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Article(id=1156983788988355428, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156983783787421903, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2309826, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1702396800000, receivedDateStr=2023-12-13, revisedDate=1731340800000, revisedDateStr=2024-11-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1753776031014, onlineDateStr=2025-07-29, pubDate=1739808000000, pubDateStr=2025-02-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753776031014, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753776031014, creator=13701087609, updateTime=1753776031014, updator=13701087609, issue=Issue{id=1156983783787421903, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='5', pageStart='1753', pageEnd='2192', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753776029774, creator=13701087609, updateTime=1769691857141, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223739602251436918, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156983783787421903, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223739602251436919, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156983783787421903, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2184, endPage=2192, ext={EN=ArticleExt(id=1156983790074680171, articleId=1156983788988355428, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Complex Network-based Identification of Incidents, Critical Causal Factors in Flight Transit Assurance Systems, columnId=1156262729993277777, journalTitle=Science Technology and Engineering, columnName=Papers·Environmental and Safe Science, runingTitle=null, highlight=null, articleAbstract=

In order to investigate the potential causative factors and mechanisms of accidents in the flight transit security system, and to further ensure the safety of civil aviation operation, based on the system theory and the gray correlation theory, and combining with the actual situation of the flight transit security operation process, the safety problems in the system were transformed into the control and feedback problems, and the safety control and feedback structure was mapped out. Using complex network theory to transform accident causation and its logical relationship, a directed weighted accident causation network model was constructed, the overall characteristics of the network and the connection of each node from different perspectives were quantitatively analyzed, such as the node degree, the network diameter and the average path length, etc., and then 16 important accident causation factors affecting the flight transit security system were selected. Through grey correlation analysis, the influence degree of each cause factor on the accident was judged, and the key cause factors that need to be prevented and controlled were finally determined. The results show that the personnel factor dominates the accidents in the flight crossing security system, and its sub-factors, such as speeding, insufficient number of personnel, error of towing personnel and illegal entry of personnel into the control area, are the key causal factors leading to the accidents.

, correspAuthors=Zi-chi ZHAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yang SONG, Zi-chi ZHAO), CN=ArticleExt(id=1156983942336303234, articleId=1156983788988355428, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于复杂网络的航班过站保障系统事故、关键致因因素识别, columnId=1156262730140078420, journalTitle=科学技术与工程, columnName=论文·环境科学、安全科学, runingTitle=null, highlight=null, articleAbstract=为了探究航班过站保障系统事故潜在致因因素和作用机理,进一步保障民航运行安全,基于系统理论和灰色关联理论并结合航班过站保障作业流程的实际情况,将系统中安全问题转化为控制和反馈问题,绘制安全控制反馈结构。运用复杂网络理论将事故致因及其逻辑关系进行转化,构建有向加权事故致因网络模型,从节点度、网络直径和平均路径长度等不同角度对网络整体特性及各节点的联系展开量化分析,遴选出影响航班过站保障系统的16个重要事故致因因素;通过灰色关联分析判断各致因因素对事故影响程度的大小,最终确定需要防控的关键致因因素。结果表明:人员因素在航班过站保障系统事故中占主导地位,其子因素中超速行驶、人员数量不足、牵引人员失误和人员违规进入控制区是导致事故的关键致因因素。, correspAuthors=赵梓池, authorNote=null, correspAuthorsNote=
*赵梓池(2000—),男,汉族,吉林长春人,硕士研究生。研究方向:安全与应急管理。E-mail:。
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宋洋(1977—),男,汉族,天津人,博士,教授。研究方向:航空应急与安防管理。E-mail:。

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宋洋(1977—),男,汉族,天津人,博士,教授。研究方向:航空应急与安防管理。E-mail:。

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Applied Sciences, 2020, 10(21): 576-585., articleTitle=A novel hazard analysis and risk assessment approach for road vehicle functional safety through integrating STPA with FMEA, refAbstract=null), Reference(id=1225467182449079015, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, doi=null, pmid=null, pmcid=null, year=2020, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=17, authorNames=中国民用航空局, journalName=事件样例:AC-396-08R2, refType=null, unstructuredReference=中国民用航空局. 事件样例:AC-396-08R2[S]. 北京: 中国民用航空局, 2020., articleTitle=null, refAbstract=null), Reference(id=1225467182583296754, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, doi=null, pmid=null, pmcid=null, year=2020, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=18, authorNames=Civil Aviation Administration of China, journalName=Sampleincident:AC-396-08R2, refType=null, unstructuredReference=Civil Aviation Administration of China. Sampleincident:AC-396-08R2[S]. 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ArticleFig(id=1225467173779452078, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 1, caption=

