A Generative Method for Earthquake Emergency Plans

YAO Liwei; REN Fu; WANG Shuangyan; WANG Yong; ZHANG Bocong; SHENG Zhaoyang; DU Qingyun

doi:10.13203/j.whugis20250127

您当前的位置：

首页 >

文章列表页 >

A Generative Method for Earthquake Emergency Plans

更新时间：2026-11-05

- A Generative Method for Earthquake Emergency Plans
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 6, Pages: 1159-1174(2025)
- 作者机构：
  
  1.武汉大学资源与环境科学学院，湖北武汉，430079
  2.中共中央党校（国家行政学院），北京，100091
  3.中国测绘科学研究院，北京，100036
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250127
  CLC： P208
- Received：29 April 2025，
  
  Published：05 June 2025
- 稿件说明：
移动端阅览
姚立伟,任福,王双燕，等.地震灾害应急预案的生成式编制方法[J].武汉大学学报（信息科学版）,2025,50(6):1159-1174.

YAO Liwei,REN Fu,WANG Shuangyan,et al.A Generative Method for Earthquake Emergency Plans[J].Geomatics and Information Science of Wuhan University,2025,50(6):1159-1174.
姚立伟,任福,王双燕，等.地震灾害应急预案的生成式编制方法[J].武汉大学学报（信息科学版）,2025,50(6):1159-1174. DOI：10.13203/j.whugis20250127

YAO Liwei,REN Fu,WANG Shuangyan,et al.A Generative Method for Earthquake Emergency Plans[J].Geomatics and Information Science of Wuhan University,2025,50(6):1159-1174. DOI：10.13203/j.whugis20250127

摘要

地震灾害应急预案的编制具有高度规范性，如何提升编制效率、适应不同情境、确保措施的可行性，仍是当前应急管理中的重要挑战。提出了一种面向标准化流程的智能预案生成方法，构建了由大模型、智能体与知识图谱协同驱动的多阶段工作流。该工作流包括4个阶段：用户需求解析与结构化输入构建、智能体驱动的震后风险分析、知识增强的响应措施生成、大模型驱动的格式优化与质量评估，支持从需求理解到预案输出的全流程自动化。为支撑工作流运行，研究设计了省、市、县3级地震应急预案模板体系，并构建了一个包含117 915个节点与182 586条关系的应急知识图谱，系统整合了应急响应场景下的风险类型、应对措施、资源配置与职责划分等核心知识。实验部分选取了20份市县级地震应急预案作为参考样本，设计6个维度的评估指标体系，通过对比分析工作流生成方法与直接大语言模型生成方法的表现，验证了所提方法在全面性、可行性与合理性等核心指标上的优势。评估结果显示，工作流生成在多个维度上得分更高，结构更加规范，逻辑更为清晰，内容更具操作性。同时，案例分析进一步展示了工作流方法在资源列举准确性、结构优化与内容一致性方面的提升，表明该方法更能满足实战场景下的应急预案需求。并开发了交互式预案编制工具，整合工作流方法用于辅助实际预案生成，具备良好的实用性与推广价值。

Abstract

Objectives

The formulation of earthquake emergency response plans is a highly standardized task that demands both procedural rigor and contextual adaptability. Existing methods often struggle to balance consistency with the need for situational specificity and actionable detail. This study aims to address these challenges by proposing an intelligent workflow for the automated generation of earthquake emergency plans. The objectives are threefold: (1) To enhance generation efficiency through automation. (2) To ensure procedural compliance and logical consistency; and (3) to improve the contextual relevance and implement ability of generated plans.

Methods

To achieve these goals

we propose a four-stage intelligent emergency plan generation framework that integrates large language models (LLM)

intelligent agents

and knowledge graph. The workflow consists of four sequential stages: User intent parsing and structured input construction

agent-driven post-earthquake risk analysis

knowledge-enhanced response strategy generation

and LLM-driven format optimization and quality evaluation

enabling end-to-end automated plan generation. To support this workflow

we design a three-tier emergency plan template system covering provincial

municipal

and county levels

and construct a comprehensive emergency knowledge graph containing 117 915 nodes and 182 586 edges

representing key entities such as risk types

response measures

resource configurations

and organizational responsibilities.

