A transformer quality assurance capability evaluation method of weight combination optimization

Abstract

The application discloses a transformer quality assurance capability evaluation method based on weight combination optimization, which comprises the following steps: 1, obtaining indexes for evaluating transformer quality assurance capability; 2, determining the relative importance weight of the transformer quality assurance capability evaluation indexes based on the priority graph method; 3, obtaining sample data corresponding to the transformer quality assurance capability evaluation indexes and determining the information weight of the indexes based on the coefficient of variation method; 4, establishing an optimization model integrating the two transformer quality assurance capability evaluation index weights; 5, solving the weight optimization model; 6, calculating the evaluation values of all transformer samples; 7, selecting the supplier of the transformer sample product corresponding to the highest evaluation value. The application is based on the deviation minimum principle of the evaluation value under subjective and objective weighting, reduces the defects of a single weighting method in use, and the index weight obtained by the method is obviously higher than the original details in the discrimination of the evaluation result, thereby providing a reference for more accurately evaluating the quality of the transformer.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power transmission and transformation equipment quality supervision, and particularly relates to a transformer quality assurance capability evaluation method based on weight combination optimization. BACKGROUND

[0002] As the core equipment for voltage conversion in the power system, the quality of the transformer directly affects the safe and stable operation of the large power grid and the power supply reliability of the regional power grid. The transformer quality assurance capability evaluation is to evaluate the consistency of the product design of the transformer equipment with the relevant technical requirements in the existing national standards, industry standards, enterprise standards and accident prevention measures, and to evaluate the design process of the transformer product in the aspects of electric field design, winding impedance short circuit capability design, magnetic field design, temperature field design, assembly design, anti-vibration design and mechanical strength design, which is an important research topic for improving the quality and safety level of the transformer equipment in the grid, and is widely used in the State Grid system. At present, the transformer quality assurance capability evaluation adopts a fixed weighting method in the aspects of electric field design, winding impedance short circuit capability design, magnetic field design, temperature field design, assembly design, anti-vibration design and mechanical strength design, mainly relies on historical evaluation experience and artificial subjective opinions, lacks systematicness and scientificity, and is difficult to meet the needs of the new power system for transformer quality evaluation and selection. In order to more truly and comprehensively reflect the actual situation of the transformer and evaluate the quality assurance capability of the transformer, many standardization institutions have stipulated the relevant existing standards of the power transformer, but at the same time, the problem of how to reasonably allocate the weights of different evaluation indexes is also faced. Unreasonable weight allocation may lead to distorted evaluation results and affect the accuracy of the transformer quality assurance capability evaluation. Therefore, it is particularly important to study a scientific optimization weighting method for the evaluation index weight of the transformer quality assurance capability.

[0003] There are several problems in the existing transformer quality assurance capability evaluation indexes and their weight allocation, first, the subjectivity of index weight allocation is strong, the existing subjective weighting methods mainly rely on expert experience or historical data, the subjective factors are large, different evaluators may obtain different weight allocation schemes, the weights given by the existing objective weighting methods have absolute objectivity, which may be inconsistent with the economic (or technical) value of the existing indexes, at the same time, once the sample changes, the corresponding weight will also change, which all lead to poor consistency and repeatability of the evaluation results. Second, the existing methods lack sufficient theoretical support and data verification when determining the weight, so that the weight setting is not scientific and reasonable. Third, the results of the evaluation work using the existing index weight allocation scheme according to the existing evaluation rules show that the final evaluation values of the transformers of each supplier have low discrimination, and cannot provide more valuable reference for the selection of transformer suppliers. The existence of these problems limits the efficiency and results of the transformer quality assurance capability evaluation. SUMMARY

[0004] The present application aims at solving the above-mentioned deficiencies and defects, and proposes a transformer quality assurance capability evaluation method with weight combination optimization, which aims at determining the most appropriate evaluation index weight, thereby improving the accuracy and reliability of the quality assurance capability evaluation results, so as to ensure that the transformer with reliable and stable operation can be selected.

