Logistics scheduling method and logistics scheduling device

Abstract

The embodiments of the invention relate to a logistics scheduling method and a logistics scheduling device. The method comprises the following steps: acquiring a first rule scoring model in a logistics scheduling system; calling a preset machine learning model to train and fit the first rule scoring model to get a first machine learning model; training the first machine learning model based on preset historical order data to get a second machine learning model; and taking the second machine learning model as a rule scoring model in the logistics scheduling system. Iteration continues until a second machine learning model tending to be stable is obtained. Moreover, the second machine learning model is called to score all relevant links in the logistic scheduling system. The accuracy of scoring can be improved greatly, and the efficiency of logistics distribution can be improved.

Description

technical field

[0001] The embodiments of the present invention relate to the technical field of logistics, and in particular, to a logistics scheduling method and device. Background technique

[0002] With the continuous development of society, more and more users handle business through O2O (Online To Offline, online to offline) platforms. Many merchants have launched their own app (Application, application program) platform. Users become registered users of the app by registering on the relevant app, and the registered user can place orders on the app, such as ordering food, buying clothes or food wait. After receiving the user's order, the merchant will assign relevant delivery personnel through the logistics dispatching system to deliver the user's ordered items to the user.

[0003] In the logistics scheduling system, there are many links, such as: the link of judging whether the order is additionally assigned to the dispatcher, the order grouping link, and the link ...

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