Results

Experiments were conducted using 20 city- and county-level earthquake emergency plans as reference cases. A six-dimensional evaluation framework was designed to compare the proposed workflow method against direct LLM generation. Results demonstrate that the workflow method significantly outperforms direct LLM generation in key metrics such as completeness

feasibility

and coherence

producing more structured

logical

and actionable outputs. Further case analysis highlights the workflow's advantages in resource detailing

paragraph structuring

and content consistency

underscoring its effectiveness in real-world scenarios.

Conclusions

We present a novel

knowledge-augmented and agent-assisted framework for intelligent emergency plan generation

offering a practical and scalable solution to a traditionally manual process. By combining structured template

semantic knowledge graphs

intelligent agent reasoning

and LLM-based generation and evaluation

the proposed workflow ensures both procedural compliance and real-world applicability. An interactive planning tool has been developed to facilitate real-time use by emergency management practitioners

enabling customizable

region-specific

and ready-to-implement emergency plans. Future work will extend the system's capabilities to other disaster types and integrate predictive analytics for dynamic risk evolution modeling.

关键词

Keywords

references

刘纪平，王琢璐，徐胜华，等 . 人工智能时代下的应急测绘［J］. 武汉大学学报（信息科学版）， 2025 ， DOI： 10.13203/j.whugis20240300 http://dx.doi.org/10.13203/j.whugis20240300 .

LIU Jiping ， WANG Zhuolu ， XU Shenghua ， et al . Emergency Surveying and Mapping in the Era of Artificial Intelligence ［J］. Geomatics and Information Science of Wuhan University ， 2025 ， DOI： 10.13203/j.whugis20240300 http://dx.doi.org/10.13203/j.whugis20240300 .

刘纪平，刘猛猛，徐胜华，等 . 大数据时代下的一体化综合减灾技术综述［J］. 武汉大学学报（信息科学版）， 2020 ， 45 （ 8 ）： 1107 - 1116 .

LIU Jiping ， LIU Mengmeng ， XU Shenghua ， et al . A Survey on Integrated and Comprehensive Disaster Reduction Technology in the Era of Big Data ［J］. Geomatics and Information Science of Wuhan University ， 2020 ， 45 （ 8 ）： 1107 - 1116 .

范维澄，刘帅 . 发展新质生产力推动中国应急管理体系和能力现代化［J］. 中国减灾， 2025 （ 1 ）： 18 - 19 .

FAN Weicheng ， LIU Shuai . Developing New Productive Forces to Promote the Modernization of China's Emergency Management System and Capabilities ［J］. Disaster Reduction in China ， 2025 （ 1 ）： 18 - 19 .

范维澄 . 落实总体国家安全观推动应急管理体系和能力现代化［J］. 中国减灾， 2024 （ 1 ）： 10 - 11 .

FAN Weicheng . Implementing the Overall National Security Concept and Promoting the Modernization of Emergency Management System and Capacity ［J］. Disaster Reduction in China ， 2024 （ 1 ）： 10 - 11 .

AI B ， ZHANG D ， JIA M X ， et al . A Decision-Support Model for the Generation of Marine Green Tide Disaster Emergency Disposal Plans ［J］. Journal of Marine Science and Engineering ， 2022 ， 10 （ 12 ）： 1890 .

NI W J ， LIU T ， CHEN S Y . Rapid Generation of Emergency Response Plans for Unconventional Emergencies ［J］. IEEE Access ， 2020 ， 8 ： 181036 - 181048 .

YU L A ， LAI K K . A Distance-Based Group Decision-Making Methodology for Multi-Person Multi-Criteria Emergency Decision Support ［J］. Decision Support Systems ， 2011 ， 51 （ 2 ）： 307 - 315 .

