Application Analysis of Multi-Agent Collaborative AI-Driven Models in Urban Emergency Management

With global urbanization on the rise, cities face complex cascading risk impacts. Traditional emergency management has bottlenecks like fragmented multi-agent collaboration and single-point AI application, making it hard to respond to dynamic emergencies. This study integrates Complex Adaptive System (CAS) theory, AI empowerment theory, and AnyLogic simulation validation to develop a multi-agent collaborative AI-driven model. It builds an “agent-data-technology-process” four-layer coupled architecture, clarifies multi-agent interaction rules and AI integration paths, and covers the full “prevention-preparedness-response-recovery” cycle. Empirical validation shows that in three typical scenarios—urban waterlogging, large-scale crowd gatherings, cross-regional public health incidents—vs. traditional models, the proposed model shortens response time, improves resource efficiency, and reduces casualties. It resolves emergency collaboration dilemmas, enriches AI-empowered emergency management's theoretical system, and guides building a resilient smart city emergency system.