[0005] In order to achieve the above-mentioned application purposes, the present application adopts the following technical solutions:

[0006] The transformer quality assurance capability evaluation method with weight combination optimization has the following characteristics:

[0007] S1, obtaining n evaluation indexes for evaluating the transformer quality assurance capability, wherein let indicates the jth transformer quality assurance capability evaluation index, and n indicates the total number of evaluation indexes, ;

[0008] S2, determining the relative importance weight vector of the transformer quality assurance capability evaluation index based on the priority graph method ; wherein, indicates the relative importance weight of , and T indicates the transpose;

[0009] S3, obtaining the sample data corresponding to the transformer quality assurance capability evaluation index, and determining the information weight vector of the transformer quality assurance capability evaluation index based on the coefficient of variation method ; wherein, indicates the information weight corresponding to ;

[0010] S4, establishing a weight optimization model that fuses the relative importance weight and the information weight by using formula (8) and formula (9):

[0011] (8)

[0012] (9)

[0013] In formula (8), is a weight coefficient, indicates the weight corresponding to ; indicates the objective function of the combined weight optimization model, indicates the weight vector; indicates the ith transformer sample after standardization processing corresponding to ;

[0014] S5, solving the weight optimization model to obtain the optimized weight vector wherein, denotes the corresponding optimized weight;

[0015] S6, calculating the quality assurance capability evaluation value of the i-th transformer sample by using formula (10) , thereby obtaining the quality assurance capability evaluation values of the m transformer samples;

[0016] (10)

[0017] S7, after sorting the quality assurance capability evaluation values of the m transformer samples in descending order, selecting the suppliers of the transformer samples corresponding to the first s quality assurance capability evaluation values as the transformer suppliers with the optimal quality.

[0018] The transformer quality assurance capability evaluation method with weight combination optimization provided by the application is also characterized in that S2 comprises:

[0019] S2.1, initializing the transformer quality assurance capability evaluation indexes ; , thereby obtaining the average score values of the n indexes;

[0020] S2.2, comparing the average score values of the n transformer quality assurance capability evaluation indexes to obtain the relative importance degrees of the indexes, thereby constructing a relative importance judgment matrix with the dimension of n x n;

[0021] S2.3, adding all the column elements of each row of the relative importance judgment matrix respectively, thereby obtaining the relative importance vectors of the n transformer quality assurance capability evaluation indexes and the relative importance sum , wherein, denotes the relative importance vector of the j-th transformer quality assurance capability evaluation index ; and = , wherein, is the element of the r-th row and the j-th column in the relative importance judgment matrix, and denotes the relative importance degree between the r-th transformer quality assurance capability evaluation index and the j-th transformer quality assurance capability evaluation index ;

[0022] S2.4, obtaining the relative importance weight of the transformer quality assurance capability evaluation index by using formula (1) , thereby obtaining the relative importance weight vector of the n transformer quality assurance capability evaluation indexes as ;

[0023] (1)

[0024] In equation (1), ,and , ; This represents the evaluation index of the quality assurance capability of the r-th transformer. The relative importance vector.

[0025] Furthermore, S3 includes:

[0026] S3.1 Obtain the quality assurance capability assessment index for the j-th transformer. The corresponding m transformer samples, thus constructing a dimension of Transformer sample matrix ,in, This represents the evaluation index of the quality assurance capability of the j-th transformer. The corresponding i-th transformer sample;

[0027] S3.2, Using formula (2) After performing forward processing, the j-th transformer quality assurance capability evaluation index is obtained. The corresponding forward-processed i-th transformer sample Thus, the transformer sample matrix after positive transformation is obtained. ';

[0028] (2)

[0029] In equation (2), k is a coefficient. express The corresponding m transformer samples, express The absolute value;

[0030] S3.3, Using formula (3) Standardization process is performed to obtain The corresponding standardized i-th transformer sample Thus, the standardized transformer sample matrix R is obtained;

[0031] (3)

[0032] S3.4, Calculate using formula (4) The mean of the corresponding m transformer samples Calculate using equation (5) The standard deviation of the corresponding m transformer samples Therefore, equation (6) is used to calculate coefficient of variation ;

[0033] (4)

[0034] (5)

[0035] (6)

[0036] S3.5, calculating by formula (7) the corresponding information weight , so that the information weight vector of n transformer quality assurance capability evaluation indexes is ;

[0037] (7)

[0038] In formula (7), , and , .

