基于Copula-BN的海上船舶碰撞风险评估方法

doi:10.16265/j.cnki.issn1003-3033.2023.09.1469

摘要/Abstract

摘要：

为有效预防海上船舶碰撞事故,提出一种基于Copula-贝叶斯网络(BN)的海上船舶碰撞动态风险评估方法,采用高斯Copula识别风险节点的边缘分布,从而更准确地描述风险因素与碰撞事件之间的复杂相互关系。首先,收集船舶碰撞事故数据,从人-船-环系统的角度,识别并分析导致船舶碰撞的风险因素;然后,利用Copula函数确定各节点的最优边际分布函数;最后,采用数据结构分析确定BN结构,构建Copula-BN模型,并基于Copula-BN模型进行相关性、前向预测和后向故障诊断分析,依据风险因素与碰撞事件之间的相依性,推出导致船舶碰撞事故发生的关键致因,并通过更新节点的状态数据,分析船舶碰撞动态风险。结果表明:该方法能够识别出导致船舶碰撞事故的关键风险因素,实现海上船舶碰撞动态风险评估。

关键词: 海上船舶碰撞, Copula-贝叶斯网络(Copula-BN), 相关性分析, 风险评估, 风险因素

Abstract:

In order to effectively prevent marine ship collisions, a Copula-BN-based dynamic risk assessment method for marine ship collisions was proposed. Gaussian Copula method was used to identify the edge distribution of each node, so that the complex relationships between risk factor and collision risk was more accurate and the Bayesian model inference results were more reasonable. Firstly, the data collected on ship collisions were combined with expert knowledge to identify and analyze the specific risk factors that led to ship collisions from the perspective of the human-ship-loop system. Gaussian Copula method was used to identify the edge distribution of each node and determine the optimal marginal distribution function of each node. Then BN structure was determined through expert experience and data structure analysis, thus completing the establishment of the Copula-BN model. Based on the correlation analysis, forward probability prediction analysis and backward fault diagnosis analysis, the key risk factors leading to the occurrence of ship collision were introduced based on the dependency between each risk factor and the target factor, and the whole risk system was dynamically analyzed through the state update of the nodes. The study shows that this method can identify the key risk factors leading to ship collision accidents and realize the dynamic assessment of ship collision risk.

Key words: marine ship collisions, Copula-Bayesian Networks(Copula-BN), correlation analysis, risk assessment, risk factors

李新宏, 付雅倩, 刘亚洲, 韩子月, 张认认. 基于Copula-BN的海上船舶碰撞风险评估方法[J]. 中国安全科学学报, 2023, 33(9): 204-213.

LI Xinhong, FU Yaqian, LIU Yazhou, HAN Ziyue, ZHANG Renren. Copula-BN based risk assessment methodology of marine ship collisions[J]. China Safety Science Journal, 2023, 33(9): 204-213.

图/表 13

表1

常见的Copula函数

Copula函数	分布函数	$ρ / θ 取值范围$
Gaussian-Copula	$Φ ρ (Φ - 1 (u 1), Φ - 1 (u 2))$	[-1,1]
t-Copula	$T p, u [T μ - 1 (u), T μ - 1 (v)]$	[-1,1]
Gumbel-Copula	$e x p (- [(- l n u 1) θ + (- l n u 1) θ] 1 θ)$	0,1
Clayton-Copula	$m a x ([u 1 - θ + u 2 - θ - 1] 1 θ, 0)$	(0, $∞$ )
Frank-Copula	$1 θ l n (1 + e x p ((- θ u 1) - 1) e x p ((- θ u 2) - 1) e x p (- θ) - 1)$	(- $∞$ ,+ $∞$ )/{0}

表1

图1

表2

表3

图2

图3

图4

表4

图5

图6

图7

表5

图8

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因素分类	具体节点内容
人为因素P	瞭望不当P₁、值守不当P₂、未及早采取避碰措施P₃、航速过快P₄、应急操作不当P₅、疲劳驾驶P₆、未履行相应义务P₇
船的因素S	导航设备故障或失灵S₁、船舶信号使用不当S₂、主机设备发生故障S₃
环境因素E	大风E₁、能见度不良E₂

风险因素	T	P₁	P₂	P₃	P₄	P₅	P₆
最优拟合函数	Normal	Weibull	Norma	Exponential	Gamma	Weibull	Norma
风险因素	P₇	S₁	S₂	S₃	E₁	E₂	—
最优拟合函数	Weibull	Gamma	Exponential	Norma	Gamma	Weibull	—

指标	P₁	P₂	P₃	P₄	P₅	P₆	P₇	S₁	S₂	S₃	E₁	E₂
各因素更新区域	[0.05,0.1]	[0.05,0.1]	[0,0.1]	[2,7]	[0.1,0.2]	[0,0.05]	[0.1,0.2]	[0.05,0.1]	[0.05,0.1]	[0,0.1]	[1,3]	[7,8]
T的均值	0.917	0.928	0.923	0.916	0.884	0.927	0.924	0.929	0.929	0.929	0.928	0.929
T的标准差	0.027	0.027	0.024	0.025	0.022	0.027	0.027	0.027	0.027	0.027	0.027	0.027

指标	P₁	P₂	P₃	P₄	P₅	P₆	P₇	S₁	S₂	S₃	E₁	E₂
原始均值	0.671	0.137	0.313	8.94	0.512	0.050	0.415	0.277	0.121	0.091	4.33	4.89
更新后均值	0.751	0.173	0.687	12.4	0.629	0.056	0.492	0.288	0.121	0.092	4.49	4.82
改变量/%	11.9	26.3	119.5	38.7	22.9	12.0	18.6	4.0	0	1.1	3.7	-1.4
原始标准差	0.254	0.158	0.4	3.38	0.109	0.020	0.22	0.171	0.073	0.062	1.77	1.22
更新后标准差	0.255	0.157	0.556	3.74	0.077	0.020	0.232	0.17	0.074	0.063	1.82	1.22
改变量/%	0.4	-0.6	39.0	10.7	-29.4	0	5.5	-0.6	1.4	1.6	2.8	0