CN106056274A - Power construction main body benefit analysis method based on PCA-DEA two-dimensional comprehensive evaluation model - Google Patents

Abstract

The invention discloses a power construction main body benefit analysis method based on a PCA-DEA (Principal Component Analysis-Data Envelopment Analysis) two-dimensional comprehensive evaluation model, comprising sample data pre-processing, and rejecting samples including abnormal data and missing data; utilizing a PCA method to screen principal component indexes from original sample data, and analyzing the internal resource transfer ability of power construction enterprises; using the principal component indexes as operating variables, selecting an input index and an output index, and using a DEA method to compare and analyze benefits among the power construction enterprises; and comparing evaluation results of two methods, respectively using a DEA evaluation value and a PCA evaluation value as an x-axis and a y-axis to establish a two-dimensional coordinate, and comprehensively evaluating and analyzing a power construction main body. The power construction main body benefit analysis method is simple and practical, and can be used as a main base for evaluating enterprise competitiveness and enterprise benefit distribution in a whole market, and provide a practical side reference for the integral analysis of an electric power engineering market.

Description

Principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model

Technical Field

The invention relates to the technical field of electric power engineering market analysis and evaluation, in particular to a power construction main body benefit analysis method based on a PCA-DEA two-dimensional comprehensive evaluation model.

Background

With the continuous development of economy in China, the power demand is continuously improved, and the investment scale of related power engineering is larger and larger. In order to standardize the order of the power engineering market, the state establishes a system of electric power facility license acceptance (repair, test). The regulation stipulates that enterprises entering the electric power engineering market must obtain the license of the electric power facility for accepting (repairing and testing) and accept the continuous supervision of the national energy agency (originally the national electric power supervision committee). The number of local (repair, trial) electric power facility certified enterprises in Guangdong province reaches 1049 as of 2013. The current monitoring means play a good role in the normative market, but have some problems and vacancies, particularly the research on the analysis method of the power engineering supply market under the condition of incomplete data. In the aspect of electric power engineering supply market, no effective channel exists, basic data are obtained mainly in the project by means of self-checking, investigation and the like, and the integrity and accuracy of the data are difficult to guarantee. How to obtain the most accurate analysis result about the market under the condition, how to scientifically and effectively evaluate the self capacity of the power construction enterprises and the relative benefits among the enterprises is a problem to be researched. For the main part of the electric power engineering market, namely, each electric power construction enterprise, the comprehensive benefits of the electric power construction enterprise are mainly evaluated, and the competitiveness of the enterprise and the benefit distribution of the enterprise in the whole market are judged by taking the comprehensive benefits as a main basis. The Principal Component Analysis (PCA) method and the Data Envelope Analysis (DEA) method each have some of their functions, and thus combining these two methods is obviously a comprehensive method suitable for evaluation of power construction enterprises.

Disclosure of Invention

The invention aims to overcome the defects in the prior art and provide a power construction main body benefit analysis method based on a PCA-DEA two-dimensional comprehensive evaluation model.

The purpose of the invention is realized by the following technical scheme:

a power construction main body benefit analysis method based on a PCA-DEA two-dimensional comprehensive evaluation model comprises the following steps:

s1, preprocessing collected sample data of the power construction enterprise, identifying abnormal data, and removing samples containing the abnormal data and missing data;

s2, screening principal component indexes from the original sample data by using a principal component analysis method, and analyzing the internal resource conversion capacity of the power construction enterprise;

s3, selecting input indexes and output indexes by taking the principal component indexes as operation variables, and performing benefit comparison analysis between electric power construction enterprises by using a data envelope analysis method;

and S4, comparing the evaluation results of the principal component analysis method and the data envelope analysis method, establishing a two-dimensional coordinate system by taking the evaluation values of the data envelope analysis method and the principal component analysis method as an abscissa axis and an ordinate axis respectively, and carrying out comprehensive evaluation and analysis of the power construction main body.

