Project quality evaluation method and system based on transmission and transformation project electronic archive

Abstract

The invention belongs to the technical field of power transmission and transformation project quality evaluation, and particularly relates to a project quality evaluation method and system based on a power transmission and transformation project electronic archive, and the method comprises the steps: firstly constructing a project quality evaluation index system, and then determining subjective and objective weights of each evaluation index in the index system based on an improved G1 method and an improved Critic method, and then determining a comprehensive weight based on the subjective weight and the objective weight of the evaluation index, then calculating a relative close degree of the to-be-evaluated project by using an improved TOPSIS method based on the comprehensive weight of the evaluation index, and finally realizing project quality evaluation of the to-be-evaluated project based on the relative close degree. According to the method, the improved G1 method and the improved Critic method are used for calculating the subjective weight and the objective weight respectively, then the comprehensive weight is determined, subjective factors of expert evaluation and objective factors contained in indexes are comprehensively considered, the influence degree of each evaluation index on quality evaluation can be analyzed more accurately, and then the project quality evaluation precision is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power transmission and transformation engineering quality evaluation, and particularly relates to an engineering quality evaluation method and system based on power transmission and transformation engineering electronic archives. BACKGROUND

[0002] As an important part of power grid engineering, the engineering quality of power transmission and transformation engineering is directly related to the safe operation of the power system. Through quality evaluation of power transmission and transformation engineering, not only can the risk be reduced, the reliability and safety of the engineering be improved, but also a reference basis can be provided for subsequent engineering construction, so the quality evaluation of power transmission and transformation engineering is one of the most important links in power grid construction. The process of power transmission and transformation engineering is relatively complex and involves a wide range of fields, so it is difficult to comprehensively evaluate the quality of power transmission and transformation engineering.

[0003] Through the search of the prior art documents, it is found that the document "Power consumption state evaluation of important power customers based on AHP-TOPSIS algorithm" determines the index weight based on AHP, and obtains the final score value through the TOPSIS comprehensive evaluation method. The document "Research on state evaluation of relay protection devices based on gray correlation degree" assigns the index weight based on the entropy method, and then quantitatively compares the states of each device using the gray correlation. However, the above documents all use a single subjective or objective evaluation method, ignoring the dual importance of subjective and objective information, which is not enough to reflect the comprehensive influence of the index. The document "Power system adequacy evaluation method based on hierarchical classification and improved AHP-EW-TOPSIS" determines the subjective and objective weights of the index by using the improved AHP method and the EW method, and combines the TOPSIS method to obtain the comprehensive score of each scene. The document "Evaluation of ship power system resilience based on IAHP-CRITIC two-dimensional cloud model" improves the judgment matrix of AHP to improve the calculation accuracy of the subjective weight, and determines the objective weight based on the Critic method, and finally combines the subjective and objective weights by using the improved game theory. However, the calculation accuracy of the combined weight and the final evaluation result still needs to be further improved. SUMMARY

[0004] The purpose of the present application is to provide an engineering quality evaluation method and system based on power transmission and transformation engineering electronic archives, which can more accurately analyze the influence of each evaluation index on the quality evaluation, and thus improve the engineering quality evaluation accuracy.

[0005] To achieve the above purpose, the technical solutions of the present application are as follows:

[0006] In a first aspect, the present application provides an engineering quality evaluation method based on power transmission and transformation engineering electronic archives, which comprises:

[0007] S1. Construct an engineering quality evaluation index system;

[0008] S2. Based on the improved G1 method, determine the subjective weights of each evaluation indicator in the engineering quality evaluation index system. Based on the improved Critic method, determine the objective weights of each evaluation indicator in the engineering quality evaluation index system. Then, based on the subjective weights and objective weights of the evaluation indicators, determine their comprehensive weights.

[0009] S3. Based on the comprehensive weight of the evaluation indicators, the relative proximity of the object to be evaluated is calculated using the improved TOPSIS method, and the engineering quality assessment of the object to be evaluated is realized based on the relative proximity.

[0010] In S2, the subjective weights of each evaluation index in the engineering quality evaluation index system determined based on the improved G1 method include:

[0011] A1. Based on expert experience, the importance of each evaluation indicator in the engineering quality evaluation index system is ranked, resulting in the following ranking: [Evaluation Indicator 1, Evaluation Indicator 2, ... Evaluation Indicator ], The total number of evaluation indicators;

[0012] A2. Calculate the coefficient of variation for each evaluation indicator based on the ranking results in A1:

[0013] ;

[0014] ;

[0015] In the above formula, For the first The coefficient of variation of each evaluation indicator; For the first The standard deviation of each evaluation indicator; For the first The average evaluation value of each evaluation indicator; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated;

[0016] A3. Calculate the relative importance of each evaluation indicator:

[0017] ;

[0018] In the above formula, For the first The relative importance of each evaluation indicator; As a correction factor;

[0019] A4. Calculate the relative weights of each evaluation indicator:

[0020] ;

[0021] ;

[0022] In the above formula, represents the relative weight of the th evaluation index; , respectively represent the relative weight of the th and the th evaluation index; represents the cumulative value from to , represents the sum of all cumulative values from to ;

[0023] A5, based on the relative weight value of each evaluation index, the evaluation indexes are sorted in descending order to obtain a descending order result: ;

[0024] A6, based on the descending order result of A5, the position weight of each evaluation index is calculated:

[0025] ;

[0026] In the above formula, is the position weight of the th evaluation index; is the combination number of taking elements from elements;

[0027] A7, the subjective weight of each evaluation index is calculated:

[0028] ;

[0029] ;

[0030] In the above formula, is the absolute weight of the th evaluation index; is the position weight of the th evaluation index; is the subjective weight of the th evaluation index.

