Power work ticket is an indispensable working document in the electric power industry, the current safety measures in the power work ticket are mainly filled out manually by the practitioners based on their experience, which lacks consistency and has the risk of omission, in order to reduce the dependence on the front-line practitioners, this paper proposes a model based on the knowledge graph and the large language model of the safety measures generation. Firstly, based on the knowledge graph of work tickets, similar work tickets are found and preliminary safety measures are generated according to the rules, and then relevant safety specifications are queried based on the semantic similarity, and the model inputs, preliminary safety measures, and relevant safety specifications are inputted into the large language model together to get the complete safety measures. From the experimental results, it can be seen that this method outperforms other models in terms of expert assessment and the accuracy of security measure generation, and the security measures generated by the model can meet the needs of invoicing, ensuring the accuracy and efficiency of filling out work tickets.
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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