脆弱性多因素耦合作用下空管亚安全态识别

doi:10.16265/j.cnki.issn1003-3033.2022.04.002

摘要/Abstract

摘要：

为有效防控空中交通管制(ATC)导致的航空事故和不安全事件,科学精准地识别空管运行亚安全态。首先,在脆弱性理论的基础上,得到ATC运行系统脆弱性的概念;然后,构建脆弱性因素耦合模型,分析特征要素演化过程中系统运行状态的变化特点,并参考免疫学理论分析亚安全态的本质;最后,利用N-K模型计算不同脆弱性因素耦合值作为评估各特征要素边界集合,利用云模型逆向云发生器评估特征要素边界,综合所有脆弱性因素,并验证系统最终运行状态。结果表明:双因素耦合系统暴露度为0.094 9,系统运行状态偏离0.077 6,系统状态恢复0.057 0,系统最终状态为0.962 1, 3因素耦合分别为0.341 7,0.308 4,0.201 5,0.859 9,4因素耦合分别为0.861 0,0.877 9,0.541 7,0.663 8,与验证结果基本一致;亚安全状态出现在系统暴露度大于敏感度阶段,此时系统脆弱性演化过程为暴露度—适应度—敏感度—适应度。

关键词: 脆弱性, 空中交通管制(ATC), 亚安全态, 多因素耦合, 云模型

Abstract:

In order to effectively prevent and control aviation accidents and unsafe events caused by ATC, the sub-safety state of ATC operation was scientifically and accurately identified. Firstly, based on vulnerability theory, the concept of ATC operation system vulnerability was obtained. Secondly, the coupling model of vulnerability factors was constructed to analyze the change characteristics of system operation state in the evolution process of characteristic factors, and the essence of sub-safety state was analyzed referring to immunology theory. Finally, N-K model was used to calculate coupling values of different vulnerability factors as boundary set of characteristic elements, and the cloud model reverse cloud generator is used to evaluate the boundary of characteristic elements. All vulnerability factors are integrated, and the final operation state of the system is verified. The results show that the exposure degree of two-factor coupling system is 0.094 9, and deviation of system operation state from 0.077 6, the recovery of system state from 0.057 0, and final state of the system is 0.962 1. Three-factor couplings are 0.341 7, 0.308 4, 0.201 5, 0.859 9, respectively, and four-factor couplings 0.861 0, 0.877 9, 0.541 7, 0.663 8,which are basically consistent with verification results. Sub-safety state occurs in the stage of system exposure greater than sensitivity, and the evolution process of system vulnerability is exposure-fitness-sensitivity-fitness.

Key words: vulnerability, air traffic control (ATC), subsafety state, multi-factor coupling, cloud model

岳仁田, 张知波. 脆弱性多因素耦合作用下空管亚安全态识别[J]. 中国安全科学学报, 2022, 32(4): 8-14.

YUE Rentian, ZHANG Zhibo. Sub-safety state identification of ATC under multi-factor coupling of vulnerability[J]. China Safety Science Journal, 2022, 32(4): 8-14.