[0031] In S2, the determination of the objective weight of each evaluation index in the engineering quality evaluation index system based on the improved Critic method comprises:

[0032] B1, constructing an evaluation matrix:

[0033] ;

[0034] In the above formula, For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators;

[0035] B2. The standardized matrix is ​​obtained by standardizing the evaluation matrix according to the following formula:

[0036] ;

[0037] ;

[0038] In the above formula, A standardized matrix; To The standardized value obtained after standardization processing; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0039] B3. Calculate the comparative strength of each evaluation indicator:

[0040] ;

[0041] ;

[0042] In the above formula, For the first The inverse entropy value of each evaluation indicator; To The normalized value obtained after normalization processing;

[0043] B4. Calculate the total information content of each evaluation indicator:

[0044] ;

[0045] In the above formula, For the first The comprehensive information content of each evaluation indicator; For the first The evaluation index and the first Pearson correlation coefficients among the evaluation indicators;

[0046] B5. Calculate the objective weights of each evaluation indicator:

[0047] ;

[0048] In the above formula, For the first Objective weights of each evaluation indicator.

[0049] In S2, determining the comprehensive weight based on the subjective and objective weights of the evaluation indicators includes:

[0050] ;

[0051] In the above formula, For the first The comprehensive weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator; For the first The objective weight of each evaluation indicator; As a neutral element, its value is... ; The total number of evaluation indicators; for and The smaller one; for and The larger one.

[0052] In S3, the calculation of the relative closeness of the objects to be evaluated using the improved TOPSIS method includes:

[0053] C1. Construct a standardized weighted matrix based on the comprehensive weights of the evaluation indicators and the standardized matrix:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] In the above formula, For a standardized weighted matrix, Represents the standardized weighted matrix The Middle Line 1 Column data; A standardized matrix; To The standardized value obtained after standardization processing; To evaluate the matrix; To the th evaluation index of the th object to be evaluated; The number of objects to be evaluated; The total number of evaluation indexes; The minimum value of all objects to be evaluated under the th index; The maximum value of all objects to be evaluated under the th index;

[0059] C2, determine the positive ideal solution and the negative ideal solution :

[0060] ;

[0061] ;

[0062] In the above formula, , are the positive ideal solution and the negative ideal solution of the th evaluation index, respectively;

[0063] C3, determine the virtual worst solution :

[0064] ;

[0065] In the above formula, is the virtual worst solution of the th evaluation index;

[0066] C4, use the grey correlation degree matrix to calculate the Mahalanobis distance between the objects to be evaluated and the positive ideal solution and the virtual worst solution:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] In the above formula, , respectively represent the Mahalanobis distance between the th object to be evaluated and the positive ideal solution and the virtual worst solution is the comprehensive weight matrix; is the grey correlation degree matrix; is the th evaluation index and the grey correlation degree of the evaluation indexes; denotes a normalized weight matrix th row and th column of the matrix ; is the minimum absolute difference between the evaluation values of the th to be evaluated object under the th and the th evaluation indexes; th to be evaluated object under the th and the th evaluation indexes; th evaluation indexes;

[0072] C5, calculating the relative closeness of the to be evaluated object:

[0073] ;

[0074] In the above formula, denotes the relative closeness of the th to be evaluated object.

[0075] In a second aspect, the present application provides an engineering quality evaluation system based on electronic archives of power transmission and transformation projects, which comprises:

[0076] an index system construction module, configured to construct an engineering quality evaluation index system;

[0077] a comprehensive weight determination module, configured to determine subjective weights of the evaluation indexes in the engineering quality evaluation index system based on an improved G1 method, determine objective weights of the evaluation indexes in the engineering quality evaluation index system based on an improved Critic method, and then determine comprehensive weights of the evaluation indexes based on the subjective weights and the objective weights of the evaluation indexes;

[0078] a quality evaluation module, configured to calculate relative closeness of a to be evaluated object by using an improved TOPSIS method based on the comprehensive weights of the evaluation indexes, and realize engineering quality evaluation of the to be evaluated object based on the relative closeness.

[0079] The comprehensive weight determination module comprises a subjective weight determination module, which is configured to determine subjective weights of the evaluation indexes in the engineering quality evaluation index system based on an improved G1 method, and the improved G1 method comprises:

[0080] A1, performing importance ordering on the evaluation indexes in the engineering quality evaluation index system according to expert experience, to obtain an ordering result: [evaluation index 1, evaluation index 2, …evaluation index ], is the total number of the evaluation indexes;

[0081] A2, calculate the coefficient of variation of each evaluation index based on the ranking result of A1:

[0082] ;

[0083] ;

[0084] In the above formula, is the coefficient of variation of the jth evaluation index; is the standard deviation of the jth evaluation index; is the average evaluation value of the jth evaluation index; is the evaluation value of the jth evaluation index in the ith to be evaluated object; is the number of to be evaluated objects; A3, calculate the relative importance of each evaluation index: ;

[0085] In the above formula, is the relative importance of the jth evaluation index;

[0086] is the correction factor; A4, calculate the relative weight of each evaluation index:

[0087] ;

[0088] In the above formula, represents the relative weight of the jth evaluation index;

[0089] , respectively represent the relative weight of the jth and the kth evaluation index; represents the cumulative value from i to j,

[0090] represents the sum of all cumulative values from i to j;

[0091] In the above formula, represents the relative weight of the jth evaluation index; , respectively represent the relative weight of the jth and the kth evaluation index; represents the cumulative value from i to j, represents the sum of all cumulative values from i to j; A5, sort each evaluation index in descending order based on the relative weight value of each evaluation index, and obtain the descending order sorting result: ;

[0092] A5, sort each evaluation index in descending order based on the relative weight value of each evaluation index, and obtain the descending order sorting result: ;

[0093] ​​​​​​A6. Calculate the positional weights of each evaluation indicator based on the descending sorting results of A5:

[0094] ;

[0095] In the above formula, For the first The positional weight of each evaluation indicator; From Take from elements The number of combinations of elements;

[0096] A7. Calculate the subjective weights of each evaluation indicator:

[0097] ;

[0098] ;

[0099] In the above formula, For the first The absolute weight of each evaluation indicator; For the first The positional weight of each evaluation indicator; For the first Subjective weights of each evaluation indicator.

