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Logistics order placing weight prediction method, system and device and storage medium

A prediction method and weight technology, applied in the field of systems, logistics order weight prediction methods, equipment and storage media, can solve the problems of low accuracy in predicting customer order weight, improve logistics scheduling efficiency, and reduce delivery recalls The effect of rate and empty run rate

Pending Publication Date: 2022-04-12
深圳市跨越新科技有限公司
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Problems solved by technology

[0004] This application provides a method, system, device, and storage medium for predicting the weight of a logistics order to solve the problem of low accuracy in predicting the weight of a customer's order by using machine learning or deep learning algorithms

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  • Logistics order placing weight prediction method, system and device and storage medium
  • Logistics order placing weight prediction method, system and device and storage medium
  • Logistics order placing weight prediction method, system and device and storage medium

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Embodiment Construction

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0028] The terms "first", "second", and "third" in this application are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, features defined as "first", "second", and "third" may explicitly or implicitly include at least one of these features. In the description of the present application, "plurality" means at least t...

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Abstract

The invention discloses a logistics order placing weight prediction method, system and device, and a storage medium. The method comprises the steps of obtaining goods types and non-zero pre-estimated piece number information in a current logistics order; calculating an estimated piece number root-mean-square error, a unit piece number weight standard deviation and a unit piece number weight mean value of the historical logistics orders of the same cargo type; judging whether the whole order weight error of the current order is not greater than a preset weight threshold value or not by combining the estimated piece number root-mean-square error and the unit piece number weight standard deviation; and if yes, determining that the predicted weight of the current order is the product value of the estimated number and the weight mean value of the unit number. When the whole weight error of the current order in the logistics orders is not larger than the preset weight threshold value, the product value of the estimated number of pieces in the order and the unit number weight mean value serves as the predicted weight of the current order, the vehicle recall rate and the empty running rate of a logistics enterprise can be reduced, correct loading space is reserved for other logistics orders of the same truck, and the loading efficiency is improved. And the overall logistics scheduling efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of logistics transportation, in particular to a method, system, device and storage medium for predicting the weight of a logistics order. Background technique [0002] According to statistics, more than 90% of customers in logistics companies do not agree with the actual weight when placing an order, and about 10% of the orders have a large difference, which makes it impossible to arrange the corresponding truck to pick up the goods, which affects the customer or even other customers. Waybill time limit. Therefore, it is particularly important to predict and correct the weight of the customer's order. It can not only arrange the correct truck to pick up the goods, but also free up loading space for other subsequent waybills and improve logistics timeliness. [0003] In the existing technology, machine learning or deep learning algorithms are mostly used to train models based on historical data of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08
Inventor 汪涵
Owner 深圳市跨越新科技有限公司
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