Intelligent Recommendation Algorithm Based on Multi-Service Power Data
Guotao Peng, Dan Wang, Jing-Ming Guo, Jianing Yu, Yuqi Zhao, Yifan Bao · 2024
Electric power companies often need to deal with a large amount of data, which are stored in the data centre stage is extremely dispersed, and the problem of difficult to fetch and use the data arises. This paper proposes an intelligent recommendation algorithm based on electric power data. Firstly, based on the constructed business logic relationship mapping, this paper calculates the similarity between the business demand information and data content, carry out correlation analysis on cross-departmental and cross-professional data, and obtain the data collection of the department where the business demand is located and the associated departments. Then this paper optimizes the intelligent recommendation algorithm based on deep neural network, pays attention to the implicit relationship between the data through the introduction of the attention mechanism to get the data collection associated with the exact business demand. Finally, it realises the intelligent extension of data fields for different business needs and provides intelligent recommendation for data with different business needs. The experimental results show that the algorithm has better intelligent recommendation performance, higher precision and superior efficiency. The method can intelligently recommend associated business data, and help users in the power problem scenario to obtain relevant information quickly and accurately, which has certain practicability and popularization value.