A substation digital twin system maturity evaluation method and system based on a multi-level evaluation system tree

Abstract

The application discloses a transformer substation digital twin system maturity evaluation method and system based on a multi-level evaluation system tree, and relates to the technical field of digital twin maturity system evaluation. The method comprises the following steps: defining maturity levels, score intervals and level standard typical constraint signs; constructing a multi-dimensional transformer substation digital twin comprehensive multi-level evaluation system; defining an evaluation index quantifiable calculation method; calculating an expert subjective weight based on FAHP; calculating an objective weight based on IEWM; dynamically combining coefficients λ ; calculating a level combination weight based on WGM λ ; layer-by-layer weighted score calculation; level standard typical sign constraint; rating radar chart and advantage and disadvantage (function clustering) analysis; obtaining iteration dimensions and contents. The application is helpful for realizing objective evaluation, the evaluation data is derived from system implementation, the score is calculated in a quantitative manner, and the application has strong accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of digital twin system maturity assessment technology, and more specifically to a method and system for assessing the maturity of a substation digital twin system based on a multi-level assessment system tree. Background Technology

[0002] With the continuous advancement of intelligent and digital transformation of power systems, digital twin technology is increasingly being applied to the operation and maintenance of substations. By constructing a two-way mapping relationship between physical equipment and virtual models, and integrating real-time monitoring data, mechanistic models, and intelligent algorithms, digital twin systems can achieve functions such as equipment status visualization, operational process simulation analysis, fault diagnosis, and decision support, becoming an important technical means to support intelligent operation and maintenance of substations. During the construction of digital twin systems, system capabilities typically evolve through stages from basic modeling, data access, and functional integration to intelligent decision support. To scientifically measure the development level of digital twin systems, various maturity level classification methods and evaluation models have been proposed domestically and internationally in fields such as intelligent manufacturing, smart cities, and asset management, using a tiered system to assess the capabilities of digital twin systems. These methods provide a reference for the evaluation of digital twin systems.

[0003] However, existing maturity assessment methods are mostly based on general domains, and their evaluation dimensions and indicator systems lack specific designs for power systems, especially substation operation and maintenance scenarios. Substation operation and maintenance is characterized by complex business logic, high system coupling, and stringent reliability requirements. Directly using general maturity assessment models makes it difficult to comprehensively reflect the capability structure and development stage of substation digital twin systems. Furthermore, existing assessment methods often use proportional divisions for maturity level intervals, failing to fully consider the differences in development difficulty at different stages of digital twin system capability evolution, easily leading to a mismatch between the level determination and the actual capability level. At the same time, most maturity assessment methods still rely on subjective expert scoring and use fixed weights for weighted calculations, making it difficult to guarantee the objectivity and repeatability of the evaluation results due to subjective factors.

[0004] Therefore, how to provide a maturity evaluation method for substation digital twin systems based on a multi-level evaluation system tree, construct a targeted indicator system and hierarchical mechanism, and establish a quantifiable weight calculation and evaluation process to achieve a scientific determination of the maturity of substation digital twin systems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree, in order to solve the problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A maturity assessment method for substation digital twin systems based on a multi-level evaluation system tree includes the following steps: A multi-level evaluation system tree for assessing the maturity of a digital twin system for substations is constructed, including an objective layer, a dimension layer, an element layer, and an indicator layer. Based on real-world data collected during substation operation, the scores of each evaluation indicator item in the multi-level evaluation system tree are calculated. The expert weights of each evaluation indicator are calculated based on the FAHP algorithm, and the objective weights of each evaluation indicator are calculated based on the IEWM algorithm and the scores of each evaluation indicator. Calculate the dynamic combination coefficient based on expert weights and objective weights. l Based on WGM- l Calculate the combined subjective and objective weights of each evaluation indicator; Based on the scores of each evaluation indicator and the weights of the subjective and objective combinations of each evaluation indicator, the scores of each evaluation element in the element layer are calculated by weighting. The weights of each evaluation element item in the element layer, the scores of each evaluation dimension item in the dimension layer, the weights of each evaluation dimension item in the dimension layer, and the scores of the evaluation target items in the target layer are calculated step by step. The resulting evaluation target item scores are the maturity scores of the substation digital twin system. Based on predefined maturity levels, nonlinear score ranges, and typical indicators of level compliance constraints, combined with the maturity score of the substation digital twin system, the current maturity level of the substation digital twin system is determined.