Potentially unsafe control behaviors

, figureFileSmall=null, figureFileBig=null, tableContent=
不安全控制行为 不安全控制行为导致的风险
未提供维护安全所需的控制行为 夜间机务巡检照明不足
未按机型对机位进行合理分配
未对机务人员配备工作防护装备
提供错误或不安全的控制行为 机务人员错误引导飞机
驾驶员未按规章驾驶车辆
人员违章进入限制区
控制行为发生顺序不一致 保障同一航班的特种车辆作业顺序不当
航油加注作业与旅客下机同时进行
货物卸载作业开始于航空器上轮档前
控制行为结束过早或执行时间过长 飞机起落架安全销过早拔出
机舱清洁、航食配备未在旅客登机前完成
牵引车未将航班推出至滑行道就结束作业
), ArticleFig(id=1225467173943029950, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表1, caption=

潜在不安全控制行为

, figureFileSmall=null, figureFileBig=null, tableContent=
不安全控制行为 不安全控制行为导致的风险
未提供维护安全所需的控制行为 夜间机务巡检照明不足
未按机型对机位进行合理分配
未对机务人员配备工作防护装备
提供错误或不安全的控制行为 机务人员错误引导飞机
驾驶员未按规章驾驶车辆
人员违章进入限制区
控制行为发生顺序不一致 保障同一航班的特种车辆作业顺序不当
航油加注作业与旅客下机同时进行
货物卸载作业开始于航空器上轮档前
控制行为结束过早或执行时间过长 飞机起落架安全销过早拔出
机舱清洁、航食配备未在旅客登机前完成
牵引车未将航班推出至滑行道就结束作业
), ArticleFig(id=1225467174106607819, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 2, caption=

Causal factors of flight transit security system accidents

, figureFileSmall=null, figureFileBig=null, tableContent=
人员因素 设备设施因素 管理因素 环境因素
身体不适时作业H1 车载设备故障F1 组织培训不足M1 标志标线模糊不清E1
超速行驶H2 机位信号装置失效或显示错误F2 机位分配不合理M2 风速过大E2
司机状态不佳H3 维修工具缺陷、损坏F3 保障作业顺序不当M3 道面不平E3
未观察H4 作业车辆违规停靠F4 作业规章制度不完善M4 地面湿滑E4
人员违规操作H5 航空器轮挡失效F5 部门协调不力M5 恶劣天气E5
引导错误H6 加油车油管老化F6 部门班组信息交流障碍M6 照明不足E6
机组失误H7 廊桥设计结构不合理F7 监督不足M7 机位附近植被未清理E7
未按规定路线行驶H8 廊桥故障F8 货物运输包装不当M8 机位垃圾清理不及时E8
人员违规进入控制区H9 车辆故障F9 车辆灯光使用不当M9 机务工作场所布局不合理E9
机务遗留工具H10 车辆配置不足F10 环境混乱E10
人员数量不足H11 设备设施摆放混乱F11
冒险、侥幸心理H12 车辆维护不当、缺乏保养F12
装卸人员野蛮装卸H13 机械原因F13
安全意识薄弱H14 设备超速运转、高负荷作业F14
技术不熟H15
机组违规H16
引导过快H17
牵引人员失误H18
廊桥人员失误H19
人员资质未达标H20
机务违规操作H21
滑错机位H22
), ArticleFig(id=1225467174349877463, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表2, caption=

航班过站保障系统事故致因因素表

, figureFileSmall=null, figureFileBig=null, tableContent=
人员因素 设备设施因素 管理因素 环境因素
身体不适时作业H1 车载设备故障F1 组织培训不足M1 标志标线模糊不清E1
超速行驶H2 机位信号装置失效或显示错误F2 机位分配不合理M2 风速过大E2
司机状态不佳H3 维修工具缺陷、损坏F3 保障作业顺序不当M3 道面不平E3
未观察H4 作业车辆违规停靠F4 作业规章制度不完善M4 地面湿滑E4
人员违规操作H5 航空器轮挡失效F5 部门协调不力M5 恶劣天气E5
引导错误H6 加油车油管老化F6 部门班组信息交流障碍M6 照明不足E6
机组失误H7 廊桥设计结构不合理F7 监督不足M7 机位附近植被未清理E7
未按规定路线行驶H8 廊桥故障F8 货物运输包装不当M8 机位垃圾清理不及时E8
人员违规进入控制区H9 车辆故障F9 车辆灯光使用不当M9 机务工作场所布局不合理E9
机务遗留工具H10 车辆配置不足F10 环境混乱E10
人员数量不足H11 设备设施摆放混乱F11
冒险、侥幸心理H12 车辆维护不当、缺乏保养F12
装卸人员野蛮装卸H13 机械原因F13
安全意识薄弱H14 设备超速运转、高负荷作业F14
技术不熟H15
机组违规H16
引导过快H17
牵引人员失误H18
廊桥人员失误H19
人员资质未达标H20
机务违规操作H21
滑错机位H22
), ArticleFig(id=1225467174706393321, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 3, caption=

Table of key causal factors for accidents in the flight overflight security system