[0039] Further, the step S5 is solved as follows to obtain :

[0040] S5.1, calculating the sum of squares of the corresponding m transformer samples , so that the sum of squares of n evaluation indexes is obtained, and then the diagonal matrix , is obtained by formula (11)

[0041] (11)

[0042] S5.2, calculating the deviation of the evaluation value of according to formula (8) , so that the deviation of n evaluation indexes is calculated, and then the vector is constructed by formula (12)

[0043] (12)

[0044] S5.3, obtaining by formula (13)

[0045] (13)

[0046] In formula (13), represents a column vector of dimension n with all elements being 1.

[0047] The electronic equipment comprises a memory and a processor, and is characterized in that the memory is used for storing a program supporting the processor to execute the transformer quality assurance capability evaluation method, and the processor is configured to execute the program stored in the memory.

[0048] The computer readable storage medium stores a computer program, and the computer program is characterized in that when the computer program is run by a processor, the steps of the transformer quality assurance capability evaluation method are executed.

[0049] Compared with the prior art, the transformer quality assurance capability evaluation method has the following beneficial effects:

[0050] 1. The transformer quality assurance capability evaluation method considers the defects of various index weighting methods in the existing transformer quality assurance capability evaluation process, and adopts a subjective and objective combined weighting method to balance the index value and the specific data value in the process of optimizing the transformer quality assurance capability evaluation index weight system, thereby reducing the deviation caused by a single method. The method provides improvement suggestions and scientific references for optimizing the transformer quality assurance capability evaluation index system, and will help users to more accurately evaluate the design quality of the transformer and timely find potential problems.

[0051] 2. The method used in the transformer quality assurance capability evaluation method promotes the flexibility of the transformer quality assurance capability evaluation process. The proposed method can be flexibly adjusted according to the actual situation of transformers of different models and suppliers, can quickly adapt to the application of new technologies on products, and can also be personalized in weight assignment for different types of transformers, and has a wide range of application scenarios. At the same time, for the evaluation party, the method reduces the consumption of computing resources and time, and enhances the efficiency and convenience of the transformer quality assurance capability evaluation work.

[0052] 3. The transformer quality assurance capability evaluation method considers improving the discrimination of the evaluation results of different evaluation objects on the basis of the existing transformer quality assurance capability evaluation scheme. By combining the historical cognition of the relative importance of the transformer quality assurance capability evaluation index in the enterprise, collecting and utilizing the statistical characteristics of the data, the obtained index weight can more finely distinguish the differences in the quality assurance capability of different transformers, and strengthen the capture of specific features between the design of transformer products of different suppliers, which ensures that the transformer quality assurance capability evaluation index is scientific and close to the actual demand in the weighting process, and can also improve the discrimination of the evaluation results of transformers of different suppliers, which is more conducive to the selection of the evaluation party. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The transformer quality assurance capability evaluation method flowchart of the weight combination optimization of the present application;

[0054] Figure 2 This is a flowchart illustrating the process of determining the weights of transformer quality assurance capability assessment indicators based on the pecking order graph method of the present invention.

[0055] Figure 3 This is a flowchart illustrating the determination of the weights of transformer quality assurance capability assessment indicators based on the coefficient of variation method according to the present invention. Detailed Implementation

[0056] In this embodiment, a weighted combination optimization method for evaluating transformer quality assurance capability is proposed, such as... Figure 1 As shown, firstly, appropriate indicators are determined based on existing documents such as the "Implementation Rules for Technical Compliance Assessment of 220 kV Transformers of State Grid Corporation of China" and the "Implementation Rules for Technical Compliance Assessment of 330~750 kV Transformers of State Grid Corporation of China". Secondly, based on the determined indicators, the relative importance weights of the indicators are determined using the pecking order method. Subsequently, sample data corresponding to the transformer quality assurance capability assessment indicators are obtained, and the information weights of the indicators are determined using the coefficient of variation method. Then, based on the weight optimization results obtained using the pecking order method and the coefficient of variation method, and based on the least squares calculation, the optimized indicator weights for each assessment indicator under the combined assessment method are calculated according to the principle of minimizing the deviation between the assessment values ​​under the fusion method and the subjective and objective weighting. Next, the assessment value of each transformer sample to be assessed is calculated based on the calculated optimized weights of each indicator. Finally, a suitable transformer supplier is selected based on the calculated assessment values. Specifically, the steps of this weight combination optimization method for assessing transformer quality assurance capability are as follows:

[0057] S1. Obtain n indicators for evaluating the transformer's quality assurance capability, among which, Let represent the j-th indicator used to assess the transformer's quality assurance capability, and n represent the total number of indicators. In this embodiment, the set of quality assurance capability indicators used to evaluate n transformers is determined based on existing regulations such as the "Implementation Rules for Technical Compliance Assessment of 220 kV Transformers of State Grid Corporation of China" and the "Implementation Rules for Technical Compliance Assessment of 330~750 kV Transformers of State Grid Corporation of China".

[0058] In this embodiment, indicators are selected based on four sources: quality management theory, quality problem tracing, supplier best practices, and company quality control requirements. This results in 13 indicators for evaluating the quality assurance capability of transformers, including: poor design selection, electric field design, winding impedance short-circuit capability design, magnetic field design, temperature field design, assembly design, seismic design, mechanical strength design, DC bias magnetic tolerance capability design, overload capability design, non-electrical quantity protection design, design process management, and R&D design tools.

[0059] S2, such as Figure 2 As shown, the relative importance weight vector of the transformer quality assurance capability assessment index is determined based on the pecking order graph method.

[0060] S2.1. Based on the scenario and requirements for using the priority graph method, it is necessary to obtain scoring data that includes the evaluator's perception of the relative importance of the quality assurance capability evaluation indicators for n transformers, and initialize the indicators. average score Thus, the average score of n indicators is obtained;

[0061] S2.2 The average scores of n transformer quality assurance capability assessment indicators are compared to determine the relative importance of each indicator. Specifically, a judgment matrix is ​​established for pairwise comparisons of different indicators. The first row and first column of the matrix list all indicators sequentially, and the numbers in the intersecting squares represent the results of pairwise comparisons. "0" and "1" represent the importance of the indicators: "1" indicates that the indicator is relatively more important in the pairwise comparison, and "0" represents that it is relatively less important. If the two indicators are roughly equal, a score of 0.5 is assigned. and ,like ,but Corresponding rows and The score of the cell where the corresponding columns intersect should be 1. Corresponding columns and The score of the cell where the corresponding row intersects should be 0; if ,but Corresponding rows and The cells where the corresponding columns intersect. Corresponding columns and The scores of the cells where the corresponding rows intersect are all 0.5, thus constructing a relative importance judgment matrix with dimensions n×n.

[0062] S2.3. After summing all column elements of each row of the judgment matrix, the total superiority number of the n indicators is obtained. and the sum of the total superior ordinal numbers ,in, This represents the evaluation index of the quality assurance capability of the j-th transformer. The relative importance vector; and = ,in, The element in the r-th row and j-th column of the relative importance judgment matrix represents the r-th transformer quality assurance capability assessment index. Evaluation indicators of the quality assurance capability of the j-th transformer The relative importance of them.

[0063] S2.4, obtaining the relative importance weight of formula (1) , so that the relative importance weight vector of n transformer quality assurance capability evaluation indexes is ;

[0064] (1)

[0065] In formula (1), , and , , The relative importance vector of the rth transformer quality assurance capability evaluation index .

[0066] S3, as shown in Figure 3 , the sample data corresponding to the transformer quality assurance capability evaluation index is obtained, and the information weight vector of the transformer quality assurance capability evaluation index is determined based on the coefficient of variation method;

[0067] S3.1, according to the scene and requirement of the coefficient of variation method, it is necessary to obtain the m transformer samples corresponding to the jth transformer quality assurance capability evaluation index from the original data of each transformer quality assurance capability evaluation index.

