CN106055525B - A kind of big data processing method based on stepwise regression analysis - Google Patents
A kind of big data processing method based on stepwise regression analysis Download PDFInfo
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- 238000000611 regression analysis Methods 0.000 title claims abstract description 20
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- 239000003245 coal Substances 0.000 description 2
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Abstract
The big data processing method based on stepwise regression analysis that the present invention relates to a kind of, follows the steps below: first collecting the data of plant operating parameters, and the operating parameter of collection is numbered；Then, using operating parameter a part of above-mentioned collection as dependent variable, other operating parameters are linear between each parameter as independent variable, list equation；Again aforesaid equation and corresponding data are imported into Matlab software one by one, carries out stepwise regression analysis operation, coefficient and intercept before calculating separate equation independent variable；Finally carry out the optimal value that interpretation of result obtains respective operations parameter.Method of the invention utilizes a large amount of data of collection, pass through stepwise regression analysis and processing big data, and R. concomitans Matlab software, the influence between each operating parameter of rational judgment DCS in factory can be passed through, rational judgment changes influence of the size to other parameters of certain parameter, and determines the optimal value of DCS in factory operating parameter.
Description
Technical field:
The present invention relates to the methods that big data is handled in industrial production, and in particular to a kind of based on the big of stepwise regression analysis
Data processing method.
Background technique:
Regression analysis is a kind of mathematical method of correlativity between handling multivariable.This correlativity is closed different from function
System, the latter reflects the stringent interdependence between variable, and the former then shows a degree of fluctuation or randomness, to independent variable
Each value, dependent variable can have multiple numerical value to correspond.When independent variable is nonrandom variable, dependent variable is to become at random
When amount, the relationship for analyzing them is known as regression analysis；When both stochastic variables, referred to as correlation analysis.Statistically grind
Regression analysis and correlation analysis can be used by studying carefully correlativity.Although uncertain with certain between the variable with correlativity
Property, still, the statistical law between them can be explored by the continuous observation to phenomenon, this kind of statistical law is known as returning
Relationship.In a multiple linear regression model, not all independent variable all has significant relation with dependent variable, sometimes some
The effect of independent variable can be ignored.This generates how from it is a large amount of may pick out to dependent variable in related independents variable have it is aobvious
The problem of writing the part independent variable influenced, there are many element in the entire set of possible independent variable, use the calculation of " optimal " subset
Method may be unworkable.It but may be effective for so gradually generating the automatic search method of the regression model X variable subset to be contained
's.When here it is seeking the good independent variable subset of appropriateness, comparing with all possible methods returned, being produced to save amount of calculation
Raw, here it is successive Regressions.
Modernization industry production link be all it is closely related, organic connections.Its it is most important characterization be automation and
Big data.In chemical industry, by modern automation technology, by production technology, equipment, control and management be linked into one it is organic
Entirety, the data of magnanimity can be generated simultaneously.The data of these magnanimity can be divided into three classes from the viewpoint of application: adjustable parameter,
Parameter and reference parameter must be controlled.Wherein, adjustable parameter is artificial adjustable, either automatically, semi-automatic or Quan Shoudong, including valve
Door aperture, voltage, electric current, resistance, frequency etc., sole purpose are to guarantee that production safety, stabilization and product quality are up to standard.It must
Controlling parameter is the technological parameter operated under prespecified working condition, i.e., in chemical production process, all kinds of technique ginsengs
Number must carry out operation under prespecified working condition just can guarantee production safety, efficiently carries out, such as storage tank and container
(including oil tank, water tank, boiler drum etc.) liquid level requires to maintain defined range；The temperature of fermentor, pressure in biochemical process
Power, pH etc. will meet technique requirement.Reference parameter is that the parameter other than two kinds of above-mentioned parameters is exactly reference parameter.Because
Each equipment, links on production process technology flowline is all closely coupled with the equipment of front and back, link, is related to entirely flowing
Journey, numerous controlled variable and manipulating variable.Conscientiously it to consider how to guarantee product quality, improve yield, energy conservation and stablize behaviour
Make, consider the connection between comprehensive each process, equipment, link and influence each other, so that each system of reasonable arrangement, is allowed to
It works in coordination, is harmonious, it is effective, it is necessary to make good use of these reference parameters.Therefore, it in order to 1. accelerate speed of production, drops
Low production cost improves the yield and quality of product；2. reducing labor intensity, improve working conditions；3. can guarantee production peace
Entirely, it prevents accident from occurring or expanding, reaches extension service life of equipment, improve the purpose of equipment utilization ability；4. production process
The realization of automation, energy radical change labor style, improves worker's culture technical level, to eliminate manual labor and brain step by step
Difference between power labour creates conditions, and it is urgent to provide a kind of methods for handling these big datas, by collecting a large amount of data
With stepwise regression analysis, to solve the above practical problem.
