KEYWORDS: Blockchain, Data storage, Design and modelling, Materials processing, Industry, Computer security, Power grids, Data acquisition, Telecommunications, Industrial applications
In recent years, China's electric power business has been booming, the scale of material procurement of electric power enterprises has been growing rapidly, and the intensity and complexity of electric power material management work have also increased significantly. In order to improve the overall level of material management of electric power enterprises, we should plan for the long term, actively introduce advanced technology and pursue high-quality development. This paper will discuss the feasibility of applying blockchain technology in the field of material management of electric power enterprises, and then propose a design scheme for a new electric power material management system based on blockchain technology for the industry of electric power material management to make reference to.
Icing of transmission lines has always been a pain point for grid companies. The economic and property losses caused by icing every winter are huge. How to make an effective prediction of transmission line icing is a difficult problem. Existing forecasting methods are often based on micro-meteorological and micro-topographic information. In the characteristic variables of micro-meteorology and micro-topography, there are often interdependencies and potential spatial correlations. However, existing icing prediction methods do not fully exploit the interactions among these characteristic variables. Therefore, this paper proposes a transmission line icing prediction model based on the feature map structure, which reveals the potential agnostic topological relationship between the feature variables by adaptively extracting the sparse adjacency matrix between the feature variables. In addition, while the dilated convolution can improve the receptive field, there is also a loss of information continuity due to the discontinuity of the convolution kernel of the dilated convolution. We propose a temporal capture module to improve the loss of information continuity through GRU and dilated convolution in parallel. End-to-end prediction is achieved by stacking a graph convolution module and a temporal capture module, and after conducting several experimental comparisons, the effective prediction of the proposed model is validated.
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