The invention provides a characteristic difference learning network traffic flow data prediction processing method and system, relates to the field of data processing, and can solve the problem of low accuracy of traffic time sequence prediction. The method comprises the steps that data preprocessing is carried out on input original traffic time sequence data, a feature difference learning sample is obtained, and the original traffic time sequence data comprises traffic data of a plurality of target road nodes in a duration range; thirdly, constructing a deep learning model DXGAN by integrating a sample positioning encoder, a feature difference generator and a discriminator; and then, inputting the feature difference learning sample into a deep learning model DXGAN to obtain a prediction result. Spatial and temporal feature difference distinguishing is performed through trending feature difference learning, and the accuracy of traffic time sequence prediction is improved by using the high accuracy of the DXGAN model.
本发明提供一种特征差异学习网络交通流量数据预测处理方法及系统,涉及数据处理领域,可解决对交通时序预测的准确性不高的问题。该方法,包括:对输入的原始交通时序数据进行数据预处理,获得特征差异学习样本,原始交通时序数据包括在一时长范围内多个目标道路节点的交通数据;接着,通过集成样本定位编码器、特征差异生成器和判别器,构建深度学习模型DXGAN;然后,将特征差异学习样本输入深度学习模型DXGAN,获得预测结果。通过趋势化特征差异学习进行时空特征差异区分,利用DXGAN模型的高准确性来提高了交通时序预测的准确性。
Characteristic difference learning network traffic flow data prediction processing method and system
一种特征差异学习网络交通流量数据预测处理方法及系统
2025-03-28
Patent
Electronic Resource
Chinese
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