[0068] S3.1.1, for the 10 evaluation indexes for evaluating the quality assurance capability of 220kV-750kV transformer products, including: design selection is bad, winding impedance short circuit capacity design, temperature field design, assembly design, anti-seismic design, mechanical strength design, DC bias magnetic resistance design, overload capacity design, non-electric quantity protection design, design process management index, specific sample data is obtained by using file checking method; wherein the file checking method: the design data related to the jth transformer quality assurance capability evaluation index is reviewed to obtain the corresponding data under each evaluation index; further, in an evaluation period, 2 contracts are drawn for transformers of different voltage grades, and if there are less than 2 contracts, all are drawn, and the data is obtained by checking the reports of the transformers corresponding to the 2 contracts. For example, for the design selection is bad index, whether the design selection is bad can be judged by checking the transformer assembly design report, and the sample data corresponding to the index is obtained, and the same is true for other indexes and will not be repeated here.

[0069] ​S3.1.2, for the acquired 3 evaluation indexes for evaluating the quality assurance capability of 220kV-750kV transformer products, including: electric field design, magnetic field design, and R&D design tool index, specific sample data is obtained by file checking and on-site verification method; wherein, the on-site verification method: on-site review of the jth transformer quality assurance capability evaluation index The actual design performance of the related equipment and the key parts are obtained to obtain the corresponding sample data under each evaluation index;

[0070] S3.1.3, based on the obtained data, the original sample data matrix of the evaluation index is composed of design selection failure, electric field design, winding impedance short circuit capability design, magnetic field design, temperature field design, assembly design, anti-seismic design, mechanical strength design, DC bias magnetic resistance capability design, overload capability design, non-electric quantity protection design, design process management, and R&D design tool, thereby constructing an original sample data matrix of transformer samples with dimensions , wherein, represents the jth transformer quality assurance capability evaluation index corresponding to the data of the ith transformer sample.

[0071] S3.2, in this embodiment, in order to convert all indexes to positive indexes, all indexes need to be normalized, then formula (2) is used for normalization processing to obtain the jth evaluation index corresponding to the ith transformer sample , thereby obtaining the data matrix of the evaluation index of the processed transformer sample ;

[0072] (2)

[0073] In formula (2), k is a coefficient, and the value is generally 0.1 or 0.2, etc.; represents the jth evaluation index corresponding to the m transformer samples, represents the absolute value.

[0074] S3.3, in this embodiment, for different indexes, the units of the corresponding data may be different, therefore, data standardization is needed to eliminate the influence of different units, so that the evaluation values of all sample data can be compared. Formula (3) is used for standardization processing to obtain the jth evaluation index corresponding to the specific data of the ith transformer sample , thereby obtaining the standardized transformer sample data matrix R;

[0075] (3)

[0076] S3.4. Based on the standardized data, calculate the j-th evaluation index using equation (4). The mean of the specific data of the m transformer samples to be evaluated The j-th evaluation index is calculated using equation (5). The standard deviation of the specific data of the m transformer samples to be evaluated Then, use equation (6) to calculate the j-th evaluation index. coefficient of variation ;

[0077] (4)

[0078] (5)

[0079] (6).

[0080] S3.5. Based on the coefficient of variation corresponding to each indicator, the j-th evaluation indicator is calculated using equation (7). Corresponding information weight Thus, the information weight vector is obtained as follows: Where T represents transpose, the calculated information weights can highlight the relative change range, i.e., the degree of variation, of each indicator;

[0081] (7)

[0082] In equation (7), ,and , .

