CN103995467A - Method for extracting main components of dredging operation energy consumption influence factors based on partial least squares - Google Patents

Abstract

The invention discloses a method for extracting main components of dredging operation energy consumption influence factors based on partial least squares. An advanced multivariate regression analysis method is adopted, and the method is mainly used for solving the practical problems that multiple variable correlations exist and independent variables are larger than sample variables in the multivariate regression analysis process. Multiple variables are synthesized into a few of representative variables can represent most of information of original variables, and the representative variables are not related, the extracted main components can well explain dependent variables and well explain independent variables, on the basis of new aggregate variables, further statistical analysis can be conducted, a theoretical basis is laid for balance optimization study of energy consumption and yield in the follow-up process, the purposes of high efficiency, high yield and low energy consumption are achieved, and the method has significance in energy consumption and yield optimization of a dredger.

Description

Dredging operation energy consumption factor Principle component extraction method based on partial least square method

Technical field

The present invention relates to the application that partial least square method is extracted major component in the numerous energy consumption factors of cutter suction dredger, belong to dredging work field.

Background technology

In actual dredging operation, scientific research personnel to a large amount of data, finds rule to analyze by corresponding equipment records.Multivariate, large sample undoubtedly can be for scientific research provides abundant information, but in most of the cases, the correlativity existing between many variablees has increased the complicacy of case study.Therefore need to find a rational method, when reducing situational variables, reduce the loss of former variable inclusion information as far as possible, collected data is done to comprehensive analysis.Partial least square method is exactly a kind of like this method, and it can find out a few comprehensive variable in numerous variablees, reflects the main information that original dependent variable and independent variable reflect, makes problem reduction.The effect of partial least square method is: the dimension that 1. can reduce studied data space; 2. the major component of extracting has good interpretability to original variable system; 3. construct regression model, can be for energy consumption forecast analysis; 4. can classify to variable.

Partial least square method (Partial least squares, PLS) is a kind of advanced person's Multiple Regression Analysis Method, is mainly used to solve in multiple regression analysis variable multiple correlation and independent variable more than practical problemss such as sample variables.By the high-dimensional data space of independent variable and dependent variable being projected to corresponding low dimensional feature space, obtain respectively the mutually orthogonal proper vector of independent variable and dependent variable, then set up the one-variable linear regression relation of independent variable and dependent variable proper vector.With principal component analysis (PCA) (Primary Component Analysis, PCA) compare: in PCA, first it bypasses dependent variable completely, consider separately independent variable system to extract major component, this just likely causes major component very low to the correlativity of dependent variable, thereby causes the correlation analysis of independent variable and dependent variable unreasonable; And PLS extracts mutually orthogonal composition from original independent variable, when extracting, composition both considered and the correlativity of dependent variable, make the covariance of composition and dependent variable reach maximum, also to have considered and the correlativity of independent variable, it is maximum that the information that makes composition comprise X reaches.It emphasizes that independent variable is to the explanation of dependent variable and predicting function when selected characteristic vector, has removed returning the impact of unhelpful noise, and be that model comprises minimum variable number, thereby PLS model have better robustness and prediction stability.It is a kind of analytical approach that integrates the basic function of multiple linear regression analysis, canonical correlation analysis, principal component analysis (PCA) that PLS analyzes.

When the problem with statistical analysis technique Study of Multivariable, variable number will increase the complicacy of problem too much.People wish that variable number information less and that obtain is more naturally.In a lot of situations, between variable, be to have certain correlationship, when having certain correlationship between two variablees, can being interpreted as these two variablees, to reflect that the information of this problem has certain overlapping.PLS is all variablees for original proposition, set up the least possible new variables, it is incoherent between two making these new variables, and extracting these new variables not only has best interpretations ability to original variable, and keeps more original information at the message context of reflection problem.

Dredging is as underwater operation, and influence factor is numerous, wherein between numerous parametric variables, usually has certain correlativity.This certainly will increase the complicacy of problem analysis.How to guarantee rationally effective process decision, reduce regulation and control parameter, reduce Operating Complexity, become dredging low energy consumption, low emission, high efficiency key issue.

