基于复杂网络的飞行区人因风险研究

为确保飞行区安全运行，收集1 233条机场飞行区事件的数据，结合事故报告及相关规范文件，利用共现分析和加权矩阵，结合复杂网络理论，建立飞行区人因风险事件网络。使用Python软件对网络节点属性进行分析，并结合天气因素，评估各个节点的重要程度。研究结果表明，飞行区人因风险事件网络符合复杂网络的特征，并确定了对飞行区事件影响较大的人为因素风险节点。根据研究结果，应加强飞行机组人员对规章的学习和对重点人员的监控。

Research on Human-factor Risks in the Flight Area Based on Complex Networks

To ensure the safe operation of the flight area, a total of 1 233 data on airport flight area incidents were collected, along with accident reports and relevant regulatory documents.By employing co-occurrence analysis and weighted matrices, combined with the theory of complex networks, a flight area human-factor risk factor network was established. The Python software was used to analyze the network node properties, and considering weather factors, the importance of each node was evaluated. The research findings indicate that the flight area human-factor risk network exhibits characteristics of a complex network, and identifies human-factor risk nodes that have significant impacts on flight area incidents. According to the results, it is suggested to strengthen the learning of regulations by flight crews and monitoring key personnel.