航班运行风险网络的传播与控制改进

doi:10.12305/j.issn.1001-506X.2021.09.22

Abstract

Abstract:

In order to explore the generation, dissemination and control process of flight operation risk, the flight operation data of North China region is counted, with a total of 76 risk nodes. Then, the partial rank correlation coefficient is used to construct the risk network, and the community module detection and triangular maximum filtering method are used to verify the applicability of the network. Furthermore, a SEIR model for flight operation risk analysis is proposed. According to the results of dynamic propagation, the key nodes in network propagation are located by clustering. Finally, two kinds of control schemes, pre prevention and tactical disposal, are adopted. The results show that the infection peak can be reduced by 18.44% when only 5 nodes are controlled, and the peak time can be postponed by two cycles, and the average infection times of important control nodes such as take-off and landing nodes can be reduced by 11.74%. The inhibition effect of this scheme was superior in three aspects: infection peak, infection cycle and infection of important nodes. The above results confirm that the proposed scheme can be effectively used for flight operation risk analysis.

Key words: flight operation risk, complex network, partial rank correlation coefficient, improved susceptible-infected-exposed-recovered (SEIR) model, network key nodes

CLC Number:

Yantao WANG, Zheng YANG. Propagation and control improvement of flight operation risk network[J]. Systems Engineering and Electronics, 2021, 43(9): 2544-2552.

Figures/Tables 16

Table 1

Risk network node"

节点	所属类别	具体含义	各工种评价依据	风险值
1	飞机维修	机务人员经验能力	评价维度包括持照、工作年限、技术级别等; 经验丰富1, 经验尚可5, 无执照且工作不足2年为9;	4	4	4
2		机务人员执勤时间	零点至2点接班为5;2~4点接班为8;4~6点为6;工作时间超过5小时风险加1, 此后再增加1小时风险加1, 最高为10;	6	5	6
⋮		⋮	⋮	⋮	⋮	⋮
8	故障保留	影响操作和飞机性能的MEL项	无故障1, 有则按手册标识的影响评5~10, 对起降有限制的为10;	2	3	5
⋮	故障保留	⋮	⋮	⋮	⋮	⋮
13	机场运行	值机身份确认	按当天差错次数, 无差错为1, 10次以内按次数, 10次及以上为10;	1	1	1
⋮	机场运行	⋮	⋮	⋮	⋮	⋮
20	飞行员及飞行操作	机组技术级别搭配	加强组为1, 教员与机长为1, 机长与副驾驶为2, 双新机长为7, 新机长与副驾为8, 等, 按手册赋值;	6	6	5
⋮		⋮	⋮	⋮	⋮	⋮
41		着陆	操作类按训练记录评分, 按机长记录中蓝黄告警的次数, 最低1, 10次以内按次数, 10次及以上为10;	2	3	5
⋮		⋮	⋮	⋮	⋮	⋮
43	管制指挥	推出指令	按当天差错计分, 无差错为1, 10次以内按次数, 10次及以上为10;	2	2	1
⋮	管制指挥	⋮	⋮	⋮	⋮	⋮
50	运行条件潜在风险	起落机场天气	实况预报高于标准500 m以上为1, 在标准上下100 m为8, 边缘天气且有雷雨等天气现象为10;	3	3	5
⋮		⋮	⋮	⋮	⋮	⋮
75		落地机场临时限制性通告	无限制为1, 影响落地操作为10, 跑道类限制为8, 滑行道飞行区限制为6, 机坪限制为3~4;	2	2	4
76	总运行状态	时段或全天运行状态	10表示已出现不安全事件; 8~9表示当天虽未发生不安全事件, 但有明显运行差错; 5~8表示运行限制较多, 运行单位安全运行压力较大; 2~5表示运行压力较小; 1表示无明显运行安全影响	7	6	6.5