[0007] Optionally, it also includes using a visual radar chart combined with an obstacle degree model to identify the core shortcomings of the substation digital twin system and the priority order for improvement.

[0008] Optionally, the dimension layer includes six dimensions: functional completeness, operational reliability, user-friendliness, expansion flexibility, migration compatibility, and security protection; the number of evaluation element items in the element layer is greater than the number of evaluation dimension items; and the number of evaluation indicator items in the indicator layer is greater than the number of evaluation element items.

[0009] Optionally, it also includes classifying each evaluation indicator item in the indicator layer into indicator categories and corresponding objective quantitative scoring methods. There are five indicator categories: tiered scoring, proportion attainment, upper limit threshold, lower limit threshold, and subjective scoring. The specific objective quantitative scoring methods are as follows: The tiered scoring system is used to measure indicators related to the functionality of a system. It employs a tiered scoring method by classifying different levels of functionality implementation. ; In the formula, si For the first i The score for each evaluation indicator ranges from [0, 6]. v Indicates the defined number of ladder levels and v ≥2, x For the actual capabilities achieved by the system, T j For the first j Step threshold, I (·) is an indicator function, indicating the achievement of the first... j The level is 1 if it is set to 1, otherwise it is 0. The percentage achievement category is used for indicators related to statistical scale and quantity. It compares the actual completion rate with the target value to obtain a standardized score. ; In the formula, x This indicates the actual quantity reached by the current system. T For the total target quantity, The penalty coefficient and ∈(0,1]; Upper limit thresholds are used for indicators with the characteristic of "the smaller the better". They employ nonlinear penalties for exceeding the limit. Some indicators are composite indicators with multiple parameters. The "weakest link effect" is introduced to determine the indicator's score more quickly and rigorously. ; In the formula, k Calculate the total number of parameters covered for this indicator item. x j For the first j The measured value of each parameter T maxj This is the upper limit threshold allowed for this parameter. j Considering the tolerance range of measurement error for this parameter and j >0, unit same x j , The attenuation coefficient and ≥1, controlling the non-linear penalty intensity of upper limit threshold-type index scores; The lower threshold is designed for metrics exhibiting a "the larger the better" characteristic, and a Sigmoid function is used to transition rewards to the threshold value; a bottleneck effect is introduced to address multi-parameter metrics.

[0010] In the formula, T minj For the first j The lower limit threshold allowed for each parameter; For subjective rating categories that address user experience metrics that are difficult to quantify fully, standardized rating cards were designed for each metric, and the final score was obtained by averaging multiple ratings.

[0011] In the formula, q This indicates the total number of raters. r j Indicates the first j The scores given by each evaluator.

[0012] Optionally, the expert subjective weights calculated based on the FAHP algorithm include the following steps: Construct a triangular fuzzy judgment matrix A: For each level, construct... n × n A dimensional judgment matrix. n The number of factors at the same level; the judgment and assignment of indicator importance refer to the triangular fuzzy number corresponding to Saaty's 1-9 scale, satisfying the following conditions. a ij =1 / a ji >0, i,j =1,2,…, n :

[0013] In the formula, a ij Indicates the first i The first indicator is relative to the first j The importance of each indicator is determined using triangular fuzzy numbers, representing a value range of [...]. l ij , u ij The most likely value is... m ij And satisfying 0 < l ij ≤ m ij ≤ u ij ,and a ji The corresponding value range is [1 / u ij , 1 / l ij The most likely value is 1 / m ij ; Calculate the fuzzy weight vector w i :

