The invention provides an off-peak parking management system based on deep learning, and the system comprises a data obtaining module which is used for obtaining the corresponding data of each parking lot through a cloud end, and summarizing the data of each vehicle into a vector; the data preprocessing module is used for performing filling calculation on missing values in the vehicle data so as to effectively process the missing values and standardize the vehicle data; the model training module is used for using a Transform model as a deep learning model, and embedding the dimension and the dimension of a feedforward neural network according to the specific architecture, the number of attention heads, the embedding dimension and the dimension of the feedforward neural network; and the demand prediction module is used for inputting real-time data into the model, and calculating correlation among time points by a self-attention mechanism. The objective of the invention is to improve the efficiency of parking management and the parking experience of vehicle owners by optimizing parking space resource allocation. According to the system, a Transform model is adopted to carry out parking demand prediction, parking data are processed through a deep learning algorithm, and high-precision parking demand prediction and peak shifting management are realized.
本发明提出了一种基于深度学习的错峰停车管理系统,包括数据获取模块,用于通过云端获取每个停车场相应的数据,并且将每个车辆数据汇总为向量;数据预处理模块,用于将车辆数据中的缺失值进行填充计算,从而有效的处理缺失值,并对车辆数据进行标准化;模型训练模块,用于使用Transformer模型作为深度学习模型,根据具体架构,注意力头数、嵌入维度、前馈神经网络的维度;需求预测模块,用于将实时数据输入模型中,自注意力机制计算各时间点之间的相关性。旨在通过优化停车位资源分配,提高停车管理的效率和车主的停车体验。系统采用Transformer模型进行停车需求预测,通过深度学习算法处理停车数据,实现高精度的停车需求预测和错峰管理。
Peak shifting parking management system based on deep learning
一种基于深度学习的错峰停车管理系统
2024-11-26
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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