Table 1

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Fig.2

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Fig.5

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Table 8

Fig.6

Fig.7

Table 9

References 31

1	COLEMAN M E , MARKS H M . Qualitative and quantitative risk assessment[J]. Food Control, 1999, (10): 289- 297.
2	Icao annex 19. Safety system management[S]. Montreal Canada: International Civil Aviation Organization, 2019.
3	LIOU J J , TZENG G H , CHANG H C . Airline safety measurement using a hybrid model[J]. Journal of Air Transport Management, 2007, 13 (4): 243- 249. doi: 10.1016/j.jairtraman.2007.04.008
4	SHYUR H J . A quantitative model for aviation safety risk assessment[J]. Computers & Industrial Engineering, 2008, 54 (1): 34- 41.
5	ZHANG X G , MAHADEVAN S . Ensemble machine learning models for aviation incident risk prediction[J]. Decision Support Systems, 2019, 116 (1): 48- 63.
6	COOMBES C , WHALE A , HUNTER R , et al. Sleepiness on the flight deck: reported rates of occurrence and predicted fatigue risk exposure associated with UK airline pilot work sche-dules[J]. Safety Science, 2020, 129 (9): 104833.
7	BLEIER M , SETTELE F , KRAUSS M , et al. Risk assessment of flight paths for automatic emergency parachute deployment in UAVs[J]. IFAC Papers on Line, 2015, 48 (9): 180- 185. doi: 10.1016/j.ifacol.2015.08.080
8	王岩韬, 李蕊, 卢飞, 等. 基于多因素分析的航班运行风险评估体系[J]. 天津工业大学学报, 2014, 33 (3): 84- 88. doi: 10.3969/j.issn.1671-024X.2014.03.017
	WANG Y T , LI R , LU F , et al. A risk assessment system of flight operation based on multiple factor analysis[J]. Journal of Tianjin Polytechnic University, 2014, 33 (3): 84- 88. doi: 10.3969/j.issn.1671-024X.2014.03.017
9	XIE Y R, YUAN L P, WANG X L, et al. Civil flight accident risk analysis based on information diffusion technique[C]//Proc. of the 5th International Conference on Transportation Information and Safety, 2019.
10	ZHANG Z Y, FENG C H, WANG Z S, et al. UAV flight risk identification and evaluation scheme[C]//Proc. of the International Conference on Unmanned Aircraft Systems, 2020.
11	WEI Y , XU H J , XUE Y , et al. Quantitative assessment and visualization of flight risk induced by coupled multi-factor under icing conditions[J]. Chinese Journal of Aeronautics, 2020, 33 (8): 2146- 2161. doi: 10.1016/j.cja.2020.03.025
12	王岩韬. 基于多变量混沌时间序列的航班运行风险预测模型[J]. 工程科学学报, 2020, 42 (12): 1664- 1673.
	WANG Y T . Flight operation risk prediction and control based on multivariate chaotic time series[J]. Chinese Journal of Engineering, 2020, 42 (12): 1664- 1673.
13	BELKOURA S , COOK A , PE NA , J M , et al. On the multi-dimensionality and sampling of air transport networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2016, 94 (1): 95- 109.
14	VOLTES D A , RODRIGUEZ D H , SUAU S P . Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: ranking the most critical airports[J]. Transportation Research, 2017, 96 (2): 119- 145.
15	LYKOU G , DEDOUSIS P , STERGIOPOULOS G , et al. Assessing Interdependencies and congestion delays in the aviation network[J]. IEEE Access, 2020, 8, 223234- 223254. doi: 10.1109/ACCESS.2020.3045340
16	DANG Y R , DING F Y , GAO F . Empirical analysis on flight flow network survivability of China[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12 (6): 177- 185. doi: 10.1016/S1570-6672(11)60239-0
17	CONG W , HU M H , DONG B , et al. Empirical analysis of airport network and critical airports[J]. Chinese Journal of Aeronautics, 2016, 29 (2): 512- 519. doi: 10.1016/j.cja.2016.01.010
18	ZHANG M Y , LIANG B Y , WANG S , et al. Analysis of flight conflicts in the Chinese air route network[J]. Chaos, Solitons & Fractals, 2018, 112 (7): 97- 102.
19	邱杨扬. 不停航施工期飞行区安全风险演化的复杂网络模型研究[D]. 武汉: 武汉理工大学, 2019.
	QIU Y Y. Research on complex network model for evolution of flight area security risks in non-stop flight construction[D]. Wuhan: Wuhan University of Technology, 2019.
20	吴明功. 基于复杂网络的空中交通复杂性识别方法[J]. 北京航空航天大学学报, 2020, 46 (5): 839- 850.
	WU M G . Air traffic complexity recognition based on complex networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46 (5): 839- 850.
21	王岩韬, 刘毓. 基于复杂网络的航班运行风险传播分析[J]. 交通运输系统工程与信息, 2020, 20 (1): 198- 205.
	WANG Y T , LIU Y . Flight operation risk propagation based on complex network[J]. Journal of Transportation Systems Enginee-ring and Information Technology, 2020, 20 (1): 198- 205.
22	CONOVER W J . Practical nonparametric statistics[M]. 3th ed New York: John Wiley & Sons, 1999: 428- 441.
23	NEWMAN M E J , GIRVAN M . Find and evaluating community structure in networks[J]. Physical Review E, 2004,
24	MASSARA G P , MATTEO T D , ASTE T . Network filtering for big data: triangulated maximally filtered graph[J]. Journal of Complex Networks, 2017, 5 (2): 161- 178.
25	SCHOCH D . Centrality without indices: partial rankings and rank probabilities in networks[J]. Social Networks, 2018, 54 (6): 50- 60.
26	MARQUIONI V M , AGUIAR M A M . Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks[J]. Chaos, Solitons & Fractals, 2020, 138 (9): 109999.
27	LÓPEZ L , FERNÁNDEZ M , GÓMEZ A , et al. An influenza epidemic model with dynamic social networks of agents with individual behaviour[J]. Ecological Complexity, 2020, 41 (1): 100810.
28	QIAN X W , UKKUSURI S V . Connecting urban transportation systems with the spread of infectious diseases: a trans-SEIR modeling approach[J]. Transportation Research Part B: Methodological, 2021, 145 (3): 185- 211.
29	CHEN L J , SUN J T . Global stability and optimal control of a SIRS epidemic model on heterogeneous networks[J]. Physica A: Statistical Mechanics and its Applications, 2014, 410 (9): 196- 204.
30	ADJEKUM D K , TOUS M F . Assessing the relationship between organizational management factors and a resilient safety culture in a collegiate aviation program with safety management systems (SMS)[J]. Safety Science, 2020, 131 (11): 104909.
31	KMET T , KMETOVA M . Bézier curve parametrisation and echo state network methods for solving optimal control problems of SIR model[J]. Biosystems, 2019, 186 (12): 104029.

