Prediction model training method, time prediction method, training device and terminal

A time prediction and training method technology, applied in the field of electronic information, can solve problems such as production impact, follow-up link impact, and assembly line failure to operate normally, and achieve the effect of reducing the probability of deviation and improving accuracy

Active Publication Date: 2020-05-01
GREE ELECTRIC APPLIANCES INC +1
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There are raw materials and (semi-)finished products in every link of production. The failure of raw materials to reach the destination in time and the untimely transshipment of (semi-)finished products will lead to the failure of the normal operation of the assembly line.
[0003] In actual production, each production activity is scheduled by its time, and whether the logistics vehicle that transports materials arrives early or late will affect production
For example, the arrival of the logistics vehicle ahead of time will cause the warehouse to explode because the materials in front have not been transferred, and the various production links in the actual production are like dominoes. If one link goes wrong, the subsequent links will be affected

Method used

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  • Prediction model training method, time prediction method, training device and terminal
  • Prediction model training method, time prediction method, training device and terminal
  • Prediction model training method, time prediction method, training device and terminal

Examples

Experimental program
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Effect test

Embodiment 1

[0059] see figure 1 , the present disclosure provides a training method of a time prediction model that can be applied to terminals such as mobile phones, computers, or tablet computers. When the training method is applied to the terminal, the following steps are performed:

[0060] Step S110: Obtain multiple pieces of sample data, wherein the sample data includes the departure time and arrival time of the logistics vehicle from the departure place to the destination, and the influencing factors affecting the departure time and arrival time, the influencing factors include traffic information factors, weather information factors, route information factors.

[0061] Step S120: Perform correlation analysis on the multiple pieces of sample data to obtain the target correlation relationship between the target time and other data except the target time in each piece of sample data, wherein the target time is the arrival time or departure time time.

[0062] Step S130: Input the d...

Embodiment 2

[0113] see Figure 5 , in the present disclosure provides a time prediction method applicable to terminals such as mobile phones, computers or tablet computers, when the time prediction method is applied to the terminal, step S210 and step S220 are executed:

[0114] Step S210: Obtain the actual influencing factors affecting the predicted target time of the target logistics vehicle and the reference time from the departure point to the destination of the target logistics vehicle.

[0115] In this embodiment, when the reference time is the departure time of the target logistics vehicle, the predicted target time is the predicted arrival time; when the reference time is the arrival time of the target logistics vehicle, the predicted target time is Estimated departure time.

[0116] Step S220: Input the actual influencing factors and the reference time into the time prediction model obtained by the training method of the time prediction model, so as to obtain the predicted targe...

Embodiment 3

[0133] see Figure 8 , the present disclosure provides a training device for a time prediction model applicable to terminals such as mobile phones, computers or tablet computers, the training device includes:

[0134] The obtaining module 301 is used to obtain multiple pieces of sample data, wherein the sample data includes the departure time and arrival time of the logistics vehicle from the departure place to the destination, and the influencing factors affecting the departure time and arrival time, the influencing factors Including traffic information factors, weather information factors, route information factors.

[0135] The implementation principle of the obtaining module 301 is similar to that of step S110, therefore, the specific description of the obtaining module 301 can refer to Embodiment 1, and details are not repeated here.

[0136] A correlation determination module 302, configured to perform correlation analysis on the multiple pieces of sample data to obtain...

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Abstract

The invention relates to the technical field of electronic information and particularly relates to a prediction model training method, a time prediction method, a training device and a terminal. Multiple pieces of sample data are obtained, the sample data comprise departure time and arrival time of the logistics vehicle from a departure place to a destination and influence factors influencing thedeparture time and the arrival time, and the influence factors comprise traffic information factors, weather information factors and route information factors; correlation analysis is carried out on the plurality of pieces of sample data; wherein the target time is arrival time or departure time, and correlation analysis is performed on the multiple pieces of sample data, so that the probability of deviation of the time prediction model is reduced, and the accuracy of the time prediction model is improved.

Description

technical field [0001] The present disclosure relates to the field of electronic information technology, and in particular to a prediction model training method, a time prediction method, a training device and a terminal. Background technique [0002] In the production process, logistics transportation is a very important link. There are raw materials and (semi-)finished products in every link of production. The failure of raw materials to reach the destination in time and the untimely transshipment of (semi-)finished products will lead to the failure of the normal operation of the assembly line. [0003] In actual production, each production activity is scheduled by its time, and whether the logistics vehicle that transports materials arrives early or late will affect production. For example, the arrival of the logistics vehicle ahead of time will cause the warehouse to explode because the materials in front have not been transferred, and the various production links in th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/08
CPCG06Q10/04G06Q10/083
Inventor 田地叶文杰叶林林石宇航吴咪咪
Owner GREE ELECTRIC APPLIANCES INC
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