Summary of the invention:
The present invention provides a kind of big data processing method based on stepwise regression analysis, can effectively carry out to mass data
Analysis is handled, and between each operating parameter of rational judgment DCS in factory and judgement changes shadow of the size to other parameters of certain parameter
It rings, so that it is determined that the optimal value of DCS in factory operating parameter.
In order to solve the above technical problems, the present invention takes following technical scheme:
A kind of big data processing method based on stepwise regression analysis, follows the steps below:
S1: collecting the data of plant operating parameters, and the operating parameter of collection be numbered, be denoted as 1 respectively, 2,
3,……,n；
S2: data processing: it regard each of operating parameter of collection as dependent variable, other remaining operation ginsengs respectively
It counts and is used as independent variable, it is linear between each parameter, it is listed below equation:
Wherein: x is independent variable；Y is dependent variable；A is the coefficient before independent variable；B is intercept；
S3: importing Matlab software for aforesaid equation and corresponding data one by one, carry out stepwise regression analysis operation,
Coefficient and intercept before calculating separate equation independent variable；
S4: interpretation of result:
(1) when the coefficient before independent variable is zero, illustrate that the independent variable does not have an impact corresponding dependent variable, coefficient is just
The both forward and reverse directions that negative reaction influences, the size that the size reaction of coefficient influences, therefore shadow can be found out by above-mentioned operation result
Ring the maximum operating parameter and operating parameter number of dependent variable；
(2) the above-mentioned equation group for acquiring coefficient is changed as shown below:
Equation group is write as to the form of matrix, such as following formula:
With Matlab software solution above formula, need to be added parameter value range in calculating as constraint condition, obtained solution is
The optimal value of respective operations parameter.
Method of the invention by stepwise regression analysis and processing big data, and combines fortune using a large amount of data are collected
With Matlab software, certain parameter can be changed by the influence between each operating parameter of rational judgment DCS in factory, rational judgment
Influence of the size to other parameters, and determine the optimal value of DCS in factory operating parameter.To solve the number occurred in actual production
According to the big analysis difficulty of amount, big, labour expends the problems such as big, production efficiency is low, working condition is bad low with utilization rate of equipment and installations, adds
Fast speed of production reduces production cost, improves the yield and quality of product；It reduces labor intensity, improves working conditions.Meanwhile
Production safety can be guaranteed by promoting and applying this method, prevented accident from occurring or expanding, reached extension service life of equipment, raising is set
The purpose of standby Utilization ability, it can be achieved that production process automation, change labor style, improve worker's culture technical level.
Specific embodiment:
Technical solution of the present invention is described in detail below.
