
科学技术与工程

中国技术经济学会
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
Complex Network-based Identification of Incidents, Critical Causal Factors in Flight Transit Assurance Systems
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。
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.
系统理论 / 航班过站保障 / 系统事故 / 关键致因 / 复杂网络
systems theory / flight crossing security / system accidents / key causal factors / complex network
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