构建多智能体系统，实现应急事件数据的实时标准化整合。
SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data
- 采用专用智能体处理原始通信数据，生成符合标准的应急事件对象。
- 支持跨源数据联动，实现事件进展的动态更新与统一视图。
- 适用于应急指挥系统，提升多部门协同效率。
应急响应系统来自多个机构和系统的数据。在实际操作中，如何按照下一代9-1-1数据标准，在不同来源间关联和更新信息仍具挑战性。理想情况下，这些数据应被视为持续的运营更新流，新信息能即时集成，以提供对不断演变事件的及时、统一视图。本文提出SentinelAI，一个用于将应急通信转化为标准化、机器可读数据集的数据集成与标准化框架，支持数据整合、复合事件构建及跨源推理。SentinelAI实现了一个可扩展的处理流水线，由多个专用智能体组成。EIDO智能体接收原始通信数据，并生成符合NENA标准的紧急事件数据对象（Emergency Incident Data Object）JSON。
原文摘要 · Abstract (English)
Emergency response systems generate data from many agencies and systems. In practice, correlating and updating this information across sources in a way that aligns with Next Generation 9-1-1 data standards remains challenging. Ideally, this data should be treated as a continuous stream of operational updates, where new facts are integrated immediately to provide a timely and unified view of an evolving incident. This paper presents SentinelAI, a data integration and standardization framework for transforming emergency communications into standardized, machine-readable datasets that support integration, composite incident construction, and cross-source reasoning. SentinelAI implements a scalable processing pipeline composed of specialized agents. The EIDO Agent ingests raw communications and produces NENA-compliant Emergency Incident Data Object JSON.
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