Preferably, the sample data preprocessing in step S1 includes the following sub-steps:

s11, dividing the electric power construction enterprise into five levels according to the level regulation of the license of the electric power facility for loading, maintaining or testing, and determining the interval range of each index in each level;

s12, traversing all electric power construction enterprises, checking whether each index of the enterprises is in an interval range corresponding to the license level of the enterprises, if the index value exceeds an interval boundary proportion threshold value, directly judging the index value to be abnormal data, and rejecting the enterprise sample;

and S13, identifying abnormal data of each index of the remaining enterprise samples by a RobustZ value method, and removing the enterprise samples containing the abnormal data.

Preferably, the step S2 includes the following sub-steps:

s21, reading a sample observation data matrix:

$X=\left(\begin{array}{cccc}{x}_{11}& x_{12}& \mathrm{...}& x_{1p}\\ x_{21}& x_{22}& \mathrm{...}& x_{2p}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ x_{n1}& x_{n2}& \mathrm{...}& x_{np}\end{array}\right),$

wherein x is_ijA value representing the jth index of the ith enterprise sample;

s22, carrying out standardization processing on the raw data:

$x_{ij}^{*}=\frac{x_{ij}-{\stackrel{\‾}{x}}_j}{\sqrt{\mathrm{var}\left(x_j\right)}},(i=1,2,...,n;j=1,2,...,p),$

wherein,

s23, calculating a sample correlation coefficient matrix:

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \mathrm{...}& r_{1p}\\ r_{21}& r_{22}& \mathrm{...}& r_{2p}\\ .& .& & .\\ .& .& \mathrm{...}& .\\ .& .& & .\\ r_{p1}& r_{p2}& \mathrm{...}& r_{pp}\end{array}\right],$

assume that x is still used after raw data normalization_ijAnd if the data is expressed, the correlation coefficient of the normalized data is as follows:

$r_{ij}=\frac{1}{n-1}\underset{t=1}{\overset{n}{\Σ}}{x}_{ti}{x}_{tj},(i,j=1,2,...,p);$

s24, calculating the eigenvalue (lambda) of the correlation coefficient matrix R by the Jacobian method₁,λ₂…λ_p) And corresponding feature vector a_i＝(a_i1,a_i2,…a_ip),i＝1,2…p；

S25, selecting the first k principal components according to the magnitude of the accumulated contribution rate of each principal component;

s26, calculating principal component scores, and respectively substituting the principal component scores into the principal component expressions according to the standardized original data and the sequence of each enterprise sample to obtain the principal component scores of each enterprise sample under each principal component, wherein the specific form is as follows:

F_i＝α₁x_i1+α₂x_i2+…+α_kx_ik,i＝1,2…n。

preferably, the step S3 includes the following sub-steps:

s31, selecting an input index and an output index of the DEA model from the principal component indexes in the step S2;

s32, setting n decision units and determining each DMU_jInput and output vectors of (j ═ 1,2, … n)

X_j＝(x_1j,x_2j,…,x_mj)^T＞0,j＝1,…,n；

Y_j＝(y_1j,y_2j,…,y_sj)^T＞0,j＝1,…,n；

S33, definition weight variable v ═ v (v)₁,v₂,…,v_m)^TAnd u ═ u (u)₁,u₂,…,u_s)^TStructure of DMU_j(j ═ 1,2, … n) benefit evaluation index

S34, for each DMU_j(j is 1,2, … n), finding E_jThe weight vector reaching the maximum value, to obtain C of DEA²Model R: for each DMU_j(j ═ 1,2, … n), the following maximization problem is constructed:

$\left\{\begin{array}{c}max\frac{{y_j}^{T}u}{{x_j}^{T}v}=E_j\\ s.t.\frac{{y_j}^{T}u}{{x_j}^{T}v}\≤1(1\≤j\≤n),u\≥0,v\≥0\end{array}\right.;$

s35, for the above-mentioned fractional planning problem, orderμ ═ tu, ω ═ t ν, then the following equivalent linear programming problem will be modeled:

