The invention relates to a traffic flow prediction method and system based on a large model, and the method comprises the steps: collecting and processing a multi-dimensional data pair, inputting a constructed prediction model through the processed data, and carrying out the training optimization of the model based on output; the traffic flow is predicted through the trained and optimized prediction model and the large model; the system comprises local hardware and a cloud server, a prediction model is deployed in cooperation with the local hardware, and a large model is deployed in the cloud server; and the local prediction model obtains the processed image data and outputs a primary prediction result, the primary prediction result, the time sequence data and the environment variable data are input into the cloud large model, and an output result of the large model is taken as a final prediction result. The method improves the accuracy and reliability of a prediction result, improves the response speed of the whole system, reduces the network burden, saves the computing resources, and enhances the flexibility of the system. The information security is effectively improved, and the risk of privacy information leakage is reduced; and the adjustability is high.
本发明涉及一种基于大模型的交通流量预测方法及系统,收集并处理多维数据对,以处理后的数据对输入构建的预测模型,基于输出对模型训练优化;以训练优化后的预测模型和大模型预测交通流量;系统包括本地硬件和云端服务器,配合本地硬件部署预测模型,云端服务器中部署大模型;本地的预测模型获取处理后的图像数据并输出初级预测结果,初级预测结果、时间序列数据和环境变量数据输入云端大模型,以大模型输出结果为最终预测结果。本发明提高预测结果的准确性和可靠性,提高整个系统的反应速度,减轻网络负担,节省计算资源,增强系统灵活性;有效提高信息安全性,减小隐私信息泄露的风险;可调性强。
Traffic flow prediction method and system based on large model
一种基于大模型的交通流量预测方法及系统
2025-03-07
Patent
Electronic Resource
Chinese
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