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.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/isneet64164.2024.10956073
Primary Topic
Advanced Algorithms and Applications
Type
article
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article

Design and Realization of Intelligent Electricity Metering Apparatus Full Life Cycle Management System Based on AI Image Recognition

Jianlin Lin, Qian Li, Xin Li, Xiang Lin
Advanced Algorithms and Applications
article

Design and Realization of Intelligent Electricity Metering Apparatus Full Life Cycle Management System Based on AI Image Recognition

Jianlin Lin, Qian Li, Xin Li, Xiang Lin
article en

Abstract

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.

State Grid Corporation of China (China) (CN), Inner Mongolia Yili Industrial Group (China) (CN)
Responsible consumption and production
Openalex Percentile: Top 63%
Advanced Algorithms and Applications
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