频数 概率 耦合方式 事件 频数 概率
单因素 0000 0 0 双因素 1100 6 0.030 6 多因素 1110 1 0.005 1
1000 13 0.066 3 1010 0 0.000 0 1101 48 0.244 9
0100 1 0.005 1 1001 79 0.403 1 1011 11 0.056 1
0010 3 0.015 3 0110 3 0.015 3 0111 2 0.010 2
0001 1 0.005 1 0101 6 0.030 6 1111 19 0.096 9
— — — 0011 3 0.015 3 — — —
), ArticleFig(id=1167815849898423241, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=EN, label=Table 2, caption=

Analysis results of coupling levels of risk factors for personal injury and death accidents in electricity production

, figureFileSmall=null, figureFileBig=null, tableContent=
耦合方式 T 4 ( a ， b ， c ， d ) T 31 ( a ， b ， c ) T 32 ( a ， b ， d ) T 33 ( a ， c ， d ) T 34 ( b ， c ， d ) T 21 ( a ， b )
耦合水平 0.162 8 0.104 7 0.040 6 0.102 3 0.021 6 0.011 6
排序 1 2 5 3 7 9
耦合方式 T 22 ( a ， c ) T 23 ( a ， d ) T 24 ( b ， c ) T 25 ( b ， d ) T 26 ( c ， d ) —
耦合水平 0.050 0 0.026 7 0.019 5 0.000 5 0.001 3 —
排序 4 6 8 11 10 —
), ArticleFig(id=1167815850015863754, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=表2, caption=

电力生产人身伤亡事故风险因素耦合水平分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
耦合方式 T 4 ( a ， b ， c ， d ) T 31 ( a ， b ， c ) T 32 ( a ， b ， d ) T 33 ( a ， c ， d ) T 34 ( b ， c ， d ) T 21 ( a ， b )
耦合水平 0.162 8 0.104 7 0.040 6 0.102 3 0.021 6 0.011 6
排序 1 2 5 3 7 9
耦合方式 T 22 ( a ， c ) T 23 ( a ， d ) T 24 ( b ， c ) T 25 ( b ， d ) T 26 ( c ， d ) —
耦合水平 0.050 0 0.026 7 0.019 5 0.000 5 0.001 3 —
排序 4 6 8 11 10 —
), ArticleFig(id=1167815850141692875, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=EN, label=Table 3, caption=

Direct impact matrix

, figureFileSmall=null, figureFileBig=null, tableContent=
因素 A1 A2 A3 A4 A5 A6 A7 … D14 D15
A1 0 0 0 0 0 0 0 … 0 0
A2 4 0 0 0 0 0 0 0 0
A3 2 2 0 0 0 0 0 0 0
A4 1 1 1 0 0 0 0 0 0
A5 4 4 0 0 0 3 0 0 0
A6 3 3 0 0 0 0 0 0 0
A7 4 4 0 0 0 0 0 0 0
︙
D14 0 0 0 0 0 0 0 … 0 0
D15 0 0 0 0 0 0 0 3 0
), ArticleFig(id=1167815850212996044, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=表3, caption=

直接影响矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
因素 A1 A2 A3 A4 A5 A6 A7 … D14 D15
A1 0 0 0 0 0 0 0 … 0 0
A2 4 0 0 0 0 0 0 0 0
A3 2 2 0 0 0 0 0 0 0
A4 1 1 1 0 0 0 0 0 0
A5 4 4 0 0 0 3 0 0 0
A6 3 3 0 0 0 0 0 0 0
A7 4 4 0 0 0 0 0 0 0
︙
D14 0 0 0 0 0 0 0 … 0 0
D15 0 0 0 0 0 0 0 3 0
), ArticleFig(id=1167815850288493517, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=EN, label=Table 4, caption=