$\left\{\begin{array}{c}\max {y_j}^T\mathrm{\μ}=E_j\\ s.t.{y_j}^T\mathrm{\μ}\≤{x_j}^T\mathrm{\ω}(1\≤j\≤n),{x_j}^T\mathrm{\ω}=1,\mathrm{\μ}\≥0\end{array}\right.;$

s36, to facilitate checking the validity of the DEA, consider the equation form of the dual model of the model:wherein s is^-＝(s₁ ^-，s₂ ^-，…，s_m ^-) Is a relaxation variable of the m-term input, s + ═(s)₁ ⁺，s₂ ⁺，…，s_s ⁺) Is a relaxation variable of the output of the s term, λ ═ λ₁,λ₂,…,λ_n) Is the combining coefficient of n DMUs, e₁ ^T＝(1，1，…，1)_m、e₂ ^T＝(1，1，…，1)_sIs a very small positive number;

s37, solving DEA model to obtain each DMU_jBenefit evaluation value E of (1, 2, … n, j)_j。

Preferably, the step S4 includes the following sub-steps:

s41, selecting proper principal component analysis boundary values and data envelope analysis boundary values according to the calculation results of the steps S2 and S3;

and S42, establishing a two-dimensional coordinate system by using the data envelope analysis score as a horizontal coordinate and the principal component analysis score as a vertical coordinate, and distributing all samples on the two-dimensional coordinate system according to the scores of the samples to perform comprehensive analysis.

Preferably, the contribution ratio refers to a ratio of a variance of a certain principal component to a total variance: $GX=\frac{{\mathrm{\λ}}_i}{\underset{i=1}{\overset{p}{\Σ}}{\mathrm{\λ}}_i}.$

preferably, the number k of the principal components is determined according to the accumulated contribution rate of the principal components, and the accumulated contribution rate is required to reach more than 85%, so that the comprehensive variables can include most information of the original variables.

Preferably, the main components include net assets (unit: ten thousand yuan), equipment total amount (unit: table), technical strength (unit: man), network access electricians (unit: man) and engineering performance (unit: ten thousand yuan).

Preferably, the threshold value of the interval boundary proportion is 15%.

Compared with the prior art, the invention has the following advantages and effects:

1. the regional power grid load margin analysis method based on the primal-dual interior point method is simple, practical, free of iterative computation, high in solving speed, capable of screening out key indexes and data under the condition of incomplete data and capable of analyzing the power engineering supply market.

2. The method can be used for scientifically and effectively evaluating the self capacity of the power construction enterprises and the relative benefits of the enterprises. And the evaluation result is displayed in a two-dimensional scale, so that the method is intuitive and clear, and the competitiveness of the enterprise and the benefit distribution of the enterprise in the whole market are judged by taking the evaluation result as a main basis.

Drawings

FIG. 1 is a flow chart of a principal benefit analysis method for power construction based on a PCA-DEA two-dimensional comprehensive evaluation model disclosed in the present invention;

FIG. 2 is a graph of sample performance of an enterprise as a function of project performance in an embodiment;

FIG. 3 is a graph illustrating the number of samples of an enterprise with different profit values according to an embodiment;

FIG. 4 is a two-dimensional comprehensive analysis chart of DEA/PCA of the enterprise sample in the example.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Examples

As shown in fig. 1, the embodiment discloses a power construction main body benefit analysis method based on a PCA-DEA two-dimensional comprehensive evaluation model, which includes the following steps:

s1, preprocessing collected sample data of the power construction enterprise, identifying abnormal data, and removing samples containing the abnormal data and missing data;

s2, screening principal component indexes from the original sample data by using a Principal Component Analysis (PCA) method, and analyzing the internal resource conversion capacity of the power construction enterprise;

s3, selecting an input index and an output index by using the principal component indexes as operation variables, and performing benefit comparison analysis between power construction enterprises by using a Data Envelope Analysis (DEA) method;

and S4, comparing the results (score and sequencing) respectively evaluated by the two methods, establishing a two-dimensional coordinate screen by respectively taking the evaluation values of DEA and PCA as the abscissa and ordinate axes, and performing comprehensive evaluation and analysis on the electric power construction main body.