LI P ， WEI C P . An Emergency Decision-Making Method Based on D-S Evidence Theory for Probabilistic Linguistic Term Sets ［J］. International Journal of Disaster Risk Reduction ， 2019 ， 37 ： 101178 .

ZHAO M ， LIU X . Development of Decision Support Tool for Optimizing Urban Emergency Rescue Facility Locations to Improve Humanitarian Logistics Management ［J］. Safety Science ， 2018 ， 102 ： 110 - 117 .

Losada J ， Sánchez S R ， García M C ， et al . Rapid Response to Large-Scale Emergencies： A Neural Network Model and a Decision-Tree Algorithm ［J］. EMERGENCIAS ， 2012 ， 24 （ 3 ）： 175 - 180 .

WANG L ， ZHANG Z X ， WANG Y M . A Prospect Theory-Based Interval Dynamic Reference Point Method for Emergency Decision Making ［J］. Expert Systems with Applications ， 2015 ， 42 （ 23 ）： 9379 - 9388 .

SHI W Q ， WANG H Z ， CHEN C ， et al . Evolutionary Game Analysis of Decision-Making Dynamics of Local Governments and Residents During Wildfires ［J］. International Journal of Disaster Risk Reduction ， 2021 ， 53 ： 101991 .

LIU Y ， FAN Z P ， YUAN Y ， et al . A FTA-Based Method for Risk Decision-Making in Emergency Response ［J］. Computers & Operations Research ， 2014 ， 42 ： 49 - 57 .

RU D Y ， WEN H Y ， ZHANG Y T . A Pre-Generation of Emergency Reference Plan Model of Public Health Emergencies with Case-Based Reasoning ［J］. Risk Management and Healthcare Policy ， 2022 ， 15 ： 2371 - 2388 .

ZHANG H ， DAI G L . Research on Traffic Decision Making Method Based on Image Analysis Case Based Reasoning ［J］. Optik ， 2018 ， 158 ： 908 - 914 .

WANG D L ， WAN K D ， MA W X . Emergency Decision-Making Model of Environmental Emergencies Based on Case-Based Reasoning Method ［J］. Journal of Environmental Management ， 2020 ， 262 ： 110382 .

YAO X ， GUO H X ， ZHU J ， et al . Dynamic Selection of Emergency Plans of Geological Disaster Based on Case-Based Reasoning and Prospect Theory ［J］. Natural Hazards ， 2022 ， 110 （ 3 ）： 2249 - 2275 .

LI R ， WANG J Q ， WANG S L ， et al . Prediction of Network Public Opinion Features in Urban Planning Based on Geographical Case-Based Reasoning ［J］. International Journal of Digital Earth ， 2022 ， 15 （ 1 ）： 890 - 910 .

CHEN W L ， WANG X L ， WANG W ， et al . A Heterogeneous GRA-CBR-Based Multi-attribute Emergency Decision-Making Model Considering Weight Optimization with Dual Information Correlation ［J］. Expert Systems with Applications ， 2021 ， 182 ： 115208 .

CHEN M Z ， TAO Z X ， TANG W T ， et al . Enhancing Emergency Decision-Making with Knowledge Graphs and Large Language Models ［J］. International Journal of Disaster Risk Reduction ， 2024 ， 113 ： 104804 .

王勇，韩遥遥，刘纪平，等 . 应急救援地理空间情报：概念特征、生成技术及应用实践［J］. 武汉大学学报（信息科学版）， 2025 ， DOI： 10.13203/j.whugis20240485 http://dx.doi.org/10.13203/j.whugis20240485 .

WANG Yong ， HAN Yaoyao ， LIU Jiping ， et al . Emergency Rescue Geospatial Intelligence： Conceptual Characteristics， Generation Technologies， and Application Practices ［J］. Geomatics and Information Science of Wuhan University ， 2025 ， DOI： 10.13203/j.whugis20240485 http://dx.doi.org/10.13203/j.whugis20240485 .