, figureFileSmall=null, figureFileBig=null, tableContent=
人员因素 设备设施因素 管理因素 环境因素
超速行驶H2 车辆维护不当、缺乏保养F12 组织培训不足M1 地面湿滑E4
司机状态不佳H3 机位分配不合理M2 机位垃圾清理不及时E8
人员违规操作H5 作业规章制度不完善M4 环境混乱E10
人员违规进入控制区H9 监督不足M7
人员数量不足H11
安全意识薄弱H14
牵引人员失误H18
机务违规操作H21
), ArticleFig(id=1225467174874165497, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表3, caption=

航班过站保障系统事故关键致因因素表

, figureFileSmall=null, figureFileBig=null, tableContent=
人员因素 设备设施因素 管理因素 环境因素
超速行驶H2 车辆维护不当、缺乏保养F12 组织培训不足M1 地面湿滑E4
司机状态不佳H3 机位分配不合理M2 机位垃圾清理不及时E8
人员违规操作H5 作业规章制度不完善M4 环境混乱E10
人员违规进入控制区H9 监督不足M7
人员数量不足H11
安全意识薄弱H14
牵引人员失误H18
机务违规操作H21
), ArticleFig(id=1225467175092269322, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 4, caption=

Reference and comparison sequences for accident frequency analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
年份 参考序列 H2 H3 H5 H9 H11 H14 H18 H21 F12 M1 M2 M4 M7 E4 E8 E10
2018 67 13 7 16 19 8 20 7 33 4 11 3 17 31 15 21 27
2019 54 17 15 27 13 3 12 0 26 2 16 0 12 34 19 24 21
2020 73 23 21 31 17 14 29 2 27 0 14 0 20 37 12 18 29
2021 42 9 12 18 8 7 19 4 19 1 9 1 11 28 6 15 20
2022 38 11 6 14 5 4 21 1 10 0 5 5 8 30 10 17 14
), ArticleFig(id=1225467175213904155, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表4, caption=

事故频次分析的参考和比较序列

, figureFileSmall=null, figureFileBig=null, tableContent=
年份 参考序列 H2 H3 H5 H9 H11 H14 H18 H21 F12 M1 M2 M4 M7 E4 E8 E10
2018 67 13 7 16 19 8 20 7 33 4 11 3 17 31 15 21 27
2019 54 17 15 27 13 3 12 0 26 2 16 0 12 34 19 24 21
2020 73 23 21 31 17 14 29 2 27 0 14 0 20 37 12 18 29
2021 42 9 12 18 8 7 19 4 19 1 9 1 11 28 6 15 20
2022 38 11 6 14 5 4 21 1 10 0 5 5 8 30 10 17 14
), ArticleFig(id=1225467175348121896, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 5, caption=

Ranking of causal factors in relation to the frequency of accidents

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 关联度 排序 致因因素 关联度 排序
H2 0.854 7 F12 0.760 14
H3 0.769 13 M1 0.850 9
H5 0.857 6 M2 0.567 16
H9 0.877 3 M4 0.933 2
H11 0.784 12 M7 0.867 5
H14 0.817 11 E4 0.820 10
H18 0.595 15 E8 0.852 8
H21 0.870 4 E10 0.958 1
), ArticleFig(id=1225467175524282682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表5, caption=

各致因因素与事故发生频次的关联度排序

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 关联度 排序 致因因素 关联度 排序
H2 0.854 7 F12 0.760 14
H3 0.769 13 M1 0.850 9
H5 0.857 6 M2 0.567 16
H9 0.877 3 M4 0.933 2
H11 0.784 12 M7 0.867 5
H14 0.817 11 E4 0.820 10
H18 0.595 15 E8 0.852 8
H21 0.870 4 E10 0.958 1
), ArticleFig(id=1225467175675277638, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 6, caption=

Ranking of the correlation between causal factors in accident severity

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 关联度 排序 致因因素 关联度 排序
H2 0.952 1 F12 0.825 7
H3 0.639 13 M1 0.824 8
H5 0.851 3 M2 0.448 16
H9 0.881 2 M4 0.793 10
H11 0.828 6 M7 0.815 9
H14 0.633 14 E4 0.783 11
H18 0.612 15 E8 0.851 4
H21 0.842 5 E10 0.678 12
), ArticleFig(id=1225467175973073253, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表6, caption=

事故严重程度中各致因因素的关联度排序

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 关联度 排序 致因因素 关联度 排序
H2 0.952 1 F12 0.825 7
H3 0.639 13 M1 0.824 8
H5 0.851 3 M2 0.448 16
H9 0.881 2 M4 0.793 10
H11 0.828 6 M7 0.815 9
H14 0.633 14 E4 0.783 11
H18 0.612 15 E8 0.851 4
H21 0.842 5 E10 0.678 12
), ArticleFig(id=1225467176098902386, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=EN, label=Table 7, caption=

