Research on discrepancy discrimination and auxiliary decision making of electric power standard terms

Abstract

The standard clauses used in the electric power industry often contain inconsistencies, making it difficult for professionals to select appropriate terms in their work. In this paper, we propose a discrepancy discrimination model based on knowledge graph and natural language processing, using standard clauses of power equipment as the dataset. Furthermore, we construct an ensemble learning model to assist decision-making in the electric power field. The experimental results show that the proposed method is effective in terms of precision and recall. The discrepancy discrimination model has a precision rate of 76.45% and recall rate of 84.72%, while the auxiliary decision-making model has a precision rate of 75.3%. The method is able to effectively identify inconsistencies in equipment standards and provide decision-making advice, providing a valuable reference for standardizing the content of standard terms in the industry.