PLS method is a kind of Projection Analysis and reductive explanation method of variable system, the core of its technology is embedding data informix and Variable Selection technology in linear least-squares algorithm, under the prerequisite guaranteeing to greatest extent with independent variable system and dependent variable system correlativity, abbreviation independent variable system, eliminate the multiple correlation of independent variable, and on new generalized variable basis, further statistical study.For theoretical foundation is laid in energy consumption afterwards and the forecasting research of output.

Summary of the invention

The present invention utilizes existing offset minimum binary method, on affecting the numerous factor of energy consumption on cutter suction dredger, carries out analytical calculation.Make it to reduce by the effect of dimensionality reduction the number of variable, analyze more enough more clear.

The concrete technical scheme of the present invention is as follows:

A dredging operation energy consumption factor Principle component extraction method based on partial least square method, comprises the following steps:

Step (1): collect the data information that affects cutter suction dredger Energy Consumption Factors variable, determine that p is analyzed energy consumption variable, lists dependent variable and independent variable sample matrix; Wherein, p is positive integer;

Step (2): sample matrix is carried out to standardization;

Step (3): according to master sample matrix, the corresponding unit character of the eigenvalue of maximum of compute matrix institute is vectorial respectively, obtains first major component of independent variable and dependent variable;

Step (4): calculate the residual matrix of the rear master sample data of reduction, repeating step (3), obtains other major components successively;

Step (5): stop judgment criterion according to extracting major component, calculate successively the interpretability of major component to independent variable, dependent variable information;

Step (6): determine major component number;

Step (7): the sample matrix data after standardization are brought into and extracted each major component expression formula, calculate respectively the variable of each major component.

In above-mentioned steps (1), sample matrix is as follows:

If to p independent variable x ₁, x ₂... x _pwith q dependent variable y ₁, y ₂... y _qcarried out n observation, remembered that respectively the data matrix of " sample point * variable " type of independent variable and dependent variable is:

X＝(x _ij) _n×p＝(x ₁,x ₂,...x _p),i＝1,2...,n；j＝1,2,...p

Y＝(y _ij) _n×q＝(y ₁,y ₂,...y _q),i＝1,2...,n；j＝1,2,...q

In above-mentioned steps (2), matrix standardization is as follows:

Data matrix after note standardization is

E ₀=(e _ij) _{n * p}and F ₀=(f _ij) _{n * q},

Wherein $e_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-\stackrel{\‾}{x_j}}{{\mathrm{sx}}_j},i=\mathrm{1,2}...,n;j=\mathrm{1,2},...p,---(1-1)$

$f_{\mathrm{ij}}=\frac{y_{\mathrm{ij}}-\stackrel{\‾}{y_j}}{{\mathrm{sy}}_j},i=\mathrm{1,2}...,n;j=\mathrm{1,2},...q,---(1-2)$

In formula in (1-1) and formula in (1-2), be respectively the mean value of the j column data of matrix X and Y, sx _j, sy _jstandard deviation for the j column data of matrix X and Y.

In above-mentioned steps (3), the calculation procedure of first Principle component extraction is as follows:

Ask matrix the corresponding unit character vector w of eigenvalue of maximum institute ₁, obtain first major component of independent variable, t ₁=E ₀w ₁

Ask matrix the corresponding unit character vector c of eigenvalue of maximum institute ₁, obtain first major component of dependent variable, u ₁=F ₀c ₁

Ask residual matrix

$E_1=E_0-t_1{p}_1^T---(1-3)$

$F_1=F_0-t_1{r}_1^T---(1-4)$

In formula in (1-3) , in formula in (1-4)

In above-mentioned steps (4), the calculation procedure of other major components is as follows:

Make E ₀=E ₁, F ₀=F ₁, residual matrix is carried out to the Principle component extraction of a new round

If the result of calculation of h step is

t _h＝E _h-1w _h (1-5)