车万翔，窦志成，冯岩松，等 . 大模型时代的自然语言处理：挑战、机遇与发展［J］. 中国科学：信息科学， 2023 ， 53 （ 9 ）： 1645 - 1687 .

CHE Wanxiang ， DOU Zhicheng ， FENG Yansong ， et al . Towards a Comprehensive Understanding of the Impact of Large Language Models on Natural Language Processing： Challenges， Opportunities and Future Directions ［J］. Scientia Sinica （Informationis）， 2023 ， 53 （ 9 ）： 1645 - 1687 .

李源，马新宇，杨国利，等 . 面向知识图谱和大语言模型的因果关系推断综述［J］. 计算机科学与探索， 2023 ， 17 （ 10 ）： 2358 - 2376 .

LI Yuan ， MA Xinyu ， YANG Guoli ， et al . Survey of Causal Inference for Knowledge Graphs and Large Language Models ［J］. Journal of Frontiers of Computer Science and Technology ， 2023 ， 17 （ 10 ）： 2358 - 2376 .

ZHU Y Q ， WANG X H ， CHEN J ， et al . LLMS for Knowledge Graph Construction and Reasoning： Recent Capabilities and Future Opportunities ［J］. World Wide Web ， 2024 ， 27 （ 5 ）： 58 .

吴昊，董福安，王雪飞，等 . 基于大模型的应急指挥辅助决策系统设计［J］. 指挥与控制学报， 2024 ， 10 （ 6 ）： 752 - 759 .

WU Hao ， DONG Fuan ， WANG Xuefei ， et al . Design of Emergency Command Aided Decision-Making System Based on LLM ［J］. Journal of Command and Control ， 2024 ， 10 （ 6 ）： 752 - 759 （）

XI Z H ， CHEN W X ， GUO X ， et al . The Rise and Potential of Large Language Model Based Agents： A Survey ［J］. Science China Information Sciences ， 2025 ， 68 （ 2 ）： 121101 .

邓云峰 . 推动应急预案有效衔接［J］. 中国应急管理， 2024 （ 3 ）： 33 - 35 .

DENG Yunfeng . Promote the Effective Convergence of Emergency Plans ［J］. China Emergency Management ， 2024 （ 3 ）： 33 - 35 .

吴宗之，刘茂 . 重大事故应急预案分级、分类体系及其基本内容［J］. 中国安全科学学报， 2003 ， 13 （ 1 ）： 18 - 21 .

WU Zongzhi ， LIU Mao . Gradation and Categorization System of Emergency Plan for Major Accidents and Their Main Contents ［J］. China Safety Science Journal ， 2003 ， 13 （ 1 ）： 18 - 21 .

ZHONG L F ， WU J ， LI Q ， et al . A Comprehensive Survey on Automatic Knowledge Graph Construction ［J］. ACM Computing Surveys ， 2024 ， 56 （ 4 ）： 1 - 62 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

From Task-Specific Models to General-Purpose Model-Driven Paradigm Shift： A Review and Future Directions of GeoAI Research

Research Progress in Eco-Geological Environmental Monitoring and Construction of an Intelligent Monitoring System in the Qinling Mountains

High-Resolution L-Band InSAR Study of Intense Near-Field Deformation Associated with Shallow Moderate Earthquakes

Spatiotemporal Evolution and Explainable Analysis of Grassland Vulnerability in the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration

A Review of Multi-scale Geospatial Data Updating Methods

Related Author

HE Jie

WANG Chaoqun

PAN Weijiang

XU Xin

Jie ZHU

Xiong YOU

Qing XIA

Hongjun ZHANG

Related Institution

School of Architecture,Harbin Institute of Technology

Guangdong Architectural Design and Research Institute Group Co., Ltd.

College of Command and Control Engineering, The Army Engineering University of PLA

Institute of Geospatial Information, Information Engineering University

Troops

AI问答

⁰