Influence，affectedness，centrality，causality and factor type

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因素 fx ex Mx Nx 类别
A1 0.045 1.796 1.842 -1.751 结果因素
A2 0.15 1.387 1.537 -1.237 结果因素
A3 0.091 0.022 0.114 0.069 原因因素
A4 0.068 0.075 0.143 -0.006 结果因素
A5 0.326 0.257 0.583 0.069 原因因素
A6 0.226 0.39 0.616 -0.164 结果因素
A7 0.35 0.42 0.77 -0.07 结果因素
A8 0.065 1.17 1.236 -1.105 结果因素
A9 0.688 0.388 1.076 0.299 原因因素
B1 0.152 0.265 0.417 -0.114 结果因素
B2 0.152 0.517 0.669 -0.366 结果因素
B3 0.252 0.763 1.014 -0.511 结果因素
B4 0.174 0.536 0.71 -0.361 结果因素
C1 0.022 0 0.022 0.022 原因因素
C2 0.07 0 0.07 0.07 原因因素
C3 0.091 0.493 0.585 -0.402 结果因素
C4 0 0.584 0.584 -0.584 结果因素
D1 0.474 0.353 0.827 0.121 原因因素
D2 0.107 0.313 0.421 -0.206 结果因素
D3 0.213 0.413 0.626 -0.201 结果因素
D4 1.155 0.104 1.258 1.051 原因因素
D5 1.586 0.243 1.829 1.343 原因因素
D6 0.805 0.446 1.251 0.358 原因因素
D7 0.25 0.196 0.445 0.054 原因因素
D8 0.333 0.196 0.528 0.137 原因因素
D9 0.96 0.196 1.156 0.765 原因因素
D10 0.316 0.196 0.512 0.121 原因因素
D11 1.063 0.196 1.258 0.867 原因因素
D12 0.813 0.203 1.016 0.61 原因因素
D13 0.544 0.196 0.739 0.348 原因因素
D14 0.47 0.063 0.533 0.408 原因因素
D15 0.366 0 0.366 0.366 原因因素
), ArticleFig(id=1167815850447877070, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735805746328195, language=CN, label=表4, caption=

影响度、被影响度、中心度、原因度及因素类别

, figureFileSmall=null, figureFileBig=null, tableContent=
因素 fx ex Mx Nx 类别
A1 0.045 1.796 1.842 -1.751 结果因素
A2 0.15 1.387 1.537 -1.237 结果因素
A3 0.091 0.022 0.114 0.069 原因因素
A4 0.068 0.075 0.143 -0.006 结果因素
A5 0.326 0.257 0.583 0.069 原因因素
A6 0.226 0.39 0.616 -0.164 结果因素
A7 0.35 0.42 0.77 -0.07 结果因素
A8 0.065 1.17 1.236 -1.105 结果因素
A9 0.688 0.388 1.076 0.299 原因因素
B1 0.152 0.265 0.417 -0.114 结果因素
B2 0.152 0.517 0.669 -0.366 结果因素
B3 0.252 0.763 1.014 -0.511 结果因素
B4 0.174 0.536 0.71 -0.361 结果因素
C1 0.022 0 0.022 0.022 原因因素
C2 0.07 0 0.07 0.07 原因因素
C3 0.091 0.493 0.585 -0.402 结果因素
C4 0 0.584 0.584 -0.584 结果因素
D1 0.474 0.353 0.827 0.121 原因因素
D2 0.107 0.313 0.421 -0.206 结果因素
D3 0.213 0.413 0.626 -0.201 结果因素
D4 1.155 0.104 1.258 1.051 原因因素
D5 1.586 0.243 1.829 1.343 原因因素
D6 0.805 0.446 1.251 0.358 原因因素
D7 0.25 0.196 0.445 0.054 原因因素
D8 0.333 0.196 0.528 0.137 原因因素
D9 0.96 0.196 1.156 0.765 原因因素
D10 0.316 0.196 0.512 0.121 原因因素
D11 1.063 0.196 1.258 0.867 原因因素
D12 0.813 0.203 1.016 0.61 原因因素
D13 0.544 0.196 0.739 0.348 原因因素
D14 0.47 0.063 0.533 0.408 原因因素
D15 0.366 0 0.366 0.366 原因因素
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电力生产人身伤亡事故风险因素耦合研究
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庞敏 1, 2 , 李琼 1 , 张益畅 1
中国安全科学学报 | 安全社会科学与安全管理 2024,34(11): 26-34
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中国安全科学学报 | 安全社会科学与安全管理 2024, 34(11): 26-34
电力生产人身伤亡事故风险因素耦合研究
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庞敏1, 2 , 李琼1, 张益畅1
作者信息
  • 1 西南石油大学 经济管理学院，四川 成都 610500
  • 2 四川石油天然气发展研究中心，四川 成都 610500