[0100] The comprehensive weight determination module further includes an objective weight determination module, which is used to determine the objective weights of each evaluation index in the engineering quality evaluation index system based on the improved Critic method; the improved Critic method includes:

[0101] B1. Constructing the evaluation matrix:

[0102] ;

[0103] In the above formula, For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators;

[0104] B2. The standardized matrix is ​​obtained by standardizing the evaluation matrix according to the following formula:

[0105] ;

[0106] ;

[0107] In the above formula, A standardized matrix; To The standardized value obtained after standardization processing; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0108] B3. Calculate the comparative strength of each evaluation indicator:

[0109] ;

[0110] ;

[0111] In the above formula, For the first The inverse entropy value of each evaluation indicator; To The normalized value obtained after normalization processing;

[0112] B4. Calculate the total information content of each evaluation indicator:

[0113] ;

[0114] In the above formula, For the first The comprehensive information content of each evaluation indicator; For the first The evaluation index and the first Pearson correlation coefficients among the evaluation indicators;

[0115] B5. Calculate the objective weights of each evaluation indicator:

[0116] ;

[0117] In the above formula, For the first Objective weights of each evaluation indicator.

[0118] The comprehensive weight determination module further includes a comprehensive weight calculation module, which is used to calculate the comprehensive weight of each evaluation index in the engineering quality evaluation index system according to the following formula:

[0119] ;

[0120] In the above formula, For the first The comprehensive weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator; For the first The objective weights of each evaluation indicator; As a neutral element, its value is... ; The total number of evaluation indicators; for and The smaller one; for and The larger one.

[0121] The quality assessment module is used to calculate the relative closeness of the objects to be assessed using the improved TOPSIS method, which includes:

[0122] C1. Construct a standardized weighted matrix based on the comprehensive weights of the evaluation indicators and the standardized matrix:

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] In the above formula, For a standardized weighted matrix, Represents the standardized weighted matrix The Middle Line number Column data; A standardized matrix; To The standardized value obtained after standardization processing; For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0128] C2. Determine the ideal solution and negative ideal solution :

[0129] ;

[0130] ;

[0131] In the above formula, , The first Positive and negative ideal solutions for each evaluation index;

[0132] C3. Determine the virtual worst solution :

[0133] ;

[0134] In the above formula, For the first The virtual worst-case solution for each evaluation metric;

[0135] C4. Calculate the Mahalanobis distance between the object to be evaluated and the positive ideal solution and the virtual worst solution using the grey relational degree matrix:

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] In the above formula, , They represent the first Mahalanobis distance between each object to be evaluated and the ideal solution and the virtual worst solution This is the comprehensive weight matrix; This is the gray relational degree matrix; For the first The first evaluation indicator and the first Grey relational degree of each evaluation indicator; Represents the standardized weighted matrix The Middle Line 1 Column data; For the first The first subject to be evaluated was in the first The and the first The minimum absolute difference between evaluation values ​​under each evaluation indicator; For the first The first subject to be evaluated was in the first The and the first The maximum absolute difference between evaluation values ​​under each evaluation indicator;

[0141] C5. Calculate the relative proximity of the objects to be evaluated:

[0142] ;

[0143] In the above formula, Indicates the first The relative closeness of the objects to be evaluated.

[0144] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0145] 1. The engineering quality assessment method based on electronic archives of power transmission and transformation projects described in this invention first constructs an engineering quality evaluation index system. Then, based on the improved G1 method and the improved Critic method, the subjective and objective weights of each evaluation index in the index system are determined respectively. Next, based on the subjective and objective weights of the evaluation indicators, their comprehensive weights are determined. Subsequently, based on the comprehensive weights of the evaluation indicators, the relative closeness of the evaluated object is calculated using the improved TOPSIS method. Finally, the engineering quality assessment of the evaluated object is achieved based on the relative closeness. The above design uses the improved G1 method and the improved Critic method to calculate the subjective and objective weights respectively, and then determines the comprehensive weight. It comprehensively considers the subjective factors of expert evaluation and the objective factors inherent in the indicators themselves, enabling a more accurate analysis of the influence of each evaluation index on the quality assessment, thereby improving the accuracy of engineering quality assessment. Therefore, this invention can more accurately analyze the influence of each evaluation index on the quality assessment, thereby improving the accuracy of engineering quality assessment.

[0146] 2. The engineering quality assessment method based on electronic archives of power transmission and transformation projects described in this invention improves the G1 method by calculating the relative importance between adjacent indices using the coefficient of variation. This replaces the subjective determination of the importance of adjacent indicators in the traditional G1 method, avoiding the subjectivity problem in determining relative importance. Simultaneously, by sorting the relative weights obtained based on relative importance in descending order, and then readjusting the weights for the evaluation indicators according to the descending order, the subjective bias caused by the initial weights is further reduced, thus determining subjective weights more reasonably. Therefore, this invention can more reasonably determine subjective weights.

[0147] 3. The engineering quality assessment method based on electronic archives of power transmission and transformation projects described in this invention introduces a reverse entropy value to replace the standard deviation in the Critic weighting method, thereby more comprehensively quantifying the information content of each indicator, more accurately assessing the comparative strength of the indicators, and thus more reasonably determining the objective weights. Therefore, this invention can more reasonably determine the objective weights.