[0083] S4. Establish and solve a combined weight optimization model that integrates relative importance weights and information content weights under the least squares principle to obtain the optimized weight vector of the transformer quality assurance capability assessment index. The specific steps for constructing the weight optimization model are as follows:

[0084] S4.1. Based on the scenarios and requirements for using the least squares method, a weight vector reflecting the relative importance of the indicator values ​​is obtained. and the information content weight vector reflecting the information content of the indicators Then, using equations (8) and (9), the deviations between the fusion weighting method, the pecking order graph method, and the coefficient of variation method are obtained:

[0085] (8)

[0086] (9)

[0087] S4.2, in order to minimize the gap between the combined weight and the weight obtained by using two methods alone, the objective function of the global model of transformer quality assurance capability evaluation index weight optimization is obtained by using formula (10) :

[0088] (10)

[0089] Formula (10) indicates that the deviation between the evaluation value under the fusion method and the evaluation value under the two weighting methods should be as small as possible for all indicators of all evaluation objects. Wherein, is the weight coefficient of the subjective and objective weighting method, indicating the proportion of the weight of the priority graph method and the coefficient of variation method in the combined weight. If , it means that the weight obtained by the coefficient of variation method accounts for a larger proportion, and the result is more affected by the objective. And if , then the weight obtained by the priority graph method accounts for a larger proportion. By controlling the value of , the user can flexibly adjust the weight optimization proportion according to the specific situation of the index and data, and obtain a more realistic weight optimization result;

[0090] S4.3, the global model of transformer quality assurance capability evaluation index weight optimization is obtained by using formula (11) and formula (12):

[0091] (11)

[0092] (12)

[0093] Combined with the solution method of Lagrange function and matrix equation, the model given by formula (11) and formula (12) can be solved;

[0094] S4.4, the specific steps of solving the global model are as follows:

[0095] S4.4.1, based on the given objective function, use formula (13) as the Lagrange function:

[0096] (13)

[0097] S4.4.2, use formula (14) and formula (15) to find the partial derivative of and respectively, and let them equal to 0:

[0098] (14)

[0099] (15)

[0100] S4.4.3, calculate the jth evaluation index The sum of the square values of the data of the corresponding m transformer samples to be evaluated , thereby obtaining the sum of the square values of the data of the corresponding m transformer samples to be evaluated for each of the n evaluation indexes, and then obtaining the diagonal matrix , ;

[0101] (16)

[0102] S4.4.4, according to formula (11), for the jth evaluation index , calculate the deviation of the evaluation value , and then calculate the deviation of the n evaluation indexes, and then use formula (17) to construct the vector :

[0103] (17)

[0104] S4.3, transform formula (14) and formula (15) into the form of a matrix equation using formula (18), and obtain , wherein represents the corresponding optimized weight;

[0105] (18)

[0106] (19)

[0107] In formula (19), represents an n-dimensional column vector with all elements being 1.

[0108] S5, calculate the evaluation value of the ith transformer sample to be evaluated using formula (20) , thereby obtaining the evaluation values of the m transformer samples to be evaluated. The calculated evaluation values make the transformer products of different suppliers comparable, and the higher the evaluation value, the better the quality of the product;

[0109] (20)

[0110] S6, sort the evaluation values of the m transformer samples to be evaluated in descending order, and select the transformer sample products corresponding to the first s evaluation values as the transformer suppliers with the optimal quality according to the actual needs of the organization.

[0111] In the embodiment, an electronic device includes a memory for storing a program supporting a processor to execute the method and the processor configured to execute the program stored in the memory.

[0112] In the embodiment, a computer readable storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of the method.

[0113] In summary, the application is based on the existing evaluation rules, considering that the determination of the existing index weight has strong subjectivity, and there is no clear scientific basis, which leads to the phenomenon that the final evaluation result has low discrimination in the actual evaluation work, and the traditional subjective and objective weighting method has defects when used alone. The combined optimization weighting method is used for weight optimization of transformer quality assurance capability evaluation index. According to the optimized index weight, the appropriate transformer supplier can be selected by comparing the evaluation values of each transformer sample to be evaluated. This method applied to transformer quality assurance capability evaluation is more scientific, improves the discrimination of the evaluation result, and provides a scientific basis for decision makers to select high-quality transformer suppliers.

