城市AI智能体与数字孪生指挥技术研究及应用

Research and Application of Urban AI Agents and Digital Twin Command Technology

摘要: 针对传统指挥系统在复杂应急场景下面临的信息孤岛化、人工依赖度高、智能决策与物理执行脱节等问题，本文提出并构建了一种“AI智能体具身孪生指挥系统”。该系统深度融合AI大模型、具身智能与数字孪生三大技术，构建了“感知–决策–执行–反馈”的闭环指挥架构，并设计了“边缘层–中央层–数字孪生层”三级系统架构。通过边缘层的多模态感知与实时控制、中央层的智能分析与决策生成、数字孪生层的高保真映射与仿真推演，系统实现了物理空间与虚拟空间的实时交互与动态优化。在危化品工厂安全监控与公安应急指挥等典型场景中的测试结果表明，该系统能够显著提升隐患排查效率、事故预警准确率与指令执行成功率，验证了其在提升指挥智能化水平和降低人员安全风险方面的有效性与实用性。本研究及技术应用为解决边缘与中央算力协同、小样本场景下模型泛化识别等技术通性问题提供了新思路，为应急指挥与公共安全领域的智能化升级提供了可行的技术路径与方法支撑。

Abstract: To address the problems faced by traditional command systems in complex emergency scenarios, such as information silos, high reliance on manual operations, and the disconnection between intelligent decision-making and physical execution, this paper proposes and constructs an “AI Agent Embodied Twin Command System”. This system deeply integrates three key technologies—AI large models, embodied intelligence, and digital twins—to establish a closed-loop command architecture of “perception-decision-execution-feedback” and designs a three-tier system architecture comprising the “edge layer, central layer, and digital twin layer”. Through multimodal perception and real-time control at the edge layer, intelligent analysis and decision generation at the central layer, and high-fidelity mapping and simulation deduction at the digital twin layer, the system achieves real-time interaction and dynamic optimization between the physical and virtual spaces. Test results in typical scenarios, including safety monitoring in hazardous chemical plants and public security emergency response, demonstrate that the system significantly improves the efficiency of hidden hazard detection, the accuracy of incident early warning, and the success rate of command execution, validating its effectiveness and practicality in enhancing the intelligence level of command processes and reducing personnel safety risks. This research and its technological applications provide new ideas for solving General technical issues such as edge-central computing synergy and model generalization in small-sample scenarios, offering a feasible technological pathway and methodological support for the intelligent upgrade of the emergency command and public safety fields.