[0014] In the formula, ( l i , m i , u i ) represents the first digit of the discriminant matrix A. i Take the geometric mean of all fuzzy numbers in the row, and use it as the first fuzzy number. i Fuzzy weight vector of each indicator; Calculate the fuzzy weight normalized vector :

[0015] In the formula, , , They represent the first i The lower bound, most likely value, and upper bound of the weight values ​​for each indicator; The weighted average method is used to defuzzify and determine the subjective weight vector. w FAHP : .

[0016] Optionally, based on the IEWM algorithm and the objective weights of the calculation index layer, the specific steps include: right m Group scoring vector s I Normalization is performed: ; In the formula, x ij For the first i The first group of score vectors j The normalized scores of each evaluation indicator i =1,2,…, m , j =1,2,…, n , e =0.0001 is used to correct for cases where the denominator is 0; Calculate the weight of evaluation indicators z ij : ; Calculate information entropy e j :

[0017] when e jWhen the value approaches 1, the original objective weight calculation is not robust to changes in entropy. Therefore, the objective weight calculation is optimized by introducing the mean information entropy. To obtain the objective weight vector w IEWM :

[0018] In the formula, d j Indicates the first j The larger the coefficient of difference for each evaluation indicator, the higher the objective weight of that indicator.

[0019] Optionally, calculate the dynamic combination coefficients. l Based on WGM- l The calculation of the subjective and objective combined weights of the indicator layer includes the following steps: The Pearson correlation coefficient method was used to calculate the correlation between subjective and objective weights. r , r ∈[-1,1]: ; in, w FAHP Represents the subjective weight vector; Determine the dynamic coefficients of subjective and objective weights based on the correlation coefficient. value, ∈[0.5,1]: ; WGM- l Calculate and normalize the combined weights to obtain the combined weight vector of the indicator layer. w I : .

[0020] Optional, evaluation feature item score vector s E The calculation is as follows: ; in, w I Represents the combined weight vector. s I This represents the rating vector.

[0021] Optional, the weights of each evaluation element in the element layer. w E Scores for each evaluation dimension item in the dimension layer s C Weights of each evaluation dimension item in the dimension layer w CThe evaluation score S of the target item in the target layer T The calculation formula is as follows: .

[0022] A maturity evaluation system for substation digital twin systems based on a multi-level evaluation tree includes: Multi-level evaluation system tree construction and evaluation index item scoring module: Used to construct a multi-level evaluation system tree specifically for evaluating the maturity of substation digital twin systems, including target layer, dimension layer, element layer and index layer. Combined with real data collected from substation operation, the score of each evaluation index item in the multi-level evaluation system tree is calculated. Weight Calculation Module: Used to calculate the expert weights of each evaluation indicator based on the FAHP algorithm, and to calculate the objective weights of each evaluation indicator based on the IEWM algorithm and the scores of each evaluation indicator. Weighting combination module: Used to calculate dynamic combination coefficients based on expert weights and objective weights. l Based on WGM- l Calculate the combined subjective and objective weights of each evaluation indicator; Element Item Score Module: Used to calculate the score of each evaluation element item in the element layer based on the scores of each evaluation indicator item and the subjective and objective combination weights of each evaluation indicator item. Maturity Score Module: Used to progressively calculate the weight of each evaluation element item in the element layer, the score of each evaluation dimension item in the dimension layer, the weight of each evaluation dimension item in the dimension layer, and the score of the evaluation target item in the target layer. The resulting evaluation target item score is the maturity score of the substation digital twin system. Maturity Level Determination Module: Based on predefined maturity levels, nonlinear score ranges, and typical indicators of level compliance constraints, combined with the maturity score of the substation digital twin system, the module determines the current maturity level of the substation digital twin system.

