基于有向權值網絡的航班運行風險傳播與控制

王巖韜; 楊志遠; 劉錕; 謝春生

doi:10.13374/j.issn2095-9389.2020.06.15.002

基于有向權值網絡的航班運行風險傳播與控制

doi: 10.13374/j.issn2095-9389.2020.06.15.002

中國民航大學航空公司人工智能民航局重點實驗室，天津 300300

基金項目: 國家自然科學基金資助項目（U1933103）

詳細信息

通訊作者:
E-mail：caucwyt@126.com

中圖分類號: N945.24；U8；V355.2
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- 文章訪問數: 1069
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- 被引次數: 0
出版歷程
- 收稿日期: 2020-06-15
- 網絡出版日期: 2020-11-14
- 刊出日期: 2022-01-01

Flight operation risk propagation and control based on a directional-weighted complex network

Airlines Artificial Intelligence Key Laboratory of Civil Aviation Administration, Civil Aviation University of China, Tianjin 300300, China

More Information

Corresponding author: E-mail: caucwyt@126.com

摘要

摘要: 為了分析航班運行風險傳播過程，進而有效控制保障飛行安全，基于復雜網絡理論，首先參照民航局咨詢通告選取機組、航空器、運行環境共29個終端因素作為網絡節點，統計民航安全監察記錄，根據事件中節點關系，構建無向網絡；統計前后節點間的作用關系和發生概率，提出一種有向帶權的航班運行風險網絡；然后，引入改進感染率和改進恢復率概念，構建一種適用于航班運行風險傳播分析的改進SIR(Susceptible-infected-recovered)模型；定義感染起始范圍，最后采取多參數控制方式，大規模計算該有向帶權網絡的傳播和控制過程。結果表明：有向網的平均最短路徑為1.788，屬于小世界網絡；參照使用民航常規管控措施，有向網節點感染下降幅度可達到37.4%；對入度值排序前3或前4的節點控制后，感染節點峰值下降率高達50.6%和58.1%，網絡傳播抑制明顯。結果證實：在該航班運行風險有向帶權網絡中，按入度值控制節點對抑制風險傳播最為有效。
- 航空運輸 /
- 航班運行風險 /
- 復雜網絡 /
- 有向帶權網絡 /
- 改進SIR模型
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.
- air transportation /
- flight operation risk /
- complex network /
- directional weighted network /
- improved SIR model

HTML全文

圖 1 無向網絡

Figure 1. Undirected network

下載: 全尺寸圖片幻燈片

圖 2 有向帶權網絡

Figure 2. Directional weighted network

下載: 全尺寸圖片幻燈片

圖 3 改進SIR模型原理圖

Figure 3. Schematic of the improved SIR model

下載: 全尺寸圖片幻燈片

圖 4 控制節點后有向帶權網絡傳播結果

Figure 4. Directed network propagation results after controlling the nodes

下載: 全尺寸圖片幻燈片

圖 5 按不同控制方式的改進SIR模型變化趨勢圖

Figure 5. Change trend of the improved SIR model based on different control methods

下載: 全尺寸圖片幻燈片

表 1 風險網絡節點

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 factors	14	Temporary air diversion
	2	Crew English level		15	Controller’s radiotelephone communication level
	3	Crew collaboration		15	Controller’s radiotelephone communication level
	4	Crew technical characteristics		16	Large areas of thunderstorms, moderate or severe icy areas, and turbulence in the airway
	5	Captain flight experience		16
	6	Captain’s familiarity with the airport		17	Rain, snow, fog, and other weather in airport
	7	Copilot flying experience		17	Rain, snow, fog, and other weather in airport
	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 failure		24	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 權值設置規則

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 ? (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 網絡參數（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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