【摘要】 面向突发事件的深度不确定性，传统应急预案的“计划-执行”逻辑难以适应动态演化的复杂灾情。本文提出并论证了大模型驱动的应急响应方案“动态-适配”生成逻辑，其核心内涵包括从“预案驱动”到“数据驱动”、从“静态计划”到“动态适应”、从“层级指挥”到“人机协同”，构建了集成“知识-事实-推理”的大模型技术框架。通过持续整合动态事实与静态知识，将应急方案从静态的“指令清单”重构为一种动态可审查的“论证式方案”。以“12·18”积石山地震为例，开展多阶段应急响应方案生成模拟实验，验证了集成“知识-事实-推理”大模型技术框架在应急响应方案生成方面的可靠性。本研究为深度不确定性情景下的应急决策模式变革及应急响应机制优化，提供了数智赋能视角的学理参考与实践启示。更多还原

【Abstract】 In view of the deep uncertainty of sudden events,the traditional “plan-execute ” logic of emergency plans is difficult to adapt to dynamically evolving and complex disaster situations. This paper proposes and demonstrates a large-model-driven “dynamic-adaptation ” generation logic for emergency response plans,whose core connotations include shifts from “plan-driven ” to “datadriven”,from “static planning ” to “dynamic adaptation ”,and from “hierarchical command ” to“human-machine collaboration ”; it constructs a large-model technical framework integrating“knowledge-facts-reasoning ”, which, by continuously integrating dynamic facts with static knowledge,reconstructs emergency plans from a static “instruction list” into a dynamically auditable“argumentation-style plan”; taking the “12·18” Jishishan earthquake as an example,it carries out multi-stage simulation experiments on the generation of emergency response plans,verifying the reliability of the integrated “knowledge-facts-reasoning ” large-model technical framework in emergency response plan generation. This study provides,from a data-and-intelligence empowerment perspective,theoretical reference and practical implications for the transformation of emergency decision-making models and the optimization of emergency response mechanisms under deep uncertainty.更多还原