[0023] As can be seen from the above technical solutions, compared with the prior art, this invention provides a method and system for assessing the maturity of substation digital twin systems based on a multi-level evaluation system tree, which has the following beneficial effects: First, addressing the lack of specificity of existing digital twin maturity assessment systems in the power sector, this invention constructs a multi-level evaluation system tree for substation digital twin systems, forming a hierarchical evaluation framework including a target layer, dimension layer, element layer, and indicator layer. The evaluation dimensions cover six core dimensions: target layer, function, performance, interaction, expansion, migration, and security, achieving a systematic characterization of the capability structure of substation digital twin systems and overcoming the technical defects of general maturity models in reflecting the high reliability and complex business characteristics of substation operation and maintenance. Second, this invention proposes a non-uniform maturity score interval division method, fully considering the evolutionary law of significant mid-term capability leaps and relatively stable two-end stages in the maturity evolution process of digital twin systems, making the maturity level division more consistent with the actual evolution characteristics of the system and improving the scientificity and accuracy of level determination. In addition, this invention establishes indicator quantification calculation rules and a dynamic fusion weighting mechanism of subjective and objective factors, by constructing FAHP-IEWM-WGM- l The multi-model fusion weight calculation method achieves comprehensive determination of indicator weights, reducing the bias caused by subjective scoring from a single expert and improving the objectivity, consistency, and repeatability of maturity evaluation results. This invention forms a complete maturity evaluation method and system, which can be implemented in stages—system construction, trial operation, and iterative optimization—accurately identifying system capability shortcomings without relying entirely on subjective expert scoring. It provides quantitative support for system function optimization and construction prioritization, possessing strong engineering feasibility and application value. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 This is a flowchart of a maturity evaluation method for a substation digital twin system based on a multi-level evaluation system tree, as disclosed in this invention. Figure 2 This is a visualization radar chart of the maturity evaluation results of a substation digital twin system disclosed in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] This invention discloses a maturity evaluation method for substation digital twin systems based on a multi-level evaluation system tree, comprising the following steps: A multi-level evaluation system tree for assessing the maturity of a digital twin system for substations is constructed, including an objective layer, a dimension layer, an element layer, and an indicator layer. Based on real-world data collected during substation operation, the scores of each evaluation indicator item in the multi-level evaluation system tree are calculated. The expert weights of each evaluation indicator are calculated based on the FAHP algorithm, and the objective weights of each evaluation indicator are calculated based on the IEWM algorithm and the scores of each evaluation indicator. Calculate the dynamic combination coefficient based on expert weights and objective weights. l Based on WGM- l Calculate the combined subjective and objective weights of each evaluation indicator; Based on the scores of each evaluation indicator and the weights of the subjective and objective combinations of each evaluation indicator, the scores of each evaluation element in the element layer are calculated by weighting. The weights of each evaluation element item in the element layer, the scores of each evaluation dimension item in the dimension layer, the weights of each evaluation dimension item in the dimension layer, and the scores of the evaluation target items in the target layer are calculated step by step. The resulting evaluation target item scores are the maturity scores of the substation digital twin system. Based on predefined maturity levels, nonlinear score ranges, and typical indicators of level compliance constraints, combined with the maturity score of the substation digital twin system, the current maturity level of the substation digital twin system is determined.

