In the ubiquitous power Internet of things, all kinds of growing power terminal equipment and business applications will generate massive data, which will cause huge pressure to the master station, and high delay and security cannot meet the requirements of new business forms. Edge computing organically integrates computing, storage, and other resources on the edge of the network and responds to the task request of the network edge node timely and effectively according to the principle of nearest service. Due to the limited resources of edge nodes, such as power monitoring camera capability, resources, bandwidth, energy, etc., computing offload is a key problem of edge computing. To solve this problem, this paper proposes a method of edge computing offload based on genetic algorithm. Firstly, in the edge-computing scenario of the power Internet of things, we analyze the computing unloading problem model under the time sequence condition. Then, aiming at the optimal decision-making problem of energy consumption and time delay of terminal equipment, we creatively transform the problem of computational offload into the problem of multi-objective optimization. In the genetic algorithm, we use NSGA-II to achieve the multi-objective optimization of the decision-making. Through conversion, time delay and energy consumption, the optimization can be achieved. Finally, we designed a simulation experiment. The results show that the unloading decision of NSGA-II can reach the best. The results show that the results of NSGA-II can be distributed in a wider range.
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