应急指挥S3DA2框架与应急数字孪生战场关键技术

doi:10.16265/j.cnki.issn1003-3033.2025.10.1646

摘要/Abstract

摘要： 为解决我国应急指挥救援体系中存在的层级割裂、数据与决策脱节及动态适应性不足等问题,聚焦一体化框架构建与数字孪生技术应用,提出4层级(应急指挥中心、现场指挥部、救援队伍、救援单兵)-5步骤(感知、模拟、谋划、决策、行动)通用模型,并进一步将其拓展为感知-模拟-谋划-决策-行动(S3DA2)框架,同时,推导得出最小化成本的最优决策公式;基于该框架,构建“应急数字孪生战场”技术体系,涵盖多源数据融合、孪生建模、流固耦合/人工智能混合仿真和多目标决策优化等;以 2024 年洞庭湖团洲垸堤防决口事件为验证场景,开发流固耦合-多智能体救援模拟器,实现物理场景与虚拟仿真的实时映射闭环。结果表明:该模型将决口扩张误差控制在 10 % 以内,将封堵时长由 82 h 缩短至 52 h,石料需求预测为 5.9 × 10⁴ m³,与实测结果一致。

关键词: 应急指挥, 感知-模拟-谋划-决策-行动(S3DA2)框架, 数字孪生战场, 关键技术, 4层级模型, 5步骤模型

Abstract:

To remedy the fragmented command tiers, a weak data-to-decision link and poor dynamic adaptability, which have long limited the effectiveness of China's emergency-response system, an integrated framework based on digital-twin technology was investigated. A generic four-tier, five-step model—comprising an emergency-command center, on-site command post, rescue teams and individual rescuers, together with the steps Sense, Simulate, Strategize, Decide and Act—was established and extended to the S3DA2 framework. A cost-minimization formula for optimal decision making was derived. Building on this framework, an emergency digital-twin battlefield technology system was established, encompassing multi-source data fusion, twin modelling, fluid-solid-coupled/artificial intelligence hybrid simulation and multi-objective decision optimization. A fluid-solid coupling, multi-agent rescue simulator was developed and validated with the 2024 Tuanzhou Dyke breach on Dongting Lake, enabling a real-time closed loop between the physical scene and its virtual replica. Results show that breach-width predictions deviate by less than 10%, total sealing time is reduced from 82 h to 52 h, and the estimated rock-fill demand of 5.9 × 10⁴ m³ matches field measurements.

Key words: emergency command, sense-simulate-strategize-decide-act (S3DA2) framework, digital twin battlefield, key technologies, four-tier model, five-step model

中图分类号:

X959其他

陈涛, 张辉, 黄丽达, 田冉冉, 闫小丽. 应急指挥S3DA2框架与应急数字孪生战场关键技术[J]. 中国安全科学学报, 2025, 35(10): 205-212.

CHEN Tao, ZHANG Hui, HUANG Lida, TIAN Ranran, YAN Xiaoli. S3DA2 framework for emergency command system and key technologies of emergency digital twin battlefield[J]. China Safety Science Journal, 2025, 35(10): 205-212.

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应急数字孪生战场的关键技术

关键技术点	主要内容	价值与应用	主要挑战	实现路径
多源数据融合	传感器、卫星、无人机、社交媒体等异构数据采集; 数据清洗、标注、时空对齐; 统一数据标准与接口协议	消除数据孤岛,形成统一的现场态势; 提升数据可用性,为仿真与决策提供高质量输入	异构数据格式多,难以实时对齐; 大规模数据传输与存储成本高	建立跨平台的数据接入协议(如基于开源标准或应用程序编程接口); 采用边缘计算来降低中心节点负载,并利用中间件实现实时数据流处理
数字孪生建模	构建灾害场景下的物理实体模型(地形、水文、建筑物等); 救援力量建模(队伍、单兵、装备); 多物理场耦合与多尺度嵌套	提供真实世界的虚拟镜像,准确反映灾害及救援要素; 支撑多场景、多阶段的应急推演	如何平衡模型精度与计算开销; 动态建模的实时更新机制	开发轻量化模型库,结合物理方程和机器学习方法,保证既有足够精度又能实时更新; 迁移学习历史案例,快速适配新场景
实时仿真与推演	动力学仿真(流体力学、结构力学等); 数据驱动预测模型(人工智能算法); 混合仿真引擎(并行、分布式计算)	预测灾害演化,辅助决策提前干预; 评估不同救援方案的执行效果	高并发、高精度的计算资源需求; 不确定性场景的模拟算法复杂度高	将物理方程(如流体力学)与数据驱动模型(如深度学习预测)相结合; 利用并行计算框架(图形处理单元、云计算)支撑高强度运算,并通过事件驱动的方式动态更新场景
决策优化与多目标调度	综合考虑人员安全、救援效率、资源消耗等多目标; 运用整数线性规划、强化学习等优化算法; 动态更新决策参数,持续迭代	提高救援方案的科学性与可行性; 支撑快速增援、资源最优配置	多目标、多约束下的算法复杂度; 快速响应灾情变化并实时更新决策	应用多目标优化算法(如强化学习、遗传算法等),结合实时反馈机制不断迭代; 综合评估资源调度、增援时机、疏散方案,以快速响应现场变化
跨层级协同与信息共享	指挥中心与现场指挥部、救援队伍之间的高效沟通; 标准化接口与权限管理; 多端协同(PC端、移动端、可穿戴设备)	减少指令滞后,提升应急响应速度; 保证决策信息从上至下、从下至上双向畅通	复杂网络环境下的通信稳定性; 不同部门、不同平台间的数据互操作	完善组织协同、标准化接口、安全防护等管理机制,确保系统在大规模、多部门联动的应急场景中稳定运行

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