Material quality assessment method based on machine learning

A quality assessment and machine learning technology, applied in the field of power systems, which can solve problems such as single linked data

Inactive Publication Date: 2017-12-22
JIANGSU ELECTRIC POWER INFORMATION TECH +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In daily work, the quality level of materials is often simply determined through linear analysis based on relevant information such as the number of problems that have occurred in materials. The associated data used is relatively single and has a certain one-sidedness.

Method used

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  • Material quality assessment method based on machine learning
  • Material quality assessment method based on machine learning
  • Material quality assessment method based on machine learning

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Experimental program
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Embodiment Construction

[0024] A method for evaluating material quality based on machine learning, comprising the following steps:

[0025] 1) Create a material quality assessment model, select an artificial neural network algorithm for reverse transfer, and construct a reverse traditional network model;

[0026] 2) Divide the collected historical sample information into two parts: training data and verification data;

[0027] 3) Use the training data to train the model to obtain the model weight parameters and adjustment factors; use the verification data to test and verify the model training results until the verification results conform to the collected information, so as to obtain the nonlinearity of material quality and quality inspection of influencing factors relation;

[0028] 4) Use the verified model and model parameters to predict the quality level of the material.

[0029] figure 1 It is a flow chart of material quality assessment based on machine learning.

[0030] details as follows...

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Abstract

The present invention discloses a material quality assessment method based on machine learning. A material quality assessment model is created, the artificial neural network algorithm of back propagation is selected, and a back propagation network model is established. The collected historical sample information is divided into two portions consisting of training data and verification data. The training data is used to perform training of the model to obtain model weight parameters and regulation factors. The verification data is used to perform test verification of a model training result until a verification result accords with collected information so as to obtain the non-linear relation of material quality and influence factor quality testing. The verified model and the model parameters are employed to predict the quality level of the materials. The material quality assessment method based on machine learning can more objectively and accurately assess the quality level of the materials.

Description

technical field [0001] The invention belongs to an electric power system, and relates to a material quality assessment method, in particular to a machine learning-based material quality assessment method. Background technique [0002] There are usually more than 4,000 items of materials purchased by the power material department, and there are as many as hundreds of thousands of suppliers. Different suppliers and different materials will have various quality problems and complaints during the use process. In daily work, the quality level of materials is often simply determined through linear analysis based on relevant information such as the number of problems that have occurred in materials. The associated data used is relatively single and has a certain one-sidedness. There are many factors related to the actual quality of materials, and the quality level of materials can be deduced through machine learning algorithms by collecting comprehensive related factors. Content...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06N3/08
CPCG06N3/084G06Q10/06395G06Q50/06
Inventor 王成现陈刚潘留兴胡天牧
Owner JIANGSU ELECTRIC POWER INFORMATION TECH
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