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参考文献 15

[1]	郭九霞, 潘传江, 廖勇. 基于GBO-Markov模型的空管不安全事件预测方法[J]. 安全与环境学报, 2016, 16(4):48-51.
	GUO Jiuxia, PAN Chuanjiang, LIAO Yong. A method for unsafe events prediction of air traffic management(ATM) based on GBO-Markov model[J]. Journal of Safety and Environment, 2016, 16(4):48-51.
[2]	岳仁田, 韩蒙. 基于四诊法的管制席安全运行状态诊断[J]. 中国安全科学学报, 2021, 31(9):52-59. doi: 10.16265/j.cnki.issn1003-3033.2021.09.008
	YUE Rentian, HAN Meng. Diagnosis of safety operation state for ATC position based on four diagnostic methods[J]. China Safety Science Journal, 2021, 31(9):52-59. doi: 10.16265/j.cnki.issn1003-3033.2021.09.008
[3]	VISMARI L F, JUNIOR J. A safety assessment methodology applied to CNS/ATM-based air traffic control system[J]. Reliability Engineering & System Safety, 2011, 96(7):727-738. doi: 10.1016/j.ress.2011.02.007
[4]	党亚茹, 李雪娇. 七大空管区域复杂网络特征对比与抗毁性分析[J]. 复杂系统与复杂性科学, 2015, 12(3):19-26.
	DANG Yaru, LI Xuejiao. Feature comparison and survivability analysis of complex networks in seven ATC regions[J]. Complex Systems and Complexity Science, 2015, 12(3):19-26.
[5]	张晓燕, 韩松臣, 杨昌其. 基于物元分析理论的空管安全风险模糊综合评价[J]. 交通信息与安全, 2016, 34(4):50-56.
	ZHANG Xiaoyan, HAN Songchen, YANG Changqi. An fuzzy synthetic evaluation model for safety risk in air traffic control based on matter element analysis[J]. Journal of Transport Information and Safety, 2016, 34 (4):50-56.
[6]	万俊强, 张敏. 基于博弈论集对分析的空管风险态势评估[J]. 中国安全科学学报, 2018, 28(11):142-148. doi: 11.16265/j.cnki.issn1003-3033.2018.11.023
	WAN Junqiang, ZHANG Min. Evaluation of air traffic control risk situation based on game theory and set pair analysis[J]. China Safety Science Journal, 2018, 28(11):142-148. doi: 11.16265/j.cnki.issn1003-3033.2018.11.023
[7]	白海卫, 宋守信, 王剑晨. 基于脆弱性的地下工程穿越施工风险评价与控制[J]. 安全与环境学报, 2019, 19(5):1504-1510.
	BAI Haiwei, SONG Shouxin, WANG Jianchen. Risk analysis and control based on the vulnerability for the new buildings[J]. Journal of Safety and Environment, 2019, 19(5):1504-1510.
[8]	吴维, 罗欣然, 魏明. 基于SD模型的跑道侵入风控网络脆弱性分析[J]. 中国安全科学学报, 2021, 31(4):41-48. doi: 10.16265/j.cnki.issn1003-3033.2021.04.006
	WU Wei, LUO Xinran, WEI Ming. Vulnerability assessment of runway intrusion risk control network based on SD model[J]. China Safety Science Journal, 2021, 31(4):41-48. doi: 10.16265/j.cnki.issn1003-3033.2021.04.006
[9]	田水承, 张成镇. 安全科学与工程视域下脆弱性研究评述[J]. 西安科技大学学报, 2018, 38(1):8-16.
	TIAN Shuicheng, ZHANG Chengzhen. Overview of vulnerability based on safety science and engineering[J]. Journal of Xi' an University of Science and Technology, 2018, 38(1):8-16.
[10]	王军武, 吴寒, 杨庭友. 基于投影寻踪的地铁车站工程暴雨内涝脆弱性评价[J]. 中国安全科学学报, 2019, 29(9):1-7. doi: 10.16265/j.cnki.issn1003-3033.2019.09.001
	WANG Junwu, WU Han, YANG Tingyou. Vulnerability assessment of rainfall waterlogging in subway stations based on projection pursuit model[J]. China Safety Science Journal, 2019, 29(9):1-7. doi: 10.16265/j.cnki.issn1003-3033.2019.09.001
[11]	BONGIOVANNI I, NEWTON C. Toward an epidemiology of safety and security risks: an organizational vulnerability assessment in international airports[J]. Risk Analysis, 2019, 39(6):1281-1297. doi: 10.1111/risa.13238
[12]	宋守信, 许葭, 陈明利, 等. 脆弱性基本原理与控制方法[J]. 安全, 2020, 41(4):1-6.
	SONG Shouxin, XU Jia, CHEN Mingli, et al. Basic principle and control method of vulnerability in safety risk[J]. Safety & Security, 2020, 41(4):1-6.
[13]	宋守信, 许葭, 陈明利, 等. 脆弱性特征要素递次演化分析与评价方法研究[J]. 北京交通大学学报:社会科学版, 2017, 16(2):57-65.
	SONG Shouxin, XU Jia, CHEN Mingli, et al. The mechanism and evaluation of vulnerability factors' progressive growth[J]. Journal of Beijing Jiaotong University :Social Science Edition, 2017, 16(2):57-65.
[14]	岳仁田, 李君尉. 基于多因素耦合的航空运输系统脆弱性分析[J]. 中国安全生产科学技术, 2020, 16(12):150-156.
	YUE Rentian, LI Junwei. Vulnerability analysis of air transportation system based on multi-factor coupling[J]. Journal of Safety Science and Technology, 2020, 16(12):150-156.
[15]	杨婷, 帅斌, 黄文成. 基于N-K模型的道路危险品运输系统耦合风险分析[J]. 中国安全科学学报, 2019, 29(9):132-137. doi: 10.16265/j.cnki.issn1003-3033.2019.09.021
	YANG Ting, SHUAI Bin, HUANG Wencheng. Coupling risk analysis of road dangerous goods transportation system based on N-K model[J]. China Safety Science Journal, 2019, 29(9):132-137. doi: 10.16265/j.cnki.issn1003-3033.2019.09.021

第1层	第2层	第3层	空管运行系统脆弱性影响因素
空管运行系统脆弱性	暴露度	人	管制员违规操作	管制员精力分配不当	机组人员违规操作
		人	地面人员工作失误	—	—
		机	设备检修不到位	地面危险施工	设备老旧
		环	自然天气复杂	空域环境复杂	管制室内环境不良
		环	无线电干扰	—	—
		管	班组管理水平不高	安全监督失职	安全培训投入不足
	敏感度	人	管制员技能熟练度	机组技能熟练度	管制员安全意识
		人	机组安全意识	管制员身心健康	—
		机	空管设备故障	飞机设备故障	—
		环	机场及跑道道面情况	飞行冲突情况	军机误入情况
		管	规章完善程度	军航协调情况	—
	适应度	人	管制员特情处理能力	机组人员特情处理能力	—
		机	机载告警系统反馈及时	—	—
		环	机场有效保障	—	—
		管	应急预案制定有效	应急演练充分	—