    • 庞敏 (1982—)，女，山东德州人，硕士，教授，博士生导师，主要从事工程项目管理、安全与风险管理等方面的研究。E-mail：。

Study on risk factors coupling of personal injury and death accidents in electricity production
Min PANG1, 2 , Qiong LI1, Yichang ZHANG1
Affiliations
  • 1 School of Economics and Management，Southwest Petroleum University，Chengdu Sichuan 610500，China
  • 2 Sichuan Oil and Natural Gas Development Research Center，Chengdu Sichuan 610500，China
出版时间: 2024-11-28 doi: 10.16265/j.cnki.issn1003-3033.2024.11.0321
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摘要
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为定量分析我国电力生产人身伤亡事故风险因素间的耦合关系，并识别关键风险因素，首先，依据4M事故致因理论将风险因素划分为人、机、环、管4类一级风险因素，采用扎根理论辨识出32个二级风险因素，将风险因素耦合方式划分为多因素、双因素和单因素耦合3个类别；其次，基于2016—2022年我国发生的196起电力生产人身伤亡事故调查报告，使用N-K模型测度风险因素耦合水平；然后，采用决策试验和评价实验法(DEMATEL)辨识关键风险因素；最后，提出风险防控对策建议。结果表明：事故发生频率与风险因素耦合水平密切相关，而因素耦合水平与参与耦合因素数量成正比，4类风险因素的风险耦合值为0.162 8，耦合水平最高；人员风险因素参与耦合的风险值较大，管理风险因素和环境风险因素参与耦合的风险值次之，三因素风险耦合中人-机-环的风险耦合值最大，数值为0.104 7，双因素风险耦合中人-环的风险耦合值最大，数值为0.05，均含有人员风险因素；安全生产投入和主体责任未落实、违规违章作业、违规违章指挥决策等人员风险因素和管理风险因素是电力生产人身伤亡事故风险因素耦合作用中的关键因素，应予以重点防控。

电力生产人身伤亡事故  /  风险因素  /  风险耦合  /  N-K模型  /  决策试验和评价实验法(DEMATEL)

To quantitatively analyze the coupling relationship between risk factors of personal injury and death accidents in Chinese electricity production and identify key risk factors，these risk factors were categorized into four dimensions: human，machine，environment and management based on the 4M accident causation theory. Utilizing grounded theory，32 secondary risk factors were identified，and the coupling methods were categorized into three types: multi-factor，double-factor and single-factor risk coupling. Drawing from investigation reports of 196 personal injury and death accidents in Chinese electricity production from 2016 to 2022，the N-K model was used to measure the coupling level of risk factors，while DEMATEL method was used to pinpoint key risk factors. Finally，the countermeasures and suggestions for risk prevention and control were put forward. This study shows that the frequency of accidents is closely related to the coupling level of risk factors，and the coupling level of risk factors is proportional to the number of participating factors. The risk coupling value of the four types of risk factors is 0.162 8，indicating the highest coupling level. The risk value of human factors participating in the risk coupling is relatively high，followed by the risk values of management and environmental factors. Among the three-factor risk couplings，the human-machine-environment risk coupling has the highest risk value at 0.104 7. For the double-factor risk couplings，the human-environment risk coupling has the highest risk value at 0.05. Both risk couplings involve human risk factors. The failure to implement safety production investment and main responsibility，illegal operations and illegal command and decision-making are the key factors in the coupling effect of risk factors of personal injury accidents in electricity production，and priority should be given to prevention and control.