U _h＝F _h-lc _h (1-6)

$E_h=E_{h-1}-t_h{p}_h^T---(1-7)$

$F_h=F_{h-1}-t_h{r}_h^T---(1-8)$

In formula (1-5)～(1-8), h=1,2 ..., m, m < < rank (E ₀),

Extraction major component in above-mentioned steps (5) stops judgment criterion and adopts coefficient of multiple determination criterion,

By statistic

$R_h^2=\frac{{\mathrm{\&Sigma;}}_{k=1}^h{\left|\right|t_k\left|\right|}^2\&times;{\left|\right|r_k\left|\right|}^2}{{\left|\right|F_0\left|\right|}^2}---(1-9)$

Whether front h major component evaluating independent variable system has enough interpretabilities to dependent variable Y system;

Coefficient of multiple determination what measure is that the variation quantity of information that can be explained by the regression equation of front h major component structure accounts for the number percent of total variation, when h=m and coefficient of multiple determination value when enough large, can stop Principle component extraction in m step and calculate, wherein

At coefficient of multiple determination application in, reference statistical amount often

$Q_h^2=\frac{{\mathrm{\&Sigma;}}_{k=1}^h{\left|\right|t_k\left|\right|}^2\&times;{\left|\right|p_k\left|\right|}^2}{{\left|\right|E_0\left|\right|}^2}---(1-10)$

The size of value, what measure is the variation quantity of information that independent variable system X is extracted.

In above-mentioned steps (5), major component is as follows to the computation process of the interpretability of independent variable, dependent variable information:

Note ρ ²(y _j; t _h) be major component t _hwith dependent variable y _jsimple correlation coefficient square, t _hinterpretability to dependent variable system Y:

Rd(y _j；t _h)＝ρ ²(y _j；t _h) (1-11)

$\mathrm{Rd}(Y;t_h)=\frac{1}{q}{\mathrm{\&Sigma;}}_{j=1}^q\mathrm{Rd}(y_j;t_h)---(1-12)$

T ₁, t ₂..., t _maccumulative total interpretability to dependent variable system Y:

$\mathrm{Rd}(Y;t_1,t_2,...t_m)={\mathrm{\&Sigma;}}_{h=1}^m\mathrm{Rd}(Y;t_h)---(1-13)$

According to formula (1-11)～(1-13), calculate the interpretability of each major component to energy consumption dependent variable Y;

Note ρ ²(x _j; t _h) be major component t _hwith independent variable x _jsimple correlation coefficient square, t _hinterpretability to independent variable system X:

Rd(x _j；t _h)＝ρ ²(x _j；t _h) (1-14)

$\mathrm{Rd}(X;t_h)=\frac{1}{p}{\mathrm{\&Sigma;}}_{j=1}^p\mathrm{Rd}(x_j;t_h)---(1-15)$

T ₁, t ₂..., t _maccumulative total interpretability to independent variable system X:

$\mathrm{Rd}(X;t_1,t_2,...t_m)={\mathrm{\&Sigma;}}_{h=1}^m\mathrm{Rd}(X;t_h)---(1-16)$

According to formula (1-14)～(1-16), calculate the interpretability of each major component to energy consumption factor X.

In above-mentioned steps (6), major component number is definite as follows:

Draw respectively major component and major component to energy consumption dependent variable Y interpretability total information and energy consumption factor X interpretability total information histogram, find out Rd _h(Cum) in the time of>=85%, both major component numbers, then to it, both get major component number common factor, have so not only guaranteed that major component has good interpretability to energy consumption dependent variable Y, and have guaranteed the good interpretability that has of major component to energy consumption factor X.

Beneficial effect is: the invention discloses a kind of dredging operation energy consumption factor Principle component extraction method based on partial least square method, take a kind of advanced person's Multiple Regression Analysis Method, be mainly used to solve in multiple regression analysis variable multiple correlation and independent variable more than practical problemss such as sample variables.Manage to be comprehensively a few representative variable in a plurality of variablees, can either represent most information of original variable, uncorrelated mutually again, the explanation dependent variable that the major component of its extraction can be taught, can well explain independent variable again, and on new generalized variable basis, further statistical study, for theoretical foundation is laid in the balance optimizing research of energy consumption afterwards and output, reach the object of high-level efficiency, high yield, low energy consumption, hog barge is carried out to energy consumption and output and optimize significant.

