Research on visual intelligent report based on big data of electricity marketing

Jianing Zhang; Yanlai Tang; Bing Dai; Pengfei Lyu

doi:10.1117/12.2626822

16 December 2021 Research on visual intelligent report based on big data of electricity marketing

Jianing Zhang, Yanlai Tang, Bing Dai, Pengfei Lyu

Proceedings Volume 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021); 121531F (2021) https://doi.org/10.1117/12.2626822
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 2021, Sanya, China

Abstract

In recent years, with the continuous development of power grids, more power marketing data is generated, and the traditional EXCEL tables can no longer effectively mine the information in big data. In order to solve these problems, for the grid marketing data, an evaluation system with the collection of support as the main indicator was established, and the information was visualized through interactive visualization technology. This can greatly improve the work efficiency of the power marketing department. In this paper, the power marketing data of 4 league cities and 48 counties in eastern Inner Mongolia is visualized by using the pyecharts module in python, and visual report is generated to evaluate the business level of power companies in each region. The results show that the visual report can analyze and evaluate the business level of various regions intuitively and efficiently, which improve work efficiency. Meanwhile, the weak links in the entire marketing process of various regions and counties can also be found in time, which greatly improving the power marketing department management efficiency and level.

Citation Download Citation

Jianing Zhang, Yanlai Tang, Bing Dai, and Pengfei Lyu "Research on visual intelligent report based on big data of electricity marketing", Proc. SPIE 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 121531F (16 December 2021); https://doi.org/10.1117/12.2626822

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