Intelligent prediction method and system for goods quantity during holidays

Abstract

The invention relates to the technical field of big data algorithms, and provides an intelligent prediction method and system for the goods volume during holidays. The method comprises the following steps: firstly, according to the week number of a prediction day, inputting historical cargo quantity sequence data, associated with the prediction day, of a to-be-predicted transport line into a trained LSTM week regulation model to obtain an initial cargo quantity prediction value of the to-be-predicted transport line on the prediction day; obtaining a holiday coefficient vector according to theactual daily commodity quantity value and the predicted daily commodity quantity value of the last year, and selecting a holiday coefficient corresponding to the predicted day according to the day information corresponding to the predicted day; and finally, according to the holiday coefficient corresponding to the prediction day, correcting the goods quantity initial prediction value to obtain a goods quantity final prediction value of the to-be-predicted transportation line on the prediction day. Functional modules in the system are arranged corresponding to the method. According to the intelligent prediction method and system provided by the invention, the predicted goods amount during holidays and festivals is more accurate, and a reliable decision basis is provided for the developmentof logistics business.

Description

technical field [0001] The invention relates to the technical field of big data algorithms, and more specifically, to an intelligent forecasting method and system for cargo volume during holidays. Background technique [0002] In the logistics industry, cargo volume forecasting is an important indicator for determining logistics demand. Correct forecasting of cargo volume can not only arrange manpower and vehicles in each distribution center in advance, but also provide logistics infrastructure construction, such as freight distribution centers. Provide basis for site selection, purchase of transport vehicles, etc. The accuracy and reliability of cargo volume forecasting results will directly affect the proportion of investment income of logistics infrastructure, thereby directly affecting the development of a logistics enterprise. Therefore, improving the accuracy of cargo volume forecasting is of great significance to the logistics industry . [0003] In the forecast of ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method and system for intelligent forecasting of cargo volume during holidays to solve the problem in the existing logistics industry that cannot provide reliable decision-making basis for the development of logistics business due to the inability to accurately predict the volume of cargo during holidays. question

Images