[0148] 4. The engineering quality assessment method based on electronic archives of power transmission and transformation projects described in this invention improves the TOPSIS method by introducing Mahalanobis distance instead of Euclidean distance. Compared to the traditional TOPSIS method, which calculates the Euclidean distance between each scheme and the positive and negative ideal points, the Mahalanobis distance is easily affected when the index information is highly repetitive, leading to evaluation results biased towards these similar indicators. Mahalanobis distance considers the distribution differences of information and can distinguish the distribution differences of points on the vertical line between the positive and negative ideal points, thereby reducing the interference of similar information. At the same time, a grey relational degree matrix is ​​used instead of the covariance matrix in the Mahalanobis distance calculation, reducing the interference of extreme values ​​on the evaluation results and forming more accurate evaluation results. Therefore, this invention can produce more accurate evaluation results. Attached Figure Description

[0149] Figure 1 This is a flowchart of the method described in this invention.

[0150] Figure 2 This is a structural block diagram of the system described in this invention.

[0151] Figure 3 The results show the relative proximity calculations of the proposed method and the traditional TOPSIS method for five power transmission and transformation projects. Detailed Implementation

[0152] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0153] Example 1:

[0154] See Figure 1 A method for assessing the quality of power transmission and transformation projects based on electronic archives is proposed, which proceeds in the following steps:

[0155] S1. Construct an engineering quality evaluation index system; the engineering quality evaluation index system includes the design phase. Construction phase Acceptance stage Design phase Including design specifications Completeness of design documents Construction phase Including construction technology level Material quality Reliability of construction equipment Construction progress Acceptance phase Including the completeness of acceptance documents Equipment durability Subsequent maintenance costs Among them, subsequent maintenance costs are a negative indicator, while all other indicators are positive indicators.

[0156] S2. Based on the improved G1 method, determine the subjective weights of each evaluation indicator in the engineering quality evaluation index system. Based on the improved Critic method, determine the objective weights of each evaluation indicator in the engineering quality evaluation index system. Then, based on the subjective weights and objective weights of the evaluation indicators, determine their comprehensive weights.

[0157] Specifically, the subjective weights of each evaluation index in the engineering quality evaluation index system determined based on the improved G1 method include:

[0158] A1. Based on expert experience, the importance of each evaluation indicator in the engineering quality evaluation index system is ranked, resulting in the following ranking: [Evaluation Indicator 1, Evaluation Indicator 2, ... Evaluation Indicator ], The total number of evaluation indicators;

[0159] A2. Calculate the coefficient of variation for each evaluation indicator based on the ranking results in A1:

[0160] ;

[0161] ;

[0162] In the above formula, For the first The coefficient of variation of each evaluation indicator; For the first The standard deviation of each evaluation indicator; For the first The average evaluation value of each evaluation indicator; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated;

[0163] A3. Calculate the relative importance of each evaluation indicator:

[0164] ;

[0165] In the above formula, For the first The relative importance of each evaluation indicator; As a correction factor, to avoid the coefficients of variation of adjacent indicators being too small, thus failing to accurately reflect their relative importance, the ratio is... or Then add a correction factor To reflect the significant differences that exist in the actual situation, The value is 0.2;

[0166] A4. Calculate the relative weights of each evaluation indicator:

[0167] ;

[0168] ;

[0169] In the above formula, Indicates the first The relative weights of each evaluation indicator; , They represent the first The, the The relative weights of each evaluation indicator; Indicates to from arrive Accumulate, Indicates to from arrive Sum all the accumulated values;

[0170] A5. Based on the relative weight values ​​of each evaluation indicator, sort the evaluation indicators in descending order to obtain the descending order ranking result: ;

[0171] A6. Calculate the positional weights of each evaluation indicator based on the descending sorting results of A5:

[0172] ;

[0173] In the above formula, For the first The positional weight of each evaluation indicator; From Take from elements The number of combinations of n elements can be represented as ;

[0174] A7. Calculate the subjective weights of each evaluation indicator:

[0175] ;

[0176] ;

[0177] In the above formula, For the first The absolute weight of each evaluation indicator; For the first The positional weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator;

[0178] The traditional Critic method measures the strength of comparison using standard deviation. Standard deviation only captures the dispersion and volatility of data, making it highly sensitive to differences between indicators. This can lead to the loss of some indicator information during calculation, failing to fully reflect all the information within the data. Standard deviation measures data volatility, not the richness of its inherent information. In contrast, this invention uses inverse entropy to assess the comparative strength of indicators. Inverse entropy is less sensitive to differences between indicators, reducing the impact of indicator variability on the calculation results. Furthermore, it can more comprehensively quantify the information contained in each indicator, thus more accurately assessing the comparative strength of indicators. Specifically, the objective weights of each evaluation indicator in the engineering quality evaluation indicator system determined based on the improved Critic method include:

[0179] B1. Constructing the evaluation matrix:

[0180] ;

[0181] In the above formula, For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators;

[0182] B2. The standardized matrix is ​​obtained by standardizing the evaluation matrix according to the following formula:

[0183] ;

[0184] ;

[0185] In the above formula, A standardized matrix; To The standardized value obtained after standardization processing; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0186] B3. Calculate the comparative strength of each evaluation indicator:

[0187] ;

[0188] ;

[0189] In the above formula, For the first The inverse entropy value of each evaluation indicator; To The normalized value obtained after normalization processing;

[0190] B4. Calculate the total information content of each evaluation indicator:

[0191] ;

[0192] In the above formula, For the first The comprehensive information content of each evaluation indicator; For the first The evaluation index and the first The Pearson correlation coefficient between the evaluation indicators; the formula for calculating the Pearson correlation coefficient is:

[0193] ;

[0194] In the above formula, For the standardized matrix The Middle The value of the k-th indicator among the evaluation objects; For the standardized matrix The mean of the k-th indicator; For the normalized matrix Y, the first... The first evaluation object The value of each indicator; For the standardized matrix The Middle The average of the indicators;

[0195] B5. Calculate the objective weights of each evaluation indicator:

[0196] ;

[0197] In the above formula, For the first The objective weights of each evaluation indicator;

