Method and system for evaluating standardization degree of construction of project department of power transmission and transformation project

Abstract

The invention belongs to the technical field of power transmission and transformation project project department standardization evaluation, and particularly relates to a power transmission and transformation project project department standardization degree evaluation method and system.The power transmission and transformation project project department standardization degree evaluation index system is constructed firstly, values of all evaluation indexes are obtained, and then a power transmission and transformation project project department standardization degree evaluation index system is established on the basis of the values of all the evaluation indexes; calculating the weight of each evaluation index in the power transmission and transformation project evaluation index system by using an improved CRITIC method; then, a membership matrix is obtained through an adaptive fuzzy clustering method, and finally, based on the membership matrix and the weight of each evaluation index, a final power transmission and transformation project evaluation result is generated through fuzzy comprehensive evaluation. According to the method, through the adaptive fuzzy clustering method, the membership degree of the membership degree matrix and the number of clustering centers can be dynamically adjusted according to actual requirements of different project conditions, so that the finally generated scoring result better fits the actual situation, and the accuracy of standardized assessment established by the project department of the power transmission and transformation project is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of standardized evaluation of power transmission and transformation project departments, and specifically relates to a method and system for evaluating the standardization degree of the establishment of power transmission and transformation project departments. Background Art

[0002] The establishment of a power transmission and transformation project department is an important link in power transmission and transformation projects, and its standardized assessment is crucial for ensuring the normal start of projects. However, the current standardized assessment methods have problems such as strong subjectivity, insufficient flexibility, lack of optimization mechanisms, and insufficient interpretability and operability of evaluation results. Therefore, how to improve the accuracy and interpretability of evaluation results, enhance the adaptive optimization ability of the evaluation system, and provide more operational and guiding support for project management and decision-making has become an urgent problem to be solved in current research.

[0003] In existing technical literature, the literature "Risk Assessment of the Construction Stage of a Warehouse Intelligent Transformation Project Based on AHP-SPA" proposed a risk assessment method for the construction stage of a warehouse intelligent transformation project that combines the analytic hierarchy process and set pair analysis. "Research on the Evaluation of the Construction Organization Plan for Highway Reconstruction and Expansion Based on the Fuzzy Comprehensive Model" calculated the weights of each evaluation index using the analytic hierarchy process and constructed an evaluation model for the construction organization plan using the fuzzy comprehensive evaluation method. The literature "Power Infrastructure Construction General Foundation Operation Safety Risk Assessment Method Based on Grey Relational Analysis" proposed a power infrastructure construction general foundation operation safety risk assessment method that combines the entropy weight method and grey relational analysis. However, the evaluation methods involved in the above literature lack the ability of dynamic adjustment and cannot fit the actual situation of the project, resulting in poor accuracy and scientificity of the standardized assessment of the establishment of power transmission and transformation project departments. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the standardization degree of the establishment of a power transmission and transformation project department that can effectively improve the accuracy of evaluation results in view of the above problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In the first aspect, the present invention provides a method for evaluating the standardization degree of the establishment of a power transmission and transformation project department, and the evaluation method includes:

[0007] S1. Construct an evaluation index system for the standardization degree of a power transmission and transformation project department, and obtain the values of each evaluation index;

[0008] S2. Based on the values of each evaluation index, use the improved CRITIC method to calculate the weights of each evaluation index in the evaluation index system of the power transmission and transformation project;

[0009] S3. Obtain the membership degree matrix through the adaptive fuzzy clustering method;

[0010] S4. Based on the membership degree matrix and the weights of each evaluation index, use fuzzy comprehensive evaluation to generate the evaluation result of the standardization degree of the final establishment of the power transmission and transformation project department.

[0011] The said S2 includes:

[0012] A1. Calculate the standard deviation of each evaluation index; for the j-th evaluation index, its standard deviation σ j The calculation formula is:

[0013]

[0014] In the above formula, Z ij is the value of the evaluation object i after preprocessing for the j-th evaluation index; is the mean value of the j-th evaluation index; N is the total number of evaluation objects;

[0015] A2. Calculate the distance matrix of each evaluation index; for the j-th evaluation index, first calculate the Euclidean distance D j (i, l) between the evaluation object i and the evaluation object l under the j-th evaluation index according to the following formula. After calculating the Euclidean distances of all evaluation objects under the j-th evaluation index, obtain the distance matrix D j of the j-th evaluation index:

[0016] D j (i, l) = |Z ij - Z lj |;

[0017] In the above formula, Z lj is the value of the evaluation object l after preprocessing for the j-th evaluation index;

[0018] A3. Centralize the distance matrix of each evaluation index; for the distance matrix D j of the j-th evaluation index, centralize it according to the following formula to obtain the centralized distance matrix A j :

[0019]

[0020] In the above formula, A j (i, l) is the element in the centralized distance matrix A j of the j-th evaluation index; represents the average distance between the evaluation object i and other evaluation objects, and m is the index variable for traversing the evaluation objects; represents the average distance between the evaluation object l and other evaluation objects; Represents the overall average distance between all evaluation objects; n is the index variable for traversing the evaluation objects;

[0021] A4. Perform linear fitting on the centered distance matrix of each evaluation index by the least squares method:

[0022]

[0023] In the above formula, A j,Z is the matrix obtained after performing linear fitting on the centered distance matrix A j of the jth evaluation index using the control variable Z; β z is the regression coefficient; is the centered distance matrix A of the control variable Z Z in the elements; L is the number of indicators of the control variable Z;

[0024] A5. Calculate the conditional distance correlation coefficient between each evaluation index:

[0025]

[0026] In the above formula, CdCor jk|Z represents the degree of non - linear correlation between the jth evaluation index and the kth evaluation index; dCov jk|Z is the conditional distance covariance between the jth evaluation index and the kth evaluation index; dVar j|Z , dVar k|Z are the conditional distance variances of the jth evaluation index and the kth evaluation index respectively; A j,Z (i, l) is the element in the matrix A j,Z ; A k,Z (i, l) is the element in the matrix A k,Z ; A k (i, l) is the element of the centered distance matrix A k of the kth evaluation index;

[0027] A6. Calculate the weights of each evaluation index:

[0028]

[0029] In the above formula, C j is the CRITIC value of the jth evaluation index; M is the total number of evaluation indexes; w j is the weight of the jth evaluation index.

