Natural disasters such as earthquakes have consecutive impacts on the smart grid because of aftershock activities. To guarantee service requirements and smart grid stable operations, it is a challenge to design a fast and survivable rerouting mechanism. There are few studies that consider concurrent rerouting aiming at multiple services in smart grid communication network, however. Firstly, we formulate the node survivability, link survivability, and path survivability model in terms of the distance from the epicenter to the node and the link of the network. Meanwhile, we introduce the indicator of site difference level which is unique in the smart grid to further restrict the service path. Secondly, to improve the algorithm efficiency and reduce rerouting time, the deep first search algorithm is utilized to obtain the available rerouting set, and then the I-DQN based on the framework of reinforcement learning is proposed to achieve concurrent rerouting for multiple services. The experimental results show that our approach has a better convergence performance and higher survivability as well as the approximate latency in comparison with other approaches.

由于余震活动，地震等自然灾害对智能电网产生了连续影响。为了保证服务要求和智能电网的稳定运行，设计快速且可生存的重新路由机制是一项挑战。但是，很少有研究考虑针对智能电网通信网络中的多种服务进行并发重新路由。首先，根据震中到节点的距离和网络的链路，建立了节点的生存能力，链路生存能力和路径生存能力模型。同时，我们介绍了智能电网中唯一的站点差异级别指标，以进一步限制服务路径。其次，为了提高算法效率并减少重路由时间，利用深度优先搜索算法获得可用的重路由集合，然后提出基于强化学习框架的I-DQN，以实现多种服务的并发重路由。实验结果表明，与其他方法相比，该方法具有更好的收敛性能，更高的生存能力以及近似的时延。