Method and device for predicting demand quantity of resource supply chain and storage medium

Abstract

The embodiment of the invention discloses a method and device for predicting the demand of a resource supply chain, and a storage medium, and the method comprises the steps: carrying out the preprocessing and feature extraction of a data source, and obtaining training data, supply data of resources based on different data distribution, and supply data of resources with different data qualities; training and predicting the hierarchical model to obtain a prediction result, wherein the prediction result comprises distribution of a plurality of prediction values of the to-be-supplied resource; outputting a prediction result to a quantile regression model; and performing regression analysis on a prediction result by utilizing a quantile regression model in a prediction stage to obtain probability distribution of resources with different data distributions under different quantile combinations and probability distribution of resources with different data qualities under different quantile combinations. According to the scheme, the future commodity supply demand can be comprehensively estimated, and the commodity stockout and waste phenomena are greatly reduced.

Description

technical field [0001] The embodiments of the present application relate to the technical field of data processing, and in particular to a method, device and storage medium for forecasting resource supply chain demand. Background technique [0002] In the existing mechanism, the demand of the retail industry may be affected by multiple factors such as seasons, holidays, weather, promotions, etc. There is a large deviation in manual estimation of the demand in the supply chain. Therefore, in order to ensure the supply of goods, at present, big data and Artificial intelligence technology optimizes the supply chain of the traditional retail industry, quantifies and predicts the demand for goods in the supply chain, and transforms the supply chain of the traditional retail industry into a smart supply chain. To a certain extent, it can predict the demand for goods to reduce out-of-stock or waste phenomenon. [0003] During the research and practice of the existing technology, t...

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Problems solved by technology

[0003] During the research and practice of the existing technology, the inventors of the embodiments of the present application found that the existing supply chain forecast is based on a single machine learning model, there is a large deviation in the forecast of supply chain demand, and it cannot be quickly obtained Accurate predictions for different data distributions and data quality

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