Intelligent Reading of Digital Meter Readings in Substation Real Scenarios Based on Sample Matching Algorithm

Aiming at the problem of intelligent reading of digital instruments in complex environments of substations, this paper proposes a reading method based on deep sample matching to solve the challenges of uneven illumination, distorted viewing angle, and diverse instrument models. The research constructs an end-to-end intelligent reading framework, including key modules such as image preprocessing, region localisation, character segmentation and reading extraction, and introduces a migration learning strategy to achieve fast adaptation in small sample scenes. In order to enhance the feature expression capability, a self-attention mechanism is combined to optimise the sample matching process, thus enhancing the robustness of the meter readings. The experiments are evaluated on a substation dataset, and the results show that the method achieves an instrument recognition accuracy of 98.2% in complex environments, which is an improvement of 23.6% compared with the traditional OCR method, significantly improves the automated reading capability of digital instruments in substations, and provides efficient and reliable technical support for smart grid data acquisition.