城市作为复杂巨系统，其地震应急响应面临灾害链动态推演不足、应急预案智能生成手段不足等问题，导致决策缺乏依据、资源配置低效，严重制约城市韧性。为提升应急响应的科学性与效率，本研究以复杂系统科学为指导，提出一套基于模型的系统工程（MBSE）与仿真深度融合的城市地震应急响应分析与优化方法，并构建了覆盖“灾害模拟-受灾评估-决策优化”全流程的仿真系统原型。研究核心在于构建了一个集MBSE建模、仿真推演和评估分析为关键环节的迭代演进的分析框架；在理论层面, 运用MBSE方法系统解析并优化应急响应流程、要素关系及动态行为；技术层面, 结合多层次、多尺度仿真模型，实现灾害、承灾体和救援实体的仿真和情景推演，可为“事前推演—事中决策—事后救援”全过程提供量化分析与智能化支持。同时，本研究开发了三个关键模型与仿真技术：首先，构建了“地震灾害-承灾体耦合模型”，结合地震烈度衰减、建筑易损性矩阵及人口活动时空分布，动态量化评估地震直接损失，为初期灾情判断和救援资源分配提供科学依据；其次，研究了城市交通、能源和通信系统级联失效的仿真模型，采用复杂流网建模与离散连续混合仿真技术，模拟地震引起城市各个子系统的破坏和系统间的级联失效，辅助评估关键基础设施损毁程度及对城市运行的影响，支持抢险救灾决策；最后，研究了大语言模型驱动的多智能体协同技术，构建指挥决策、营救等智能体交互框架，实现应急策略的动态优化和预案的智能化生成。基于上述研究成果，研制了城市地震应急响应仿真原型平台。通过蒙特卡洛仿真实验，验证了该平台在多场景地震灾害动态演化和应急预案智能化生成方面的能力及决策支持可行性。本研究方法与原型平台为城市应急管理部门提供了集定性分析、定量计算与智能决策支持于一体的工具，能够有效支撑灾害链预测、预案可行性评估以及资源动态优化配置，提升应急响应效率和决策科学性。研究成果为构建未来韧性城市应急管理体系提供了理论创新、技术支撑和实践指导，具有重要学术意义和实际应用价值。
As complex megasystems, cities often face challenges in earthquake emergency response, particularly due to inadequate dynamic forecasting of disaster chains and insufficient intelligent generation methods for emergency plans. This leads to a lack of evidence-based decision-making and inefficient resource allocation, significantly hindering urban resilience. To enhance the scientific rigor and efficiency of emergency response, guided by complex systems science, this study proposes an urban earthquake emergency response analysis and optimization method that deeply integrates Model-Based Systems Engineering (MBSE) with simulation. A simulation system prototype covering the entire workflow of "disaster simulation - damage assessment - decision optimization" has been developed. The core of this research lies in establishing an iteratively evolving analytical framework that integrates MBSE modeling, simulation-based scenario analysis, and evaluation. Theoretically, the MBSE approach is employed to systematically analyze and optimize emergency response processes, element relationships, and dynamic behaviors. Technically, multi-level and multi-scale simulation models are combined to simulate hazards, vulnerable systems, and rescue entities, enabling comprehensive scenario analysis. This framework provides quantitative analysis and intelligent support for the entire spectrum of "pre-disaster planning - in-disaster decision-making - post-disaster rescue." Furthermore, this study developed three critical models and simulation technologies. First, an "earthquake hazard-vulnerable system coupling model" was constructed. This model integrates seismic intensity attenuation, building vulnerability matrices, and spatiotemporal population distribution to dynamically quantify direct earthquake losses, providing a scientific basis for initial damage assessment and rescue resource allocation. Second, a simulation model for cascading failures in urban transportation, energy, and communication systems was developed. Utilizing complex flow network modeling and discrete-continuous hybrid simulation techniques, this model simulates earthquake-induced damage to various urban subsystems and their inter-system cascading failures. It assists in assessing the damage extent of critical infrastructure and its impact on urban operations, supporting emergency response and disaster relief decisions. Finally, a large language model (LLM)-driven multi-agent collaboration technology was investigated. An intelligent agent interaction framework, encompassing command, decision-making, and rescue agents, was established to achieve dynamic optimization of emergency strategies and intelligent generation of response plans. Based on these research findings, an urban earthquake emergency response simulation prototype platform was developed. Monte Carlo simulation experiments validated the platform's capabilities in simulating the dynamic evolution of multi-scenario earthquake disasters, intelligently generating emergency plans, and providing feasible decision support. The proposed method and prototype platform offer urban emergency management departments a comprehensive tool that integrates qualitative analysis, quantitative computation, and intelligent decision support. This tool can effectively support disaster chain prediction, emergency plan feasibility assessment, and dynamic optimization of resource allocation, thereby enhancing emergency response efficiency and the scientific basis of decision-making. The findings provide theoretical innovation, technical support, and practical guidance for constructing future resilient urban emergency management systems, demonstrating significant academic merit and practical application value.
