Article replenishment method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the field of artificial intelligence, and relates to an article replenishment method, which comprises the following steps: obtaining the stock quantity, the demand quantity and the order quantity of historical articles in different stages, and calculating a historical state variable according to the stock quantity, the demand quantity and the order quantity; obtaining a reward function and a target constraint capacity, and constructing a basic deep neural network according to the target constraint capacity and the reward function; training the basic deep neural network based on the historical state variable, determining that the training of the basic deep neural network is completed when the loss function of the basic deep neural network is converged, and obtaining a target deep neural network; obtaining a current state variable of the target article, and calculating according to the target deep neural network to obtain predicted reward values of the target article under different replenishment quantities; and selecting the replenishment quantity corresponding to the maximum predicted reward value as the target replenishment quantity. The invention further provides an article replenishment device, computer equipment and a storage medium. According to the invention, accurate prediction of the replenishment quantity of articles is realized.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to an item replenishment method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of the offline retail market, major offline retail stores usually adopt a series of measures to attract customers to the store, including promotions, fresh food, etc., and increase the flow of customers in the store through these drainage strategies. However, most of the products in the store are mainly products that are in demand at room temperature. These products will be affected by promotions, other brands, the amount of goods placed, and the natural climate. However, most products are not affected by promotions and natural weather. Or the amount of merchandise on the shelf. [0003] At present, in practical applications, there are mainly two ways to determine the item replenishment strategy: one is to set the dis...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiment of the present application is to propose a method, device, computer equipment and storage medium for item replenishment, so as to solve the technical problem that the quantity of item replenishment is not accurate enough

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