Intelligent Synergy in Urban Public Health and Safety Governance: A Multi-Agent Simulation Framework with Edge Computing and Optimization Algorithm

Urban public health and safety governance is facing challenges such as increasing risk complexity, inefficient cross-sectoral coordination, and imbalance in resource allocation, and the construction of a intelligent logistics system has become a key path to enhance governance resilience. This study proposes an intelligent synergy framework integrating multi-agent simulation and edge computing technologies. Through Python and NetLogo-based simulations, we validate three core technical contributions: (1) A hybrid integer programming algorithm achieving 92% medical resource matching accuracy during outbreaks, with performance degradation to 77% under >1-hour data delays; (2) Edge computing deployment reducing governance response latency by 40% compared to traditional models; (3) Community participation thresholds showing 12% early-warning accuracy improvement when grassroots data reporting exceeds 80%. The proposed "structure-function-technology-institution" framework demonstrates 20-30% efficiency gains in resource allocation through multi-agent collaboration mechanisms. Simulation results reveal critical thresholds: data processing delays <1 hour and community participation >80% are essential for maintaining governance effectiveness. This research establishes a computational foundation for intelligent urban governance through the integration of distributed edge nodes, adaptive optimization algorithms, and scenario-driven simulation protocols.