系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (9): 2544-2552.doi: 10.12305/j.issn.1001-506X.2021.09.22

• 系统工程 • 上一篇    下一篇

航班运行风险网络的传播与控制改进

王岩韬1,*, 杨拯2   

  1. 1. 中国民航大学空管学院, 天津 300300
    2. 四川航空股份有限公司运行风险控制中心, 四川 成都 610202
  • 收稿日期:2020-10-26 出版日期:2021-08-20 发布日期:2021-08-26
  • 通讯作者: 王岩韬
  • 作者简介:王岩韬(1982—), 男, 副教授, 硕士, 主要研究方向为航班运行安全与民航智慧运行|杨拯(1997—), 男, 工程师, 硕士研究生, 主要研究方向为航班运行控制
  • 基金资助:
    国家自然科学基金(U1933103);国家自然科学基金(U1733103);国家重点研发项目(2016YFB0502400)

Propagation and control improvement of flight operation risk network

Yantao WANG1,*, Zheng YANG2   

  1. 1. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
    2. Operational Risk Control Center, Sichuan Airlines Comany Limited, Chengdu 610202, China
  • Received:2020-10-26 Online:2021-08-20 Published:2021-08-26
  • Contact: Yantao WANG

摘要:

为探究航班运行风险的产生、传播与控制过程, 首先统计华北区域航班运行数据共计76个风险节点。然后,采用偏秩相关系数构建风险网络, 再使用社团模块探测算法与三角最大滤波法验证网络适用性。并且, 提出一种适用于航班运行风险分析的SEIR(susceptible-infected-exposed-recovered)模型。根据动力学传播结果, 聚类定位网络传播中关键节点。最后, 采用前置预防与战术处置两类控制方案。计算结果表明,仅控制5个节点后, 感染峰值可降低18.44%, 峰值时间推后两个周期, 起降等重要操纵节点被感染次数平均下降11.74%。该方案在感染峰值、感染周期、重要节点感染3个方面的抑制效果均占优。以上结果证实, 所提方案可有效用于航班运行风险问题分析。

关键词: 航班运行风险, 复杂网络, 偏秩相关系数, 改进SEIR模型, 网络关键节点

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

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