[0198] Traditional weighted combination methods typically involve directly summing the weighted values ​​of various indicators. This method is susceptible to extreme values ​​of certain indicators, leading to an imbalance in the overall weights. The Uninorm operator, however, exhibits greater robustness and, through its non-linear characteristics, balances the mutual influence between indicators, reducing excessive interference from extreme values ​​and enhancing the rationality of the combined results. Specifically, based on the subjective and objective weights of the evaluation indicators, the overall weights are determined using the Uninorm operator, including:

[0199] ;

[0200] In the above formula, For the first The comprehensive weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator; For the first The objective weight of each evaluation indicator; As a neutral element, its value is... ; The total number of evaluation indicators; for and The smaller one; for and The larger one;

[0201] S3. Based on the comprehensive weight of the evaluation indicators, the relative proximity of the object to be evaluated is calculated using the improved TOPSIS method. The engineering quality of the object to be evaluated is then assessed based on the relative proximity. The larger the value of the relative proximity, the better the engineering quality.

[0202] Specifically, calculating the relative closeness of the objects to be evaluated using the improved TOPSIS method includes:

[0203] C1. Construct a standardized weighted matrix based on the comprehensive weights of the evaluation indicators and the standardized matrix:

[0204] ;

[0205] ;

[0206] ;

[0207] ;

[0208] In the above formula, For a standardized weighted matrix, Represents the standardized weighted matrix The Middle Line 1 Column data; A standardized matrix; To The standardized value obtained after standardization processing; For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0209] C2. Determine the ideal solution and negative ideal solution :

[0210] ;

[0211] ;

[0212] In the above formula, , The first Positive and negative ideal solutions for each evaluation index;

[0213] C3. Determine the virtual worst solution The traditional TOPSIS method evaluates each solution by calculating its distance from the ideal solution and the negative ideal solution; the closer a solution is to the ideal solution and the farther it is from the negative ideal solution, the better. However, if the difference between the positive and negative ideal solutions is small, the merits of the solutions are not obvious. This invention introduces a virtual worst-case solution, combining the optimal and worst solutions to further amplify the gap. It expands the worst-case solution to a wider range, making the evaluation not only limited to existing data but also extending to potential extreme worst-case scenarios. This makes the comparison between different solutions more obvious and enhances the ability to differentiate between them.

[0214] ;

[0215] In the above formula, For the first The virtual worst-case solution for each evaluation metric;

[0216] C4. Calculate the Mahalanobis distance between the object to be evaluated and the positive ideal solution and the virtual worst solution using the grey relational degree matrix. Traditional Mahalanobis distance calculates the covariance matrix by the deviation of data from the mean, which is greatly affected by extreme values, significantly raising or lowering the mean and amplifying the deviation. After introducing grey relational degree, the difference between each pair of indicators is normalized by the global maximum and global minimum differences, making the calculation process independent of the absolute magnitude of the variables, thus effectively reducing the interference of extreme values.

[0217] ;

[0218] ;

[0219] ;

[0220] ;

[0221] In the above formula, , They represent the first Mahalanobis distance between each object to be evaluated and the ideal solution and the virtual worst solution This is the comprehensive weight matrix; This is the gray relational degree matrix; For the first The first evaluation indicator and the first Grey relational degree of each evaluation indicator; Represents the standardized weighted matrix The Middle Line 1 Column data; For the first The first subject to be evaluated was in the first The and the first The minimum absolute difference between evaluation values ​​under each evaluation indicator; For the first The first subject to be evaluated was in the first The and the first The maximum absolute difference between evaluation values ​​under each evaluation indicator;

[0222] C5. Calculate the relative proximity of the objects to be evaluated:

[0223] ;

[0224] In the above formula, Indicates the first The relative closeness of the objects to be evaluated.

[0225] Performance verification:

[0226] The quality assessment of five power transmission and transformation projects was conducted using the method proposed in this invention and the traditional TOPSIS method. The subjective weight, objective weight, and comprehensive weight obtained by the method proposed in this invention are shown in Tables 1 and 2, respectively. The results of the relative closeness calculation are as follows: Figure 3 As shown.

[0227] Table 1 Subjective weights obtained by the method proposed in this invention

[0228]

[0229] Table 2. Objective weights and comprehensive weights obtained by the method proposed in this invention.

[0230]

[0231] As shown in Table 1, the construction phase has the highest weight (0.498) among all phases. This is because the construction phase is the core part affecting project quality, involving more and broader influencing factors compared to the basic design and acceptance phases. Among these, the level of construction technology directly determines the final physical quality of the project, hence its highest weight (0.364). The weights for material quality, construction equipment reliability, and construction progress are 0.243, 0.229, and 0.164, respectively. In the design phase, design standardization is the core indicator for evaluating design quality. Non-standard design may lead to deviations from the overall project quality, thus its weight is even higher (0.542). The weight for the completeness of design documents is 0.458. In the acceptance phase, project durability has the highest weight (0.398) because equipment durability is crucial for the long-term operation of the project, thus its importance is higher. The weights for the completeness of acceptance documents and subsequent maintenance costs are 0.345 and 0.257, respectively.

[0232] Depend on Figure 3 It can be seen that the engineering quality ranking results obtained by the method proposed in this invention are: power transmission and transformation project 5 > power transmission and transformation project 4 > power transmission and transformation project 3 > power transmission and transformation project 2 > power transmission and transformation project 1, while the engineering quality ranking results obtained by the traditional TOPSIS method are: power transmission and transformation project 5 > power transmission and transformation project 3 > power transmission and transformation project 4 > power transmission and transformation project 2 > power transmission and transformation project 1. There is a certain difference between the two. This is because the method proposed in this invention uses the improved G1 method and the improved Critic method to achieve a weight allocation with higher accuracy and smaller error. At the same time, the improved TOPSIS method solves the distance closeness problem in the traditional TOPSIS method, which can more accurately reflect the real differences between different projects.

