管制-飞行状态相依网络演化过程

李昂; 聂党民; 温祥西; 韩宝华; 曾裕景

doi:10.7527/S1000-6893.2021.24726

航空学报 >

2021 , Vol. 42 >Issue 9: 324726 - 324726

DOI: https://doi.org/10.7527/S1000-6893.2021.24726

电子电气工程与控制

管制-飞行状态相依网络演化过程

李昂 ,
聂党民 ,
温祥西 ,
韩宝华 ,
曾裕景

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1. 空军工程大学空管领航学院, 西安 710051;
2. 中国人民解放军 95429部队, 昆明 650000

收稿日期: 2020-09-07

修回日期: 2020-10-06

网络出版日期: 2021-09-29

基金资助

国家自然科学基金（71801221）

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Evolution process of control-aircraft state interdependent network

LI Ang ,
NIE Dangmin ,
WEN Xiangxi ,
HAN Baohua ,
ZENG Yujing

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1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China;
2. Unit 95429 of the PLA, Kunming 650000, China

Received date: 2020-09-07

Revised date: 2020-10-06

Online published: 2021-09-29

Supported by

National Natural Science Foundation of China (71801221)

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摘要

动态、准确的管制系统运行态势预测是航空运输系统各相关单位开展协同决策的关键基础。基于航空器间的冲突情况、管制员对航空器的管控情况以及管制移交情况构建管制-飞行状态相依网络，探究、分析其演化规律，采用相关性分析和主成分分析证明了所选5项指标的合理性。设置自由飞行和固定航线飞行两种仿真场景，通过计算平均节点度、平均点强等拓扑指标的最大李雅普诺夫指数证明各时间序列均具有混沌特性，选择长短期记忆（LSTM）人工神经网络方法对各时间序列的演化规律进行预测，并与其他预测算法进行对比。仿真结果表明LSTM算法能对管制系统的演化过程进行有效的预测，且预测精度高于贝叶斯算法和支持向量机算法；在自由飞行条件下，5项指标的预测误差绝大部分在20%以内，固定航线飞行的预测效果优于自由飞行。

关键词： 相依网络; 预测; 演化过程; 李雅普诺夫指数; 长短期记忆(LSTM); 人工神经网络

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李昂 , 聂党民 , 温祥西 , 韩宝华 , 曾裕景 . 管制-飞行状态相依网络演化过程[J]. 航空学报, 2021 , 42(9) : 324726 -324726 . DOI: 10.7527/S1000-6893.2021.24726

Abstract

Prediction of dynamic and accurate operation situation of the control system is essential for collaborative decision-making among relevant units of the air transport system. In this paper, a model for interdependent network of aircraft control/flight status is established based on the conflict of aircrafts, control of aircrafts by controllers, and transfer of controllers. The evolution law of the interdependent network is explored. Correlation analysis and principal component analysis are used to prove the rationality of the five indicators selected. Two simulation scenes of free flight and flight on route are set up. It is proved that each time series is chaotic by calculating the maximum Lyapunov exponent of topological indexes such as average node degree and average point weight. The Long Short-Term Memory (LSTM) method is employed to predict the evolution law of each time series, and is compared with other prediction algorithms. The simulation results show that the LSTM algorithm can predict the evolution process of the control system effectively, and the prediction accuracy is higher than that of the Bayes algorithm. The LSTM algorithm also supports the vector machine algorithm. The prediction error of the five indexes with the LSTM algorithm is mostly within 20% under the condition of free flight, and the prediction effect for flight on route is better than that for free flight.

Key words： interdependent network; prediction; evolution process; Lyapunov exponent; Long Short-Term Memory (LSTM); artificial neural network

