Intelligent prediction method for warehouse management data

A technology of intelligent forecasting and warehouse management, applied in forecasting, data processing applications, instruments, etc., can solve problems such as no material forecasting

Inactive Publication Date: 2018-04-17
LIAOCHENG VOCATIONAL & TECHN COLLEGE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There is currently no method to predict whether materials will need to be resupplied in advance

Method used

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  • Intelligent prediction method for warehouse management data
  • Intelligent prediction method for warehouse management data
  • Intelligent prediction method for warehouse management data

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0027] Embodiment 1, an intelligent prediction method for warehouse management data, knowing a certain conditional probability, how to obtain the probability after the exchange of two events, that is, how to obtain P(H when P(X|H) is known |X); use the Naive Bayes Theorem to predict the classification algorithm, and the calculation formula of the Naive Bayes Theorem is:

[0028] P(H|X)=P(X|H)P(H) / P(X)

[0029] P(X|H) represents the probability of event X occurring under the premise that event H has already occurred, which is called the conditional probability of event X when event H occurs;

[0030] The basic solution formula is: P(X|H)=P(XH) / P(H).

[0031] The Naive Bayes Theorem is useful because we often encounter this situation in life: it is easy to get P(X|H) directly, but it is difficult to get P(H|X) directly, but we If we care more about P(H|X), Bayes' theorem will open the way for us to get P(H|X) from P(X|H).

[0032] Naive Bayesian classification is a very simpl...

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PUM

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Abstract

The invention relates to an intelligent prediction method for warehouse management data. Classification algorithm prediction is performed through a Naive Bayes theorem. A calculation formula is as follows: P(H/X)=P(X/H)P(H)/P(X), wherein P(X/H) represents an occurrence probability of an event X on the premise that an event H occurs already, and is called as a conditional probability of the event Xunder the condition that the event H occurs. A basic solving formula is as follows: P(X/H)=P(XH)/P(H). Naive Bayes classification algorithm prediction can predict a status of each material, and comprises a process of comparing a probability of borrowing the materials by teachers with a probability of not borrowing the materials by the teachers to obtain a probability of borrowing the materials next time; and the probability of borrowing each material by each teacher is predicted, so that the materials are supplemented in advance.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and big data mining, and in particular relates to an intelligent prediction method for warehouse management data. Background technique [0002] With the popularization of computer applications and the continuous development of computer technology, it is becoming more and more necessary to use computers to process rich, complex and unpredictable data information. Traditional enterprise warehouse management can no longer meet the needs of modern management. It is necessary to realize comprehensive, scientific and systematic management of storage, storage, query, statistics and other affairs in warehouse management. Through this warehouse management system to understand the flow of materials, we can make correct decisions, ensure the safety and normal operation of funds, and improve economic efficiency. The school needs to go to the warehouse to borrow materials for every practical ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08
CPCG06Q10/04G06Q10/087
Inventor 马振吴英宾李跃田岳宗辉束华娜郑桂昌范国渠赵华丽贾兆立靳璐璐
Owner LIAOCHENG VOCATIONAL & TECHN COLLEGE
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