Braiding drive data processing through rote learning

Article type: Research Article

Authors: Chen, Bin^* | Xu, Huan | Yang, Qiuyong | Zhao, Zhiyu | You, Shaohua

Affiliations: China Southern Power Grid Internet Service Co. LTD, Guangzhou, Guangdong, China

Correspondence: [*] Corresponding author: Bin Chen, China Southern Power Grid Internet Service Co. LTD, Guangzhou 510060, Guangdong, China. E-mail: chenbin95598@163.com.

Abstract: It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.

Keywords: Rote learning, braiding drive data processing, traditional drive data processing, feature extraction, data acquisition, rote learning

DOI: 10.3233/IDT-230374

Journal: Intelligent Decision Technologies, vol. 18, no. 1, pp. 91-104, 2024