With the rapid development and popularization of electric vehicles (EVs), the increased demand for charging stations (CSs) has made configuring charging facilities and optimizing charging station recommendations crucial. Consequently, personalized recommendation services for charging stations have emerged as a crucial strategy to mitigate the "mileage anxiety" experienced by drivers. Previous work has typically focused on optimizing CSs’ resource allocation or drivers’ costs, with little consideration of the complexity of the charging behavior of individuals, such as the distribution of Point Of Interests (POIs) surrounding the CS, the similarity of driver selection in different temporal scales, and the tradeoff between different users’ inherent preferences and external cost (e.g., travel times and electric prices), which may result in lower satisfaction, decreased accuracy of recommendations, and sub-optimal decision-making. To address this problem, we propose a charging station recommendation framework based on Actor-Critic Deep Reinforcement Learning, called DST, to assist electric vehicle drivers in finding the proper spots for charging. In the DST, both the actor and critic networks are implemented by Deep Neural Networks (DNNs). The Actor networks, utilizing a Semantic-based Bidirectional Long Short-Term Memory Network and a Stacked Convolutional Neural Network with Multiple Channels, extract the geographical semantic features of CSs and specific preference patterns from different temporal scales as hidden state representations. The critic networks employ a Twin-Critic architecture for joint optimization of inherent preference rewards and external environmental rewards. Extensive experiments on two real-world datasets demonstrate that the DST achieves the best comprehensive performance compared with seven baseline approaches.

Due to the limitation of time and space, the traditional photographic image intelligent fuzzy teaching system can not provide targeted auxiliary teaching for students with different learning abilities. The design of intelligent fuzzy assistant teaching system of photographic image based on augmented reality and web is carried out, including the hardware design of server and peripheral equipment selection, and the design of intelligent fuzzy assistant teaching system of photographic image based on Web and augmented reality, such as intelligent fuzzy assistant teaching display, student learning behavior evaluation software design, etc. Experiments show that, compared with the traditional system, the photographic image intelligent fuzzy assistant teaching system based on augmented reality and web has more correct allocation of teaching resources, stronger pertinence and practical value.