The main part of the electric power engineering market, namely, each electric power construction enterprise, mainly evaluates the comprehensive benefits of the electric power construction enterprise, and judges the competitiveness of the enterprise and the benefit distribution of the enterprise in the whole market by taking the comprehensive benefits as a main basis. The electric power construction main body benefit analysis method is developed based on mature mathematical theory and stable mathematical programming algorithm, is high in calculation speed and good in stability, and is a simple, convenient and practical analysis method.

Specifically, the method comprises the following steps:

step S1, sample data preprocessing, which comprises the following steps:

and S11, dividing the power construction enterprise into five levels, namely a first level, a second level, a third level, a fourth level and a fifth level according to the level specification of the license of the power facility for bearing (repairing and testing), wherein the first level is the highest level, and the fifth level is the lowest level. Determining the interval range of each index in each grade;

and S12, traversing all the electric power construction enterprises, and checking whether each index of the electric power construction enterprises is in the range corresponding to the license level. If the index value exceeds the interval boundary by more than 15%, directly judging the index value to be abnormal data, and rejecting the enterprise sample;

and S13, for the remaining enterprise samples, identifying abnormal data of each index by adopting a RobustZ value method (robust statistical method), and removing the enterprise samples containing the abnormal data. In the embodiment, enterprises with five-level qualification are selected as analysis samples, and the result of sample data preprocessing is shown in table 1. Table 1 shows the results of the enterprise sample data preprocessing in this embodiment.

TABLE 1

Qualification of an enterprise	Total number of data	Deleting data	Residual data	Remaining data fraction
					Five stages	344	26	318	92.44％

S2, screening principal component indexes from the original sample data by using a Principal Component Analysis (PCA) method, and analyzing the internal resource conversion capacity of the power construction enterprise, wherein the method specifically comprises the following steps:

s21, reading a sample observation data matrix:

$X=\left(\begin{array}{cccc}{x}_{11}& x_{12}& \mathrm{...}& x_{1p}\\ x_{21}& x_{22}& \mathrm{...}& x_{2p}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ x_{n1}& x_{n2}& \mathrm{...}& x_{np}\end{array}\right),$

wherein x is_ijA value representing the jth index of the ith enterprise sample;

s22, carrying out standardization processing on the raw data:

$x_{ij}^{*}=\frac{x_{ij}-{\stackrel{\‾}{x}}_j}{\sqrt{\mathrm{var}\left(x_j\right)}},(i=1,2,...,n;j=1,2,...,p),$

wherein,

s23, calculating a sample correlation coefficient matrix:

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \mathrm{...}& r_{1p}\\ r_{21}& r_{22}& \mathrm{...}& r_{2p}\\ .& .& & .\\ .& .& \mathrm{...}& .\\ .& .& & .\\ r_{p1}& r_{p2}& \mathrm{...}& r_{pp}\end{array}\right],$

for convenience, assume that x is still used after raw data is normalized_ijAnd if the data is expressed, the correlation coefficient of the normalized data is as follows:

$r_{ij}=\frac{1}{n-1}\underset{t=1}{\overset{n}{\Σ}}{x}_{ti}{x}_{tj},(i,j=1,2,...,p);$

s24, calculating the eigenvalue (lambda) of the correlation coefficient matrix R by the Jacobian method₁,λ₂…λ_p) And corresponding feature vector a_i＝(a_i1,a_i2,…a_ip),i＝1,2…p；

S25, selecting the first k principal components according to the accumulated contribution rate of each principal component, wherein the contribution rate refers to the proportion of the variance of a certain principal component in the total variances:

the selection of the number k of the principal components is mainly determined according to the accumulated contribution rate of the principal components, and the accumulated contribution rate is required to reach more than 85 percent, so that the comprehensive variables can comprise most of information of the original variables. Analyzing the implementation example, and selecting five indexes of net assets (ten thousand yuan), equipment total amount (platform), technical strength (people), network access electricians (people) and engineering performance (ten thousand yuan) as main components;

and S26, calculating the principal component score. And respectively substituting the main component expressions according to the standardized original data and the sequence of each enterprise sample, so as to obtain the main component score of each enterprise sample under each main component. The concrete form is as follows:

F_i＝α₁x_i1+α₂x_i2+…+α_kx_iki is 1,2 … n. In this embodiment, some enterprises are selected and listed as principal component indicators and PCA scores as shown in table 2. Table 2 shows the principal component index and PCA score of some of the enterprise samples in this example.

