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

A cluster analysis and forecasting model technology, applied in forecasting, character and pattern recognition, calculation, etc., can solve problems such as algorithm inefficiency

Inactive Publication Date: 2016-11-09
WUHAN BAOSTEEL CENT CHINA TRADE
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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

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

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Embodiment Construction

[0085]The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0086] The invention provides a loading man-hour prediction model based on cluster analysis and decision tree algorithm, which includes a cluster analysis algorithm module and a decision tree algorithm module. The prediction model divides historical data into time intervals through a cluster analysis algorithm, Then the classification scheme of the decision tree algorithm is obtained, and then through the input of historical data, including the packaging method, hoisting method and other attribute sets that affect the final loading man-hours, it is connected with the classification scheme of the cluster analysis, and the final result is generated by using the C4.5 decision tree algorithm. The decision tree data model to finally predict the future loading hours;

[0087] Cluster analysis algorithm module:

[0088] The cluster analysis ...

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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 ...

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62
CPCG06Q10/04G06F18/2321
Inventor 车静王永川姚琳高山
Owner WUHAN BAOSTEEL CENT CHINA TRADE
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