[0030] The said S3 includes:

[0031] B1. Define the membership function and construct the initial membership matrix:

[0032]

[0033] In the above formula, R is the membership matrix, and the elements in the membership matrix are μ ic , μ ic represents the membership degree of the evaluation object i to the clustering center c; N is the total number of evaluation objects; C is the total number of clustering centers;

[0034] B2. Initialize the fuzzy clustering center and its number;

[0035] B3. Update the membership matrix according to the following formula:

[0036]

[0037] In the above formula, ||x i -v c || is the Euclidean distance between the evaluation object i and the c-th fuzzy clustering center; ||x i -v h || is the Euclidean distance between the evaluation object i and the h-th fuzzy clustering center; β is the parameter controlling the fuzziness of the membership degree; λ is the characteristic parameter; η is the adaptive parameter; x i , v c , v h are the feature vectors of the evaluation object i, the c-th fuzzy clustering center, and the h-th fuzzy clustering center respectively; x ij is the value of the evaluation object i on the j-th evaluation index; v cj , v hj are the values of the c-th and h-th fuzzy clustering centers on the j-th evaluation index respectively;

[0038] B4. Update the clustering center based on the updated membership matrix:

[0039]

[0040] In the above formula, v′ c is the updated c-th fuzzy clustering center; α is the adjustment parameter; g(c j ) is the regularization term;

[0041] B5. Adjust the adaptive parameter η and the number C of fuzzy clustering centers based on the updated fuzzy clustering center;

[0042] The adjustment formula for the adaptive parameter is:

[0043]

[0044] In the above formula, γ is the constant used to adjust the influence of η; mean(||x i -v C ||) is the average Euclidean distance from the evaluation object i to all clustering centers;

[0045] The adjustment strategy for the number C of fuzzy clustering centers is as follows: First, calculate the clustering mean square error. If the clustering mean square error is greater than the error threshold, increase the number of fuzzy clustering centers; otherwise, stop increasing the number of fuzzy clustering centers. The calculation formula for the clustering mean square error is as follows:

[0046]

[0047] B6. Return to B3 and perform iterative calculation until the iterative termination condition is satisfied, and output the membership matrix obtained in the last iteration.

[0048] The above-mentioned S4 includes:

[0049] C1. Based on the weight vector W = (w1, w2,..., w M ) obtained in S2 and the membership matrix obtained in S3, calculate the comprehensive evaluation vector B of each evaluation object;

[0050] C2. Calculate the score value of each evaluation object according to the following formula:

[0051]

[0052] In the above formula, V i is the score value of evaluation object i; B r is the r-th element in the comprehensive evaluation vector B, representing the comprehensive membership degree of the evaluation object at the r-th evaluation level; va r is the score value of the r-th evaluation level;

[0053] C3. Evaluate the standardization degree based on the score values of each evaluation object.

[0054] In the second aspect, the present invention provides a system for evaluating the standardization degree of the establishment of a transmission and transformation project department. The evaluation system includes:

[0055] A data acquisition and construction module for constructing an evaluation index system for the standardization degree of a transmission and transformation project department and obtaining the values of each evaluation index;

[0056] A weight determination module for calculating the weights of each evaluation index in the evaluation index system of the transmission and transformation project by using the improved CRITIC method based on the values of each evaluation index;

[0057] A membership matrix calculation module for obtaining the membership matrix by using the adaptive fuzzy clustering method;

[0058] An evaluation module for generating the final evaluation result of the standardization degree of the establishment of a transmission and transformation project department by using fuzzy comprehensive evaluation based on the membership matrix and the weights of each evaluation index.

[0059] The weight determination module is used to calculate the weights of each evaluation index according to the following steps:

[0060] A1. Calculate the standard deviation of each evaluation index; for the j-th evaluation index, its standard deviation σ j is calculated by the formula:

[0061]

[0062] In the above formula, Z ij is the value of the evaluation object i after preprocessing for the j-th evaluation index; is the mean value of the j-th evaluation index; N is the total number of evaluation objects;

[0063] A2. Calculate the distance matrix of each evaluation index; for the j-th evaluation index, first calculate the Euclidean distance D j (i, l) between the evaluation object i and the evaluation object l under the j-th evaluation index. After calculating the Euclidean distances of all evaluation objects under the j-th evaluation index, the distance matrix D j of the j-th evaluation index is obtained:

[0064] D j (i, l) = |Z ij -Z lj |;

[0065] In the above formula, Z lj is the value of the evaluation object l after preprocessing for the j-th evaluation index;

[0066] A3. Centralize the distance matrix of each evaluation index; for the distance matrix D j of the j-th evaluation index, centralize it according to the following formula to obtain the centralized distance matrix A j :

[0067]

[0068] In the above formula, A j (i, l) is the element in the centralized distance matrix A j of the j-th evaluation index; represents the average distance between the evaluation object i and other evaluation objects, and m is the index variable for traversing the evaluation objects; represents the average distance between the evaluation object l and other evaluation objects, and n is the index variable for traversing the evaluation objects; represents the overall average distance between all evaluation objects;

[0069] A4. Perform linear fitting on the centralized distance matrix of each evaluation index by the least squares method:

[0070]

[0071] In the above formula, A j,Z is the centralized distance matrix A of the j-th evaluation index using the control variable Z j obtained after linear fitting; β z is the regression coefficient; is the centralized distance matrix A of the control variable Z Z in the elements; L is the number of indicators of the control variable Z;

[0072] A5. Calculate the conditional distance correlation coefficient between each evaluation index:

[0073]

[0074] In the above formula, CdCor jk|Z represents the degree of non-linear correlation between the j-th evaluation index and the k-th evaluation index; dCov jk|Z is the conditional distance covariance between the j-th evaluation index and the k-th evaluation index; dVar j|Z , dVar k|Z are the conditional distance variances of the j-th evaluation index and the k-th evaluation index respectively; A j,Z (i, l) is an element in the matrix A j,Z ; A k,Z (i, l) is an element in the matrix A k,Z ; A k (i, l) is an element of the centralized distance matrix A of the k-th evaluation index k ;

[0075] A6. Calculate the weight of each evaluation index:

[0076]

[0077] In the above formula, C j is the CRITIC value of the j-th evaluation index; M is the total number of evaluation indexes; w j is the weight of the j-th evaluation index.