TABLE 2

Step S3, selecting input indexes and output indexes by using the principal component indexes as operation variables, and performing benefit comparison analysis between electric power construction enterprises by using a Data Envelope Analysis (DEA) method, wherein the method specifically comprises the following steps:

and S31, selecting net assets, equipment total amount, technical strength and the number of net electricians to enter the network as input indexes, and selecting engineering performance as output indexes.

S32, 318 decision units are totally arranged, and each DMU is determined_jInput and output vectors of (j-1, …,318)

X_j＝(x_1j,x_2j,…,x_mj)^T＞0,j＝1,…,318

Y_j＝(y_1j,y_2j,…,y_sj)^T＞0,j＝1,…,318；

S33, definition weight variable v ═ v (v)₁,v₂,…,v_m)^TAnd u ═ u (u)₁,u₂,…,u_s)^TStructure of DMU_j(j ═ 1,2, … n) benefit evaluation index

S34, for each DMU_j(j is 1,2, … n), finding E_jThe weight vector that reaches the maximum value. Thus obtaining C of DEA²Model R: for each DMU_j(j ═ 1,2, … n), the following maximization problem is constructed:

$\left\{\begin{array}{c}max\frac{{y_j}^{T}u}{{x_j}^{T}v}=E_j\\ s.t.\frac{{y_j}^{T}u}{{x_j}^{T}v}\≤1(1\≤j\≤n),u\≥0,v\≥0\end{array}\right.;$

s35, for the above-mentioned fractional planning problem, orderμ ═ tu, ω ═ t ν, then the following equivalent linear programming problem will be modeled:

$\{\begin{array}{c}\max {y_j}^T\mathrm{\μ}=E_j\\ s.t.{y_j}^T\mathrm{\μ}\≤{x_j}^T\mathrm{\ω}(1\≤j\≤n),{x_j}^T\mathrm{\ω}=1,\mathrm{\μ}\≥0\end{array};$

s36, to facilitate the verification of the validity of the DEA, consider the equation form of the dual model of the model (with relaxation variables and with non-archimedes infinitesimal):wherein s is^-＝(s₁ ^-,s₂ ^-，…，s_m ^-) Is the relaxation variable of the m-term input; s⁺＝(s₁ ⁺,s₂ ⁺，…，s_s ⁺) Is the relaxation variable of the s term output; λ ═ λ₁,λ₂,…,λ_n) Is the combining coefficient of n DMUs; e.g. of the type₁ ^T＝(1,1,…,1)_m、e₂ ^T＝(1,1,…,1)_sIs a very small positive number;

s37, solving DEA model to obtain each DMU_jBenefit evaluation value E of (1, 2, … n, j)_j. For this example, some of the enterprises were selected and the DEA scores thereof were listed as shown in table 3. Table 3 shows the DEA score values of some of the enterprise samples in this example.

TABLE 3

Enterprise number	18	32	43	49	65	68	87	198
									Clean assets	1162	5279	2055	7724	13012	1269	1180	122
Total amount of equipment	319	53	67	135	195	169	162	103
									Technical strength	12	5	9	12	7	8	8	14
Number of electrician entering network	11	10	9	11	10	10	10	18
									Engineering achievement	3903	4448	4839	12763	19821	23662	34327	3630
Efficiency of enterprise	0.115	0.396	0.341	0.446	0.660	0.689	1	1

The variation of the enterprise profits with the engineering performance is shown in fig. 2, and the benefit distribution listing all enterprise samples is shown in fig. 3.

