Flight operation risk propagation and control based on a directional-weighted complex network
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摘要: 为了分析航班运行风险传播过程，进而有效控制保障飞行安全，基于复杂网络理论，首先参照民航局咨询通告选取机组、航空器、运行环境共29个终端因素作为网络节点，统计民航安全监察记录，根据事件中节点关系，构建无向网络；统计前后节点间的作用关系和发生概率，提出一种有向带权的航班运行风险网络；然后，引入改进感染率和改进恢复率概念，构建一种适用于航班运行风险传播分析的改进SIR(Susceptible-infected-recovered)模型；定义感染起始范围，最后采取多参数控制方式，大规模计算该有向带权网络的传播和控制过程。结果表明：有向网的平均最短路径为1.788，属于小世界网络；参照使用民航常规管控措施，有向网节点感染下降幅度可达到37.4%；对入度值排序前3或前4的节点控制后，感染节点峰值下降率高达50.6%和58.1%，网络传播抑制明显。结果证实：在该航班运行风险有向带权网络中，按入度值控制节点对抑制风险传播最为有效。Abstract: The flight operation risk is equal to the occurrence probability multiplied by the severity of the consequences. Flight operation risks include many types, forms, and numbers, and they frequently change with conditions. In the face of this complex system, through principle analysis, the risk formation mechanism research, and the spreading process, a scientific risk management and control method can be constructed. Based on the risk management technology, an informative and automated management control system can be developed and applied. The overall safety level of flight operations will be effectively improved. To analyze and study the flight operations risk propagation and then effectively control flight safety based on the complex network theory, 29 terminal factors were selected as network nodes according to the Civil Aviation Administration's advisory notice, initially including the flight cabin crew, civil aviation aircraft, and operating environment. Civil aviation safety monitoring records from 2009 to 2014 were counted, and an undirected network was constructed based on node relationships. The relationships and occurrence probability between the nodes were counted, and a directed and weighted network was constructed. The concepts of improved infection rate and improved recovery rate were introduced, and an improved susceptible-infected-recovered (SIR) model suitable for flight operation risks was proposed. Finally, the initial infection range was clearly defined, and a multi-parameter control method was adopted. For directed networks, large-scale propagation and control simulations were calculated. The results indicate that the average shortest path of the directed network was 1.788, which belonged to the small-world network. The directed network infection node decreased to 37.4% with conventional control measures. After controlling top three or four nodes of the entry degree value sequence, the infected nodes peak drop rate was the biggest, as high as 50.6%/58.1%, the risk spread in the network was significantly suppressed. The results confirm that controlling nodes based on the entry degree value is the most effective method to suppress risk propagation in the directed and weighted network.
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图 4 控制节点后有向带权网络传播结果
Figure 4. Directed network propagation results after controlling the nodes
图 5 按不同控制方式的改进SIR模型变化趋势图
Figure 5. Change trend of the improved SIR model based on different control methods
表 1 风险网络节点
Table 1. Risk network node
Node type Node number Node name Node type Node number Node name Crew risk factors 1 Crew qualification level matching Operational environment
risk factors14 Temporary air diversion 2 Crew English level 15 Controller’s radiotelephone communication level 3 Crew collaboration 4 Crew technical characteristics 16 Large areas of thunderstorms, moderate
or severe icy areas, and turbulence
in the airway5 Captain flight experience 6 Captain's familiarity with the airport 17 Rain, snow, fog, and other
weather in airport7 Copilot flying experience 8 Copilot’s familiarity with the airport 18 Runway friction effect 9 Transient fatigue 19 Airport landing standards 10 Cumulative fatigue 20 Flight procedure complexity 11 Special passenger pressure 21 Approach terrain and obstacles 12 Flight inspection 22 Airport equipment and facilities status 13 Change route before takeoff 23 Runway length and slope Aircraft risk factors 27 Landing approach involves
equipment failure24 Airport temporary restriction notice 28 Aircraft failure rate 25 Destination airport busyness 29 Navigation database encoding 26 Alternate airport busyness 
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表 2 权值设置规则
Table 2. Weight setting rules
Weight setting Probability of previous node affecting the next node Statistical frequency/probability 1 High probability Statistical frequency ≥ 100, occurrence probability ϵ (3.94 × 10−3,1] 0.8 More likely [50, 100; 1.97 × 10−3, 3.94 × 10−3] 0.5 May occur [10, 50; 3.94 × 10−4, 1.97 × 10−3] 0.2 Low probability [1, 10; 3.94 × 10−5, 3.94 × 10−4] 0 Typically does not affect the next node Statistical frequency = 0; [0, 3.94 × 10−5]
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表 3 网络参数（部分）
Table 3. Network parameters (partial)
Node number Total degree value In-degree value Out-degree value Clustering coefficient Betweenness 1 11 7 4 0.803571 0.000374 2 5 1 4 0.809524 0.000220 3 28 21 7 0.380952 0.037671 4 22 13 9 0.528571 0.013475 5 23 16 7 0.455882 0.016636 … … … … … … 9 39 26 13 0.304615 0.152202 10 39 24 15 0.336957 0.182254 … … … … … … 20 25 9 16 0.411765 0.066644 … … … … … … 25 10 5 5 0.619048 0.005644 26 10 1 9 0.680556 0.001102 27 13 4 9 0.700000 0.001223
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