Power Big Data Imbalance Analysis Based on Deep Learning OCR Algorithm

With the rapid development of information technology and intelligent equipment, the power industry generates a large amount of real-time data, which constitutes “power big data”. These data are widely used in power system optimization, fault diagnosis, energy efficiency management and other fields. However, in practical applications, power big data often suffers from data imbalance. In this paper, an unbalanced analysis method for power big data based on deep learning OCR algorithm is proposed. First, the common types of data information and their imbalance phenomenon in electric power big data are analyzed, and the impact of data imbalance on the electric power big data analysis model is discussed. Secondly, the basic principles of deep learning technology and OCR algorithm are introduced, and an improved processing method is proposed for the imbalance problem in electric power big data by combining deep learning OCR technology. Finally, by optimizing data preprocessing, feature extraction, sampling strategy and loss function, the application effect of OCR algorithm in electric power data is effectively improved.