Embodiment 1
With the big data processing method based on stepwise regression analysis, certain coal dust factory grinding machine data is first collected, the grinding machine is total
There are 19 parameters, to each parameter number such as the following table 1:
| Number | Parameter | Number | Parameter | Number | Parameter |
| 1 | Secondary air fan frequency | 2 | End flue temperature | 3 | Combustion chamber draft |
| 4 | Furnace tail temperature | 5 | Furnace exit temperature | 6 | Distribution plenum outlet temperature |
| 7 | Feeder frequency | 8 | Actual flow | 9 | Mill entrance temperature |
| 10 | Mill entrance pressure | 11 | Mill entrance oxygen amount | 12 | Mill entrance oxygen amount |
| 13 | Grinding machine outlet pressure | 14 | Whirlwind temperature | 15 | Cloth bag inlet temperature |
| 16 | Cloth bag outlet temperature | 17 | Fan frequency | 18 | 1# stores up powder tower temperature degree |
| 19 | 2# stores up powder tower temperature degree |
Respectively by 1-19 parameter in table 1 as dependent variable, rest parameter can obtain following equation as independent variable:
Wherein: the number of y and x subscript expression parameter, b are intercept.
Aforesaid equation and corresponding data are imported into Matlab software one by one, stepwise regression analysis operation is carried out, asks
Coefficient before independent variable out, the coefficient if certain independent variable does not influence dependent variable before the independent variable is zero, finally it is as follows
As a result:
y19=9.01+0.7459x18
y18=-110.699-0.1x2-0.007x4-0.013x6+0.373x8+0.527x15-0.02x16+4.91x17+
0.25x19
y17=19.427-0.0013x3-0.0007x4+0.1567x7+0.0195x8+0.0021x9+0.0179x14+
0.0132x15+0.0009x16+0.0203x18
y16=5.77+0.0356x6+0.8161x14
y15=0.481+0.0061x9+0.7972x12+0.0659x14+0.1041x18
y14=29.407-0.3469x7+0.0097x9+0.6419x12+0.1659x15+1.8141x17
y13=-1185.61-34.212x7-25.249x8
y12=13.3586+0.0395x1-0.0047x3+0.0029x4+0.0046x6-0.28031x7+0.0052x9+
0.2043x14+0.5627x15
y11=1.8995-0.0303x14
y10=-35.0614-0.5407x3-0.0570x6+2.2395x7+0.5132x18
y9=-522.62+0.1531x4+0.0437x6-6.7397x7+2.5759x8+2.0845x16+19.4179x17+
1.8276x12-0.005x13-0.0655x16
y8=-16.355-0.0063x4-0.0009x5+0.0145x6+0.5751x7+0.0177x9-0.0005x13-
0.0636x15+1.1788x17+0.0819x18
y7=-44.0176+0.0992x1+0.0082x4+0.0003x5+0.0023x6+0.1533x8-0.0133x9-
0.0761x12+0.0526x14+2.4382x17+0.0246x18
y6=-459.437-2.9828x1+0.0072x2+0.2244x3-0.1105x4+0.0278x5+7.155x7+
13.47x8+0.26x9-0.302x10+6.302x12+2.771x15-3.235x18
y5=-429.54+1.1742x6+41.7394x7-35.5218x8
y4=1244.5-5.695x1+5.5625x3-0.2278x6+46.7195x7-8.4274x8+1.7503x9+
3.3865x12-56.1883x17-4.4394x18
y3=194.78+2.5697x1+0.1021x4-1.0547x10-2.7895x12+1.6788x15-12.0286x17
y2=-0.596x4+1.0683x6
y1=-1.779+0.0273x3-0.0099x4-0.0101x6+1.0875x7+0.1942x8+0.2186x12-
0.0872x18
The reaction of above-mentioned separate equation formula is influenced as the parameter of dependent variable by independent variable parameter, and according to independent variable before
The direction and size that coefficient judgement influences.
Following form is converted by above-mentioned equation group:
Matrix is converted by equation group:
The matrix is solved with Matlab software, needs to be added parameter value range in calculating as constraint condition, obtained solution
The as optimal value of respective operations parameter.
Interpretation of result: You Shangbiao 2 is it is found that first collect 19 operating parameters of the grinding machine by this method, by gradually returning
Return analytic operation, the coefficient before finding out independent variable obtains the optimal value of respective operations parameter with Matlab software solution matrix.