S4, comparing the results (score and sequencing) of the two methods, respectively establishing a two-dimensional coordinate screen by taking the evaluation values of DEA and PCA as the abscissa and ordinate axes, and carrying out comprehensive evaluation and analysis on the electric power construction main body, wherein the specific steps comprise:

s41, listing PCA and DEA scores for the first 11 business samples are shown in table 4. Table 4 shows the PCA score and DEA score of the first 11 business samples in this example.

TABLE 4

Based on the calculation results of steps S2 and S3, 0.5DEA value and 0.5PCA value are selected as the limits.

S42, establishing a two-dimensional coordinate system by taking the DEA score as the abscissa and the PCA score as the ordinate, and distributing all samples on a two-dimensional screen according to the scores to obtain a DEA/PCA two-dimensional comprehensive analysis chart of the enterprise, wherein the DEA/PCA two-dimensional comprehensive analysis chart is shown in figure 4. As can be seen from the figure, the enterprises in the upper right area (high comprehensive benefit area) have 26 families, the evaluation results of DEA and PCA of the enterprises belong to high levels, which shows that the enterprises have significant effectiveness relative to other enterprises, the enterprises have higher input-output ratio than the comprehensive indexes with the largest difference, the relative benefits are excellent, and the enterprise market competitiveness and the enterprise resource conversion capability of the area are higher. But such businesses are relatively few.

The upper left area and the lower right area are defined as a middle comprehensive benefit area. The enterprises in the upper left area have 11 enterprises, the DEA evaluation scores are low, but the PCA scores are high, which shows that the enterprises in the area have poor relative benefits, but the investment and production with the largest difference are better than the comprehensive indexes, the competitiveness is poor in the process of competing with other enterprises, and the internal resource conversion capacity of the enterprises is high. Enterprises in the lower right area are opposite, DEA scores reflecting relative effectiveness are high, but the DEA scores are poorer in the input-output ratio with the largest difference than the comprehensive indexes, the competitiveness is strong in the process of competing with other enterprises, the conversion capability of resources in the enterprises is not high, and the enterprises have 27 in total. Enterprises in the two areas should be further analyzed according to specific situations, and if a decision maker wants to take DEA evaluation as the main and PCA evaluation as the auxiliary, enterprises falling in the right area should be selected for analysis.

The rest of enterprises are all located in the lower left area (low comprehensive benefit area), the number of enterprises in the area is the largest, the area is an area with smaller DEA and PCA values, namely the area with poorer overall benefits of the enterprises, and the enterprise market competitiveness and the enterprise resource conversion capability of the area are lower.

The rest of the enterprises are all in the lower left area, the number of the enterprises in the area is the largest, and the area is an area with smaller DEA and PCA values, namely an area with poorer overall enterprise benefit. This shows that the overall benefit of the construction enterprises in the electric power engineering market of the embodiment is low, and the reason may be that the number of construction enterprises at the present stage is too large, and the quantity of the projects to be built is too small, which causes the phenomenon of "more and less congealed", thereby causing the low general benefit of the construction enterprises.

The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (9)

1. A power construction main body benefit analysis method based on a PCA-DEA two-dimensional comprehensive evaluation model is characterized by comprising the following steps:

s1, preprocessing collected sample data of the power construction enterprise, identifying abnormal data, and removing samples containing the abnormal data and missing data;

s2, screening principal component indexes from the original sample data by using a principal component analysis method, and analyzing the internal resource conversion capacity of the power construction enterprise;

s3, selecting input indexes and output indexes by taking the principal component indexes as operation variables, and performing benefit comparison analysis between electric power construction enterprises by using a data envelope analysis method;

and S4, comparing the evaluation results of the principal component analysis method and the data envelope analysis method, establishing a two-dimensional coordinate system by taking the evaluation values of the data envelope analysis method and the principal component analysis method as an abscissa axis and an ordinate axis respectively, and carrying out comprehensive evaluation and analysis of the power construction main body.

