突发事件连锁演化单一情景构建与分析

doi:10.16265/j.cnki.issn1003-3033.2026.04.0033

摘要/Abstract

摘要：

针对突发事件情景建模粒度固定、多情景属性和结构描述不清晰等问题,从微观情景视角出发,基于底线思维提出一种自顶向下的单一情景构建方法。通过状态要素和驱动要素2类知识元建立演化关系,结合分层Petri网(PN)逐层求精,系统刻画突发事件连锁演化情景,模拟情景演化态势;以暴雨影响下的生命线灾害级联事件为例,构建分层情景模型,刻画级联失效、共因失效和升级失效特征并分析其演化态势,识别出城市生命线失效与应急响应间的互锁困境。结果表明:该方法可通过分层情景结构满足不同层次决策需求,通过同构马尔可夫链识别影响情景态势发展的关键要素。

关键词: 突发事件, 单一情景, Petri网(PN), 连锁演化, 马尔可夫链

Abstract:

To address problems such as fixed granularity in emergency scenario modelling and unclear descriptions of multi-scenario attributes and structures, a top-down unitary-scenario construction method was proposed from a microscopic perspective based on baseline thinking. Evolution relationships were established based on two types of knowledge elements, namely state elements and trigger elements. Combined with a hierarchical PN and refined layer by layer, the cascading evolution scenarios of emergencies were systematically characterized, and the evolution dynamics of scenarios were simulated. Taking cascading lifeline disaster events under heavy rainfall as an example, a hierarchical scenario model was constructed.. The characteristics of cascading failure, common cause failure and escalation failure features were characterized, and the evolution trends were analyzed. The interlocking dilemma between urban lifeline failure and emergency response was identified. The results indicate that the proposed scenario construction method can meet different levels of decision-making needs through a hierarchical scenario structure. The isomorphic Markov chain can identify the critical elements affecting the scenario evolution.

Key words: emergency, unitary scenario, Petri net(PN), cascading evolution, Markov chain

中图分类号:

X913.4安全系统工程

宋英华, 徐弘倩, 桑小艳. 突发事件连锁演化单一情景构建与分析[J]. 中国安全科学学报, 2026, 36(4): 184-193.

Song Yinghua, Xu Hongqian, Sang Xiaoyan. Construction and analysis of a unitary scenario for cascading evolution of emergencies[J]. China Safety Science Journal, 2026, 36(4): 184-193.

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模型方法	多情景模型	单一情景模型
连锁特征	级联	级联和耦合
刻画粒度	固定粒度	自上而下多粒度
时间特征	基于时刻	基于时间
反馈回路	无	有
情景结构	固定	可变
事件数量	单一事件	多事件耦合

π₁	π₂	π₃	π₄	π₅	π₆	π₇	π₈	π₉	π₁₀	π₁₁
0.015 1	0.030 1	0.039 9	0.211 0	0.023 6	0.032 9	0.011 8	0.013 5	0.031 1	0.023 4	0.044 6
π₁₂	π₁₃	π₁₄	π₁₅	π₁₆	π₁₇	π₁₈	π₁₉	π₂₀	π₂₁	—
0.006 1	0.014 9	0.009 3	0.007 3	0.008 9	0.009 7	0.008 5	0.003 2	0.003 1	0.452 1	—

${\stackrel{-}{u}}_{1}$	${\stackrel{-}{u}}_{2}$	${\stackrel{-}{u}}_{3}$	${\stackrel{-}{u}}_{4}$	${\stackrel{-}{u}}_{5}$	${\stackrel{-}{u}}_{6}$	${\stackrel{-}{u}}_{7}$
0.015	0.045	0.162	0.189	0.132	0.178	0.370
${\stackrel{-}{u}}_{8}$	${\stackrel{-}{u}}_{9}$	${\stackrel{-}{u}}_{10}$	${\stackrel{-}{u}}_{11}$	${\stackrel{-}{u}}_{12}$	${\stackrel{-}{u}}_{13}$	${\stackrel{-}{u}}_{14}$
0.141	0.141	0.407	0.056	0.046	0.314	0.452

U(t₁)	U(t₂)	U(t₃)	U(t₄)	U(t₅)	U(t₆)	U(t₇)
0.015	0.030	0.132	0.083	0.094	0.046	0.046
U(t₈)	U(t₉)	U(t₁₀)	U(t₁₁)	U(t₁₂)	U(t₁₃)	—
0.132	0.141	0.056	0.056	0.211	0.452	—

λ_t	λ₁	λ₂	λ₃	λ₄	λ₅	λ₆	λ₇
Δ_k	1.94	1.94	0.70	0.45	0.45	0.21	2.04
λ_t	λ₈	λ₉	λ₁₀	λ₁₁	λ₁₂	λ₁₃	—
Δ_k	2.05	2.07	0.26	1.98	1.93	1.90	—