Research on the Differentiation Analysis Tool of Standard Clauses of Power Grid Equipment Based on Deep Learning

This paper studies the differentiation analysis tool of standard clauses of power grid equipment based on deep learning. The tool extracts, classifies and compares data for various indicators in the standard clauses of power grid equipment by designing a specific deep learning algorithm model, so as to identify and quantify the indicator differences between different standards. The system architecture of this paper includes multiple modules such as data preprocessing, feature extraction, and difference analysis. Combined with convolutional neural network and recurrent neural network algorithms, it effectively improves the recognition accuracy of complex indicator differentiation. In order to verify the effectiveness of the tool, a large number of model simulation experiments were carried out, and the simulation results were verified and compared using precise data analysis methods. The experimental results show that the proposed tool has high accuracy and practicality in identifying and analyzing the indicator differences of standard clauses of power grid equipment, and can provide reliable technical support for the optimization of power grid equipment standards.