This paper addresses the digitization of power standards in the power industry through the development of knowledge graphs and the application of intelligent technologies. We explore the transformation of standard management, leveraging technologies like cloud computing, big data, and AI, and discuss the strategies of international standardization bodies like ISO and IEC towards machine-readable standards. The core of our research is the construction and application of a power standard knowledge graph, aimed at enhancing the management of standardized knowledge. This involves stages from data collection, incorporating OCR technology, to data management with an emphasis on data quality and security. A significant contribution of this work is the development of the PLM-based Knowledge Graph Completion Model, particularly focusing on the KG-SBERT model, which integrates semantic and structured features from pre-trained language models and knowledge graphs. Our experiments demonstrate the superiority of KG-SBERT over traditional KGE models in knowledge graph completion tasks, highlighting the effectiveness of prompt information in improving model performance. surpassing traditional knowledge graph embedding techniques in link prediction accuracy, attaining an MRR of 47.67, and Hits@5 and Hits@10 scores of 25.15, 81.54, and 93.91, respectively. This study underlines the critical role of digital transformation in the power industry, demonstrating how advanced technologies can significantly enhance the management, security, and accessibility of power standards, with broad implications for the industry's future development.
This article addresses the challenges of data privacy protection and information security in the context of standard digitization, systematically examining the current status and practical applications of relevant algorithmic technologies. It elucidates the essence and characteristics of standard digitization, analyzes the necessity and urgency of strengthening data privacy protection and information security. The article reviews the current situation of data privacy protection and information security both domestically and internationally, highlighting the heightened requirements imposed by standard digitization on both fronts. It focuses on exploring advanced privacy protection and security algorithmic technologies such as homomorphic encryption, differential privacy, federated learning, cryptographic algorithms, artificial intelligence, and evaluates their performance through experiments. Finally, the article analyzes the application effects of algorithmic technologies in real scenarios, using a case study from the electricity industry, confirming their effectiveness and practicality. This research holds significant reference value for promoting technological innovation and practical applications in data privacy protection and information security under the conditions of digital economy.
With the development of electric power industries, the number of standards has grown rapidly. However, the contents of standard clauses extracted from various fields are often inconsistent. It is difficult for the staff to choose the proper standard clauses in their work. Therefore, it is significant to provide staff with consistent electric power standard clauses. This paper takes the standard documents in the electric power field as the data source, and focuses on how to find out the related but inconstant clauses in the documents. We take advantage of the entity relationships of knowledge graph to get the discrepancies of electric power standard clauses. The experimental results illustrate the good performance of the proposed method in terms of precision and recall. The precision rate is 76.45%, and the recall rate reaches 84.72%. In addition, the proposed approach could also provide a solution to the differential discrimination of standard documents in various industries.
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