[0233] Example 2:

[0234] See Figure 2 A system for evaluating the quality of power transmission and transformation projects based on electronic archives includes an indicator system construction module, a comprehensive weight determination module, and a quality evaluation module. The indicator system construction module is used to construct an indicator system for evaluating the quality of the project. The comprehensive weight determination module is used to determine the subjective weights of each evaluation indicator in the indicator system based on an improved G1 method, determine the objective weights of each evaluation indicator based on an improved Critic method, and then determine the comprehensive weight based on the subjective and objective weights of the evaluation indicators. The comprehensive weight determination module includes a subjective weight determination module, an objective weight determination module, and a comprehensive weight calculation module. The subjective weight determination module is used to determine the subjective weights of each evaluation indicator in the indicator system based on the improved G1 method, which includes:

[0235] A1. Based on expert experience, the importance of each evaluation indicator in the engineering quality evaluation index system is ranked, resulting in the following ranking: [Evaluation Indicator 1, Evaluation Indicator 2, ... Evaluation Indicator ], The total number of evaluation indicators;

[0236] A2. Calculate the coefficient of variation for each evaluation indicator based on the ranking results in A1:

[0237] ;

[0238] ;

[0239] In the above formula, For the first The coefficient of variation of each evaluation indicator; For the first The standard deviation of each evaluation indicator; For the first The average evaluation value of each evaluation indicator; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated;

[0240] A3. Calculate the relative importance of each evaluation indicator:

[0241] ;

[0242] In the above formula, For the first The relative importance of each evaluation indicator; As a correction factor;

[0243] A4. Calculate the relative weights of each evaluation indicator:

[0244] ;

[0245] ;

[0246] In the above formula, Indicates the first The relative weights of each evaluation indicator; , They represent the first The, the The relative weights of each evaluation indicator; Indicates to from arrive Accumulate, Indicates to from arrive Sum all the accumulated values;

[0247] A5. Based on the relative weight values ​​of each evaluation indicator, sort the evaluation indicators in descending order to obtain the descending order ranking result: ;

[0248] A6. Calculate the positional weights of each evaluation indicator based on the descending sorting results of A5:

[0249] ;

[0250] In the above formula, For the first The positional weight of each evaluation indicator; From Take from elements The number of combinations of elements;

[0251] A7. Calculate the subjective weights of each evaluation indicator:

[0252] ;

[0253] ;

[0254] In the above formula, For the first The absolute weight of each evaluation indicator; For the first The positional weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator;

[0255] The objective weight determination module is used to determine the objective weights of each evaluation index in the engineering quality evaluation index system based on the improved Critic method; the improved Critic method includes:

[0256] B1. Constructing the evaluation matrix:

[0257] ;

[0258] In the above formula, For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators;

[0259] B2. The standardized matrix is ​​obtained by standardizing the evaluation matrix according to the following formula:

[0260] ;

[0261] ;

[0262] In the above formula, A standardized matrix; To The standardized value obtained after standardization processing; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0263] B3. Calculate the comparative strength of each evaluation indicator:

[0264] ;

[0265] ;

[0266] In the above formula, For the first The inverse entropy value of each evaluation indicator; To The normalized value obtained after normalization processing;

[0267] B4. Calculate the total information content of each evaluation indicator:

[0268] ;

[0269] In the above formula, For the first The comprehensive information content of each evaluation indicator; For the first The evaluation index and the first Pearson correlation coefficients among the evaluation indicators;

[0270] B5. Calculate the objective weights of each evaluation indicator:

[0271] ;

[0272] In the above formula, For the first The objective weight of each evaluation indicator;

[0273] The comprehensive weight calculation module is used to calculate the comprehensive weight of each evaluation indicator in the engineering quality evaluation index system according to the following formula:

[0274] ;

[0275] In the above formula, For the first The comprehensive weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator; For the first The objective weight of each evaluation indicator; As a neutral element, its value is... ; The total number of evaluation indicators; for and The smaller one; for and The larger one;

[0276] The quality assessment module is used to calculate the relative proximity of the object to be assessed based on the comprehensive weight of the evaluation indicators using the improved TOPSIS method, and to achieve the engineering quality assessment of the object to be assessed based on the relative proximity; the improved TOPSIS method includes:

[0277] C1. Construct a standardized weighted matrix based on the comprehensive weights of the evaluation indicators and the standardized matrix:

[0278] ;

[0279] ;

[0280] ;

[0281] ;

[0282] In the above formula, For a standardized weighted matrix, Represents the standardized weighted matrix The Middle Line 1 Column data; A standardized matrix; To The standardized value obtained after standardization processing; For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator;

[0283] C2. Determine the ideal solution and negative ideal solution :

[0284] ;

[0285] ;

[0286] In the above formula, , The first Positive and negative ideal solutions for each evaluation index;

[0287] C3. Determine the virtual worst solution :

[0288] ;

[0289] In the above formula, For the first The virtual worst-case solution for each evaluation metric;

[0290] C4. Calculate the Mahalanobis distance between the object to be evaluated and the positive ideal solution and the virtual worst solution using the grey relational degree matrix:

[0291] ;

[0292] ;

[0293] ;

[0294] ;

[0295] In the above formula, , They represent the first Mahalanobis distance between each object to be evaluated and the ideal solution and the virtual worst solution This is the comprehensive weight matrix; This is the gray relational degree matrix; For the first The first evaluation indicator and the first Grey relational degree of each evaluation indicator; Represents the standardized weighted matrix The Middle Line 1 Column data; For the first The first subject to be evaluated was in the first The and the first The minimum absolute difference between evaluation values ​​under each evaluation indicator; For the first The first subject to be evaluated was in the first The and the first The maximum absolute difference between evaluation values ​​under each evaluation indicator;

[0296] C5. Calculate the relative proximity of the objects to be evaluated:

[0297] ;

[0298] In the above formula, Indicates the first The relative closeness of the objects to be evaluated.