[0028] Reference Figure 1 As shown, the specific steps include: S1. Define the maturity level, nonlinearity score range, and typical indicators of level compliance constraints for substation digital twin systems; S2. Construct a multi-level evaluation system tree specifically for evaluating the maturity of digital twin systems in substations. The multi-level evaluation system tree includes an objective layer, a dimension layer, an element layer, and an indicator layer. S3. For each evaluation indicator item in the indicator layer established in S2, classify the indicator categories and corresponding objective quantitative scoring methods; S4. Based on the S3 calculation method and combined with real data collected during system operation, objectively calculate the scores of each evaluation indicator item. S5. For the indicator layer in S2, calculate the expert weight of each evaluation indicator based on the FAHP algorithm, and calculate the objective weight of each evaluation indicator based on the IEWM algorithm and the scores of each evaluation indicator in S4. S6. Based on the expert weights and objective weights of each evaluation indicator in S5, calculate the dynamic combination coefficient. l Based on WGM- l Calculate the combined subjective and objective weights of each evaluation indicator; S7. Based on the scores of each evaluation indicator item in S4 and the subjective and objective combination weights of each evaluation indicator item in S7, the scores of each evaluation element item in the element layer are calculated by weighting. S8. Repeat S5-S8 to calculate the weight of each evaluation element in the element layer, the score of each evaluation dimension in the dimension layer, the weight of each evaluation dimension in the dimension layer, and the score of the evaluation target in the target layer. The obtained evaluation target score is the maturity score of the substation digital twin system. S9. Based on the maturity level, nonlinear score range and typical indicators of level compliance constraints defined in S1, and combined with the maturity score of the substation digital twin system in S8, determine the current maturity level of the substation digital twin system. S10. Based on the visualized radar chart and combined with the obstacle degree model, identify the core shortcomings of the substation digital twin system and the priority order for improvement.

[0029] Furthermore, the maturity levels described in S1 L set ={Virtual Mirror Level L1, Dynamic Perception Level L2, Simulation and Inference Level L3, Closed-Loop Feedback Level L4, Predictive Optimization Level L5, Twin Intelligence Level L6}, a total of six levels.

[0030] Furthermore, the nonlinear fractional interval described in S1 R set ={[0,0.8), [0.8,1.8), [1.8,3), [3,4.2), [4.2,5.2), [5.2,6]}, corresponding to six maturity levels.

[0031] Furthermore, the typical indicators of the level compliance constraints mentioned in S1 include having a visualized 3D model, supporting real-time mapping of virtual and real data, supporting multi-field coupling or simulation analysis, supporting closed-loop control of operation and maintenance decision-making, supporting state prediction and autonomous optimization, and supporting cross-system collaboration and ecological interconnection, corresponding to six maturity levels.

[0032] Furthermore, the number of target items in the target layer described in S2 is 1, namely the maturity score of the substation digital twin system, which is the first layer of the multi-level evaluation system tree.

[0033] Furthermore, the dimension layer described in S2 includes six dimensions: functional completeness, operational reliability, user-friendliness, expansion flexibility, migration compatibility, and security protection. It is the second layer of the multi-level evaluation system tree.

[0034] Furthermore, the number of evaluation element items in the element layer described in S2 is greater than the number of evaluation dimension items. It is the third layer of the multi-level evaluation system tree, specifically containing 32 evaluation element items, as shown in Table 1.

[0035] Furthermore, the number of evaluation indicator items in the indicator layer described in S2 is greater than the number of evaluation element items. It is the fourth layer of the multi-level evaluation system tree, specifically containing 120 evaluation indicator items, as shown in Table 1.

[0036] Table 1. Classification of evaluation indicators and weights at each level of the SIOM-DTS maturity assessment system.

[0037] Furthermore, the index categories described in S3 are divided into five categories, including tiered scoring (Category I), proportion attainment (Category II), upper limit threshold (Category III), lower limit threshold (Category IV), and subjective scoring (Category V). For ease of calculation, the specific category division of each evaluation index item is shown in Table 1.

[0038] Furthermore, the specific objective quantitative scoring method described in S3 is as follows: 1) The tiered scoring system is used to measure the relevant indicators of system functionality. By classifying different levels of functionality, a tiered scoring method is used to ensure a clear quantitative distinction between different maturity stages.

