Visual recognition and comparison system and method of intelligent watt hour meter chip based on convolutional neural network

Abstract

To enhance the performance of intelligent watt hour meters, a visual recognition and comparison system based on convolutional neural networks is proposed for intelligent watt hour meter chips. Firstly, the overall framework of the chip visual recognition comparison system is designed. Secondly, the hardware part of the system comprises the image acquisition module and image data transmission module of intelligent watt hour meter chips. In the software part, the classification function is selected based on the structural characteristics and operational principle of convolutional neural networks, and iterative training is used to complete the identification and comparison of smart meter chips. The experimental results demonstrate that this proposed system can significantly improve the accuracy of visual recognition and comparison, while also reducing the time consumption when compared to traditional recognition and comparison systems.