[0078] The membership degree matrix calculation module is used to calculate the membership degree matrix according to the following steps:

[0079] B1. Define the membership degree function and construct the initial membership degree matrix:

[0080]

[0081] In the above formula, R is the membership degree matrix, and the elements in the membership degree matrix are μ ic , μ icDenote the membership degree of evaluation object \(i\) to clustering center \(c\); \(N\) is the total number of evaluation objects; \(C\) is the total number of clustering centers;

[0082] B2. Initialize the fuzzy clustering centers and their numbers;

[0083] B3. Update the membership matrix according to the following formula:

[0084]

[0085] In the above formula, \(\|x\) i \(-v\) c \| is the Euclidean distance between evaluation object \(i\) and the \(c\)-th fuzzy clustering center; \(\|x\) i \(-v\) h \| is the Euclidean distance between evaluation object \(i\) and the \(h\)-th fuzzy clustering center; \(\beta\) is the parameter controlling the fuzziness of the membership degree; \(\lambda\) is the characteristic parameter; \(\eta\) is the adaptive parameter; \(x\) i 、\(v\) c 、\(v\) h are the feature vectors of evaluation object \(i\), the \(c\)-th fuzzy clustering center, and the \(h\)-th fuzzy clustering center respectively; \(x\) ij is the value of evaluation object \(i\) on the \(j\)-th evaluation index; \(v\) cj 、\(v\) hj are the values of the \(c\)-th and \(h\)-th fuzzy clustering centers on the \(j\)-th evaluation index respectively;

[0086] B4. Update the clustering centers based on the updated membership matrix:

[0087]

[0088] In the above formula, \(v'\) c is the updated \(c\)-th fuzzy clustering center; \(\alpha\) is the adjustment parameter; \(g(c\) j ) is the regularization term;

[0089] B5. Adjust the adaptive parameter \(\eta\) and the number \(C\) of fuzzy clustering centers based on the updated fuzzy clustering centers;

[0090] The adjustment formula for the adaptive parameter is:

[0091]

[0092] In the above formula, \(\gamma\) is a constant used to adjust the influence of \(\eta\); \(mean(\|x\) i \(-v\) C \|) is the average Euclidean distance from evaluation object \(i\) to all clustering centers;

[0093] The adjustment strategy for the number C of the fuzzy clustering centers is as follows: First, calculate the clustering mean square error. If the clustering mean square error is greater than the error threshold, increase the number of fuzzy clustering centers; otherwise, stop increasing the number of fuzzy clustering centers. The calculation formula for the clustering mean square error is:

[0094]

[0095] B6. Return to B3 for iterative calculation until the iterative termination condition is met, and output the membership degree matrix obtained in the last iteration.

[0096] The evaluation module is used to generate the evaluation result of the standardization degree of the final establishment of the power transmission and transformation project department according to the following steps:

[0097] C1. Based on the weight vector W=(w1, w2,..., w M ) obtained from S2 and the membership degree matrix obtained from S3, calculate the comprehensive evaluation vector B of each evaluation object;

[0098] C2. Calculate the score value of each evaluation object according to the following formula:

[0099]

[0100] In the above formula, V i is the score value of the evaluation object i; B r is the r-th element in the comprehensive evaluation vector B, representing the comprehensive membership degree of the evaluation object at the r-th evaluation level; va r is the score value of the r-th evaluation level;

[0101] C3. Generate the evaluation result of the standardization degree of the final establishment of the power transmission and transformation project department based on the score values of each evaluation object.

[0102] In the third aspect, the present invention provides an evaluation device for the standardization degree of the establishment of a power transmission and transformation project department. The evaluation device includes a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the foregoing evaluation method according to the instructions in the computer program code.

[0103] In the fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the foregoing evaluation method is implemented.

[0104] Compared with the prior art, the beneficial effects of the present invention are:

[0105] 1. The evaluation method for the standardization degree of the construction of a power transmission and transformation project department in the present invention first constructs an evaluation index system for the standardization degree of the power transmission and transformation project department and obtains the values of each evaluation index. Then, based on the values of each evaluation index, the improved CRITIC method is used to calculate the weights of each evaluation index in the evaluation index system of the power transmission and transformation project. Then, the membership degree matrix is obtained through the adaptive fuzzy clustering method. Finally, based on the membership degree matrix and the weights of each evaluation index, the fuzzy comprehensive evaluation is used to generate the final evaluation result of the construction of the power transmission and transformation project department. Through the adaptive fuzzy clustering method in the above design, the membership degree and the number of clustering centers of the membership degree matrix can be dynamically adjusted according to the actual needs of different project conditions, which can better reflect the actual characteristics of the evaluation object, so that the finally generated scoring result is more in line with the actual situation, effectively improving the accuracy of the standardization assessment of the construction of the power transmission and transformation project department. Therefore, the present invention can make the finally generated scoring result more in line with the actual situation, thereby effectively improving the accuracy of the standardization evaluation result of the construction of the power transmission and transformation project department.

[0106] 2. The evaluation method for the standardization degree of the construction of a power transmission and transformation project department in the present invention, on the one hand, integrates the variability and correlation of each evaluation index to ensure the objectivity and reasonableness of weight distribution; on the other hand, the conditional distance correlation coefficient is used to replace the Pearson correlation coefficient to describe the correlation between indicators. The conditional distance correlation coefficient can not only identify linear and non-linear relationships, but also effectively reduce the index redundancy by eliminating the influence of control variables, so as to improve the accuracy and scientific nature of the evaluation, and thus can more comprehensively reflect the independence and importance between indicators, and allocate weights more reasonably, especially suitable for complex and multi-dimensional evaluation systems. Therefore, the present invention can allocate weights more reasonably. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is a flowchart of the evaluation method described in the present invention.

[0108] Figure 2 It is a structural block diagram of the evaluation system described in the present invention.

[0109] Figure 3 It is a structural block diagram of the evaluation device described in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0110] The present invention will be further described in detail below in conjunction with the specific embodiments and the accompanying drawings.