[0299] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied 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.

[0300] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0301] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0302] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0303] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the quality of power transmission and transformation projects based on electronic archives, characterized in that: The engineering quality assessment methods include: S1. Construct an engineering quality evaluation index system; S2. Based on the improved G1 method, determine the subjective weights of each evaluation indicator in the engineering quality evaluation index system. Based on the improved Critic method, determine the objective weights of each evaluation indicator in the engineering quality evaluation index system. Then, based on the subjective weights and objective weights of the evaluation indicators, determine their comprehensive weights. S3. Based on the comprehensive weight of the evaluation indicators, the relative proximity of the object to be evaluated is calculated using the improved TOPSIS method, and the engineering quality assessment of the object to be evaluated is realized based on the relative proximity.

2. The engineering quality assessment method based on electronic archives of power transmission and transformation projects according to claim 1, characterized in that: In S2, the subjective weights of each evaluation index in the engineering quality evaluation index system determined based on the improved G1 method include: A1. Based on expert experience, the importance of each evaluation indicator in the engineering quality evaluation index system is ranked, resulting in the following ranking: [Evaluation Indicator 1, Evaluation Indicator 2, ... Evaluation Indicator ], The total number of evaluation indicators; A2. Calculate the coefficient of variation for each evaluation indicator based on the ranking results in A1: ； ； In the above formula, For the first The coefficient of variation of each evaluation indicator; For the first The standard deviation of each evaluation indicator; For the first The average evaluation value of each evaluation indicator; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; A3. Calculate the relative importance of each evaluation indicator: ； In the above formula, For the first The relative importance of each evaluation indicator; As a correction factor; A4. Calculate the relative weights of each evaluation indicator: ； ； In the above formula, Indicates the first The relative weights of each evaluation indicator; , They represent the first The, the The relative weights of each evaluation indicator; Indicates to from arrive Accumulate, Indicates to from arrive Sum all the accumulated values; A5. Based on the relative weight values ​​of each evaluation indicator, sort the evaluation indicators in descending order to obtain the descending order ranking result: ; A6. Calculate the positional weights of each evaluation indicator based on the descending sorting results of A5: ； In the above formula, For the first The positional weight of each evaluation indicator; From Take from elements The number of combinations of elements; A7. Calculate the subjective weights of each evaluation indicator: ； ； In the above formula, For the first The absolute weight of each evaluation indicator; For the first The positional weight of each evaluation indicator; For the first Subjective weights of each evaluation indicator.

3. A method for engineering quality assessment based on electronic archives of power transmission and transformation projects according to claim 1 or 2, characterized in that: In S2, the objective weights of each evaluation index in the engineering quality evaluation index system determined based on the improved Critic method include: B1. Constructing the evaluation matrix: ； In the above formula, For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; B2. The standardized matrix is ​​obtained by standardizing the evaluation matrix according to the following formula: ； ； In the above formula, A standardized matrix; To The standardized value obtained after standardization processing; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator; B3. Calculate the comparative strength of each evaluation indicator: ； ； In the above formula, For the first The inverse entropy value of each evaluation indicator; To The normalized value obtained after normalization processing; B4. Calculate the total information content of each evaluation indicator: ； In the above formula, For the first The comprehensive information content of each evaluation indicator; For the first The evaluation index and the first Pearson correlation coefficients among the evaluation indicators; B5. Calculate the objective weights of each evaluation indicator: ； In the above formula, For the first Objective weights of each evaluation indicator.

4. A method for engineering quality assessment based on electronic archives of power transmission and transformation projects according to claim 1 or 2, characterized in that: In S2, determining the comprehensive weight based on the subjective and objective weights of the evaluation indicators includes: ； In the above formula, For the first The comprehensive weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator; For the first The objective weights of each evaluation indicator; As a neutral element, its value is... ; The total number of evaluation indicators; for and The smaller one; for and The larger one.

5. A method for engineering quality assessment based on electronic archives of power transmission and transformation projects according to claim 1 or 2, characterized in that: In S3, the calculation of the relative closeness of the objects to be evaluated using the improved TOPSIS method includes: C1. Construct a standardized weighted matrix based on the comprehensive weights of the evaluation indicators and the standardized matrix: ； ； ； ； In the above formula, For a standardized weighted matrix, Represents the standardized weighted matrix The Middle Line number Column data; A standardized matrix; To The standardized value obtained after standardization processing; For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator; C2. Determine the ideal solution and negative ideal solution : ； ； In the above formula, , The first Positive and negative ideal solutions for each evaluation index; C3. Determine the virtual worst solution : ； In the above formula, For the first The virtual worst-case solution for each evaluation metric; C4. Calculate the Mahalanobis distance between the object to be evaluated and the positive ideal solution and the virtual worst solution using the grey relational degree matrix: ； ； ； ； In the above formula, , They represent the first Mahalanobis distance between each object to be evaluated and the ideal solution and the virtual worst solution This is the comprehensive weight matrix; This is the gray relational degree matrix; For the first The first evaluation indicator and the first Grey relational degree of each evaluation indicator; Represents the standardized weighted matrix The Middle Line number Column data; For the first The first subject to be evaluated in the first The and the first The minimum absolute difference between evaluation values ​​under each evaluation indicator; For the first The first subject to be evaluated in the first The and the first The maximum absolute difference between evaluation values ​​under each evaluation indicator; C5. Calculate the relative proximity of the object to be evaluated: ； In the above formula, Indicates the first The relative closeness of the objects to be evaluated.

