Data Label System Classification Method of Power Equipment and Customers Based on Data Middle Platform
Pages 1538 - 1543
Abstract
The development of digital information technology promotes the reform and reform of information digitization of electric power enterprises in the power industry. The role of data in business process is highlighted. As a data product, data label is more and more applied to business system. Power equipment and customers are the two core subjects of power grid power enterprises, including "generation, transmission, distribution, transformation and utilization". Through the construction of data label system, the application efficiency and application value of power data can be improved. The construction of corresponding label system can effectively enhance the safe and stable operation level of power grid and the lean management and service level of power grid enterprises. Data label classification is the basis of the construction of the label system, and the scientificity and rationality of the classification are very important to the promotion and application of data labels. This paper focuses on the core management resources of power enterprises, based on the data center of power enterprises, takes the business demand as the core, combines the typical classification methods and relevant theoretical basis, studies the classification method and benchmarking of big data label system of power enterprises Through the statistics of user tags and the calculation of root mean square error, the relevant conclusion is drawn that label classification can optimize customer energy efficiency and realize Internet precision marketing.
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Published In
October 2021
3136 pages
ISBN:9781450385046
DOI:10.1145/3495018
Copyright © 2021 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 14 March 2022
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AIAM2021
AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
October 23 - 25, 2021
Manchester, United Kingdom
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Overall Acceptance Rate 100 of 285 submissions, 35%
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