Results of correlation ratios of causal factors

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 关联度 排序 致因因素 关联度 排序
H2 1.114 8 1 H21 0.967 9 9
F12 1.085 5 2 E4 0.954 9 10
H11 1.056 1 3 M7 0.940 0 11
H18 1.028 6 4 M4 0.849 9 12
H9 1.004 6 5 H3 0.830 9 13
E8 0.998 8 6 M2 0.790 1 14
H5 0.993 0 7 H14 0.774 8 15
M1 0.969 4 8 E10 0.707 7 16
), ArticleFig(id=1225467176266674564, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983788988355428, language=CN, label=表7, caption=

各致因因素关联度比值结果

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 关联度 排序 致因因素 关联度 排序
H2 1.114 8 1 H21 0.967 9 9
F12 1.085 5 2 E4 0.954 9 10
H11 1.056 1 3 M7 0.940 0 11
H18 1.028 6 4 M4 0.849 9 12
H9 1.004 6 5 H3 0.830 9 13
E8 0.998 8 6 M2 0.790 1 14
H5 0.993 0 7 H14 0.774 8 15
M1 0.969 4 8 E10 0.707 7 16
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基于复杂网络的航班过站保障系统事故、关键致因因素识别
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宋洋 , 赵梓池 *
科学技术与工程 | 论文·环境科学、安全科学 2025,25(5): 2184-2192
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科学技术与工程 | 论文·环境科学、安全科学 2025, 25(5): 2184-2192
基于复杂网络的航班过站保障系统事故、关键致因因素识别
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宋洋 , 赵梓池*
作者信息
  • 中国民航大学安全科学与工程学院, 天津 300000

    • 宋洋(1977—),男,汉族,天津人,博士,教授。研究方向:航空应急与安防管理。E-mail:。

通讯作者:

*赵梓池(2000—),男,汉族,吉林长春人,硕士研究生。研究方向:安全与应急管理。E-mail:。
Complex Network-based Identification of Incidents, Critical Causal Factors in Flight Transit Assurance Systems
Yang SONG , Zi-chi ZHAO*
Affiliations
  • Safety Science and Engineering College, Civil Aviation University of China, Tianjin 300000, China
出版时间: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2309826
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摘要
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为了探究航班过站保障系统事故潜在致因因素和作用机理,进一步保障民航运行安全,基于系统理论和灰色关联理论并结合航班过站保障作业流程的实际情况,将系统中安全问题转化为控制和反馈问题,绘制安全控制反馈结构。运用复杂网络理论将事故致因及其逻辑关系进行转化,构建有向加权事故致因网络模型,从节点度、网络直径和平均路径长度等不同角度对网络整体特性及各节点的联系展开量化分析,遴选出影响航班过站保障系统的16个重要事故致因因素;通过灰色关联分析判断各致因因素对事故影响程度的大小,最终确定需要防控的关键致因因素。结果表明:人员因素在航班过站保障系统事故中占主导地位,其子因素中超速行驶、人员数量不足、牵引人员失误和人员违规进入控制区是导致事故的关键致因因素。
系统理论  /  航班过站保障  /  系统事故  /  关键致因  /  复杂网络

In order to investigate the potential causative factors and mechanisms of accidents in the flight transit security system, and to further ensure the safety of civil aviation operation, based on the system theory and the gray correlation theory, and combining with the actual situation of the flight transit security operation process, the safety problems in the system were transformed into the control and feedback problems, and the safety control and feedback structure was mapped out. Using complex network theory to transform accident causation and its logical relationship, a directed weighted accident causation network model was constructed, the overall characteristics of the network and the connection of each node from different perspectives were quantitatively analyzed, such as the node degree, the network diameter and the average path length, etc., and then 16 important accident causation factors affecting the flight transit security system were selected. Through grey correlation analysis, the influence degree of each cause factor on the accident was judged, and the key cause factors that need to be prevented and controlled were finally determined. The results show that the personnel factor dominates the accidents in the flight crossing security system, and its sub-factors, such as speeding, insufficient number of personnel, error of towing personnel and illegal entry of personnel into the control area, are the key causal factors leading to the accidents.

