Daily short-term express delivery business volume prediction method for logistics enterprises

Abstract

The invention belongs to the technical field of logistics express delivery business volume prediction, discloses a daily short-term express delivery business volume prediction method for logistics enterprises, uses a particle swarm optimization algorithm with an improved inertia weight to optimize the back propagation neural network, and adopts a new horizontal data selection method to select andinput data for the BP neural network. The optimized BP neural network is used to predict the daily short-term express delivery business volume of the logistics company, a proper amount of cloud computing resources can be dynamically applied in different time periods to process the express parcel data and monitor the parcel transportation process. The method can predict the daily short-term expressdelivery business volume, apply for a proper amount of cloud resources, and can process the business data of all express parcels on time without causing waste of cloudy resources. The method can be applied to the daily short-term express delivery business volume forecast of the logistics company, and is of great significance for reducing the cost of the logistics company and improving the qualityof the user service.

Description

technical field [0001] The invention belongs to the technical field of forecasting business volume of logistics and express delivery, and in particular relates to a method for forecasting daily short-term express business volume of a logistics enterprise. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Predicting the future express business volume based on the historical data of logistics express companies is a kind of time series forecasting, and the current backpropagation (BP) neural network has a wide range of applications in time series forecasting. BP neural network is a single feed-forward network with an error back propagation mechanism, which has strong nonlinear, self-learning, self-adaptive, generalization and other capabilities. However, it has shortcomings such as easy to fall into local optimum, slow convergence speed and difficult to determine the network structure, so overcoming thes...

AI Technical Summary

Problems solved by technology

However, it has shortcomings such as easy to fall into local optimum, slow convergence speed and difficult to determine the network structure, so overcoming these shortcomings is an extremely important issue.

On the other hand, predicting the future long-term express business volume based on historical data may not only lead to an increase in the cost of logistics companies, but also may lead to delayed processing of express package data, which in turn will reduce user satisfaction

Images