为提高高速铁路应急处置效率，实现资源统一调配，融合云计算和大数据技术，研究了高速铁路应急服务资源的调度问题。在应急云资源建模、应急云服务虚拟化建模、云服务池建模、高速铁路现有应急救援系统的资源使用建模、云计算模式构建的基础上，建立了跨总部和区域的高速铁路应急资源服务池的全域优化调度模型，提出了云资源传输总距离最小、资源协调成本最低、运行处理时间最短、当资源缺乏时需增加的资源数最小等4个优化目标；针对问题的特征，研究提出基于双层粒子群的高铁应急服务优化调度方法。对于给定的案例，选取距离最小作为优化目标，运用提出的方法可给出优化的资源调度方向和资源调度数量，验证了所提出的方法对于求解分布式应急云资源调度问题的有效性。

In order to improve the emergency response efficiency of high-speed railway and realize the unified allocation of resources, cloud computing and big data technology were integrated to study the dispatching problem of emergency service resources of high-speed railway. A global optimization dispatching model of high-speed railway emergency resource service pool across headquarters and regions was established, based on the emergency cloud resource modeling, the virtualization of emergency cloud services modeling, cloud service pool modeling, the resource use of the existing emergency rescue system for high-speed rail modeling and cloud computing pattern construction. The optimization goals of the model were minimizing the total cloud resource transport distance, the resource coordination cost, the operational processing time and the number of additional resources needed when resources were scarce. In view of the characteristics of the problem, the optimization dispatching method of high-speed railway emergency service based on double-layer particle swarm was proposed. For a given case, the minimum distance is selected as the optimization objective, and the optimal resource dispatching direction and resource dispatching quantity can be obtained by using the method proposed, which verifies the effectiveness of the proposed method for solving the distributed emergency cloud resource dispatching problem.