Implementation and Optimization of Automatic Inspection Tool for Maintenance Applications Empowered by Big Data Processing

In response to the low efficiency and high misjudgment rate of traditional manual inspection in power grid maintenance applications, this paper designs and implements an automatic inspection tool based on big data processing and artificial intelligence. This tool utilizes the Spark platform to process massive maintenance data and constructs a CNN based graph (single line graph) comparison model and an RNN based text (maintenance application) compliance review model. Meanwhile, an intelligent risk warning model was constructed by integrating machine learning algorithms. After testing and verifying 150000 maintenance application data in the local power grid in 2024, the tool has reduced the average inspection time from 30 minutes manually to 45 seconds; The comprehensive inspection accuracy of text and graphics reached 98.7%, which is about 8.2 percentage points higher than manual inspection; The underreporting rate for high-risk (Level I) applications has been reduced to below 0.1%. This tool has successfully replaced local traditional manual inspection methods, significantly improving the efficiency and safety of power grid operation and maintenance, and verifying the effectiveness of big data and AI technology in the intelligent transformation of power operation and maintenance.