[0111] Example 1:

[0112] Refer to Figure 1 , an evaluation method for the standardization degree of the construction of a power transmission and transformation project department, which is carried out in the following steps in sequence:

[0113] S1. Construct a standardization evaluation index system for power transmission and transformation project departments, obtain the value of each evaluation index and pre-process it; the standardization evaluation index system includes whether there are appointment documents, whether the project department is established in a timely manner, whether the qualifications of the incumbent personnel meet the requirements, whether the number of management personnel meets the requirements, whether the personnel are configured or concurrently appointed as required, and whether the personnel replacement procedures are followed; the pre-processing refers to: first use the interquartile range method to monitor outliers to eliminate outliers, and then perform Z-Score standardization on the values ​​of the evaluation indicators;

[0114] S2. Based on the value of each evaluation index, the improved CRITIC method is used to calculate the weight of each evaluation index in the power transmission and transformation project evaluation index system; in the traditional CRITIC method, the correlation is usually measured by the Pearson correlation coefficient, but it is only applicable to linear relationships and it is difficult to capture complex nonlinear dependencies; in order to improve the accuracy and scientificity of the evaluation, the conditional distance correlation coefficient is introduced to replace the Pearson correlation coefficient. The conditional distance correlation coefficient can not only identify linear and nonlinear relationships, but also avoid the wrong weight allocation caused by ignoring the influence of potential variables, and also quantify the direct correlation between indicators by eliminating the influence of control variables, thereby effectively reducing the redundancy of indicators, which helps to improve the accuracy and scientificity of correlation evaluation, and is especially suitable for the standardization degree evaluation system involving multiple related variables and complex dependencies;

[0115] The specific steps of the improved CRITIC method are:

[0116] A1. Calculate the standard deviation of each evaluation index; for the jth evaluation index, its standard deviation σ j The calculation formula is:

[0117]

[0118] In the above formula, Z ij is the value of evaluation object i after preprocessing of the jth evaluation index; is the mean value of the jth evaluation index; N is the total number of evaluation objects;

[0119] A2. Calculate the distance matrix of each evaluation index. For the jth evaluation index, first calculate the Euclidean distance D between evaluation object i and evaluation object l under the jth evaluation index according to the following formula: j (i, l), after completing the Euclidean distance calculation of all evaluation objects under the jth evaluation index, the distance matrix D of the jth evaluation index is obtained j :

[0120] D j (i,l)=|Z ij -Z lj |;

[0121] In the above formula, Z lj is the preprocessed value of the evaluation object l under the j-th evaluation index;

[0122] A3. Centralize the distance matrix of each evaluation index; for the distance matrix D j of the j-th evaluation index, the centralized distance matrix A is obtained by centralization according to the following formula j :

[0123]

[0124] In the above formula, A j (i, l) is the element in the centralized distance matrix A j of the j-th evaluation index; represents the average distance between the evaluation object i and other evaluation objects under the control variable Z, and m is the index variable for traversing the evaluation objects; represents the average distance between the evaluation object l and other evaluation objects under the control variable Z, and n is the index variable for traversing the evaluation objects; represents the overall average distance between all evaluation objects under the control variable Z;

[0125] A4. Perform linear fitting on the centralized distance matrix of each evaluation index by the least squares method:

[0126]

[0127] In the above formula, A j,Z is the matrix obtained by linearly fitting the centralized distance matrix A j of the j-th evaluation index using the control variable Z; β z is the regression coefficient; is the element in the centralized distance matrix A Z of the control variable Z; L is the number of indices of the control variable Z; the control variable Z is a general term for external factors that may affect the evaluation index system. In the standardized assessment of the establishment of the power transmission and transformation project department, the control variable Z includes geographical location, climate conditions, the number of personnel, etc. By introducing the above control variable Z, the interference of these external factors can be removed when calculating the relationship between evaluation indices, so as to more accurately reflect the essential association between indices. The calculation process of the centralized distance matrix A Z of the control variable Z is similar to that of the centralized distance matrix A j , that is:

[0128]

[0129] In the above formula, D Z (i, l) represents the Euclidean distance between the evaluation object i and the evaluation object l under the control variable Z; represents the average Euclidean distance between the evaluation object \(i\) and other evaluation objects under the control variable \(Z\), where \(m\) is the index variable for traversing the evaluation objects; represents the average Euclidean distance between the evaluation object \(l\) and other evaluation objects under the control variable \(Z\); represents the overall average distance between all evaluation objects under the control variable \(Z\); \(n\) is the index variable for traversing the evaluation objects; \(Z\) i and \(Z\) l are the values of the control variable on the evaluation object \(i\) and the evaluation object \(l\); Exemplarily, when the control variable \(Z\) is the number of configured personnel in the department, assuming the personnel configuration in the departments is: 50 people in the owner's project department, 30 people in the supervision project department, and 40 people in the construction project department, then the values of the control variable \(Z\) on the three evaluation objects are 50, 30, and 40 respectively;

[0130] The regression coefficient \(\beta\) z is calculated by the formula:

[0131]

[0132] A5. Calculate the conditional distance correlation coefficient between each evaluation index:

[0133]

[0134]

[0135] In the above formula, \(CdCor\) jk|Z represents the degree of non - linear correlation between the \(j\) - th evaluation index and the \(k\) - th evaluation index; \(dCov\) jk|Z is the conditional distance covariance between the \(j\) - th evaluation index and the \(k\) - th evaluation index; \(dVar\) j|Z and \(dVar\) k|Z are the conditional distance variances of the \(j\) - th evaluation index and the \(k\) - th evaluation index respectively; \(A\) j,Z (\(i,l\)) is an element in the matrix \(A\) j,Z ; \(A\) k,Z (\(i,l\)) is an element in the matrix \(A\) k,Z ; \(A\) k (\(i,l\)) is an element of the centered distance matrix \(A\) k of the \(k\) - th evaluation index;

[0136] A6. Calculate the weights of each evaluation index:

[0137]

[0138] In the above formula, \(C\) j is the CRITIC value of the \(j\) - th evaluation index; \(M\) is the total number of evaluation indexes; \(w\) j is the weight of the \(j\) - th evaluation index.

