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The existing power system does not have a unified grid resource management model. The basic data of equipment assets cannot be stored, managed, and maintained in a unified manner. The production systems of different departments are developed by different manufacturers at different times, which leads to the problem of production data silos and high maintenance costs. To solve this problem, a grid resource business middle platform based on State grid-corporation public data model (SG-CIM, CIM) model is designed in this paper. Firstly, the concept and core technology of the middle platform in the power grid resource business are introduced. Then a dynamic data migration strategy based on the FA algorithm is proposed. Finally, the model based on SG-CIM and the application scenario of power transmission and distribution are built and its effectiveness is analyzed.

Convolutional neural networks (CNN) have made rapid progress for a series of visual tasks, but the bottom-up convolutional feature extraction process is not qualified to mimic the human visual perception process, which takes advantages of discriminative features. Although attention modules extract the top-down discriminative features and have been widely investigated in the past few years, current attention modules interrupt the bottom-up convolutional feature extraction process. To tackle this challenge, in this paper, we introduce dense connection structure to fuse discriminative features from attention modules and convolutional features, which we term as dense attention learning. Also, to alleviate the over-fitting problem caused by rapid feature dimension growth, we propose a channel-wise attention module to compress and refine the convolutional features. Based on these strategies, we build a dense attention convolutional neural network (DA-CNN) for visual recognition. Exhaustive experiments on four challenging datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our DA-CNN outperforms many state-of-the-art methods. Moreover, the effectiveness of our dense attention learning and channel-wise attention module is also validated.

Blockchain technology is a new type of distributed database technology solution, which has unique advantages in terms of decentralization, security and transparency. These characteristics of the blockchain can solve some typical technical problems in the construction of the current power distribution IoT cloud master station. In this context, the architecture design and implementation of the power distribution IoT cloud master station based on blockchain is first discussed; then, the blockchain is integrated from three aspects: security performance, state estimation algorithm, and deep search engine. The new framework of the technology is analyzed. Finally, the application of the blockchain technology in the cloud master station of the distribution network is summarized.

By employing perturbation theory, a technique called the 'equivalent rectangular waveguide method' is proposed for analyzing the transmission characteristics of polymeric arrayed waveguide grating (AWG) multiplexers with non-ideal core cross sections, which are modeled by the 'tanh' function. Simulation results show that the non-ideal core cross sections cause the shift of the transmission spectrum, and result in the increase of crosstalk compared with the device originally designed with exact rectangular core cross sections. In order to eliminate the shift of the transmission spectrum, reduce the crosstalk and further realize accurate demultiplexing, the redesign of the AWG device is carried out.