2. The principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model according to claim 1, wherein the sample data preprocessing in the step S1 comprises the following sub-steps:

s11, dividing the electric power construction enterprise into five levels according to the level regulation of the license of the electric power facility for loading, maintaining or testing, and determining the interval range of each index in each level;

s12, traversing all electric power construction enterprises, checking whether each index of the enterprises is in an interval range corresponding to the license level of the enterprises, if the index value exceeds an interval boundary proportion threshold value, directly judging the index value to be abnormal data, and rejecting the enterprise sample;

and S13, identifying abnormal data of each index of the remaining enterprise samples by a RobustZ value method, and removing the enterprise samples containing the abnormal data.

3. The principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 1, wherein the step S2 comprises the following sub-steps:

s21, reading a sample observation data matrix:

wherein x is_ijA value representing the jth index of the ith enterprise sample;

s22, carrying out standardization processing on the raw data:

wherein,

s23, calculating a sample correlation coefficient matrix:

assume that x is still used after raw data normalization_ijAnd if the data is expressed, the correlation coefficient of the normalized data is as follows:

s24, calculating the eigenvalue (lambda) of the correlation coefficient matrix R by the Jacobian method₁,λ₂…λ_p) And corresponding feature vector a_i＝(a_i1，a_i2，…a_ip),i＝1,2…p；

S25, selecting the first k principal components according to the magnitude of the accumulated contribution rate of each principal component;

s26, calculating principal component scores, and respectively substituting the principal component scores into the principal component expressions according to the standardized original data and the sequence of each enterprise sample to obtain the principal component scores of each enterprise sample under each principal component, wherein the specific form is as follows:

F_i＝α₁x_i1+α₂x_i2+…+α_kx_ik,i＝1,2…n。

4. the principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 1, wherein the step S3 comprises the following sub-steps:

s31, selecting an input index and an output index of the DEA model from the principal component indexes in the step S2;

s32, setting n decision units and determining each DMU_jInput and output vectors of (j ═ 1,2, … n)

S33, definition weight variable v ═ v (v)₁,v₂,…,v_m)^TAnd u ═ u (u)₁,u₂,...,u_s)^TStructure of DMU_j(j ═ 1,2, … n) benefit evaluation index

S34, for each DMU_j(j is 1,2, … n), finding E_jThe weight vector reaching the maximum value, to obtain C of DEA²Model R: for each DMU_j(j ═ 1,2, … n), the following maximization problem is constructed:

s35, for the above-mentioned fractional planning problem, orderμ ═ tu, ω ═ t ν, then the following equivalent linear programming problem will be modeled:

s36, to facilitate checking the validity of the DEA, consider the equation form of the dual model of the model:wherein s is^-＝(s₁ ^-，s₂ ^-，…，s_m ^-) Is a relaxation variable of the m-term input, s⁺＝(s₁ ⁺，s₂ ⁺，…，s_s ⁺) Is a relaxation variable of the output of the s term, λ ═ λ₁,λ₂,…,λ_n) Is the combining coefficient of n DMUs, e₁ ^T＝(1，1，…，1)_m、e₂ ^T＝(1，1，…，1)_sIs a very small positive number;

s37, solving DEA model to obtain each DMU_jBenefit evaluation value E of (1, 2, … n, j)_j。

5. The principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 1, wherein the step S4 comprises the following sub-steps:

s41, selecting proper principal component analysis boundary values and data envelope analysis boundary values according to the calculation results of the steps S2 and S3;

and S42, establishing a two-dimensional coordinate system by using the data envelope analysis score as a horizontal coordinate and the principal component analysis score as a vertical coordinate, and distributing all samples on the two-dimensional coordinate system according to the scores of the samples to perform comprehensive analysis.

6. The principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 3,

the contribution ratio refers to the proportion of the variance of a certain principal component in the total variances:

7. the principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 6,

the number k of the principal components is selected according to the accumulated contribution rate of the principal components, and the accumulated contribution rate is required to reach more than 85%, so that the comprehensive variables can comprise most of information of the original variables.

8. The principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 3,

the main components comprise net assets, equipment total amount, technical strength, network access electricians and engineering performance.

9. The principal benefit analysis method for power construction based on PCA-DEA two-dimensional comprehensive evaluation model as claimed in claim 2,

the interval boundary proportion threshold is 15%.