systems theory  /  flight crossing security  /  system accidents  /  key causal factors  /  complex network
宋洋, 赵梓池. 基于复杂网络的航班过站保障系统事故、关键致因因素识别. 科学技术与工程, 2025 , 25 (5) : 2184 -2192 . DOI: 10.12404/j.issn.1671-1815.2309826
Yang SONG, Zi-chi ZHAO. Complex Network-based Identification of Incidents, Critical Causal Factors in Flight Transit Assurance Systems[J]. Science Technology and Engineering, 2025 , 25 (5) : 2184 -2192 . DOI: 10.12404/j.issn.1671-1815.2309826
正文
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随着航空运输量的高速发展,机场航班日均起降量的持续攀升给航班保障作业带来了巨大挑战。作为机场地面保障服务的主体,航班过站保障系统运行呈现多种保障对象协同并行、保障时间愈发紧凑、各项保障工作内容繁杂等明显特点。据不完全统计,民航运行不安全事件每年给航空业带来直接或间接损失超过100亿美元[1]。如何从源头上挖掘航班过站保障系统的关键成因,找到系统中各保障作业的薄弱环节并施以有效约束,对降低航班过站保障系统事故发生率具有重要意义。
目前,鲜有学者专门针对航班过站保障这一系统进行事故致因分析,大多数是从其涵盖的各个保障作业环节、业务保障部门和操作流程入手进行致因因素识别与分析。Huseyin等[2]采用定性分析方法得出人为因素是导致土耳其机场停机坪安全的根本原因,但由于缺乏量化研究,无法将其结果推广到其他机场;Vicente等[3]以普卡尔帕机场事故为例,强调了恶劣天气对飞行安全的重要性,发现除飞机自身的气象设备外,机场地面设备设施对提高机场复杂气象条件下的航班过站保障作业安全有着重要影响;Towa等[4]通过识别航空货邮装卸作业中的关键因素-散落机坪上的异物,促使机坪监管员采取高效的处置措施,确保过站航班的顺利起降;Chikha等[5]借助模糊推理技术将已识别的变量进行参数化处理,揭示出导致地面服务车辆滑行事故的主要原因是地勤人员缺乏态势感知,但此方法尚不足以深入挖掘航班过站保障等复杂系统事故的根源因素;邵荃等[6]采用事故树方法分析廊桥刮碰航空器事故致因,发现廊桥对接人员违章作业等是影响廊桥刮碰航空器的主导因素;王永刚等[7]运用FTA-TOPSIS法对机场各业务风险防控进行评价,发现机坪引导工作是导致航空器受损的重要原因;李雨桐[8]指出航班保障作业顺序变化和设施设备老化是影响机坪事故的主要诱发因素,而对于由天气状况等纯客观原因所造成的不安全事件则较难进行深入分析。
综上所述,目前虽有众多专家学者对航班过站保障各作业环节事故致因机理展开调查研究,但大多数分析对象较单一且角度宏观,难以理清复杂系统事故成因的内在联系机制。航班过站保障作为机场运营的关键环节,其系统功能正常与否直接影响后续航班运行保障计划的执行进程,继而造成旅客滞留、后续航班延误乃至被迫取消。当下学界对该系统研究主要集中在优化地勤保障车辆协同调度[9]和评估多航班地面保障作业流程效率等方面[10],缺乏系统性地探究导致航班过站保障作业事故发生的潜在致因因素和作用机理,且尚未考虑其系统内部各个环节操作失误可能引发的安全问题和不良后果,如:员工人身伤害、操作中断或环境破坏等。对此,将航班过站保障系统按保障流程划分为26个标准作业节点,综合考虑从航班到港入位至航空器推出等一系列保障作业环节中可能引起航班过站保障系统事故的致因因素,建立系统理论过程分析-灰色关联分析(system theory process analysis-grey relational analysis,STPA-GRA)模型,并采用复杂网络理论对STPA进行扩展,以便更清晰、直观地展现事故网络中重要度较高的节点和节点间相互作用关系的强弱;同时,利用灰色关联分析法判断上述重要节点与航班过站保障系统事故关联的强弱次序,最终确定导致系统功能失效的关键致因因素,以期为降低航班过站保障作业事故发生率,提升机场和航司整体安全运行水平提供参考。
1 事故致因分析与模型构建
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1.1 航班过站保障作业流程分析
航班过站保障作业是一个以并行工作流为主、多种保障部门和人员协同进行的动态复杂过程[11],其实际保障作业流程大致可抽象如图1所示。整个保障流程必须严格按地面保障标准执行,各项保障作业间具有一定的逻辑时序和因果关联,且受保障作业性质、环境等条件制约。倘若某个保障环节出现问题,则会对整个保障流程和机场安全水平产生重大影响。
以深圳宝安国际机场起降次数最为频繁的B737、A320等机型为研究对象,将上述各保障环节细化成26个保障作业节点,如图2所示。可知航班过站保障作业项目繁多且各作业间的联系复杂,是一个多方保障部门之间相互协作、串并联作业同时进行的保障作业流程。