[0139] S3. Obtain the membership matrix through the adaptive fuzzy clustering method; the adaptive fuzzy clustering method introduces an adaptive mechanism on the basis of the standard fuzzy C-means clustering. By adjusting the adaptive parameter η and the number of fuzzy clustering centers, the membership can better reflect the actual characteristics of the evaluation object. The specific steps of the adaptive fuzzy clustering method are as follows:

[0140] B1. For each evaluation index, define the corresponding membership function according to the preset fuzzy evaluation levels (such as excellent, good, general), and then construct the initial membership matrix:

[0141]

[0142] In the above formula, R is the membership matrix, and the elements in the membership matrix are μ ic , μ ic represents the membership degree of the evaluation object i to the clustering center c; N is the total number of evaluation objects; C is the total number of clustering centers;

[0143] B2. Initialize the fuzzy clustering centers and their numbers;

[0144] B3. Update the membership matrix according to the following formula:

[0145]

[0146] In the above formula, ||x i -v c || is the Euclidean distance between the evaluation object i and the c-th fuzzy clustering center; ||x i -v h || is the Euclidean distance between the evaluation object i and the h-th fuzzy clustering center; β is the parameter controlling the fuzziness of the membership; λ is the characteristic parameter; η is the adaptive parameter; x i , v c , v h are the feature vectors of the evaluation object i, the c-th fuzzy clustering center, and the h-th fuzzy clustering center respectively; x ij is the value of the evaluation object i on the j-th evaluation index; v cj , v hj are the values of the c-th and h-th fuzzy clustering centers on the j-th evaluation index respectively; v c The calculation formula of j is:

[0147]

[0148] The calculation formula of the said v cj is different from the clustering center update formula in that the feature vector v cThe value of the c-th fuzzy clustering center on the j-th evaluation index represents the relationship between the whole and the part. Specifically: v c is the position of the clustering center in the entire multi-dimensional feature space, which is composed of values on multiple evaluation indexes; while v cj is the value of the c-th clustering center on the j-th evaluation index, representing the specific coordinate of v c on the j-th dimension;

[0149] B4. Update the clustering center based on the updated membership matrix to make it more accurately represent different fuzzy evaluation levels:

[0150]

[0151] In the above formula, v c ′ is the updated c-th fuzzy clustering center;; α is an adjustment parameter used to balance the model complexity and data fitting, and its usual value range is [1, 5]; g(cj) is a regularization term used to prevent unreasonable deviation of the clustering center;

[0152] B5. Adjust the adaptive parameter η and the number C of fuzzy clustering centers based on the updated fuzzy clustering centers to ensure that the number of fuzzy evaluation levels is more suitable for the actual situation;

[0153] The adjustment formula for the adaptive parameter is:

[0154]

[0155] In the above formula, γ is a constant used to adjust the influence of η; mean(||x i -v C ||) is the average Euclidean distance from the evaluation object i to all clustering centers;

[0156] The adjustment strategy for the number C of fuzzy clustering centers is: first calculate the clustering mean square error. If the clustering mean square error is greater than the error threshold, increase the number of fuzzy clustering centers, otherwise stop increasing the number of fuzzy clustering centers; The calculation formula for the clustering mean square error is:

[0157]

[0158] B6. Return to B3 and perform iterative calculation until the iteration termination condition is met, and output the membership matrix obtained in the last iteration; The iteration termination condition can be that the change in the membership matrix between two consecutive iterations is less than a predetermined threshold;

[0159] S4. Based on the membership matrix and the weights of each evaluation index, use fuzzy comprehensive evaluation to generate the final evaluation result of the establishment of the transmission and transformation engineering project department; The specific steps are:

[0160] C1. Based on the weight vector W = (w1, w2,..., w M ) obtained from S2 and the membership degree matrix obtained from S3, calculate the comprehensive evaluation vector B of each evaluation object;

[0161] C2. Calculate the scoring values of each evaluation object according to the following formula:

[0162]

[0163] In the above formula, V i is the scoring value of evaluation object i; B r is the r-th element in the comprehensive evaluation vector B, representing the comprehensive membership degree of the evaluation object at the r-th evaluation level; va r is the scoring value of the r-th evaluation level;

[0164] C3. Conduct a standardization degree assessment based on the scoring values of each evaluation object.

[0165] Performance verification:

[0166] Use the method proposed in the present invention to evaluate the standardization degree of the owner's project department, supervision project department, and construction project department of the power transmission and transformation project. Table 1 shows the weights of each evaluation index in the index system. It can be seen from Table 1 that key influencing factors such as the management of appointment documents and the timeliness of project department establishment are given higher weights, while factors with less impact on the evaluation results, such as the procedures for personnel replacement, have lower weights. Table 2 shows the standard scoring values of the three evaluation levels, and Table 3 shows the final scoring intervals of the three evaluation levels, which are used to determine the final evaluation level of the project department. Table 4 shows the final evaluation results of the three project departments. It can be seen from Table 4 that in this evaluation, the owner's project department performed the best and had the highest score. This is because the main advantages of the owner's project department lie in document management and the timeliness of project department establishment, but there is still room for improvement in personnel qualifications and management compliance; the supervision project department performed moderately. Although it is relatively stable in document management and personnel qualifications, there are certain deficiencies in project department establishment and personnel allocation; the construction project department had the lowest score because there are relatively large problems in document management, personnel management, and project department establishment, and urgent improvement is needed. Generally speaking, each project department should focus on strengthening document management, time control, and personnel management to improve the overall management level and ensure the smooth progress of the project.

[0167] Table 1 Weight distribution of each evaluation index

[0168] Evaluation indicators Weight Is there any appointment document? 0.35 Whether the project department is established in time 0.25 Whether the qualifications of the personnel meet the requirements 0.15 Does the number of management personnel meet the requirements? 0.10 Are the personnel assigned as required or concurrently assigned? 0.10 Whether the personnel replacement procedures are followed 0.05

[0169] Table 2 Standard scoring values of the three evaluation levels

[0170] grade Rating value excellent 0.85 good 0.65 generally 0.45

[0171] Table 3 Final Scoring Ranges for Different Evaluation Levels

[0172] Rating Interval excellent 0.75-1.0 good 0.5-0.74 generally 0-0.49

[0173] Table 4 Final Evaluation Results of Three Project Departments

[0174] department Final score Evaluation results Owner Project Department 0.63 good Supervision Project Department 0.54 good Construction Project Department 0.31 generally

[0175] Example 2:

[0176] See Figure 2 , a standardization degree evaluation system for the establishment of a power transmission and transformation project department, including a data acquisition and construction module, a weight determination module, a membership degree matrix calculation module, and an evaluation module; the data acquisition and construction module is used to construct an evaluation index system for the standardization degree of the power transmission and transformation project department and obtain the values of each evaluation index;

