Transportation scheduling algorithm generation method and system based on large model technology

Abstract

The invention is suitable for the technical field of logistics transportation, and provides a transportation scheduling algorithm generation method and system based on a large model technology, and the method comprises the following steps: collecting historical transportation order data, and extracting key features of the historical transportation order data according to a transportation scheduling demand; constructing a transportation scheduling algorithm model; historical transportation order data are input into a transportation scheduling algorithm model for training, and model parameters are adjusted through a back propagation algorithm, so that the model can accurately predict an optimal transportation path and scheduling scheme; real-time order information, vehicle state and road condition information are input, input data are processed based on a transportation scheduling algorithm model, and an optimal transportation path is calculated; and according to the optimal transportation path, the order information is allocated to a matched vehicle, and a vehicle scheduling plan is determined in combination with the transportation cost of the vehicle. Through the deep learning model, a large amount of historical transportation order data can be efficiently processed and analyzed, and the optimal transportation path and scheduling scheme can be quickly generated.

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