KEYWORDS: Data modeling, Education and training, Detection and tracking algorithms, Image processing, Image enhancement, Evolutionary algorithms, Target detection, RGB color model, Target recognition, Standards development
With the proposal of the "dual carbon" strategy, the development of China's new energy vehicle industry has ushered in new opportunities. This article focuses on the charging business scenarios in microgrid parks, relying on digital technologies such as artificial intelligence and big data, and deeply explores the value of data. By constructing a two-stage model of YOLOv5+LPRNnet, electric vehicle recognition is achieved, improving recognition accuracy while ensuring recognition speed, and achieving automated and standardized management of vehicle entry and exit charging stations, Effectively solving the problem of occupying space for gasoline vehicles, empowering business development with data, and significantly improving operational service efficiency.
New energy vehicle charging site coverage is an important indicator to measure the overall development level and travel convenience of new energy vehicles. Based on Geopandas spatial data analysis technology, this paper aims to improve the calculation method of charging stations. Geopandas can solve the existing problems in the calculation of complex site coverage efficiently and accurately. The relevant experience can be used for reference by relevant professionals such as the site selection of charging stations.
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