The rapid advancement of cloud-edge-end collaboration offers a feasible solution to realize low-delay and low-energy-consumption data processing for internet of things (IoT)-based smart distribution grid. The major concern of cloud-edge-end collaboration lies on resource management. However, the joint optimization of heterogeneous resources involves multiple timescales, and the optimization decisions of different timescales are intertwined. In addition, burst electromagnetic interference will affect the channel environment of the distribution grid, leading to inaccuracies in optimization decisions, which can result in negative influences such as slow convergence and strong fluctuations. Hence, we propose a cloud-edge-end collaborative multi-timescale multi-service resource management algorithm. Large-timescale device scheduling is optimized by sliding window pricing matching, which enables accurate matching estimation and effective conflict elimination. Small-timescale compression level selection and power control are jointly optimized by disturbance-robust upper confidence bound (UCB), which perceives the presence of electromagnetic interference and adjusts exploration tendency for convergence improvement. Simulation outcomes illustrate the excellent performance of the proposed algorithm.

The distribution station serves as a foundational component for managing the power system. However, there are missing data in the areas without collection devices due to the limitation of device deployment, leading to an adverse impact on the real-time and precise monitoring of distribution stations. The problem of missing data can be solved by the pseudo measurement data deduction method. Traditional pseudo measurement data deduction methods overlook the temporal and contextual correlations of distribution station data, resulting in a lower restoration accuracy. Motivated by the above challenges, this paper proposes a novel pseudo measurement data deduction model for minimal data collection requirements in distribution stations. Compared to the traditional GAN, the proposed enhanced GAN improves the architecture by decomposing the input tensor of the generator, allowing it to handle high-dimensional and intricate data. Furthermore, we enhance the loss function to accelerate the model’s convergence speed. Our proposed approach allows GAN to be trained within a supervised environment, effectively enhancing the accuracy of model training. The simulation result shows that the proposed algorithm achieves better performances compared with existing methods.