personal injury and death accidents in electricity production  /  risk factors  /  risk coupling  /  N-K model  /  decision-making trial and evaluation laboratory (DEMATEL) method
庞敏, 李琼, 张益畅. 电力生产人身伤亡事故风险因素耦合研究. 中国安全科学学报, 2024 , 34 (11) : 26 -34 . DOI: 10.16265/j.cnki.issn1003-3033.2024.11.0321
Min PANG, Qiong LI, Yichang ZHANG. Study on risk factors coupling of personal injury and death accidents in electricity production[J]. China Safety Science Journal, 2024 , 34 (11) : 26 -34 . DOI: 10.16265/j.cnki.issn1003-3033.2024.11.0321
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0 引言
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“双碳”目标下，我国风能、水能、太阳能等可再生能源发电产业快速发展[1]。同时，随着电力产业规模的不断扩大，电力生产过程中事故频发，带来严重的人员伤亡和巨大的经济损失。因此，深入研究电力生产人身伤亡事故风险因素，并辨识关键风险点，对指导该类事故防控具有重要意义。
国内外学者在电力生产风险及事故方面已展开了较多研究。在风险因素识别方面，孙志春等[2]建立了基于失效模式的电力企业事故分析系统，分析了导致事故发生的关键风险；关城等[3]运用行为安全24Model，分析了电力企业人身事故致因，并提出风险防控对策；赵楚凡等[4]集成关联规则挖掘和灰色关联分析方法，构建关键致因链筛选模型，研究了电力人身事故演化机制。在风险评价方面，崔威等[5]从建筑与设备、自然环境、电力运行等方面评价了开都河流域上的水电站风险；徐庆等[6]构建电力生产事故隐患风险评估指标体系，结合风险可接受准则划分电力生产事故隐患风险等级；王东等[7]采用基于贝叶斯网络的机器学习方法评价电网运行风险。在风险防控方面，林穿等[8]提出一种基于事理图谱的电力安全事故预控方法；鲍颜红等[9]以防控措施代价最小为目标，针对有大规模风电的电力系统，制定了一个安全且稳定的风险在线防控思路。上述研究成果对预防电力生产人身伤亡事故具有一定理论价值和借鉴意义，但仍存在以下不足：一是针对电力生产事故风险因素的研究多为定性分析，未能从量化视角识别关键因素，且研究并不完全针对电力生产人身伤亡事故，研究结果针对性不强；二是针对电力生产人身伤亡事故风险因素耦合的研究处于空白，缺乏对风险因素间耦合度的测量。
鉴于此，笔者拟以我国2016—2022年发生的196起电力生产人身伤亡事故的调查报告为依据，在分析电力生产人身伤亡事故风险因素基础上，首先，运用N-K模型评估风险耦合特性，测度耦合水平，探讨关键风险耦合类型；然后，采用决策试验和评价实验法(Decision-making Trial and Evaluation Laboratory，DEMATEL)分析风险因素影响关系，辨识关键风险点；最后，基于分析结果，提出风险防控对策建议，以期为电力生产人身伤亡事故防控提供参考。
1 基于N-K模型和DEMATEL的风险因素耦合分析
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在使用N-K模型测度一级风险因素耦合水平基础上，采用DEMATEL法研究电力生产人身伤亡事故一级风险因素分类下的二级风险因素，探究具体风险因素在系统中的重要程度，辨识出具有重要性的风险因素。技术路线如图1所示。
1.1 基于N-K模型的风险因素耦合模型
1) 风险因素耦合过程分析。基于安全系统工程理论，将电力生产人身伤亡事故视作由人、机、环、管4类子系统风险因素组成的复杂系统。电力生产人身伤亡事故风险耦合是指电力生产过程中各子系统风险因素之间相互依赖、相互影响和相互作用的关系。电力生产人身伤亡事故风险因素耦合过程如图2所示。由图2可知：各类风险因素同其他类别风险因素发生耦合后将破坏系统原本的稳定状态，产生耦合风险，当耦合风险值超过系统整体风险阈值后，将使风险增强或产生新的风险，导致电力生产人身伤亡事故发生。
2) 风险因素耦合测度模型构建。N-K模型[10]能够较好地解决复杂性问题。选用N-K模型研究电力生产人身伤亡事故风险因素间的耦合关系，其中，N表示系统中的元素个数，且每个元素有n种状态；K表示系统元素间相互耦合关系的数量，最小取0，最大取N-1[11]。运用信息理论中的交互信息值 T量化风险因素间的耦合水平[12]， T的计算[13]如下：