[0039]

[0040] In the formula, s i For the first i The score for each evaluation indicator ranges from [0, 6]. v Indicates the defined number of ladder levels and v ≥2, x For the actual capabilities achieved by the system, T j For the first j Step threshold, I (·) is an indicator function, indicating the achievement of the first... j The level is 1 if it is 1 otherwise it is 0.

[0041] 2) The proportion-based target category (Category II) is mainly used for indicators related to statistical scale and quantity. It compares the actual completion rate with the target value to obtain a standardized score.

[0042]

[0043] In the formula, x This indicates the actual quantity reached by the current system. T For the total target quantity, The penalty coefficient and ∈(0,1).

[0044] 3) Upper limit threshold type (Type III) is mainly used for indicators with the characteristic of "the smaller the better". To reduce the excessive influence of extreme bias on the score, a nonlinear penalty is used for data exceeding the limit. At the same time, considering that some indicators contain composite indicators with multiple parameters, the weakest link effect is introduced to determine the score of the indicator more quickly and rigorously.

[0045]

[0046] In the formula, k Calculate the total number of parameters covered for this indicator item. x j For the first j The measured value of each parameter T maxj This is the upper limit threshold allowed for this parameter. j Considering the tolerance range of measurement error for this parameter and j >0, unit same x j , The attenuation coefficient and ≥1 indicates the non-linear penalty intensity of the upper limit threshold-type index score.

[0047] 4) The lower limit threshold class (Class IV) mainly targets indicators with the characteristic of "the larger the better," and uses the Sigmoid function to transition the reward to the limit value. For multiple parameters, the weakest link effect is also introduced, and strict scoring is used to highlight the impact of parameters with poor performance.

[0048]

[0049] In the formula, T minj For the first j The lower limit threshold allowed for each parameter.

[0050] 5) Subjective rating category (Category V) is for user subjective experience indicators that are difficult to fully quantify. To reduce the impact of individual differences, a standardized scorecard can be designed for each indicator item, and the final score is obtained by averaging the scores from multiple people.

[0051]

[0052] In the formula, q This indicates the total number of raters. r j Indicates the first j The scores given by each evaluator.

[0053] Furthermore, the actual system operation data collected in S4 includes two categories: one is objective operation data, covering 115 evaluation indicators corresponding to categories I-IV, accounting for 95.8% of all indicators; the other is expert subjective scoring, including only evaluation indicators corresponding to category V, totaling 5 items, accounting for 4.2%. The objective operation data originates from actual system operation records or on-site test data. For the expert subjective scoring data, 10 experts in substation operation and maintenance were invited to anonymously and independently score the data. A standardized scoring card was developed before the formal scoring, and standard deviation was used to measure the dispersion of the scores. The proportion of objective operation data indicators effectively reduces the potential bias of expert subjective scoring on the overall evaluation results, improving the objectivity and reliability of the evaluation results. In the embodiment, the scoring results of the 10 experts are shown in Table 2. The standard deviation of the scores for the 5 subjective indicators is less than 0.8, indicating a high degree of consistency in the expert opinions.

[0054] Table 2. Statistics of Anonymous Expert Scores for Category V Indicators

[0055] Furthermore, the expert subjective weights calculated based on the FAHP algorithm as described in S5 include the following steps: 1) Construct the triangular fuzzy judgment matrix A: For each level, construct... n × n A dimensional judgment matrix. n This refers to the number of factors at the same level. The importance assessment and value assignment for indicators reference the triangular fuzzy number corresponding to Saaty's 1-9 scale, satisfying the following conditions: a ij =1 / a ji >0, i,j =1,2,…, n .

[0056]

[0057] 2) Calculate the fuzzy weight vector wi :

[0058] 3) Calculate the fuzzy weight normalization vector :

[0059] 4) Use the maximum membership method to defuzzify and determine the subjective weight vector. w FAHP :

[0060] Furthermore, the objective weights of the indicator layer calculated based on the IEBM algorithm and S4, as described in S5, specifically include the following steps: 1) To m Group scoring vector s I Normalization is performed:

[0061] In the formula, x ij For the first i The first group of score vectors j The normalized scores of each evaluation indicator i =1,2,…, m , j =1,2,…, n , e =0.001.

