A method for integrating operation and maintenance data of power information systems based on artificial intelligence technology

The conventional method of integrating operation and maintenance data for power information systems is often time-consuming and suffers from limited data coverage. Consequently, this paper proposes a new method for integrating such data using artificial intelligence technologies. Initially, we set a unified standard and classification guideline for the integration process of operation and maintenance data in power information systems, identifying data types based on their interrelationships. We then organize and process the operation and maintenance data, removing any inconsistencies and redundancies to minimize the impact of irrelevant data and enhance integration efficiency. Furthermore, we implement a standardized coding system to verify and encode the organized data, ensuring it corresponds accurately with the actual datasets. By leveraging deep learning algorithms on offline data, this approach not only minimizes system resource utilization but also increases the extent of effective data coverage. Experimental outcomes indicate that this integration approach not only accelerates the process but also achieves greater data inclusivity compared to traditional methods, thus offering significant practical value.