According to the United Nations, wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming, posing critical threats to life, infrastructure, and ecosystems. Conventional disaster management frameworks rely on static simulations and passive data acquisition, hindering their ability to adapt to arbitrarily evolving wildfire episodes in real-time. To address these limitations, we introduce the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin (DT) platform augmented by autonomous AI agents. The IVSR continuously ingests multisource sensor imagery, weather data, and 3D forest models to create a live virtual replica of the fire environment. A similarity engine powered by AI aligns emerging conditions with a precomputed Disaster Simulation Library, retrieving and calibrating intervention tactics under the watchful eyes of experts. Authorized action-ranging from UAV redeployment to crew reallocation-is cycled back through standardized procedures to the physical layer, completing the loop between response and analysis. We validate IVSR through detailed case-study simulations provided by an industrial partner, demonstrating capabilities in localized incident detection, privacy-preserving playback, collider-based fire-spread projection, and site-specific ML retraining. Our results indicate marked reductions in detection-to-intervention latency and more effective resource coordination versus traditional systems. By uniting real-time bidirectional DTs with agentic AI, IVSR offers a scalable, semi-automated decision-support paradigm for proactive, adaptive wildfire disaster management.

翻译：根据联合国报告，由于全球变暖，预计到2030年野火发生频率和强度将增加约14%，到2050年将增加30%，对生命、基础设施和生态系统构成严重威胁。传统的灾害管理框架依赖静态模拟和被动数据采集，难以实时适应不断演变的野火态势。为克服这些局限，我们提出了智能虚拟态势研判室（IVSR），这是一个由自主人工智能代理增强的双向数字孪生平台。IVSR持续整合多源传感器图像、气象数据和三维森林模型，创建火灾环境的实时虚拟副本。基于人工智能的相似性引擎将实时态势与预计算的灾害模拟库进行匹配，在专家监督下检索并校准干预策略。经授权的行动——从无人机重新部署到人员调度——通过标准化程序反馈至物理层，形成响应与分析的闭环。我们通过工业合作伙伴提供的详细案例模拟验证了IVSR系统，展示了其在局部事件检测、隐私保护回放、基于碰撞器的火势蔓延预测以及现场特定机器学习模型重训练等方面的能力。实验结果表明，与传统系统相比，IVSR显著降低了从检测到干预的延迟，并实现了更高效的资源协调。通过将实时双向数字孪生与自主人工智能相结合，IVSR为主动、自适应的野火灾害管理提供了一个可扩展的半自动化决策支持范式。