6. An engineering quality assessment system based on electronic archives of power transmission and transformation projects, characterized in that: The engineering quality assessment system includes: The indicator system construction module is used to construct an engineering quality evaluation indicator system; The comprehensive weight determination module is used to determine the subjective weights of each evaluation indicator in the engineering quality evaluation index system based on the improved G1 method, determine the objective weights of each evaluation indicator in the engineering quality evaluation index system based on the improved Critic method, and then determine the comprehensive weight based on the subjective weights and objective weights of the evaluation indicators. The quality assessment module is used to calculate the relative proximity of the object to be assessed using the improved TOPSIS method based on the comprehensive weight of the evaluation indicators, and to realize the engineering quality assessment of the object to be assessed based on the relative proximity.

7. The engineering quality assessment system based on electronic archives of power transmission and transformation projects according to claim 6, characterized in that: The comprehensive weight determination module includes a subjective weight determination module, which is used to determine the subjective weights of each evaluation index in the engineering quality evaluation index system based on the improved G1 method. The improved G1 method includes: A1. Based on expert experience, the importance of each evaluation indicator in the engineering quality evaluation index system is ranked, resulting in the following ranking: [Evaluation Indicator 1, Evaluation Indicator 2, ... Evaluation Indicator ], The total number of evaluation indicators; A2. Calculate the coefficient of variation for each evaluation indicator based on the ranking results in A1: ； ； In the above formula, For the first The coefficient of variation of each evaluation indicator; For the first The standard deviation of each evaluation indicator; For the first The average evaluation value of each evaluation indicator; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; A3. Calculate the relative importance of each evaluation indicator: ； In the above formula, For the first The relative importance of each evaluation indicator; As a correction factor; A4. Calculate the relative weights of each evaluation indicator: ； ； In the above formula, Indicates the first The relative weights of each evaluation indicator; , They represent the first The, the The relative weights of each evaluation indicator; Indicates to from arrive Accumulate, Indicates to from arrive Sum all the accumulated values; A5. Based on the relative weight values ​​of each evaluation indicator, sort the evaluation indicators in descending order to obtain the descending order ranking result: ; A6. Calculate the positional weights of each evaluation indicator based on the descending sorting results of A5: ； In the above formula, For the first The positional weight of each evaluation indicator; From Take from elements The number of combinations of elements; A7. Calculate the subjective weights of each evaluation indicator: ； ； In the above formula, For the first The absolute weight of each evaluation indicator; For the first The positional weight of each evaluation indicator; For the first Subjective weights of each evaluation indicator.

8. A project quality assessment system based on electronic archives of power transmission and transformation projects according to claim 6 or 7, characterized in that: The comprehensive weight determination module also includes an objective weight determination module, which is used to determine the objective weights of each evaluation index in the engineering quality evaluation index system based on the improved Critic method. The improved Critic method includes: B1. Constructing the evaluation matrix: ； In the above formula, For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; B2. The standardized matrix is ​​obtained by standardizing the evaluation matrix according to the following formula: ； ； In the above formula, A standardized matrix; To The standardized value obtained after standardization processing; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator; B3. Calculate the comparative strength of each evaluation indicator: ； ； In the above formula, For the first The inverse entropy value of each evaluation indicator; To The normalized value obtained after normalization processing; B4. Calculate the total information content of each evaluation indicator: ； In the above formula, For the first The comprehensive information content of each evaluation indicator; For the first The evaluation index and the first Pearson correlation coefficients among the evaluation indicators; B5. Calculate the objective weights of each evaluation indicator: ； In the above formula, For the first Objective weights of each evaluation indicator.

9. A project quality assessment system based on electronic archives of power transmission and transformation projects according to claim 6 or 7, characterized in that: The comprehensive weight determination module further includes a comprehensive weight calculation module, which is used to calculate the comprehensive weight of each evaluation index in the engineering quality evaluation index system according to the following formula: ； In the above formula, For the first The comprehensive weight of each evaluation indicator; For the first Subjective weighting of each evaluation indicator; For the first The objective weights of each evaluation indicator; As a neutral element, its value is... ; The total number of evaluation indicators; for and The smaller one; for and The larger one.

10. An engineering quality assessment system based on electronic archives of power transmission and transformation projects according to claim 6 or 7, characterized in that: The quality assessment module is used to calculate the relative closeness of the objects to be assessed using the improved TOPSIS method, which includes: C1. Construct a standardized weighted matrix based on the comprehensive weights of the evaluation indicators and the standardized matrix: ； ； ； ； In the above formula, For a standardized weighted matrix, Represents the standardized weighted matrix The Middle Line number Column data; A standardized matrix; To The standardized value obtained after standardization processing; For evaluation matrix; For the first The first of the objects to be evaluated The evaluation value of each evaluation indicator; The number of objects to be evaluated; The total number of evaluation indicators; For the first The minimum value of all objects to be evaluated under each indicator; For the first The maximum value of all objects to be evaluated under each indicator; C2. Determine the ideal solution and negative ideal solution : ； ； In the above formula, , The first Positive and negative ideal solutions for each evaluation index; C3. Determine the virtual worst solution : ； In the above formula, For the first The virtual worst-case solution for each evaluation metric; C4. Calculate the Mahalanobis distance between the object to be evaluated and the positive ideal solution and the virtual worst solution using the grey relational degree matrix: ； ； ； ； In the above formula, , They represent the first Mahalanobis distance between each object to be evaluated and the ideal solution and the virtual worst solution This is the comprehensive weight matrix; This is the gray relational degree matrix; For the first The first evaluation indicator and the first Grey relational degree of each evaluation indicator; Represents the standardized weighted matrix The Middle Line number Column data; For the first The first subject to be evaluated in the first The and the first The minimum absolute difference between evaluation values ​​under each evaluation indicator; For the first The first subject to be evaluated in the first The and the first The maximum absolute difference between evaluation values ​​under each evaluation indicator; C5. Calculate the relative proximity of the object to be evaluated: ； In the above formula, Indicates the first The relative closeness of the objects to be evaluated.

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