Accompanying drawing explanation

Fig. 1 is analytical approach process flow diagram of the present invention.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.Following examples are only for technical scheme of the present invention is more clearly described, and can not limit the scope of the invention with this.

PLS method is extracted major component, is not the algorithm of indiscriminately imitating principal component analysis (PCA), but when taking into account the correlativity of independent variable system and dependent variable system, progressively extracts the major component separately of two variable systems.

PLS modeling criterion is that principal component analysis (PCA) criterion and principal component regression criterion are combined and form a new residual sum of squares (RSS) index.Its criterion function being widely used is:

$J=\underset{\left|\right|w\left|\right|=1,\left|\right|c\left|\right|=1}{\max }\mathrm{cov}(u_i,t_i)=\underset{\left|\right|w\left|\right|=1,\left|\right|c\left|\right|=1}{\max }\sqrt{\mathrm{Var}\left(u_i\right)\mathrm{Var}\left(t_i\right)}\mathrm{Corr}(u_i,t_i)$

Note: claim in formula that w is model effect weight, c is dependent variable weight u _i, t _ibe respectively the major component of dependent variable and independent variable.(i＝1,2,...m)。

A dredging operation energy consumption factor Principle component extraction method based on partial least square method, comprises the following steps:

Step (1): collect the data information that affects cutter suction dredger Energy Consumption Factors variable, determine that p is analyzed energy consumption variable, lists dependent variable and independent variable sample matrix; Wherein, p is positive integer;

Step (2): sample matrix is carried out to standardization;

Step (3): according to master sample matrix, the corresponding unit character of the eigenvalue of maximum of compute matrix institute is vectorial respectively, obtains first major component of independent variable and dependent variable;

Step (4): calculate the residual matrix of the rear master sample data of reduction, repeating step (3), obtains other major components successively;

Step (5): stop judgment criterion according to extracting major component, calculate successively the interpretability of major component to independent variable, dependent variable information;

Step (6): determine major component number;

Step (7): the sample matrix data after standardization are brought into and extracted each major component expression formula, calculate respectively the variable of each major component.

In above-mentioned steps (1), sample matrix is as follows:

If to p independent variable x ₁, x ₂... x _pwith q dependent variable y ₁, y ₂... y _qcarried out n observation, remembered that respectively the data matrix of " sample point * variable " type of independent variable and dependent variable is:

X＝(x _ij) _n×p＝(x ₁,x ₂,...x _p),i＝1,2...,n；j＝1,2,...p

Y＝(y _ij) _n×q＝(y ₁,y ₂,...y _q),i＝1,2...,n；j＝1,2,...q

In above-mentioned steps (2), matrix standardization is as follows:

Data matrix after note standardization is

E ₀=(e _ij) _{n * p}and F0=(f _ij) _{n * q},

Wherein $e_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-\stackrel{\&OverBar;}{x_j}}{{\mathrm{sx}}_j},i=\mathrm{1,2}...,n;j=\mathrm{1,2},...p,---(1-1)$

$f_{\mathrm{ij}}=\frac{y_{\mathrm{ij}}-\stackrel{\&OverBar;}{y_j}}{{\mathrm{sy}}_j},i=\mathrm{1,2}...,n;j=\mathrm{1,2},...q,---(1-2)$

In formula in (1-1) and formula in (1-2), be respectively the mean value of the j column data of matrix X and Y, sx _j, sy _jstandard deviation for the j column data of matrix X and Y.