[0177] The weight determination module is used to calculate the weights of each evaluation index in the evaluation index system of the power transmission and transformation project based on the values of each evaluation index by using the improved CRITIC method; the weight determination module is used to calculate the weights of each evaluation index according to the following steps:

[0178] A1. Calculate the standard deviation of each evaluation index; for the j-th evaluation index, its standard deviation σ j The calculation formula is:

[0179]

[0180] In the above formula, Z ij is the value of the evaluation object i after preprocessing in the j-th evaluation index; is the mean value of the j-th evaluation index; N is the total number of evaluation objects;

[0181] A2. Calculate the distance matrix of each evaluation index; for the j-th evaluation index, first calculate the Euclidean distance D j (i, l) between the evaluation object i and the evaluation object l under the j-th evaluation index according to the following formula. After calculating the Euclidean distances of all evaluation objects under the j-th evaluation index, the distance matrix D j of the j-th evaluation index is obtained:

[0182] D j (i, l) = |Z ij -Z lj |;

[0183] In the above formula, Z lj is the value of the evaluation object l after preprocessing in the j-th evaluation index;

[0184] A3. Centralize the distance matrix for each evaluation index; for the distance matrix D of the j-th evaluation index j , centralize it according to the following formula to obtain the centralized distance matrix A j :

[0185]

[0186] In the above formula, A j (i, l) is the element in the centralized distance matrix A j of the j-th evaluation index; represents the average distance between the evaluation object i and other evaluation objects, and m is the index variable for traversing the evaluation objects; represents the average distance between the evaluation object l and other evaluation objects; represents the overall average distance between all evaluation objects; n is the index variable for traversing the evaluation objects;

[0187] A4. Perform linear fitting on the centralized distance matrix of each evaluation index by the least squares method:

[0188]

[0189] In the above formula, A j,Z is the matrix obtained by performing linear fitting on the centralized distance matrix A j of the j-th evaluation index using the control variable Z; β z is the regression coefficient; is the element in the centralized distance matrix A Z of the control variable Z; L is the number of indicators of the control variable Z;

[0190] A5. Calculate the conditional distance correlation coefficient between each evaluation index:

[0191]

[0192] In the above formula, CdCor jk|Z represents the degree of non-linear correlation between the j-th evaluation index and the k-th evaluation index; dCov jk|Z is the conditional distance covariance between the j-th evaluation index and the k-th evaluation index; dVar j|Z , dVar k|Z are the conditional distance variances of the j-th evaluation index and the k-th evaluation index respectively; A j,Z (i, l) is the element in the matrix A j,Z ; A k,Z (i, l) is the element in the matrix A k,Z ; A k (i, l) is the centralized distance matrix A kelements;

[0193] A6. Calculate the weights of each evaluation index:

[0194]

[0195] In the above formula, C j is the CRITIC value of the j-th evaluation index; M is the total number of evaluation indexes; w j is the weight of the j-th evaluation index;

[0196] The membership degree matrix calculation module is used to obtain the membership degree matrix through the adaptive fuzzy clustering method; specifically, the membership degree matrix is calculated according to the following steps:

[0197] B1. Define the membership degree function and construct the initial membership degree matrix:

[0198]

[0199] In the above formula, R is the membership degree matrix, and the elements in the membership degree matrix are μ ic , μ ic represents the membership degree of the evaluation object i to the clustering center c; N is the total number of evaluation objects; C is the total number of clustering centers;

[0200] B2. Initialize the fuzzy clustering center and its number;

[0201] B3. Update the membership degree matrix according to the following formula:

[0202]

[0203] In the above formula, ||x i -v c || is the Euclidean distance between the evaluation object i and the c-th fuzzy clustering center; ||x i -v h || is the Euclidean distance between the evaluation object i and the h-th fuzzy clustering center; β is the parameter for controlling the fuzziness of the membership degree; λ is the characteristic parameter; η is the adaptive parameter; x i , v c , v h are the feature vectors of the evaluation object i, the c-th fuzzy clustering center, and the h-th fuzzy clustering center respectively; x ij is the value of the evaluation object i in the j-th evaluation index; v cj , v hj are the values of the c-th and h-th fuzzy clustering centers in the j-th evaluation index respectively;

[0204] B4. Update the clustering center based on the updated membership degree matrix:

[0205]

[0206] In the above formula, v′ c is the updated c-th fuzzy clustering center; α is an adjustment parameter; g(c j ) is a regularization term;

[0207] B5. Adjust the adaptive parameter η and the number C of fuzzy clustering centers based on the updated fuzzy clustering centers;

[0208] The adjustment formula for the adaptive parameter is:

[0209]

[0210] In the above formula, γ is a constant used to adjust the influence of η; mean(||x i - v C ||) is the average Euclidean distance from the evaluation object i to all clustering centers;

[0211] The adjustment strategy for the number C of fuzzy clustering centers is: first calculate the clustering mean square error. If the clustering mean square error is greater than the error threshold, increase the number of fuzzy clustering centers; otherwise, stop increasing the number of fuzzy clustering centers. The calculation formula for the clustering mean square error is:

[0212]

[0213] B6. Return to B3 for iterative calculation until the iterative termination condition is met, and output the membership matrix obtained in the last iteration;

[0214] The evaluation module is used to generate the evaluation result of the standardization degree of the formation of the final transmission and transformation project department based on the membership matrix and the weights of each evaluation index. Specifically, the evaluation result of the standardization degree of the formation of the final transmission and transformation project department is generated according to the following steps:

[0215] C1. Based on the weight vector W = (w1, w2,..., w M ) obtained in S2 and the membership matrix obtained in S3, calculate the comprehensive evaluation vector B of each evaluation object;

[0216] C2. Calculate the score value of each evaluation object according to the following formula:

[0217]

[0218] In the above formula, V i is the score value of the evaluation object i; B r is the r-th element in the comprehensive evaluation vector B, representing the comprehensive membership degree of the evaluation object in the r-th evaluation level; va ris the scoring value for the r-th evaluation level;

[0219] C3. Generate the evaluation result of the standardization degree of the establishment of the transmission and transformation project department based on the scoring values of each evaluation object.

