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Real-time scheduling method of weight sharing depth network based on dimension optimal conversion

A deep network and dimensional technology, applied in the direction of biological neural network models, instruments, data processing applications, etc., to achieve the effect of fast response speed

Active Publication Date: 2018-06-22
ZHEJIANG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, it seems that the traditional scheduling algorithm is difficult to meet the requirements of the industry in terms of processing massive scheduling data and quickly responding to scheduling problems.

Method used

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  • Real-time scheduling method of weight sharing depth network based on dimension optimal conversion
  • Real-time scheduling method of weight sharing depth network based on dimension optimal conversion
  • Real-time scheduling method of weight sharing depth network based on dimension optimal conversion

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Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] Attached below Figure 1-5 The technical scheme of the present invention is further described.

[0058] figure 1 A flow diagram of the method of the invention is shown.

[0059] Example overview

[0060] Set up the workshop production scheduling problem, n machines, p workpieces, and the workpieces have q processing processes. The purpose is to arrange processing machines for each process of each workpiece. Changing the values ​​of n, p, and q can change the scale of the problem, and set n=3, q=3, and q=3 for small problems. Medium-sized problem n=30, q=30, q=30. Large problems n=300, q=300, q=300. The specific data format is shown in Table 1 below:

[0061] Table 1

[0062] Workflow

time consuming (input)

Sequence number (output)

Artifact 1 Process 1

5

5

Artifact 1 Process 2

12

6

Artifact 1 Process 3

3

2

…

…

…

Artifact 2 Process 1

4

1

Artifact 2 Process 1

8

4

Ar...

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Abstract

The invention provides a real-time scheduling method of a weight sharing depth network based on dimension optimal conversion. The method comprises the following steps of 1, obtaining and collecting real-time data and scheduling data of a practical scheduling occasion, and using the obtained data as training data; 2, processing the real-time data obtained in the step 1, and processing the data intoa multilayer two-dimensional matrix form meeting the depth network input; 3, respectively using a multilayer two-dimensional matrix in the step 2 as the input of the depth network, using the scheduling data obtained in the step 1 as the output of the depth network, and training the depth network; and 4, using a convolutional neural network trained in the step 3 into the practical scheduling environment, and performing practical network scheduling.

Description

[0001] The invention relates to a real-time scheduling method of a weight sharing deep network. Background technique [0002] For the production scheduling problem, the current mainstream method uses a mathematical model combined with an optimization heuristic algorithm to solve it, which can obtain high solution accuracy. However, in the context of big data, the explosive growth of production parameters in the production environment and strict scheduling time indicators put forward further requirements for scheduling methods. At present, it seems that the traditional scheduling algorithm is difficult to meet the industry requirements in terms of processing massive scheduling data and quickly responding to scheduling problems. Contents of the invention [0003] The present invention overcomes the above-mentioned shortcomings of the prior art, and proposes a real-time big data scheduling technology method based on a deep convolutional network. [0004] The present invention ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/04
CPCG06Q10/06312G06N3/045
Inventor 王万良臧泽林李伟琨王宇乐赵燕伟
Owner ZHEJIANG UNIV OF TECH