1.2 STPA方法分析流程构建
1.2.1 航班过站保障系统安全风险与约束
航班过站保障作业受保障资源、时间等方面的严格制约,使保障人员和设备常需在同一时刻对多架航班进行保障作业,其过程既涉及航空器和相关设备的运行,也涉及各种作业和乘客人员,多种地面车辆混杂,运行环境复杂,驻场单位众多,极大地增加了航班过站保障系统事故发生的可能性。
1.2.2 绘制安全控制反馈结构
控制反馈结构的建立是识别系统不安全控制行为的前提条件,通常由控制器、传感器、执行器和受控过程组成[12];航班过站保障系统的安全控制反馈结构如图3所示。通过分析各部件的安全控制情况可为系统级无效或不充分的控制行为和对应事故致因因素的查找提供关键线索。
1.2.3 识别潜在不安全控制行为
利用上述已绘制的系统安全控制反馈结构,辨识出导致航班过站保障系统约束失效的潜在不安全控制行为。将航班过站保障系统的不安全控制行为分为:①控制器未提供维护安全所需的控制行为;②控制器提供了错误或不安全的控制行为;③正确的控制行为发生次序不一致;④正确的控制行为结束过早或执行时间过长。识别出的部分不安全控制行为如表1所示。
1.2.4 事故致因因素分析
在确定不安全控制行为导致的危险后,将安全控制反馈结构划分为人员、设备设施、管理和环境层次,剖析整个航班过站保障系统事故中不安全控制行为的产生过程和组件间的交互作用关系,最终提取了55个事故主要致因因素,结果如表2所示。
1.3 事故致因网络模型建立
1.3.1 网络节点
根据复杂网络理论,将在安全控制反馈结构分析下得出的事故主要致因因素均视为独立网络节点。由于各事故致因最终传递和指向事故,因此,从事故后果的角度出发将航班过站保障系统事故类型分成航空器受损、人员伤亡、车辆受损和设备设施受损四大类,并且将其也作为网络节点。网络节点层(事故类型层)依次标记为:A1(航空器受损)、A2(人员伤亡)、A3(车辆受损)和A4(设备设施受损)。最终,航班过站保障系统事故致因网络模型共计55+4个网络节点。
1.3.2 网络有向边和权值
通过搜集近10年民用航空器事故调查报告和典型事故案例共528份,从中筛选出有关航班过站保障作业过程中产生的事故征候和不安全事件数365起。结合事故致因因素和网络节点识别航班过站保障系统事故的致因链路,节点间的有向边连接表示各致因因素在事件链中的因果关系,网络边的权值为相同有向边在不同事故致因链路中出现的次数之和,其值越大则边的权值越高。
1.3.3 网络模型建立
整理航班过站保障系统事故类型的所有致因因素,叠加、组合365起事故致因链,借助Gephi软件建立如图4所示的航班过站保障系统事故致因网络结构模型图。模型图由59个节点和243条有向加权边构成,可以明显看出,各节点周围的有向边分布疏密不均,表明不同节点在网络中的重要度有所差异。为深入探究致因网络节点的重要程度和准确找出保障作业过程中诱发事故的关键环节,还需进一步计算复杂网络的各个统计指标,分析网络特性,以判断关键致因节点和链路对整个网络的影响程度。
用不同颜色对事故致因因素和事故类型所属的不同网络节点层加以区分,红色代表人员因素层,绿色代表设备设施因素层,蓝色代表管理因素层,黄色代表环境因素层,粉色代表事故类型层;边的权值数据为各项因果关系在事故中呈现次数
2 事故致因网络结构模型分析
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2.1 节点度的统计分析
节点度作为网络的基本特征之一,是表示每个节点在网络中的中心性和对其他节点和受其他节点的影响程度,节点的度越大,与之相连的节点越多,则该节点在网络中的影响力越大[13]。由于节点较多,为方便识别度值较大的节点并使展示更加清晰,统计航班过站保障系统事故致因模型中总度值大于5的致因节点的度分布如图5所示。
分析图5可知,入度值排序在前5位的节点依次为:A1“航空器受损”、A2“人员伤亡”、A3“车辆受损”、A4“设备设施受损”和H22“滑错机位”。其中,事故致因节点A1的入度值最高,表明将风险传至该节点的因素节点最多。如H6“引导错误”、E2“风速过大”等节点在事故致因网络中均指向A1节点,这说明A1容易受到其他较多因素的影响,进而诱发根源众多,形成了多通道到达路径,增大事故发生的可能性。出度值排序在前5位的节点依次为:M7“监督不足”、M1“组织培训不足”、M4“作业规章制度不完善”、H14“安全意识薄弱”和H18“牵引人员失误”。其中,事故致因节点M7的出度值最高,表明该节点在传导风险方面具有较强的主动性,既具有发散性的不稳定因素,又是导致多种事故发生的主要原因。上述这些度值较大的因素常常在事故发生过程中与更多的因素有直接的因果关系,系统一旦出现异常,这些因素更易受到其他因素的影响或者对其他因素带来危险,导致系统中不安全因素急剧增多,并使其整体安全性严重下降,进而引发风险事故发生。
2.2 节点强度的统计分析