[0220] Example 3:

[0221] See Figure 3 , an evaluation device for the standardization degree of the establishment of a transmission and transformation project department, including a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the evaluation method as described in Example 1 according to the instructions in the computer program code.

[0222] Example 4:

[0223] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the evaluation method as described in Example 1.

[0224] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0225] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0226] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the flowchart(s) Figure 1 a flowchart or flowcharts and / or block(s) Figure 1 a block or blocks.

[0227] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in the flowchart(s) Figure 1 a flowchart or flowcharts and / or block(s) Figure 1 a block or blocks.

[0228] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0229] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating the degree of standardization of power transmission and transformation project department establishment, characterized by: The evaluation method includes: S1. Construct a standardization evaluation index system for power transmission and transformation project departments and obtain the value of each evaluation index; S2. Based on the values ​​of each evaluation index, the weight of each evaluation index in the power transmission and transformation project evaluation index system is calculated using the improved CRITIC method; S3, obtaining the membership matrix through adaptive fuzzy clustering method; S4. Based on the membership matrix and the weights of each evaluation index, the fuzzy comprehensive evaluation is used to generate the final evaluation results of the standardization degree of the power transmission and transformation project department.

2. A method for evaluating the degree of standardization of power transmission and transformation project department establishment according to claim 1, characterized in that: The S2 includes: A1. Calculate the standard deviation of each evaluation index; for the jth evaluation index, its standard deviation σ j The calculation formula is: In the above formula, Z ij is the value of evaluation object i after preprocessing of the jth evaluation index; is the mean value of the jth evaluation index; N is the total number of evaluation objects; A2. Calculate the distance matrix of each evaluation index. For the jth evaluation index, first calculate the Euclidean distance D between evaluation object i and evaluation object l under the jth evaluation index according to the following formula: j (i, l), after completing the Euclidean distance calculation of all evaluation objects under the jth evaluation index, the distance matrix D of the jth evaluation index is obtained j : D j (i,l)=|Z ij -Z lj |; In the above formula, Z lj is the value of the evaluation object l after preprocessing of the jth evaluation index; A3. Centralize the distance matrix of each evaluation index; for the distance matrix D of the jth evaluation index j , according to the following formula, the centralized distance matrix A is obtained j : In the above formula, A j (i,l) is the central distance matrix A of the jth evaluation index j Elements in represents the average distance between evaluation object i and other evaluation objects, and m is the index variable for traversing evaluation objects; Represents the average distance between evaluation object l and other evaluation objects; Represents the overall average distance between all evaluation objects; n is the index variable for traversing the evaluation objects; A4. Perform linear fitting on the centralized distance matrix of each evaluation index using the least squares method: In the above formula, A j,Z The central distance matrix A of the jth evaluation index using the control variable Z is j The matrix obtained after linear fitting; β z is the regression coefficient; is the centralized distance matrix A of the control variable Z Z The elements in; L is the number of indicators of the control variable Z; A5. Calculate the conditional distance correlation coefficient between each evaluation index: In the above formula, CdCor jk|Z Indicates the degree of nonlinear correlation between the jth evaluation index and the kth evaluation index; dCov jk|Z is the conditional distance covariance between the jth evaluation index and the kth evaluation index; dVar j|Z , dVar k|Z are the conditional distance variances of the jth and kth evaluation indicators respectively; A j,Z (i,l) is the matrix A j,Z Elements in A k,Z (i,l) is the matrix A k,Z Elements in A k (i,l) is the central distance matrix A of the kth evaluation index k Elements of A6. Calculate the weight of each evaluation index: In the above formula, C j is the CRITIC value of the jth evaluation index; M is the total number of evaluation indicators; w j is the weight of the jth evaluation index.

3. The method for evaluating the degree of standardization of power transmission and transformation project department establishment according to claim 1 is characterized by: The S3 includes: B1. Define the membership function and construct the initial membership matrix: In the above formula, R is the membership matrix, and the elements in the membership matrix are μ ic , μ ic It represents the membership of evaluation object i to cluster center c; N is the total number of evaluation objects; C is the total number of cluster centers; B2. Initialize the fuzzy cluster centers and their number; B3. Update the membership matrix according to the following formula: In the above formula, ||x i -v c || is the Euclidean distance between the evaluation object i and the cth fuzzy cluster center; ||x i -v h || is the Euclidean distance between the evaluation object i and the hth fuzzy cluster center; β is the parameter for controlling the fuzziness of membership; λ is the characteristic parameter; η is the adaptive parameter; x i 、v c 、v h are the characteristic vectors of evaluation object i, cth fuzzy cluster center, and hth fuzzy cluster center respectively; x ij is the value of the evaluation index of the evaluation object i at the jth level; v cj 、v hj are the values ​​of the cth and hth fuzzy cluster centers at the jth evaluation index respectively; B4. Update the cluster center based on the updated membership matrix: In the above formula, v′ c is the updated cth fuzzy cluster center; α is the adjustment parameter; g(c j ) is the regularization term; B5. Adjust the adaptive parameter η and the number C of fuzzy clustering centers based on the updated fuzzy clustering centers; The adjustment formula of the adaptive parameter is: In the above formula, γ is a constant used to adjust the influence of η; mean(||x i -v C ||) is the average Euclidean distance from evaluation object i to all cluster centers; The adjustment strategy of the number C of fuzzy cluster centers is: first calculate the cluster mean square error, if the cluster mean square error is greater than the error threshold, then increase the number of fuzzy cluster centers, otherwise stop increasing the number of fuzzy cluster centers; the calculation formula of the cluster mean square error is: B6. Return to B3 to iterate until the iteration termination condition is met, and output the membership matrix obtained from the last iteration.

4. The method for evaluating the degree of standardization of power transmission and transformation project department establishment according to claim 1 is characterized by: The S4 includes: C1, weight vector W obtained based on S2 = (w1,w2,…,w M ) and the membership matrix obtained by S3, and calculate the comprehensive evaluation vector B of each evaluation object; C2. Calculate the score of each evaluation object according to the following formula: In the above formula, V i is the score of the evaluation object i; B r is the rth element in the comprehensive evaluation vector B, indicating the comprehensive membership of the evaluation object at the rth evaluation level; va r is the score value of the rth evaluation level; C3. Evaluate the degree of standardization based on the score of each evaluation object.

