突发性泄漏事故已成为我国城市公共安全的一个潜在威胁。本文以福建省福州市某街区为案例,采用高分辨率遥感影像、各种敏感目标和人口的分布信息等空间数据,利用事件触发机制,建立基础数据库与虚拟现实仿真试验平台。针对威胁城市公共安全的突发性泄漏事故,选择一种适合城市街区尺度的点源气体扩散模型与GIS集成,以构建泄漏气体扩散的三维时空仿真模型;模拟泄漏气体的扩散范围,以便实时监测污染可能影响区域并进行数据解读分析,进而虚拟推演突发事故对公众的危害程度及可能的发展趋势;构建突发事件应急辅助决策支持系统。并设计疏散撤离位置与救援路径,优化救援资源配置方案,以实现应急处置向导、应急资源整合与行动协调。这可快速高效地为城市突发性泄漏污染事故的模拟、应急救援提供数据验证和决策辅助支持,同时,也为完善我国突发性公共事件的应急管理体系和特大灾害的风险管理体制提供借鉴。

Sudden leakage has become a growing threat to public safety in cities. In this paper, using the emergency response to sudden leak accidents, which is a practical problem threatening public safety in cities as the entry point, we studied the simulation technology of sudden gas diffusion process on city-block-scale. Focused on the leakages that threaten urban public security, we suggested a point source gas diffusion model suit for city-block-scale and constructed a 3D spatio-temporal gas diffusion simulate model coupled with GIS. We use it to simulate the extent of the diffusion area for pollution monitoring and data analysis, and virtually reason the accident's damage degree to public and the subsequent development. So that the accident response support decision system can be constructed, the evacuation path and time can be calculated, the emergency rescue plan can be optimized. The most remarkable characteristic of the emergency rescue problem is the time urgency that decision-makers should propose emergency rescue plan within the shortest possible time, and that adequate rescue vehicles, personnel, and supplies would be mobilized and arrive at the accident sites at the fastest speed to carry out rescues. Faced with massive sudden leakages, it is very essential to optimally distribute the emergency rescue points for being able to effectively control disaster and to successfully carry out rescue activities. According to the spatial distribution of the emergency rescue points around the accident areas, we explored the algorithm for seeking the best path of aid resource allocation, obtaining the optimal solution to the distribution of the emergency rescue points through dynamic optimization strategies.