基于动态贝叶斯网络的客舱颠簸事件应急处置分析

doi:10.16265/j.cnki.issn1003-3033.2024.12.0411

摘要/Abstract

摘要：

为有效降低客舱颠簸事件造成的人员伤亡与财产损失,提出基于动态贝叶斯网络(DBN)的客舱颠簸应急处置决策分析方法。首先,依据国内外相关法律法规,结合地面、空中的关键人员应急职责,从航前、航中、航后分析颠簸应急处置流程,选择24个关键事件构建结构化的蝴蝶结(BT)模型;其次,建立映射条件和转换规则,形成DBN模型;然后,统计客观的直接节点先验概率以及三角模糊数专家评判法得到的补充节点模糊概率,得出所有节点的先验概率;最后,选择时间片间隔1和2 min重点对中度、重度颠簸进行仿真推理,研究各动态要素对客舱颠簸事件处置失效的影响特征。结果表明:各应急处置节点受颠簸程度和时间变化影响显著,应急处置最佳时间在5 min之内,其中,乘员固定措施不到位的失效概率随颠簸程度加重而加大;乘员没有系好安全带及客舱乘务人员的过度服务等人为因素是导致处置失效的关键原因。

关键词: 动态贝叶斯网络(DBN), 颠簸事件, 应急处置, 蝴蝶结(BT)模型, 三角模糊数

Abstract:

In order to effectively reduce the casualties and property losses caused by cabin turbulence, a decision analysis method based on a dynamic Bayesian network is proposed for cabin turbulence emergency response. Firstly, according to the relevant laws and regulations at domestic and international, combined with the emergency duties of key personnel on the ground and in the air, the turbulence emergency disposal process is analysed from pre-flight, in-flight and post-flight, and 24 key events are selected to construct a structured BT model. Secondly, the mapping conditions and transformation rules are established to form a DBN model. Then, the objective direct node a priori probability and the supplementary node fuzzy probability obtained by the triangular fuzzy probabilities of supplementary nodes obtained by the fuzzy number expert judgement method to obtain the a priori probabilities of all nodes. Finally, the time slice intervals of 1 and 2 min are selected to focus on the simulation inference of moderate and heavy turbulence, and to study the characteristics of the influence of each dynamic element on the failure of cabin turbulence event disposal. The results show that: the emergency response nodes are significantly affected by the degree of turbulence and time changes, and the optimal time for emergency response is within 5 min. Among them, the probability of failure for the failure of the crew fixation measures in place increases with the increase of the degree of turbulence, human factors such as the failure of the crew to fasten the seat belts and the over-servicing by the cabin crew are the key reasons for the failure of the response.

Key words: dynamic Bayesian network(DBN), turbulence event, emergency response, bow-tie(BT)model, triangular fuzzy number

中图分类号:

X928.03事故预防与预测

吴煜, 吴欣怡, 解江. 基于动态贝叶斯网络的客舱颠簸事件应急处置分析[J]. 中国安全科学学报, 2024, 34(12): 203-212.

WU Yu, WU Xinyi, XIE Jiang. Analysis of emergency response to cabin turbulence based on dynamic Bayesian network[J]. China Safety Science Journal, 2024, 34(12): 203-212.

