Edge computing has a promising application prospect in the field of Internet of Things, especially cable real-time online monitoring business in a smart grid. However, for the relatively limited resources and capabilities of the edge nodes, such as computing resource and storage resource, it is hard to highly and comprehensively satisfy the high real-time requirements of cable online monitoring tasks. To solve this problem, effectively dynamic task allocation is needed on the basis of efficient utilization and optimization of resource and capabilities of the edge nodes. In this article, a task allocation mechanism for cable real-time online monitoring business based on edge computing is proposed. First, considering the linear distribution characteristics of the cable, the statuses of edge nodes, the processing overhead of tasks, and the scheduling strategy of delay-sensitive tasks, we establish a task allocation model based on edge computing. Second, a task allocation strategy based on improved discrete particles warm optimization is proposed. In our strategy, we focus on the task queuing problem in edge nodes and the optimized task allocation problem among edge nodes. Simulation results show that the task allocation mechanism proposed in this article can effectively reduce the average delay of cable real-time online monitoring businesses, and further improve the security and reliability of the smart grid.