T ( a ， b ， c ， d ) = ∑ h = 1 H ∑ i = 1 I ∑ j = 1 J ∑ k = 1 K p h i j k . l o g 2 [ p h i j k / ( p h * * * . p * i * * . p   * * j * . p   * * * k ) ]
式中： a 、 b 、 c 、 d分别为人、机、环、管4类风险因素； h = 1，2 ， … ， H; i = 1，2 ， … ， I; j = 1，2 ， … ， J; k = 1，2 ， … ， K; H 、 I 、 J 、 K分别为人、机、环、管各类风险因素的状态数目；*为因素处于未知状态； p h i j k为4种风险因素发生耦合的概率。 T值越高表示风险耦合值越高，该风险耦合诱发事故的概率越高。
参考朱敬宇[14]、方俊[15]、张建设[16]等的研究成果，将电力生产人身伤亡事故风险耦合方式划分为3类：多因素、双因素和单因素风险耦合。
多因素风险耦合指3类及以上因素风险耦合的方式，主要包括人-机-环-管、人-机-环、人-机-管、人-环-管、机-环-管等5种耦合形式，对应风险耦合值分别为 T 4 ( a ， b ， c ， d ) 、 T 31 ( a ， b ， c ) 、 T 32 ( a ， b ， d ) 、 T 33 ( a ， c ， d )和 T 34 ( b ， c ， d )，计算公式为：
T 4 ( a ， b ， c ， d ) = ∑ h = 1 H ∑ i = 1 I ∑ j = 1 J ∑ k = 1 K p h i j k . l o g 2 [ p h i j k / ( p h * * * . p * i * * . p   * * j * . p   * * * k ) ]
T 31 ( a ， b ， c ) = ∑ h = 1 H ∑ i = 1 I ∑ j = 1 J p h i j * . l o g 2 [ p h i j * / ( p h * * * . p * i * * . p   * * j * ) ]
T 32 ( a ， b ， d ) = ∑ h = 1 H ∑ i = 1 I ∑ k = 1 K p h i * k . l o g 2 [ p h i * k / ( p h * * * . p * i * * . p   * * * k ) ]
T 33 ( a ， c ， d ) = ∑ h = 1 H ∑ j = 1 J ∑ k = 1 K p h * j k . l o g 2 [ p h * j k / ( p h * * * . p   * * j * . p   * * * k ) ]
T 34 ( b ， c ， d ) = ∑ i = 1 I ∑ j = 1 J ∑ k = 1 K p * i j k . l o g 2 [ p * i j k / ( p * i * * . p   * * j * . p   * * * k ) ]
双因素风险耦合指2类风险因素间产生耦合的情形，主要包括人-机、人-环、人-管、机-环、机-管、环-管等6种耦合情况，对应风险耦合值分别为 T 21 ( a ， b ) 、 T 22 ( a ， c ) 、 T 23 ( a ， d ) 、 T 24 ( b ， c ) 、 T 25 ( b ， d ) 、 T 26 ( c ， d )，计算公式如下：
T 21 ( a ， b ) = ∑ h = 1 H ∑ i = 1 I p h i * * . l o g 2 [ p h i * * / ( p h * * * . p * i * * ) ]
T 22 ( a ， c ) = ∑ h = 1 H ∑ j = 1 J p h * j * . l o g 2 [ p h * j * / ( p h * * * . p   * * j * ) ]
T 23 ( a ， d ) = ∑ h = 1 H ∑ k = 1 K p h * * k . l o g 2 [ p h * * k / ( p h * * * . p   * * * k ) ]
T 24 ( b ， c ) = ∑ i = 1 I ∑ j = 1 J p * i j * . l o g 2 [ p * i j * / ( p * i * * . p   * * j * ) ]
T 25 ( b ， d ) = ∑ i = 1 I ∑ k = 1 K p * i * k . l o g 2 [ p * i * k / ( p * i * * . p   * * * k ) ]
T 26 ( c ， d ) = ∑ j = 1 J ∑ k = 1 K p   * * j k . l o g 2 [ p   * * j k / ( p   * * j * . p   * * * k ) ]
单因素风险耦合指4类风险因素系统内部因素间产生耦合作用，无法利用交互信息的方式衡量其耦合情况[17]，故构建N-K模型分析双因素风险耦合和多因素风险耦合2类耦合方式。
1.2 基于DEMATEL的关键风险因素分析方法