图/表 8

图1

表1

图2

图3

图4

表2

节点先验概

节点	直接节点先验概率 $P (C b)$		三角模糊数 $G = (k, l, u)$		补充节点模糊概率 $P g$		先验概率 $P A = 0.7 P (C b) + 0.3 P g$
节点	中度	重度	中度	重度	中度	重度	中度	重度
A₁₁₁	—	—	(0.012,0.010,0.005)	(0.100,0.150,0.050)	0.009	0.100	0.009	0.100
A₁₁₂	0.120	0.260	(0.100,0.020,0.039)	(0.400,0.450,0.320)	0.053	0.390	0.100	0.300
A₁₁₃	0.070	0.100	(0.210,0.100,0.107)	(0.150,0.100,0.050)	0.136	0.100	0.090	0.100
A₁₁₄	—	—	(0.011,0.100,0.107)	(0.110,0.130,0.060)	0.009	0.010	0.009	0.010
A₁₁₅	—	—	(0.120,0.800,0.055)	(0.050,0.150,0.100)	0.085	0.010	0.085	0.010
A₁₂₁	—	—	(0.120,0.050,0.100)	(0.120,0.120,0.060)	0.090	0.100	0.090	0.010
A₁₂₂	0.090	0.120	(0.100,0.120,0.149)	(0.180,0.240,0.240)	0.123	0.220	0.100	0.150
A₁₂₃	0.150	0.360	(0.350,0.354,0.244)	(0.400,0.450,0.620)	0.316	0.490	0.200	0.400
A₁₂₅	0.100	0.200	(0.120,0.100,0.060)	(0.520,0.490,0.580)	0.100	0.530	0.100	0.300
A₁₂₄₁	0.200	0.420	(0.280,0.310,0.310)	(0.500,0.530,0.530)	0.200	0.520	0.200	0.450
A₁₂₄₂	0.160	0.260	(0.300,0.340,0.330)	(0.420,0.380,0.370)	0.290	0.390	0.200	0.300
A₁₃₁	0.100	0.200	(0.100,0.120,0.080)	(0.180,0.250,0.170)	0.100	0.200	0.100	0.200
A₁₃₂₁	0.100	0.250	(0.120,0.120,0.060)	(0.400,0.380,0.468)	0.100	0.416	0.100	0.300
A₁₃₂₂	0.140	0.350	(0.210,0.208,0.100)	(0.350,0.320,0.479)	0.173	0.383	0.150	0.360
A₁₃₃	0.160	0.420	(0.310,0.320,0.486)	(0.400,0.390,0.490	0.293	0.353	0.200	0.400
A₂₁₁	—	—	(0.100,0.100,0.100)	(0.100,0.100,0.100)	0.100	0.100	0.100	0.100
A₂₁₂	0.27	0.460	(0.310,0.320,0.486)	(0.400,0.390,0.490)	0.372	0.426	0.300	0.450
A₂₁₃	—	—	(0.100,0.100.0.100)	(0.150,0.210,0.240)	0.100	0.200	0.100	0.200
A₂₂₁	0.010	0.010	(0.010,0.010,0.010)	(0.001,0.001,0.001)	0.010	0.010	0.010	0.010
A₂₂₂	—	—	(0.030,0.040,0.080)	(0.030,0.040,0.080)	0.050	0.050	0.050	0.050
A₃₁	0.100	0.300	(0.100,0.150,0.050)	(0.250,0.300,0.350)	0.100	0.300	0.100	0.300
A₃₂	0.060	0.080	(0.020,0.032,0.029)	(0.042,0.030,0.066)	0.027	0.046	0.050	0.070
A₄₁	0.100	0.100	(0.120,0.100,0.080)	(0.120,0.100,0.080)	0.100	0.100	0.100	0.100
A₄₂	0.010	0.010	(0.010,0.010,0.010)	(0.010,0.010,0.010)	0.010	0.010	0.010	0.010

表2

图5

图6

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代号	事件	代号	事件	代号	事件	代号	事件
A	颠簸事件发生	A₁₁₁	判断颠簸等级失误	A₁₂₄₁	没有固定餐车	A₂₁₂	乘务人员过度服务
A₁	人为因素	A₁₁₂	机长没做好固定措施	A₁₂₄₂	乘务人员没有固定自己	A₂₁₃	机组人员心理问题
A₂	管理因素	A₁₁₃	没有对颠簸进行广播	A₁₃₁	没有遵守安全防护	A₂₂₁	法规制订缺漏
A₃	环境因素	A₁₁₄	没有进行三方会议	A₁₃₂	不在座椅上	A₂₂₂	监管力度不够
A₄	机载设备因素	A₁₁₅	机长疲劳飞行	A₁₃₂₁	没有抓紧扶手	A₃₁	天气环境恶劣
A₁₁	机长应急处置失效	A₁₂₁	乘务人员疲劳飞行	A₁₃₂₂	旅客没有固定自己	A₃₂	飞机无法绕开
A₁₂	乘务人员应急处置失效	A₁₂₂	没检查飞机物品固定	A₁₃₃	旅客没有系好安全带	A₄₁	固定装置故障
A₁₃	旅客应急失效	A₁₂₃	没有做好自身防护	A₂₁	内部原因	A₄₂	天气雷达故障
A₅	航前阶段	A₁₂₄	服务中处置失效	A₂₂	外部原因
A₆	航中阶段	A₁₂₅	乘务人员没有系好安全带	A₂₁₁	机组成员训练不合格