Research on Model Matching and Anomaly Detection Method of Distribution Network Equipment Based on OCR and Image Recognition

The acceptance work of distribution network engineering lines is an important link to ensure the quality of distribution network engineering lines and effectively improve the safe and stable operation level of the distribution network. However, traditional on-site verification methods for distribution network engineering suffer from time-consuming, labor-intensive, and error prone issues, making it difficult to efficiently identify potential hazards such as construction process defects and equipment model mismatches, which pose a threat to the safe operation of the distribution network. Therefore, this article proposes a model matching and anomaly detection method for distribution network equipment based on OCR and image recognition technology. Firstly, key text information on equipment nameplates and identification plates is automatically extracted through OCR technology; Secondly, by combining image recognition models to analyze the appearance characteristics of the equipment, precise matching and anomaly detection of the equipment model can be achieved. This method integrates image processing, deep learning, and database matching techniques, which can effectively improve the efficiency and accuracy of line engineering quality acceptance. The experimental results show that this method has good robustness and practicality in various complex scenarios, providing important technical support for the intelligent acceptance and management of distribution network engineering.