Claims

1. A method of evaluating the quality assurance capability of a transformer with weight combination optimization, characterized by, Comprising the following steps: S1, acquire n evaluation indexes for evaluating transformer quality assurance capability, wherein let represents the jth transformer quality assurance capability evaluation index, n represents the total number of evaluation indexes, ; S2, determining the relative importance weight vector of the transformer quality assurance capability evaluation index based on the priority graph method ; wherein, represents the relative importance weight of , and T represents transposition; S3, acquire sample data corresponding to the transformer quality assurance capability evaluation index, and determine an information quantity weight vector of the transformer quality assurance capability evaluation index based on a coefficient of variation method ; wherein, represents corresponding information quantity weights; S4, using formula (8) and formula (9) to establish a weight optimization model of fusing relative importance weight and information amount weight: (8) (9) In formula (8), is a weight coefficient, denotes a corresponding weight; denotes an objective function of a combined weight optimization model, denotes a weight vector; denotes a corresponding normalized i-th transformer sample; S5, solving the weight optimization model to obtain an optimized weight vector wherein, denotes corresponding optimized weight; S6, calculating the quality assurance capability evaluation value of the ith transformer sample by using formula (10) Thus, the quality assurance capability evaluation values of the m transformer samples are obtained. (10) S7, after the quality assurance capability evaluation values of the m transformer samples are sorted in descending order, the suppliers of the transformer samples corresponding to the first s quality assurance capability evaluation values are selected as the transformer suppliers with optimal quality.

2. The method for evaluating the quality assurance capability of a transformer according to claim 1, wherein S2 Comprising: S2.1, initializing transformer quality assurance capability evaluation index average score value of the importance degree , thereby obtaining the average score value of the n indexes; S2.2, comparing the average score values of the n transformer quality assurance capability evaluation indexes to obtain the relative importance degree of each index, thereby constructing a relative importance judgment matrix with a dimension of n x n; S2.3, adding all column elements of each row of the relative importance decision matrix respectively, a relative importance vector of the n transformer quality assurance capability evaluation indexes is obtained and a relative importance sum wherein, represents a relative importance vector of the jth transformer quality assurance capability evaluation index ; and = wherein, is an element of the rth row and the jth column of the relative importance decision matrix, representing a relative importance degree between the rth transformer quality assurance capability evaluation index and the jth transformer quality assurance capability evaluation index ; S2.4, obtaining the relative importance weight of , so that the relative importance weight vector of the n transformer quality assurance capability evaluation indexes is ; (1) In equation (1), ,and , ; This represents the evaluation index of the quality assurance capability of the r-th transformer. The relative importance vector.

3. The method for evaluating the quality assurance capability of a transformer according to claim 2, wherein S3 Comprising: S3.1, obtaining a jth transformer quality assurance capability evaluation index corresponding m transformer samples, thereby constructing a transformer sample matrix with dimensions wherein, represents a jth transformer quality assurance capability evaluation index corresponding ith transformer sample;​ S3.2, using formula (2) on S3.2, using formula (2) on S3.2, using formula (2) on S3.2, using formula (2) on S3.2, using formula (2) on (2) In formula (2), k is a coefficient, denotes corresponding m transformer samples, denotes an absolute value; S3.3, the formula (3) is utilized to standardize The corresponding standardized i-th transformer sample , thereby obtaining the standardized transformer sample matrix R;​ (3) S3.4, calculating with formula (4) the mean of the m transformer samples corresponding , calculating with formula (5) the standard deviation of the m transformer samples corresponding , calculating with formula (6) the coefficient of variation of ; (4) (5) (6) S3.5, calculating using formula (7) corresponding information weight , so that the information weight vector of n transformer quality assurance capability evaluation indexes is ; (7) In formula (7), , and , .

4. The method of claim 3, wherein, The step S5 is solved by the following steps, obtaining : S5.1, calculating the sum of the square values of the corresponding m transformer samples , thereby obtaining the sum of the square values of the n evaluation indices, and further obtaining the diagonal matrix , : (11) S5.2, calculate the bias of the evaluation values according to formula (8) , thereby calculating the bias of the n evaluation indicators, and then constructing the vector :​ (12) S5.3, using formula (13) : (13) In formula (13), is an n-dimensional column vector representing all elements being 1.

5. An electronic device comprising a memory and a processor, characterized in that The memory is configured to store a program supporting the processor to execute the transformer quality assurance capability evaluation method of any one of claims 1-4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to execute the steps of the transformer quality assurance capability evaluation method of any one of claims 1-4.

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