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Stepwise regression analysis-based big data processing method

A big data processing and step-by-step regression technology, applied in the field of big data processing, can solve problems such as unworkable algorithms, and achieve the effects of ensuring production safety, prolonging service life, and improving workers' cultural and technical levels

Inactive Publication Date: 2016-10-26
CHINA UNIV OF MINING & TECH YINCHUAN COLLEGE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This creates a problem of how to select some independent variables that have a significant impact on the dependent variable from a large number of possible independent variables. There are many elements in the entire set of possible independent variables, and the algorithm using the "optimal" subset may work. unreasonable
Then the automatic search method that gradually generates the subset of X variables to be included in the regression model may be effective

Method used

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  • Stepwise regression analysis-based big data processing method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] Using the big data processing method based on stepwise regression analysis, first collect the mill data of a pulverized coal plant. The mill has 19 parameters. The number of each parameter is as follows in Table 1:

[0023]

[0024]

[0025] Taking parameters 1-19 in Table 1 as dependent variables and the remaining parameters as independent variables, the following equation can be obtained:

[0026]

[0027] Among them: m=18, n=19, the subscripts of y and x indicate the number of parameters, and b is the intercept.

[0028] Import the above equations and corresponding data into Matlab software one by one, and perform stepwise regression analysis to find the coefficient before the independent variable. If an independent variable has no influence on the dependent variable, the coefficient before the independent variable is zero, and finally the following results are obtained:

[0029] the y 19 =9.01+0.7459x 18

[0030] the y 18 =-110.699-0.1x 2 -0.007x 4 -0...

Embodiment 2

[0056] Using the big data processing method based on stepwise regression analysis, the operation data of a pulverized coal boiler in a heating company is collected. The pulverized coal boiler has a total of 65 parameters. The number of each parameter is as follows in Table 2:

[0057] serial number

parameters

serial number

parameters

serial number

parameters

1

Filling valve opening

2

Water supply valve opening setting

3

Water temperature

4

Dust differential pressure

5

Dust blowing set point

6

Secondary air valve opening

7

Secondary air valve opening setting

8

Secondary air volume

9

Powder bin differential pressure

10

Powder bin weight

11

Powder bin weight low alarm setting value

12

Feed water flow

13

water pressure

14

boiler water level

15

after superheater temperature

16

Return air valve 1 opening

17...

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Abstract

The present invention relates to a stepwise regression analysis-based big data processing method. The method includes the following steps of firstly, collecting data of factory operation parameters, and numbering the collected operation parameters; then, using some of the collected operation parameters as dependent variables, using other operation parameters as independent variables, and writing equations, wherein the parameters are in a linear relationship; then, importing the equations and corresponding data into Matlab software one by one, performing stepwise regression analysis operation, and calculating a coefficient and an intercept of each equation before the independent variable; and finally, performing result analysis so as to obtain an optimal value of a corresponding operation parameter. According to the method, a lot of data is collected, stepwise regression analysis and big data processing are performed, the Matlab software is used, and the impact of all operation parameters of a factory DCS is determined quantitatively so as to quantitatively determine and change the impact of a certain parameter on other parameters to determine the optimal value of a factory DCS operation parameter.

Description

Technical field: [0001] The invention relates to a method for processing big data in industrial production, in particular to a method for processing big data based on stepwise regression analysis. Background technique: [0002] Regression analysis is a mathematical method to deal with the correlation among multiple variables. This correlation is different from the functional relationship. The latter reflects the strict dependence between variables, while the former shows a certain degree of volatility or randomness. For each value of the independent variable, the dependent variable can have multiple values ​​and corresponding to it. When the independent variable is a non-random variable and the dependent variable is a random variable, analyzing their relationship is called regression analysis; when both are random variables, it is called correlation analysis. Regression analysis and correlation analysis can be used to study correlation in statistics. Although there is som...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 魏亚玲李东张学梅苗泽凯程实马青华
Owner CHINA UNIV OF MINING & TECH YINCHUAN COLLEGE
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