In above-mentioned steps (3), the calculation procedure of first Principle component extraction is as follows:

Ask matrix the corresponding unit character vector w of eigenvalue of maximum institute ₁, obtain first of independent variable

Composition, t ₁=E0w ₁

Ask matrix the corresponding unit character vector c of eigenvalue of maximum institute ₁, obtain first major component of dependent variable, u ₁=F ₀c ₁

Ask residual matrix

$E_1=E_0-t_1{p}_1^T---(1-3)$

$F_1=F_0-t_1{r}_1^T---(1-4)$

In formula in (1-3) , in formula in (1-4)

In above-mentioned steps (4), the calculation procedure of other major components is as follows:

Make E ₀=E ₁, F ₀=F ₁, residual matrix is carried out to the Principle component extraction of a new round

If the result of calculation of h step is

t _h＝E _h-1w _h (1-5)

u _h＝F _h-1c _h (1-6)

$E_h=E_{h-1}-t_h{p}_h^T---(1-7)$

$F_h=F_{h-1}-t_h{r}_h^T---(1-8)$

In formula (1-5)～(1-8), h=1,2 ..., m, m < < rank (E ₀),

Extraction major component in above-mentioned steps (5) stops judgment criterion and adopts coefficient of multiple determination criterion,

By statistic

$R_h^2=\frac{{\mathrm{\&Sigma;}}_{k=1}^h{\left|\right|t_k\left|\right|}^2\&times;{\left|\right|r_k\left|\right|}^2}{{\left|\right|F_0\left|\right|}^2}---(1-9)$

Whether front h major component evaluating independent variable system has enough interpretabilities to dependent variable Y system;

Coefficient of multiple determination what measure is that the variation quantity of information that can be explained by the regression equation of front h major component structure accounts for the number percent of total variation, when h=m and coefficient of multiple determination value when enough large, can stop Principle component extraction in m step and calculate, wherein

At coefficient of multiple determination application in, reference statistical amount often

$Q_h^2=\frac{{\mathrm{\&Sigma;}}_{k=1}^h{\left|\right|t_k\left|\right|}^2\&times;{\left|\right|p_k\left|\right|}^2}{{\left|\right|E_0\left|\right|}^2}---(1-10)$

The size of value, what measure is the variation quantity of information that independent variable system X is extracted.

In above-mentioned steps (5), major component is as follows to the computation process of the interpretability of independent variable, dependent variable information:

Note ρ ²(y _j; t _h) be major component t _hwith dependent variable y _jsimple correlation coefficient square, t _hinterpretability to dependent variable system Y:

Rd(y _j；t _h)＝ρ ²(y _j；t _h) (1-11)

$\mathrm{Rd}(Y;t_h)=\frac{1}{q}{\mathrm{\&Sigma;}}_{j=1}^q\mathrm{Rd}(y_j;t_h)---(1-12)$

T ₁, t ₂..., t _maccumulative total interpretability to dependent variable system Y:

$\mathrm{Rd}(Y;t_1,t_2,...t_m)={\mathrm{\&Sigma;}}_{h=1}^m\mathrm{Rd}(Y;t_h)---(1-13)$

According to formula (1-11)～(1-13), calculate the interpretability of each major component to energy consumption dependent variable Y;

Note ρ ²(x _j; t _h) be major component t _hwith independent variable x _jsimple correlation coefficient square, t _hinterpretability to independent variable system X:

Rd(x _j；t _h)＝ρ ²(x _j；t _h) (1-14)

$\mathrm{Rd}(X;t_h)=\frac{1}{p}{\mathrm{\&Sigma;}}_{j=1}^p\mathrm{Rd}(x_j;t_h)---(1-15)$

T ₁, t ₂..., t _maccumulative total interpretability to independent variable system X:

$\mathrm{Rd}(X;t_1,t_2,...t_m)={\mathrm{\&Sigma;}}_{h=1}^m\mathrm{Rd}(X;t_h)---(1-16)$

According to formula (1-14)～(1-16), calculate the interpretability of each major component to energy consumption factor X.

