Construction and analysis of a unitary scenario for cascading evolution of emergencies

doi:10.16265/j.cnki.issn1003-3033.2026.04.0033

Abstract

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

CLC Number:

X913.4安全系统工程

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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References 15

[1]	姜卉. 应急实时决策中的情景表达及情景间关系研究[J]. 电子科技大学学报:社科版, 2012, 14(1): 48-52.
	Jiang Hui. Expression of scenarios and the relationship among scenarios in emergency real-time decision-making[J]. Journal of University of Electronic Science and Technology of China:Social Sciences Edition, 2012, 14(1): 48-52.
[2]	宋英华, 刘含笑, 蒋新宇, 等. 基于知识元与贝叶斯网络的食品安全事故情景推演研究[J]. 情报学报, 2018, 37(7): 712-720.
	Song Yinghua, Liu Hanxiao, Jiang Xinyu, et al. Research on scenario evolution of food safety incidents based on knowledge element and bayesian network[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(7): 712-720.
[3]	钱静, 刘奕. 突发事件分析的EOC概念模型[J]. 清华大学学报:自然科学版, 2022, 62(2): 259-265.
	Qian Jing, Liu Yi. EOC model: a conceptual model to analyze emergencies[J]. Journal of Tsinghua University:Science and Technology, 2022, 62(2): 259-265.
[4]	Xie Xiaoliang, Huang Linglu, Marson S M, et al. Emergency response process for sudden rainstorm and flooding: scenario deduction and bayesian network analysis using evidence theory and knowledge meta-theory[J]. Natural Hazards, 2023, 117: 3307-3329. doi: 10.1007/s11069-023-05988-x
[5]	Ricci F, Yang Ming, Reniers G, et al. Emergency response in cascading scenarios triggered by natural events[J]. Reliability Engineering & System Safety, 2024, 243: DOI: 10.1016/j.ress.2023.109820.
[6]	Pang Lei, Zhang Mengxi, Yang Kai, et al. Scenario derivation and consequence evaluation of dust explosion accident based on dynamic bayesian network[J]. Journal of Loss Prevention in the Process Industries, 2023, 83: DOI: 10.1016/j.jlp.2023.105055.
[7]	Shi Lingyuan, Yang Xin, Li Jue, et al. Scenario construction and deduction for railway emergency response decision-making based on network models[J]. Information Sciences, 2022, 588: 331-349. doi: 10.1016/j.ins.2021.12.071
[8]	陈雪龙, 姜坤. 基于贝叶斯网络的并发型突发事件链建模方法[J]. 中国管理科学, 2021, 29(10): 165-177.
	Chen Xuelong, Jiang Kun. Modeling method of concurrent emergency chain based on bayesian network[J]. Chinese Journal of Management Science, 2021, 29(10): 165-177. doi: 10.16381/j.cnki.issn1003-207x.2017.1561
[9]	刘艺, 李茂源, 王欣芝, 等. 大型活动踩踏事故“态-势”情景构建与组合推演方法[J]. 中国安全科学学报, 2024, 34(9): 183-190. doi: 10.16265/j.cnki.issn1003-3033.2024.09.0575
	Liu Yi, Li Maoyuan, Wang Xinzhi, et al. Research on 'status and trends' scenario construction and combination deduction method of stampede accident in large-scale activities[J]. China Safety Science Journal, 2024, 34(9): 183-190.
[10]	Mietzner D, Reger G. Advantages and disadvantages of scenario approaches for strategic foresight[J]. International Journal of Technology Intelligence and Planning, 2005, 1(2): DOI:10.1504/IJTIP.2005.006516.
[11]	李仕明, 张志英, 刘樑, 等. 非常规突发事件情景概念研究[J]. 电子科技大学学报:社科版, 2014, 16(1): 1-5.
	Li Shiming, Zhang Zhiying, Liu Liang, et al. Research on scenario concepts of unconventional emergencies[J]. Journal of University of Electronic Science and Technology of China:Social Sciences Edition, 2014, 16(1): 1-5.
[12]	Chen Chen, Yang Yabin, Wang Mengtong, et al. Characterization and evolution of emergency scenarios using hybrid petri net[J]. Process Safety and Environmental Protection, 2018, 114: 133-142. doi: 10.1016/j.psep.2017.12.016
[13]	Liu Qiong, Guo Kai, Wu Xianguo, et al. Simulation-based rescue plan modeling and performance assessment towards resilient metro systems under emergency[J]. Reliability Engineering & System Safety, 2024: DOI: 10.1016/j.ress.2023.109642.
[14]	Lee H, Romero J. Synthesis report: climate change 2023[R]. Intergovernmental Panelon Climate Change, 2023.
[15]	Rinaldi S A, Peerenboom J P, Kelly T K. Identifying, understanding, and analyzing critical infrastructure interdependencies[J]. IEEE Control Systems Magazine, 2001, 21(6): 11-25.

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	—