Design and Development of Anti-Occupation System for Charging Piles of New Energy Vehicles Based on Deep Learning

Aiming at the problem that New Energy Vehicles (NEV) charging piles are illegally occupied, this paper puts forward a design and development scheme of anti-occupation system based on deep learning. Firstly, a comprehensive system architecture is designed, including license plate recognition module and occupancy judgment module, in order to realize the recognition and state monitoring of charging piles. Secondly, a license plate recognition algorithm based on deep learning is proposed, which combines the mixed model of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to realize the accurate recognition of the license plates of electric vehicles and fuel vehicles in China. Then, a real-time evaluation system is designed to comprehensively evaluate the processing efficiency and response speed of the system. Finally, through a large number of experiments, the results show that the system has high recognition accuracy and reliable real-time performance, which can effectively prevent charging piles from being illegally occupied and improve the utilization efficiency and management effect of charging piles. This design and development scheme provides an innovative solution for the intelligent management of NEV charging piles, which has important theoretical significance and practical application value.