基于注意力机制的Seq2seq泊位占有率预测方法

• Format: Patent
• Language: Chinese
• Published: 30.06.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

本发明公开的基于注意力机制的Seq2seq泊位占有率预测方法，包括接受请求、预处理数据、构建模型、优化模型等步骤，通过双向长短期记忆网络BiLSTM解决长期依赖学习能力不足的问题，通过卷积神经网络CNN捕获时间模式，获得学习序列与目标序列的相关性，以此增强模型局部特征的学习能力。本发明解决了现有技术中对泊位占有率预测结果不稳定、准确度低的问题。 The invention discloses an Seq2seq berth occupancy prediction method based on attention mechanism. The method comprises the steps of receiving a request, preprocessing data, constructing a model, optimizing the model and the like, solves the problem of insufficient long-term dependence learning ability through a bidirectional long-short-term memory network BiLSTM, captures a time mode through a convolutional neural network CNN, and obtains the correlation between a learning sequence and a target sequence so as to enhance the learning ability of the local features of a model. According to the invention, the problems of unstable parking space occupancy prediction result and low accuracy in the prior art are solved.