5. A system for evaluating the degree of standardization of power transmission and transformation project department, characterized by: The evaluation system includes: The data acquisition construction module is used to construct the standardization evaluation index system of the power transmission and transformation project department and obtain the value of each evaluation index; A weight determination module is used to calculate the weight of each evaluation index in the power transmission and transformation project evaluation index system based on the value of each evaluation index using the improved CRITIC method; A membership matrix calculation module is used to obtain the membership matrix through an adaptive fuzzy clustering method; The evaluation module is used to generate the final evaluation results of the standardization degree of the power transmission and transformation project department based on the membership matrix and the weights of each evaluation index by using fuzzy comprehensive evaluation.

6. A power transmission and transformation project department establishment standardization evaluation system according to claim 5, characterized in that: The weight determination module is used to calculate the weight of each evaluation index according to the following steps: A1. Calculate the standard deviation of each evaluation index; for the jth evaluation index, its standard deviation σ j The calculation formula is: In the above formula, Z ij is the value of evaluation object i after preprocessing of the jth evaluation index; is the mean value of the jth evaluation index; N is the total number of evaluation objects; A2. Calculate the distance matrix of each evaluation index. For the jth evaluation index, first calculate the Euclidean distance D between evaluation object i and evaluation object l under the jth evaluation index according to the following formula: j (i, l), after completing the Euclidean distance calculation of all evaluation objects under the jth evaluation index, the distance matrix D of the jth evaluation index is obtained j : D j (i,l)=|Z ij -Z lj |; In the above formula, Z lj is the value of the evaluation object l after preprocessing of the jth evaluation index; A3. Centralize the distance matrix of each evaluation index; for the distance matrix D of the jth evaluation index j , according to the following formula, the centralized distance matrix A is obtained by centralization j : In the above formula, A j (i,l) is the central distance matrix A of the jth evaluation index j Elements in represents the average distance between evaluation object i and other evaluation objects, and m is the index variable for traversing evaluation objects; Represents the average distance between evaluation object l and other evaluation objects; Represents the overall average distance between all evaluation objects; n is the index variable for traversing the evaluation objects; A4. Perform linear fitting on the centralized distance matrix of each evaluation index using the least squares method: In the above formula, A j,Z The central distance matrix A of the jth evaluation index using the control variable Z is j The matrix obtained after linear fitting; β z is the regression coefficient; is the centralized distance matrix A of the control variable Z Z The elements in; L is the number of indicators of the control variable Z; A5. Calculate the conditional distance correlation coefficient between each evaluation index: In the above formula, CdCor jk|Z Indicates the degree of nonlinear correlation between the jth evaluation index and the kth evaluation index; dCov jk|Z is the conditional distance covariance between the jth evaluation index and the kth evaluation index; dVar j|Z , dVar k|Z are the conditional distance variances of the jth and kth evaluation indicators respectively; A j,Z (i,l) is the matrix A j,Z Elements in A k,Z (i,l) is the matrix A k,Z Elements in A k (i,l) is the central distance matrix A of the kth evaluation index k Elements of A6. Calculate the weight of each evaluation index: In the above formula, C j is the CRITIC value of the jth evaluation index; M is the total number of evaluation indicators; w j is the weight of the jth evaluation index.

7. A power transmission and transformation project department establishment standardization evaluation system according to claim 5, characterized in that: The membership matrix calculation module is used to calculate the membership matrix according to the following steps: B1. Define the membership function and construct the initial membership matrix: In the above formula, R is the membership matrix, and the elements in the membership matrix are μ ic , μ ic It represents the membership of evaluation object i to cluster center c; N is the total number of evaluation objects; C is the total number of cluster centers; B2. Initialize the fuzzy cluster centers and their number; B3. Update the membership matrix according to the following formula: In the above formula, ||x i -v c || is the Euclidean distance between the evaluation object i and the cth fuzzy cluster center; ||x i -v h || is the Euclidean distance between the evaluation object i and the hth fuzzy cluster center; β is the parameter for controlling the fuzziness of membership; λ is the characteristic parameter; η is the adaptive parameter; x i 、v c 、v h are the characteristic vectors of evaluation object i, cth fuzzy cluster center, and hth fuzzy cluster center respectively; x ij is the value of the evaluation index of the evaluation object i at the jth level; v cj 、v hj are the values ​​of the cth and hth fuzzy cluster centers at the jth evaluation index respectively; B4. Update the cluster center based on the updated membership matrix: In the above formula, v′ c is the updated cth fuzzy cluster center; α is the adjustment parameter; g(c j ) is the regularization term; B5. Adjust the adaptive parameter η and the number C of fuzzy clustering centers based on the updated fuzzy clustering centers; The adjustment formula of the adaptive parameter is: In the above formula, γ is a constant used to adjust the influence of η; mean(||x i -v C ||) is the average Euclidean distance from evaluation object i to all cluster centers; The adjustment strategy of the number C of fuzzy cluster centers is: first calculate the cluster mean square error, if the cluster mean square error is greater than the error threshold, then increase the number of fuzzy cluster centers, otherwise stop increasing the number of fuzzy cluster centers; the calculation formula of the cluster mean square error is: B6. Return to B3 to iterate until the iteration termination condition is met, and output the membership matrix obtained from the last iteration.

8. A power transmission and transformation project department establishment standardization evaluation system according to claim 5, characterized in that: The evaluation module is used to generate the final standardization evaluation result of the power transmission and transformation project department according to the following steps: C1, weight vector W obtained based on S2 = (w1, w2, ..., w M ) and the membership matrix obtained by S3, and calculate the comprehensive evaluation vector B of each evaluation object; C2. Calculate the score of each evaluation object according to the following formula: In the above formula, V i is the score of the evaluation object i; B r is the rth element in the comprehensive evaluation vector B, indicating the comprehensive membership of the evaluation object at the rth evaluation level; va r is the score value of the rth evaluation level; C3. Generate the final evaluation result of the degree of standardization of the power transmission and transformation project department based on the score value of each evaluation object.

9. A device for evaluating the degree of standardization of power transmission and transformation project department, characterized by: The evaluation device includes a memory and a processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute the evaluation method according to any one of claims 1 to 4 according to the instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the evaluation method according to any one of claims 1 to 4 is implemented.

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