The invention provides a flow prediction model training method and device, and a traffic flow prediction method and device, and relates to the technical field of road traffic monitoring, and the method comprises the steps: integrating information before a convolution layer through adding the convolution layer, and carrying out the further extraction of spatial features outputted by a target basic model, by combining the dynamic time module, the channel attention module and the space attention module, refining of input features is achieved, the model can pay attention to meaningful channels, space positions and time sequences at the same time, and therefore the representation capacity and decision accuracy of the model are improved. The dynamic time module and the convolution attention module can dynamically adjust the importance of each channel and spatial position in the features according to task requirements, so that the model can better pay attention to key features, and the recognition performance is improved.
本申请提供一种流量预测模型训练方法、装置、交通流量预测方法、装置,涉及道路交通监测技术领域,该流量预测模型训练方法通过增加卷积层对卷积层之前的信息进行整合,对目标基础模型输出的空间特征做进一步提取,通过结合动态时间模块,通道注意力模块和空间注意力模块,实现了对输入特征的精炼,使模型能够同时关注有意义的通道、空间位置和时间序列,从而提高了模型的表征能力和决策准确性。动态时间模块和卷积注意力模块能够根据任务需求动态地调整特征中每个通道和空间位置的重要性,有助于模型更好地关注于关键特征,提高识别性能。
Training method and device of flow prediction model, and traffic flow prediction method and device
流量预测模型训练方法、装置、交通流量预测方法、装置
2025-01-07
Patent
Electronic Resource
Chinese
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