Mr. Yuxiang Cai Profile

Yuxiang Cai

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Proceedings Article | 19 July 2024 Paper

Enhancing standardization in the electricity industry: an intelligent analysis of standard discrepancy

Wenshu Ni, Zhijian Fang, Jie Fu, Xin Jiang, Yuxiang Cai, Wangning Guan

Proceedings Volume 13181, 131814F (2024) https://doi.org/10.1117/12.3031353

KEYWORDS: Matrices, Data modeling, Standards development, Performance modeling, Industry, Education and training, Machine learning, Semantics, Visualization, Visual process modeling

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This paper proposes an intelligent discrepancy analysis method based on natural language processing to address the issue of standard discrepancy in the electricity industry. The objective of this method is to identify discrepancies between standards and pinpoint their distinguishing factors, in order to achieve better standardization, regulation, and consistency. This will enhance the safety and reliability of electricity equipment and systems, while reducing production, operation, and management costs. The paper first builds an electricity standard discrepancy dataset using the open-world assumption theory. Then, it uses a noisy method to fine-tune the SBERT model for identifying discrepancies in electricity standard clauses. Finally, by optimizing the SimCSE model with relaxed optimal transport distance, the interpretability of the model is improved and a text similarity matrix is obtained, enabling the visualization of discrepancies in clause text. The precision and recall rates of standard discrepancy identification achieved by this method are 81.54% and 82.78%, respectively. This method not only helps to improve the sustainable development of the electricity industry, but also provides more data support and decision-making references for electricity enterprises to better address issues related to standard management and implementation.

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