Design and Realization of Intelligent Electricity Metering Apparatus Full Life Cycle Management System Based on AI Image Recognition
With the continuous improvement of the power grid lean management requirements, the existing smart turnover cabinet has limitations in the degree of automation, failure rate, etc., and has been unable to meet the needs of the full life cycle management of electric energy measuring instruments. For this reason, this paper designs and realizes a smart electric energy measuring apparatus full life cycle management system based on AI image recognition. First, the system adopts a model architecture combining ResNet and EfficientNet, using the deep network robustness of ResNet and the efficient deployment capability of EfficientNet to ensure the accuracy and real-time performance of the system in complex tasks; second, combining with the attention mechanism to optimize the extraction capability of key features, the system designs a customized classification layer to meet the demand of electric energy measuring instruments for multi-category recognition; finally, this paper designs and implements an intelligent electric energy measuring instruments life cycle management system based on AI image recognition. Finally, the system enhances the adaptability to low-light, reflective and other complex scenes through the image preprocessing module. The system realizes the functions of automatic storage and retrieval, real-time monitoring and early warning of measuring instruments, effectively improves the lean level of the whole life cycle management of measuring assets, and provides important support for promoting the modernization of the management of intelligent electric energy measuring instruments.
Authors
- Jianlin Lin (ORCID: https://orcid.org/0000-0002-4391-9346)
- Qian Li (ORCID: https://orcid.org/0000-0002-1486-9773)
- Xin Li (ORCID: https://orcid.org/0000-0002-9244-6485)
- Xiang Lin (ORCID: https://orcid.org/0000-0002-7454-8197)
Institutions
- State Grid Corporation of China (China) (CN)
- Inner Mongolia Yili Industrial Group (China) (CN)
Publication Details
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1109/isneet64164.2024.10956073
- Primary Topic
- Advanced Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00