DEMATEL法[18]能够利用图论和矩阵工具探究系统内各要素间相互关系，应用该方法可以分析电力生产人身伤亡事故风险因素间的相互关联、相互影响，以确定关键风险点。主要计算步骤如下[19]:
1) 构建直接影响矩阵Z。根据风险因素指标体系，邀请专家使用5级标度法测度二级风险因素间相互影响程度，0~4依次表示无影响、低影响、中等影响、较高影响、高影响，通过计算因素间平均影响强度，得到直接影响矩阵 Z = | z x y | q * q ， z x y为因素x对y的平均影响程度，q为二级风险因素总数。
2) 规范化直接影响矩阵。按下式规范化处理电力生产人身伤亡事故风险因素的直接影响矩阵，获得规范化影响矩阵G。
λ = 1 / m a x 1 ≤ x ≤ q ∑ y = 1 q z x y ， G = λ Z = | g x y | q * q
3) 计算综合影响矩阵R。基于规范化后的影响矩阵G，综合考虑风险因素间直接影响与间接影响的效应，利用下式计算得到综合影响矩阵R。
R = G + G 2 + … + G q = G ( E - G ) - 1 = | r x y | q * q
式中E为单位矩阵。
4) 计算中心度、原因度和权重值，明确因素属性及重要程度。将综合影响矩阵R分别按下式计算得到各风险因素的影响度fx、被影响度ex、中心度Mx、原因度Nx和权重值Qx。
f x = ∑ y = 1 q t x y ， x = 1，2 ， … ， q
e x = ∑ x = 1 q t x y ， y = 1，2 ， … ， q
M x = f x + e x ， x = 1，2 ， … ， q
N x = f x - e x ， x = 1，2 ， … ， q
Q x = M x ∑ x = 1 x = q M x ， x = 1，2 ， … ， q
2 人身伤亡事故风险因素辨识
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将4M理论应用到电力生产人身伤亡事故风险因素分析中，并根据我国2016—2022年196起电力生产人身伤亡事故调查报告分析具体风险。围绕人、机、环、管4个一级风险因素，通过扎根理论思想辨识出32个二级风险因素，具体如图3所示。
1) 人的风险因素。系统内较活跃且最难以把控的因素，其主体可分为电力企业从业人员和第三方单位工作人员，两者间有着密切联系。
2) 机的风险因素。以电力生产系统为核心，以安全生产作业防护措施、安全警示标识等附属设备为补充，其主体即是事故的载体，也是主要致因，且受其他风险的影响较大。
3) 环的风险因素。包括自然环境和作业环境，前者指雷电等自然条件不佳所引起的风险，后者指现场照明不足、存在有毒气体等不良环境以及物体坠落或坍塌等突发情况所造成的威胁。
4) 管的风险因素。主要指由政府部门、电力企业、第三方单位在安全管理方面的缺陷或漏洞所带来的风险。
3 人身伤亡事故风险耦合分析
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3.1 数据统计
分析2016—2022年间我国196起电力生产人身伤亡事故的调查报告，总结不同风险耦合方式的频数和概率，结果见表1。表1中，每种因素有0和1这2种状态，其中0表示未发生耦合，1表示发生耦合，参与耦合的因素对应顺序为人员、设备、环境 和管理因素。例如：1110表示人员、设备和环境风险发生，而管理风险不发生，其他情况同理。
3.2 耦合水平测度
基于表1中风险因素耦合形式频数与概率的统计结果和式(2)—式(12)测度出电力生产人身伤亡事故多因素风险耦合、双因素风险耦合不同风险因素耦合方式下的耦合水平，计算结果及排序见表2，风险因素耦合水平从高到低的总排序为：T4(a，b，c，d)>T31(a，b，c)>T33(a，c，d)>T22(a，c)>T32(a，b，d)>T23(a，d)>T34(b，c，d)>T24(b，c)>T21(a，b)>T26(c，d)>T25(b，d)。
3.3 耦合测度结果分析
1) 四因素风险发生耦合的耦合水平最高，其次是三因素风险耦合，虽然人-环和人-管双因素风险耦合水平高于部分三因素风险耦合水平，但总体而言，随着参与耦合因素的增加耦合水平随之增加，风险逐渐增大。
2) 三因素风险耦合值从大到小的总排序为： T 31 ( a ， b ， c ) > T 33 ( a ， c ， d ) > T 32 ( a ， b ， d ) > T 34 ( b ， c ， d )，其中排名前3的耦合均有人员风险因素参与，说明人员风险因素是威胁电力生产安全的重要因素，也是导致事故发生的重要原因。
3) 双因素风险耦合值从大到小的总排序为： T 22 ( a ， c ) > T 23 ( a ， d ) > T 24 ( b ， c ) > T 21 ( a ， b ) > T 26 ( c ， d ) > T 25 ( b ， d )。其中，人-环风险因素耦合形式造成的风险最高，人-管风险因素耦合次之，可见，人、环和管风险因素存在较大耦合性，表明人员易受其所处环境和管理的影响，即当其所处环境不佳、管理存在漏洞时容易导致事故发生。
4 人身伤亡事故关键风险因素分析
