Clustering analysis and decision tree algorithm-based truck loading work time prediction model

Abstract

The invention discloses a clustering analysis and decision tree algorithm-based truck loading work time prediction model. A clustering analysis and decision tree mixed algorithm is introduced, factors influencing inventory control are abstracted out, related historical data serves as a training sample, and finally the truck loading work time can be effectively predicted by using a trained decision tree data model; and the historical data of truck loading is deeply mined by utilizing a data mining technology based on a demand, and an available, easy-to-use and high-accuracy data model is generated. The clustering analysis and the decision tree algorithm are combined and complement each other, so that the accuracy of the data model is improved; an optimization policy is adopted for an original decision tree algorithm under the condition of establishing a simple and accurate data model, so that the calculation amount is reduced and the algorithm efficiency is improved; and through the data model, a relatively accurate time interval of cargo loading can be predicted and used for better manual decision-making.

Description

technical field [0001] The present invention introduces a decision tree algorithm that approximates discrete function values, abstracts the factors that affect inventory control, and uses relevant historical data as training samples, and finally uses the trained decision tree data model to effectively predict the loading hours Specifically, it refers to a loading man-hour prediction model based on cluster analysis and decision tree algorithm. Background technique [0002] Loading is a key link in the distribution of goods, and an excellent loading plan is the embodiment of improving the delivery rate of warehouse goods. Data mining is carried out based on the historical data of cargo loading, and a data model that can predict the loading time of goods is established to predict the loading time. [0003] Through the analysis of various factors in the entire loading process, we have found eight factors that affect the loading time, namely: cargo type, packaging method, cargo ...

AI Technical Summary

Problems solved by technology

However, in the process of constructing the tree, the data set needs to be scanned and sorted multiple times in sequence, which will lead to the inefficiency of the algorithm in practical applications, so we need to optimize the details of the original algorithm in this aspect

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