中国安全科学学报 ›› 2025, Vol. 35 ›› Issue (11): 220-227.doi: 10.16265/j.cnki.issn1003-3033.2025.11.1632

• 应急技术与工程 • 上一篇    下一篇

面向灾害风险研判的应急事理图谱构建及应用

朱莉1,2(), 杨耀星1,2   

  1. 1 南京信息工程大学 管理工程学院,江苏 南京 210044
    2 南京信息工程大学 风险治理与应急决策研究院,江苏 南京 210044
  • 收稿日期:2025-07-11 修回日期:2025-09-11 出版日期:2025-11-28
  • 作者简介:

    朱莉 教授 (1983—),女,江西上饶人,博士,教授,主要从事应急管理方面的研究。E-mail:

  • 基金资助:
    国家社会科学基金资助(22BGL242); 江苏省研究生科研与实践创新计划项目(KYCX25_1547)

Construction and application of emergency event evolutionary graph for disaster risk assessment

ZHU Li1,2(), YANG Yaoxing1,2   

  1. 1 School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China
    2 Research Institute for Risk Governance and Emergency Decision-Making, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China
  • Received:2025-07-11 Revised:2025-09-11 Published:2025-11-28

摘要:

为精准捕捉灾害风险的动态演化特性与模式特征,实现科学高效的灾害风险研判,创新融合灾害的自然与社会双重属性考量,依托事件抽取、关系识别及事件泛化成果,提出一种基于因果关联强度的事理模式抽象方法,构建出面向链式灾害风险研判的应急事理图谱;结合覆盖灾害事件多维属性的相似度匹配策略,选择以地震灾害为例进行实证分析。研究结果表明:所提出的基于事理图谱的灾害风险研判框架,不仅能为目标案例泸定地震快速匹配高相似度的历史事件,还可依据图谱揭示的风险间内在复杂关联与演化路径,进一步预测堰塞湖等次生灾害风险以及谣言传播等衍生社会风险。

关键词: 灾害风险研判, 应急事理图谱, 风险演化, 因果关联, 次生灾害

Abstract:

In order to more accurately capture the evolutionary patterns of disaster risks and facilitate scientific risk assessment, this study innovatively integrated the dual natural and social attributes of disasters. By leveraging the outcomes from event extraction, event relationship identification, and event generalization, a method based on causal association strength was developed to mine event logic. Subsequently, an emergency event evolutionary graph was constructed for assessing chain-reaction disaster risks. Combined with a similarity matching method that incorporates the multi-dimensional attributes of disaster events, an empirical study was conducted using earthquake disasters as a case study. The results demonstrate that the proposed disaster risk assessment framework based on the event evolutionary graph can rapidly identify historical earthquake events with high similarity to the Luding earthquake case. Furthermore, by leveraging the intrinsic interconnections and evolutionary paths among risks revealed by the graph, the framework can predict secondary disasters (e.g., landslide-dammed lakes) and derivative social risks (e.g., rumor propagation).

Key words: disaster risk assessment, emergency event evolutionary graph, risk evolution, causal association, secondary disaster

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