节点强度本质上是反映节点在整个网络中的重要程度。在有向网络中,某一节点指向其他节点的边的权值之和称为该节点的出强度;由其他节点指向该节点的边的权值之和称为入强度,两者之和为总强度。同样为使展示更清晰,统计总强度值大于40的节点的强度分布如图6所示。入强度排在前5位的节点分别为:H9“人员违规进入控制区”、H5“人员违规操作”、H2“超速行驶”、A2“人员伤亡”和H21“机务违规操作”。出强度排在前5位的节点分别为:H14“安全意识薄弱”、M1“组织培训不足”、M7“监督不足”、M4“作业规章制度不完善”和H21“机务违规操作”。从强度与度的结果对比中不难发现,人的不安全行为和管理失效是系统事故致因因素中较为关键的节点,尤其是管理失效所占比例更甚,在航班过站保障作业过程中,机务违规操作与监督不足往往对于事故发生是并联关系,而人员违规进入控制区与组织培训不足展现的是相互之间的因果关系。通过节点强度分析可以综合分析两个节点间的相关性,在制定控制措施时,应切断节点连接数目多且强度大的边,阻断对其他相关联节点的作用,并联关系的节点间可选择控制效果好的节点进行控制消除,因果关系的节点间可通过从源头上控制风险因素,以有效减少事故发生的可能性。
2.3 网络直径和平均路径长度统计分析
网络直径和平均路径长度共同反映航班过站保障系统事故致因网络中因素间相互影响的紧密程度。构建的航班过站保障系统事故致因网络的直径为3,平均路径长度为1.63。从该结果可以看出,事故致因网络直径和平均路径长度比节点数量小得多,反映出航班过站保障系统事故致因因素之间的关联关系较为紧密,平均分离程度较小且相互作用速度较快,各危险因素一旦失效后具有较强的传播效率和扩散性,极易引发因素之间发生交互反应。因此,为有效降低事故的发生率,应建立快速、高效的反映机制,在某个节点不受控时,迅速切断其向下一个或多个节点的传播路径,防止事故态势进一步扩大。
安全控制反馈结构层中各影响因素节点到每种事故类型节点的平均长度如图7所示。其中,环境因素层到各类事故的平均路径长度最短,在实际保障作业中,如遇地面标志标线模糊不清、照明不足等,会导致作业车辆错误驶入不当区域、机务人员难以在夜间持续进行航班维保工作等隐患或事故的发生,并往往会引发较为严重的后果。总体来看,人员因素和环境因素到各事故类型的平均路径长度较其他2种因素层短,表明这两种类型因素是导致事故发生的最快因素,设备设施因素和管理因素的平均路径长度较长是因为它们一般属于较深层次的间接因素,通过影响其他因素间接导致事故的发生。
2.4 聚类系数统计分析
聚类系数是描述网络中与同一致因节点相连的节点之间相互连接的情况,网络中节点的聚集程度越高,其聚类系数越大,节点与周围节点的关系就更密切。经计算,航班过站保障系统事故致因网络的平均聚类系数为0.112,其中,从该系统事故致因网络中筛选出聚类系数大于0.15的节点分布如图8所示。节点H11“人员数量不足”的聚类系数最大为0.5,节点H18“牵引人员失误”、M2“机位分配不合理”和E4“地面湿滑”聚类系数也达到0.3以上,说明这4个节点与其邻近节点之间联系最为紧密且易相互作用;聚类系数较大的节点大多都处在人员因素层,说明人员因素层具有更强的事故致因聚集特性,其次是管理因素层和环境因素层。对于这些聚集性较强的事故致因因素,为防止其发生异常并诱发其邻居因素也暴露在危险之中,安全管理人员应重点关注聚类系数较高的节点,加强对这些节点的控制措施,做好事故的预防与应急处理方案,避免事故网络发生连锁反应并及时阻断事故发生。
2.5 介数中心性统计分析
事故模型中各致因因素节点的介数中心度值如图9所示,取值范围介于0~0.13486。介数中心度不低于0.05的节点共有7个,分别为E10“环境混乱”、H18“牵引人员失误”、E8“机位垃圾清理不及时”、H5“人员违规操作”、H3“司机状态不佳”、F12“车辆维护不当、缺乏保养”和M7“监督不足”,这7个节点的累积介中心度已达到介数中心度总和的半数之上,表明这些节点在系统中因素之间相互影响的过程中起到了至关重要的作用,他们的存在使得因素之间的影响更加快速,容易将其他节点暴露在风险当中,大大增加了直接或间接导致事故发生的概率。因此,可针对性地对这些节点进行有效管控,以便切断更多致因因素间风险传播和扩大的途径,削弱致因因素间的连锁反应,提高航班过站保障系统的整体安全性。
3 系统事故致因因素灰色关联分析
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由于导致航班过站保障系统事故的致因因素种类众多,若分析时将所有因素全部纳入其中,既会增加网络复杂度、降低分析效率,又难以准确找出变量间的关联性。因此,通过对航班过站保障系统事故致因网络的各个统计指标分析,最终得到整合的事故致因重要节点16个,结果如表3所示。
3.1 基于事故频次的灰色关联分析
统计2018—2022年近5年发生的机坪作业事故案例共279份,并从每份事故调查报告中提取上述与事故发生有关的重要节点,将其作为比较序列与事故发生频次进行灰色关联分析。航班过站保障系统事故灰色关联分析的参考序列和比较序列具体统计结果如表4所示。对表4数据进行无量纲化处理后,计算灰色关联系数,最终求得2018—2022年航班过站保障系统事故发生频次与各致因因素的灰色关联度并对其进行综合排序,具体结果如表5所示。