In above-mentioned steps (6), major component number is definite as follows:

Draw respectively major component and major component to energy consumption dependent variable Y interpretability total information and energy consumption factor X interpretability total information histogram, find out Rd _h(Cum) in the time of>=85%, both major component numbers, then to it, both get major component number common factor, have so not only guaranteed that major component has good interpretability to energy consumption dependent variable Y, and have guaranteed the good interpretability that has of major component to energy consumption factor X.

Embodiment

Cutter suction dredger energy consumption factor Principle component extraction

(1) cutter suction dredger energy consumption factor has numerous parametric variables, first collects data information, determines situational variables.The main energy consumption variable of cutter suction dredger is as shown in table 1.

The main energy consumption of table 1 cutter suction dredger

Cutter suction dredger energy consumption factor is as shown in table 2.

Table 2 cutter suction dredger energy consumption factor

(2) raw data is carried out to standardization

It is some irrational impacts that may bring due to the difference of dimension in order to eliminate that raw data is carried out aims of standardization.

According to (1-1) and (1-2) formula respectively energy consumption dependent variable Y and energy consumption factor X to be carried out can obtaining dependent variable after data normalization be Y ', independent variable is X '.Its data matrix is:

X '=(x ' _ij) _{n * 12}=(x ' ₁, x ' ₂..., x ' ₁₂) and Y '=(y ' _ij) _{n * 6}=(y ' ₁, y ' ₂... y ' ₆)

(3) extract major component

Make E ₀=X ', F ₀=Y ', asks matrix the corresponding unit character vector w of eigenvalue of maximum institute ₁, obtain first major component of independent variable.

t ₁＝E ₀w ₁

Ask matrix the corresponding unit character vector c of eigenvalue of maximum institute ₁, obtain first major component of dependent variable.u ₁＝F ₀c ₁

According to the method for formula (1-3)～(1-8), obtain successively other major components t of independent variable and dependent variable again _hand u _h(h=l, 2 ..., m, m < < rank (E ₀)).

(4) calculate respectively major component dependent variable and independent variable are obtained to interpretability

According to formula (1-9), (1-11)～(1-13), calculate the interpretability of each major component to energy consumption dependent variable Y, its result is as shown in table 3.

The interpretability of table 3 major component to energy consumption variable Y

According to formula (1-10), (1-14)～(1-16) calculate the interpretability of each major component to energy consumption factor X, its result is as shown in table 4.

The interpretability of table 4 major component to energy consumption factor X

(5) determine major component number

Draw respectively major component and major component to energy consumption dependent variable Y interpretability total information and energy consumption factor X interpretability total information histogram.Find out Rd _h(Cum) in the time of>=85%, both major component numbers, then to it, both get major component number common factor, have so not only guaranteed that major component has good interpretability to energy consumption dependent variable Y, and have guaranteed the good interpretability that has of major component to energy consumption factor X.

(6) sample data is brought into the corresponding expression formula of extracting major component, can obtain the major component number of cutter suction dredger energy consumption factor, its major component number had both been considered the correlativity with energy consumption dependent variable, make the covariance of composition and dependent variable reach maximum, also considered and the correlativity of independent variable, it is maximum that the information that makes composition comprise X reaches.Can, use these major components in many analyses, further do comprehensive evaluation, cluster analysis and regression analysis.

The above is only the preferred embodiment of the present invention; be noted that for those skilled in the art; under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.

Claims (8)

1. the dredging operation energy consumption factor Principle component extraction method based on partial least square method, is characterized in that comprising the following steps:

Step (1): collect the data information that affects cutter suction dredger Energy Consumption Factors variable, determine that p is analyzed energy consumption variable, lists dependent variable and independent variable sample matrix; Wherein, p is positive integer;

Step (2): sample matrix is carried out to standardization;

Step (3): according to master sample matrix, the corresponding unit character of the eigenvalue of maximum of compute matrix institute is vectorial respectively, obtains first major component of independent variable and dependent variable;

Step (4): calculate the residual matrix of the rear master sample data of reduction, repeating step (3), obtains other major components successively;

Step (5): stop judgment criterion according to extracting major component, calculate successively the interpretability of major component to independent variable, dependent variable information;

Step (6): determine major component number;

Step (7): the sample matrix data after standardization are brought into and extracted each major component expression formula, calculate respectively the variable of each major component.

2. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that the sample matrix in described step (1) is as follows:

If to p independent variable x ₁, x ₂... x _pwith q dependent variable y ₁, y ₂... y _qgone n observation, remembered that respectively the data matrix of " sample point * variable " type of independent variable and dependent variable is:

X＝(x _ij) _n×p＝(x ₁,x ₂,...x _p),i＝1,2...,n；j＝1,2,...p

Y＝(y _ij) _n×q＝(y ₁,y ₂,...y _q),i＝1,2...,n；j＝1,2,...q 。

3. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that in described step (2), matrix standardization is as follows:

Data matrix after note standardization is:

E ₀=(e _ij) _{n * p}and F ₀=(f _ij) _{n * q}

Wherein

In formula in (1-1) and formula in (1-2), be respectively the mean value of the j column data of matrix X and Y, sx _j, sy _jstandard deviation for the j column data of matrix X and Y.

4. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that the calculation procedure of first Principle component extraction in described step (3) is as follows:

Ask matrix the corresponding unit character vector w of eigenvalue of maximum institute ₁, obtain first major component of independent variable, t ₁=E ₀w ₁

Ask matrix the corresponding unit character vector c of eigenvalue of maximum institute ₁, obtain first major component of dependent variable, u ₁=F ₀c ₁

Ask residual matrix

In formula in (1-3) , in formula in (1-4)

5. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that the calculation procedure of other major components in described step (4) is as follows:

Make E ₀=E ₁, F ₀=F ₁, residual matrix is carried out to the Principle component extraction of a new round

If the result of calculation of h step is

t _h＝E _h-lw _h (1-5)

u _h＝F _h-lc _h (1-6)

In formula (1-5)～(1-8), h=1,2 ..., m, m < < rank (E0),

6. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that the extraction major component in described step (5) stops judgment criterion employing coefficient of multiple determination criterion,

By statistic

Whether front h major component evaluating independent variable system has enough interpretabilities to dependent variable Y system.

Coefficient of multiple determination what measure is that the variation quantity of information that can be explained by the regression equation of front h major component structure accounts for the number percent of total variation, when h=m and coefficient of multiple determination value when enough large, can stop Principle component extraction in m step and calculate, wherein

At coefficient of multiple determination application in, reference statistical amount often

The size of value, what measure is the variation quantity of information that independent variable system X is extracted.

7. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that in described step (5), major component is as follows to the computation process of the interpretability of independent variable, dependent variable information:

Note ρ ²(y _j; t _h) be major component t _hwith dependent variable y _jsimple correlation coefficient square, t _hinterpretability to dependent variable system Y:

Rd(y _j；t _h)＝ρ ²(y _j；t _h) (1-11)

T ₁, t ₂..., t _maccumulative total interpretability to dependent variable system Y:

According to formula (1-11)～(1-13), calculate the interpretability of each major component to energy consumption dependent variable Y;

Note ρ ²(x _j; t _h) be major component t _hwith independent variable x _jsimple correlation coefficient square, t _hinterpretability to independent variable system X:

Rd(x _；；t _h)＝ρ ²(x _j；t _h) (1-14)

T ₁, t ₂..., t _maccumulative total interpretability to independent variable system X

According to formula (1-14)～(1-16), calculate the interpretability of each major component to energy consumption factor X.

8. the dredging operation energy consumption factor Principle component extraction method based on partial least square method according to claim 1, is characterized in that determining of major component number in described step (6) is as follows:

Draw respectively major component and major component to energy consumption dependent variable Y interpretability total information and energy consumption factor X interpretability total information histogram, find out Rd _h(Cum) in the time of>=85%, both major component numbers, then to it, both get major component number common factor, have so not only guaranteed that major component has good interpretability to energy consumption dependent variable Y, and have guaranteed the good interpretability that has of major component to energy consumption factor X.