	节点1	节点2	节点3	节点4	节点5	⋯
节点1	0	0	0	0.15	0	⋯
节点2	0	0	0.94	0	0	⋯
节点3	0	0.94	0	0	0	⋯
节点4	0.15	0	0	0	0	⋯
节点5	0	0	0	0	0	⋯
⋮	⋮	⋮	⋮	⋮	⋮	⋱

节点	度值	聚类系数	介数	紧密中心度	社团
1	2	0.00	10.03	0.30	4
2	2	0.00	28.08	0.27	0
3	3	0.00	7.57	0.25	0
⋮	⋮	⋮	⋮	⋮	⋮
71	28	0.59	62.04	0.49	7
72	5	0.50	6.59	0.36	6
73	14	0.72	14.17	0.38	7
74	2	0.29	89.55	0.38	4
75	10	0.42	50.42	0.36	8
76	6	0.24	181.86	0.42	4

标准差	感染率
0	0.2
1	0.4
2	0.6
3	0.8
4	1.0

节点编号	被感染次数	感染次数	总和
8	1 001	2 416	3 417
11	1 161	2 053	3 214
24	1 159	2 213	3 372
25	1 525	2 607	4 132
29	1 521	2 890	4 411
30	1 517	2 697	4 214
34	1 577	2 537	4 114
45	1 515	2 629	4 144
50	1 270	3 865	5 135
55	1 523	2 472	3 995
58	1 216	2 047	3 263
59	1 276	2 128	3 404
60	1 535	2 419	3 954
61	1 495	2 983	4 478
71	1 530	2 606	4 136

节点	节点类别	可采取措施类型	具体方法
50 起落机场天气	运行环境	前置预防-系统与制度建设	建立低能见运行程序;
50 起落机场天气	运行环境	战术处置	实时跟踪现场天气以及起降状况;
61 机载燃油监控	飞行中监控	前置预防-人员资质提升	强化最后储备燃油的情景训练;
		前置预防-系统与制度建设	①采用高精度的计算机飞行计划系统; ②监控系统中完善燃油监控功能;
		战术处置	空地配合优化高度与航径, 降低燃油消耗;
29 核对计算机飞行计划	飞行前计划	前置预防-人员资质提升	加强飞行计划手工与计算机实训;
		前置预防-系统与制度建设	①监控系统中完善实时飞行与计划偏差计算功能; ②制定并执行交叉检查程序;
		战术处置	遇特殊情况, 空地配合优化改航航路;
30 飞行前设备检查	飞行前准备	前置预防-系统与制度建设	①严格执行绕机检查标准操作; ②严格执行交叉检查程序;
45 位置监控与指挥	飞行中指挥	战术处置	遇特殊情况, 空地配合优化改航航路;
45 位置监控与指挥	飞行中指挥	前置预防-系统与制度建设	①使用秒级刷新的高精度飞行监控系统; ②制定各类特殊情况下的监控操作程序

Propagation and control improvement of flight operation risk network

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控制参数	感染峰值下降/%	感染周期推后	重要操纵感染降低/%
总度值	8.94	0	0.51
入度	7.37	0	0.43
出度	9.36	0	0.86
聚类系数	5.36	0	-0.28(增加)
介数	18.27	1	5.18
紧密中心度	3.32	1	-1.05(增加)
本方案	18.44	2	11.74

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标准差	条件概率/%
0	10
1	20
2	30
3, 4	40

均值	进入感染期概率/%
1	10
2	20
3	30
4	40
5	50
6	60
7	70