	耦合类型	频次
暴露度	双因素耦合	N(1100)=73 P₁₁₀₀=0.132 2	N(1010)=1 P₁₀₁₀=0.001 8	N(1001)=32 P₁₀₀₁=0.058 0	N(0110)=1 P₀₁₁₀=0.001 8	N(0101)=8 P₀₁₀₁=0.014 5	N(0011)=9 P₀₀₁₁=0.016 3
暴露度	多因素耦合	N(1110)=3 P₁₁₁₀=0.005 4	N(1101)=6 P₁₁₀₁=0.010 9	N(1011)=2 P₁₀₁₁=0.003 6	N(0111)=2 P₀₁₁₁=0.003 6	N(1111)=3 P₁₁₁₁=0.005 4	—
敏感度	双因素耦合	N(1100)=24 P₁₁₀₀=0.043 5	N(1010)=10 P₁₀₁₀=0.018 1	N(1001)=38 P₁₀₀₁=0.068 8	N(0110)=2 P₀₁₁₀=0.003 6	N(0101)=1 P₀₁₀₁=0.001 8	N(0011)=0 P₀₀₁₁=0
敏感度	多因素耦合	N(1110)=3 P₁₁₁₀=0.005 4	N(1101)=4 P₁₁₀₁=0.007 2	N(1011)=8 P₁₀₁₁=0.014 5	N(0111)=1 P₀₁₁₁=0.001 8	N(1111)=3 P₁₁₁₁=0.005 4	—
适应度	双因素耦合	N(1100)=35 P₁₁₀₀=0.063 4	N(1010)=16 P₁₀₁₀=0.029 0	N(1001)=32 P₁₀₀₁=0.058 0	N(0110)=22 P₀₁₁₀=0.039 9	N(0101)=8 P₀₁₀₁=0.014 5	N(0011)=74 P₀₀₁₁=0.134 1
适应度	多因素耦合	N(1110)=5 P₁₁₁₀=0.009 1	N(1101)=3 P₁₁₀₁=0.005 4	N(1011)=8 P₁₀₁₁=0.014 5	N(0111)=2 P₀₁₁₁=0.003 6	N(1111)=6 P₁₁₁₁=0.010 9	—
脆弱性	双因素耦合	N(1100)=27 P₁₁₀₀=0.048 91	N(1010)=33 P₁₀₁₀=0.059 78	N(1001)=46 P₁₀₀₁=0.083 33	N(0110)=65 P₀₁₁₀=0.117 75	N(0101)=34 P₀₁₀₁=0.061 59	N(0011)=24 P₀₀₁₁=0.043 48
脆弱性	多因素耦合	N(1110)=6 P₁₁₁₀=0.010 87	N(1101)=17 P₁₁₀₁=0.030 8	N(1011)=12 P₁₀₁₁=0.021 74	N(0111)=19 P₀₁₁₁=0.034 42	N(1111)=11 P₁₁₁₁=0.019 93	—

耦合类型	暴露度	敏感度	适应度	脆弱性	亚安全
人-机-环-管	0.860 95	0.877 89	0.541 65	0.345 99	-0.016 94
耦合类型	暴露度	敏感度	适应度	脆弱性	亚安全
人-机-环	0.645 56	0.420 99	0.365 56	0.241 59	0.224 57
机-环-管	0.417 36	0.444 01	0.248 48	0.005 06	-0.026 65
人-环-管	0.264 29	0.276 73	0.128 18	0.077 76	-0.012 44
人-机-管	0.039 41	0.091 92	0.063 76	0.204 80	-0.052 51

耦合值	上界云参数	中间层云参数	下界云参数
暴露度	(0.094 9, 0.134 4, 0.068 6)	(0.341 7, 0.237 9, 0.087 9)	(0.861 0, 0, 0)
敏感度	(0.077 6, 0.065 5, 0.024 5)	(0.308 4, 0.155 5, 0.066 7)	(0.877 9, 0, 0)
适应度	(0.057 0, 0.071 3, 0.032 0)	(0.201 5, 0.132 3, 0.064 2)	(0.541 7, 0, 0)
脆弱性	(0.038 4, 0.060 7, 0.017 0)	(0.132 3, 0.113 9, 0.062 3)	(0.346 0, 0, 0)
亚安全	(0.017 3, 0.073 8, 0.034 1)	(0.033 2, 0.119 9, 0.020 3)	(-0.016 9, 0, 0)