参考文献

[1] 张兆宁, 张佳. 大面积航班延误发生的预测方法[J]. 系统工程, 2020, 38(4):115-121. ZHANG Z N, ZHANG J. Prediction method for the occurrence of large-scale flight delays[J]. Systems Engineering, 2020, 38(4):115-121(in Chinese).
[2] 高旗, 初建宇, 李印凤. 基于PSO-BP神经网络的终端区拥堵等级预测模型[J]. 航空计算技术, 2019, 49(6):57-61. GAO Q, CHU J Y, LI Y F. Prediction model of congestion level in terminal area based onPSO-BP neural network[J]. Aeronautical Computing Technique, 2019, 49(6):57-61(in Chinese).
[3] 杨东玲. 基于ADS-B的4D航迹预测及应用[D]. 天津:中国民航大学, 2017. YANG D L. 4D track prediction based on ADS-B and application[D]. Tianjin:Civil Aviation University of China, 2017(in Chinese).
[4] 刘继新, 曾逍宇, 尹旻嘉, 等. 基于累积logistic回归模型的管制员应激程度预测[J]. 重庆交通大学学报(自然科学版), 2019, 38(3):97-102, 115. LIU J X, ZENG X Y, YIN M J, et al. Stress level prediction of controller based on cumulative logistic regression model[J]. Journal of Chongqing Jiaotong University (Natural Science), 2019, 38(3):97-102, 115(in Chinese).
[5] VANDERHAEGEN F. Mirroreffect based learning systems to predict human errors-application to the air traffic control[J]. IFAC-Papers OnLine, 2016, 49(19):295-300.
[6] CORVER S C, UNGER D, GROTE G. Predicting air traffic controller workload[J]. Human Factors:the Journal of the Human Factors and Ergonomics Society, 2016, 58(4):560-573.
[7] 刘大伟, 吕元娜, 余智华. 一种改进的复杂网络链路预测算法[J]. 小型微型计算机系统, 2016, 37(5):1071-1074. LIU D W, LV Y N, YU Z H. An improved link prediction algorithm for complex networks[J]. Journal of Chinese Computer Systems, 2016, 37(5):1071-1074(in Chinese).
[8] LIU B, XU S, LI T, et al. Quantifying the effects of topology and weight for link prediction in weighted complex networks[J]. Entropy, 2018, 20(5):363.
[9] LÜ L, PAN L M, ZHOU T, et al. Toward link predictability of complex networks[J]. PNAS, 2015, 112(8):2325-2330.
[10] LI Y M, ZHAO L M, YU Z Y, et al. Traffic flow prediction with big data:a learning approach based on SIS-complex networks[C]//2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Piscataway:IEEE Press, 2017.
[11] 伍杰华, 沈静, 周蓓. 改进朴素贝叶斯模型的复杂网络关系预测[J]. 计算机工程与科学, 2017, 39(10):1825-1831. WU J H, SHEN J, ZHOU B. An enhanced naiveBayesian relationship prediction model in complex networks[J]. Computer Engineering & Science, 2017, 39(10):1825-1831(in Chinese).
[12] 王超, 朱明. 基于复杂网络表征ATF时间序列动力学特性[J]. 计算机仿真, 2018, 35(6):81-85. WANG C, ZHU M. Characterizing dynamic property of airtraffic flow time series based on complex network. computer engineering and applications[J]. Computer Simulation, 2018, 35(6):81-85(in Chinese).
[13] 徐肖豪, 李善梅. 空中交通拥挤的识别与预测方法研究[J]. 航空学报, 2015, 36(8):2753-2763. XU X H, LI S M. Identification and prediction of air traffic congestion[J]. ActaAeronautica et Astronautica Sinica, 2015, 36(8):2753-2763(in Chinese).
[14] BULDYREV S V, PARSHANI R, PAUL G, et al. Catastrophic cascade of failures in interdependent networks[J]. Nature, 2010, 464(7291):1025-1028.
[15] 李昂, 聂党民, 温祥西, 等. 管制-飞行状态相依网络模型及特性分析[J]. 北京航空航天大学学报, 2020, 46(6):1204-1213. LI A, NIE D M, WEN X X, et al. Control-aircraft state interdependent network model and characteristic analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6):1204-1213(in Chinese).
[16] 王新迎, 韩敏. 基于极端学习机的多变量混沌时间序列预测[J]. 物理学报, 2012, 61(8):080507. WANG X Y, HAN M. Multivariate chaotic time series prediction based on extreme learning machine[J]. ActaPhysica Sinica, 2012, 61(8):080507(in Chinese).
[17] 周永道, 马洪, 吕王勇, 等. 基于多元局部多项式方法的混沌时间序列预测[J]. 物理学报, 2007, 56(12):6809-6814. ZHOU Y D, MA H, LÜ W Y, et al. Prediction of the chaotic time series using multivariate local polynomial regression[J]. Acta Physica Sinica, 2007, 56(12):6809-6814(in Chinese).
[18] 毛文涛, 蒋梦雪, 李源, 等. 基于异常序列剔除的多变量时间序列结构化预测[J]. 自动化学报, 2018, 44(4):619-634. MAO W T, JIANG M X, LI Y, et al. Structural prediction of multivariate time series through outlier elimination[J]. ActaAutomatica Sinica, 2018, 44(4):619-634(in Chinese).
[19] THISSEN U, VAN BRAKEL R, DE WEIJER A P, et al. Using support vector machines for time series prediction[J]. Chemometrics and Intelligent Laboratory Systems, 2003, 69(1-2):35-49.
[20] 陈建婷. 一种基于深度学习的数据预测方法[J]. 电子技术与软件工程, 2019(6):151-152. CHEN J T.Data prediction method based on deep learning[J]. Electronic Technology & Software Engineering, 2019(6):151-152(in Chinese).
[21] 宋捷, 杨磊, 胡明华, 等. 基于深度学习的航班起降延误预测方法[J]. 航空计算技术, 2020, 50(3):30-34. SONG J, YANG L, HU M H, et al. Departure and landing delay prediction based on deep learning technique[J]. Aeronautical Computing Technique, 2020, 50(3):30-34(in Chinese).
[22] ZHANG Q C, YANG L T, YAN Z, et al. An efficient deep learning model to predict cloud workload for industry informatics[J]. IEEE Transactions on Industrial Informatics, 2018, 14(7):3170-3178.
[23] NIELSEN AA K, VOIGT C A. Deep learning to predict the lab-of-origin of engineered DNA[J]. Nature Communications, 2018, 9:3135.
[24] 张进, 胡明华, 张晨, 等. 空域复杂性建模[J]. 南京航空航天大学学报, 2010, 42(4):454-460. ZHANG J, HU M H, ZHANG C, et al. Airspace complexity modeling[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2010, 42(4):454-460(in Chinese).
[25] KIM T, KING B R. Time series prediction using deep echo state networks[J]. Neural Computing and Applications, 2020, 32(23):17769-17787.

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