Application of Deep Learning in Automatic Comparison and Synchronization of Remote Communication Database

With the development of power system, telemechanical communication plays an increasingly important role in power dispatching and monitoring. However, the existing telemechanical communication system has the problem of low efficiency and insufficient accuracy in the automatic comparison and synchronization of database and information point table. In order to solve these problems, this paper proposes an automatic comparison and synchronization method based on deep learning. In this method, multiple convolution layers and pooling layers are used as feature extraction modules to obtain potential features of data. Using Bi Long Short-Term Memory (Bi-LSTM) to mine the temporal correlation characteristics of the data. Through this model, the information point table in the database is identified and compared, and the data is synchronized in real time. The experimental results show that this method has significant advantages in improving the efficiency and accuracy of data verification and synchronization, and the accuracy rate is more than 95%.