Abstract:Concerning the real-time monitoring problem of warping dams in northwest China, this work studies the scheduling method of warping dam monitoring and early warning tasks. To avoid the delay in discovering the hidden trouble of warping dams and improve the timeliness of the warning system, this study considers the average waiting time from task unloading to edge servers and proposes a collaborative task scheduling method based on edge computing in warping dam monitoring. A task completion time model is built according to the task computation, computing power of edge servers, and other information. Then, a simulated annealing algorithm is used to optimize the unloading position of computing tasks. A task scheduling strategy is designed in which multiple edge computing servers cooperate. Experimental results show that this method can greatly reduce the calculation time of monitoring tasks and improve the timeliness of monitoring and early warning.