航空器离场管制关键环节识别及风险传播研究

航空器离场风险识别是民航安全生产的重要环节，但目前研究多面向航空器运动过程和静态危险源，忽略了管制业务影响和风险动态传播机理．针对此问题，本文提出了基于复杂网络的离场管制关键点识别及风险传播模型．首先，抽象管制信息交互和业务逻辑，构建复杂网络并量化各节点重要度，然后考虑安全管控延迟效应，定义风险产生、传播及恢复规则，形成离场管制风险动态传播模型．实验结果表明：本文模型能再现风险传播机理和规律，机组等10个节点为风险传播关键环节，机组对风险扩散作用最强，传播成功率峰值为0.519，塔台管制员和地面管制员为危险源时能激发其他节点进行风险控制，针对关键节点实施安全管理能有效抑制风险传播．

Key node identification and risk propagation of aircraft departure control

The detection of aircraft departure risk is an important part of civil aviation safety production, but the current research focuses mostly on the aircraft movement process and static hazard sources, ignoring the control business impact and risk dynamic propagation mechanism. Aiming at this problem, this paper proposes a complex network-based model of departure control key point identification and risk transmission. First, we abstract the control information interaction and business logic, construct a complex network and quantify the importance of each node. Secondly, considering the safety control postponement effect, the rules of risk generation, propagation, and recovery are defined, and a dynamic propagation model of departure control risk is created. The experimental results show that the proposed model can reproduce the risk propagation mechanism and law, 10 nodes, such as the crew, are the key links in risk spreading, the crew has the strongest effect on risk propagation, the peak propagation success rate is 0.519, and other nodes can be stimulated to carry out risk control when the tower controller and ground controller are the source of risk, implementing safety management for key nodes can effectively inhibit risk propagation.