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4.1 数据收集
邀请5位从事相关领域研究或工作的专家评价32个电力生产人身伤亡事故二级风险因素间的相互影响关系强度，每位专家均有7年以上的科研或从业经验。计算各专家评价结果的平均值，得到直接影响矩阵Z，见表3。
4.2 关联度测算
基于式(13)和式(14)得到综合影响矩阵R，依据式(15)—式(18)计算出各二级风险因素的影响度fx、被影响度ex、中心度Mx、原因度Nx，计算结果见表4。中心度越高，表明该因素在系统中的作用越关键，核心程度越强；原因度越高，表明该因素对其他因素的作用越强，原因度为正数的因素是原因因素，原因度为负数的因素是结果因素。
根据计算出的中心度和原因度绘制原因-结果图，如图4所示。
根据式(19)计算得出各风险因素的权重值 Q x，并绘制雷达图，如图5所示。
4.3 关键风险因素分析
由表4和图4可知：32个二级风险因素中包含心理状态不佳A3、安全意识淡薄A5和作业现场安全管理不到位D1等18个原因因素，以及违规违章作业A1、违规违章指挥决策A2和技术水平存在不足A7等14个结果因素。其中，安全生产法执行不到位D4、安全生产投入和主体责任未落实D5的原因度最高，表明两者最容易对其他风险因素产生不良影响。
风险因素权重值越高，表明该因素越关键。从图5可以看出，权重值排名前10的风险因素依次为：D5、A1、A2、D11、D6、D4、A8、D9、A9、D12，其在系统中的重要程度较高。
结合3.3节的N-K模型分析结果，对比不同二级风险因素的原因度和权重值可知：首先，人员风险因素参与耦合的积极性比管理风险因素高，但这并不代表管理风险因素的重要程度低于人员风险因素。相反，权重值排名前10位的二级风险因素中，管理风险因素占6个，主要原因在于大部分人员风险因素属于结果因素，是浅层显性直接致因，而大部分管理风险因素属于原因因素，是深层隐性根源致因，这是N-K模型与DEMATEL法分析结果存在差异的原因。其次，人员风险因素中权重值最大的为A1，设备风险因素中权重值最大的为B3，环境风险因素中C3与C4的权重值相等且最大，管理风险因素中权重值最大的为D5。
5 结论
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1) 电力生产人身伤亡事故风险因素耦合水平与参与耦合风险因素数量成正相关，因素耦合水平随耦合因素数量的增加而增加，4因素风险耦合值最大，3因素风险耦合中人-机-环风险因素的耦合值最大，双因素风险耦合中人-环风险因素的耦合值最高。
2) 人员和管理风险因素对电力生产安全造成的威胁较大，其中安全生产投入和主体责任未落实、违规违章作业、违规违章指挥决策、监理企业未切实履行监理职责和安全生产法执行不到位是风险因素耦合作用中的关键因素；安全生产投入和主体责任未落实、安全生产法执行不到位的原因度最高，应进行重点防控以降低其对其他风险因素的作用。
3) 文中只辨识出了电力生产人身伤亡事故关键耦合风险因素，后续学者可深入探讨关键耦合路径，也可针对火电、水电等不同领域电力生产人身伤亡事故进行风险耦合分析。
4) 针对电力生产人身伤亡事故风险因素耦合提出如下建议：一方面，应尽量避免多因素风险耦合的发生，在注意从源头消解单风险因素时，也需遏制各风险因素与外部风险因素间的交互，以降低风险因素间发生多因素耦合的概率，防止其他因素与其发生耦合作用产生新的风险或加大风险强度。另一方面，应从危险源源头防止风险耦合的发生。例如：环境方面，通过监督与考核等手段提高对作业环境安全隐患辨识的全面性和准确率，针对恶劣天气和自然灾害以预防为主，确保能够迅速有效进行应急救援。管理方面，电力生产企业切实履行安全生产主体责任，并健全安全操作规程、安全教育培训以及外包监督管理等相关规章制度，相关政府部门完善自身安全生产监督管理机构和人员配置，并加大检查和安全宣贯力度。
基金
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  • 国家社会科学基金(20XJY006)
文献
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2024年第34卷第11期
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doi: 10.16265/j.cnki.issn1003-3033.2024.11.0321
  • 接收时间：2024-05-17
  • 首发时间：2025-07-09
  • 出版时间：2024-11-28
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  • 收稿日期：2024-05-17
  • 修回日期：2024-08-20
基金
国家社会科学基金(20XJY006)
作者信息
    1 西南石油大学 经济管理学院，四川 成都 610500
    2 四川石油天然气发展研究中心，四川 成都 610500
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