由表5可知,各因素与事故发生频次之间的关联程度是不同的,取关联程度较大的前5个致因因素按影响程度大小依次进行排序,E10>M4>H9>H21>M7。这表明环境混乱的关联度最大,其次是作业规章制度不完善、人员违规进入控制区、机务违规操作和监督不足。所以,由航班过站保障作业环境混乱引发的事故发生率要远大于其他事故致因因素,应更加注重对作业环境的改善,采取有效措施尽量减少或避免事故的发生。
3.2 基于事故后果严重度的灰色关联分析
在现实情况中,环境混乱等具有较高事故发生频次的致因因素,一旦及时采取有效的预防和控制措施,可能不会造成严重的事故后果;反之,在某些特定条件下,导致事故发生频次较低的事故致因因素,则极有可能引发更加严重的事故后果。因此,为确定导致航班过站保障系统失效的关键致因因素,按事件样例[13]中飞机运行阶段和机场范围内发生的飞机受损以及导致的人员伤亡和经济损失情况,将事件严重程度划分为事故、征候和一般事件并以此作为事故后果严重度的统计指标,根据4.1节中同样的分析步骤,得到各致因因素的关联度,结果如表6所示。
由表6可知,当以事故后果严重度为分析对象时,超速行驶、人员违规进入控制区、人员违规操作、机位垃圾清理不及时和机务违规操作等为造成事故的主要致因因素,由此可见,基于事故频次和后果严重度的关联度分析,导致最终确定的事故致因因素并不一致。因此,为甄别出引发事故的关键性致因因素,选取事故后果严重度关联度和事故频次关联度的比值作为判断标准,计算公式为
B=$\frac{{C}_{s}}{{C}_{f}}$
式(1)中:B为关联度比值;Cs为事故后果严重度关联度;Cf为事故频次关联度。
根据式(1)计算求得各因素的事故后果严重度关联度和事故频次关联度的比值并进行排序,结果如表7所示。
由表7中可以得出,超速行驶是导致航班过站保障系统事故的关键致因因素,车辆维护不当、人员数量不足等也是促使事故发生的重要致因因素。在实际保障作业过程中,各个保障环节往往需涉及众多类型的保障人员和特种车辆,如摆渡车、清污车、牵引车等,且每项保障环节都对人员及车辆有着严格的时序约束限制,因此,由超速行驶引发的事故在航班过站保障系统中占有很大比例,应加强驾驶员的技能和安全意识方面的培训,养成良好的驾驶习惯,同时,也要促进各保障部门间的有效控制和协调,增加部门间的信息交互透明度。
4 结论
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(1)基于系统理论思维构建了航班过站保障系统控制反馈结构,辨识出导致此系统约束失效的潜在不安全控制行为及其可能引发的风险,分别从人员层次、设备设施层次、管理层次和环境层次4个方面入手,共提取出55个事故主要致因因素。
(2)结合复杂网络理论对STPA方法的逻辑关系和作用机制进行系统性的扩展,构建包含59个节点和243条有向加权边的航班过站保障系统事故致因网络模型。通过定量计算节点度、节点强度、网络直径和平均路径长度等复杂网络相关统计指标得出人员因素层中的H11“人员数量不足”、H21“机务违规操作”和H9“人员违规进入控制区”等致因因素与事故发生有着紧密联系。
(3)运用灰色关联分析法,最终确定由事故致因网络遴选出的16个重要致因因素对航班过站保障系统事故的影响程度,H2“超速行驶”是影响事故发生的关键因素,管理者应建立并落实对驾驶员的日常监督检查制度,积极协同配合各保障部门间的信息交流工作,警惕并阻断每一个不安全因素的发生和传播路径,降低致因相互作用的能力,以期从根源上遏制风险并提高航班过站保障作业的安全水平。
基金
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  • 国家重点研发计划(2021YFB2600500)
  • 民航热灾害防控与应急重点实验室开放基金课题(RZH2022-KF-04)
文献
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参考文献 引证文献
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2025年第25卷第5期
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doi: 10.12404/j.issn.1671-1815.2309826
  • 接收时间：2023-12-13
  • 首发时间：2025-07-29
  • 出版时间：2025-02-18
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  • 收稿日期：2023-12-13
  • 修回日期：2024-11-12
基金
国家重点研发计划(2021YFB2600500)
民航热灾害防控与应急重点实验室开放基金课题(RZH2022-KF-04)
作者信息
    中国民航大学安全科学与工程学院, 天津 300000

通讯作者:

*赵梓池(2000—),男,汉族,吉林长春人,硕士研究生。研究方向:安全与应急管理。E-mail:。
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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