So that the big analysis difficulty of data volume for solving appearance in the actual production process is big, labour's consuming is big, production efficiency is low, life
The problems such as production condition is bad low with utilization rate of equipment and installations.
Embodiment 2
With the big data processing method based on stepwise regression analysis, certain heat supply company pulverized-coal fired boiler operation data is collected,
The pulverized-coal fired boiler shares 65 parameters, to each parameter number such as the following table 2:
Respectively by above-mentioned 1-65 parameter as dependent variable, rest parameter can obtain following equation as independent variable:
Wherein: the number of y and x subscript expression parameter, b are intercept.
Aforesaid equation and corresponding data are imported into Matlab software one by one, stepwise regression analysis operation is carried out, asks
Coefficient before independent variable out, the coefficient if certain independent variable does not influence dependent variable before the independent variable is zero, finally it is as follows
As a result:
The reaction of above-mentioned separate equation formula is influenced as the parameter of dependent variable by independent variable parameter, and according to independent variable before
The direction and size that coefficient judgement influences.
Following form is converted by above-mentioned equation group:
Matrix is converted by equation group:
The matrix is solved with Matlab software, needs to be added parameter value range in calculating as constraint condition, obtained solution
The as optimal value of respective operations parameter.
Claims (1)
1. a kind of big data processing method based on stepwise regression analysis, it is characterised in that: follow the steps below:
S1: collecting the data of plant operating parameters, and the operating parameter of collection be numbered, be denoted as 1 respectively, 2,3 ...,
n；
S2: data processing: regarding each of operating parameter of collection as dependent variable respectively, and other remaining operating parameters are made
It is linear between each parameter for independent variable, it is listed below equation:
Wherein: x is independent variable；Y is dependent variable；A is the coefficient before independent variable；B is intercept；
S3: aforesaid equation and corresponding data are imported into Matlab software one by one, carry out stepwise regression analysis operation, is calculated
Coefficient and intercept before separate equation independent variable out；
S4: interpretation of result:
(1) when the coefficient before independent variable is zero, illustrate that the independent variable does not have an impact corresponding dependent variable, coefficient is positive and negative anti-
The both forward and reverse directions that should be influenced, coefficient size reaction influence size, therefore by above-mentioned operation result can find out influence because
The maximum operating parameter and operating parameter number of variable；
(2) the above-mentioned equation group for acquiring coefficient is changed as shown below:
Equation group is write as to the form of matrix, such as following formula:
With Matlab software solution above formula, obtained solution is the optimal value of respective operations parameter.
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| CN114398734A (en) * | 2022-01-17 | 2022-04-26 | 华侨大学 | Method, device and equipment for optimizing parameters of micro-feeding and readable storage medium |
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| CN101719195A (en) * | 2009-12-03 | 2010-06-02 | 上海大学 | Inference method of stepwise regression gene regulatory network |
| CN103761420A (en) * | 2013-12-31 | 2014-04-30 | 湖南大唐先一科技有限公司 | Evaluation method for stepwise regression of thermal power equipment performances |
| CN104593540A (en) * | 2015-01-30 | 2015-05-06 | 冶金自动化研究设计院 | Method for evaluating energy efficiency in converter steelmaking process |
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| CN101719195A (en) * | 2009-12-03 | 2010-06-02 | 上海大学 | Inference method of stepwise regression gene regulatory network |
| CN103761420A (en) * | 2013-12-31 | 2014-04-30 | 湖南大唐先一科技有限公司 | Evaluation method for stepwise regression of thermal power equipment performances |
| CN104593540A (en) * | 2015-01-30 | 2015-05-06 | 冶金自动化研究设计院 | Method for evaluating energy efficiency in converter steelmaking process |
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| Symbolic and numerical regression: experiments and applications;J.W.Davidson等;《Information Sciences》;20030331;第150卷(第1-2期);95-117 * |
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