[0062] 2) Calculate the weight of each evaluation indicator:

[0063] 3) Calculate information entropy:

[0064] 4) When e j When the value approaches 1, the original objective weight calculation is not robust to changes in entropy. Therefore, the objective weight calculation is optimized by introducing the mean information entropy. To obtain the objective weight vector w IEWM :

[0065] Furthermore, in S6, the calculation of dynamic combination coefficients... l Based on WGM- l The calculation of the subjective and objective combined weights of the indicator layer includes the following steps: 1) The Pearson correlation coefficient method was used to calculate the correlation between subjective and objective weights. r , r ∈[-1,1]:

[0066] 2) Determine the dynamic coefficients of subjective and objective weights based on the correlation coefficient. value, ∈[0.5,1]:

[0067] 3) WGM- l Calculate and normalize the combined weights to obtain the combined weight vector of the indicator layer. w I :

[0068] Furthermore, the score calculation for each evaluation element item described in S7 specifically includes the following steps:

[0069] Furthermore, the weights of each evaluation element item in the element layer described in S8 w E Scores for each evaluation dimension item in the dimension layer s C Weights of each evaluation dimension item in the dimension layer w C The evaluation score S of the target item in the target layer T The combined weights of the indicator layer, element layer, and dimension layer in the embodiment are calculated and shown in Table 1.

[0070]

[0071] Furthermore, the final digital twin system maturity level described in S9 L final The judgment is as follows:

[0072] In the formula, L set ={L1, L2, L3, L4, L5, L6} is the set of maturity levels. L int Indicates according to S T The initial maturity level is determined. I (·) is an indicator function, possessing the first... l A typical indicator for meeting the level-one standard is 1; otherwise, it is 0. This represents the logical AND operator. If the system does not have this feature...L int Typical indicators lead to a downgrade in maturity level assessment. In the example, the evaluation results and visualization radar charts for substation digital twin system 1 and substation digital twin system 2 are shown below. Figure 2 As shown, the initial maturity scores of the two systems are 3.9 and 3.7, respectively, both corresponding to maturity level L4. Based on the typical indicators of level achievement constraints defined in S1, it can be further determined that the former still requires manual intervention in the closed-loop feedback stage and has not yet met the requirements for L4 autonomous closed-loop decision-making; the latter lacks physical field simulation support and does not meet the typical indicators of L3 level achievement. After comprehensive assessment, the final maturity levels of substation digital twin system 1 and substation digital twin system 2 are determined to be L3 simulation level and L2 dynamic perception level, respectively.

[0073] Furthermore, in S10, the obstacle degree model specifically includes the following steps: 1) Calculate the first i Deviation of each evaluation indicator item s i Score for this evaluation indicator:

[0074] 2) Calculate obstacle degree O i :

[0075] 3) Sort the obstacle degree in descending order and select the top ten obstacle degree evaluation indicators as the key evaluation indicators restricting the evolution of system maturity. In the embodiment, by performing obstacle degree model analysis on each evaluation indicator of substation digital twin system 1 and substation digital twin system 2, the top 10 obstacle degree evaluation indicators are selected as shown in Table 3, which serve as important targets for the next stage of substation digital twin system iterative optimization.

[0076] Table 3 Key Evaluation Indicators Restricting the Evolution of Substation Digital Twin Systems

[0077] Finally, this embodiment also discloses a maturity evaluation system for substation digital twin systems based on a multi-level evaluation system tree, including: Multi-level evaluation system tree construction and evaluation index item scoring module: Used to construct a multi-level evaluation system tree specifically for evaluating the maturity of substation digital twin systems, including target layer, dimension layer, element layer and index layer. Combined with real data collected from substation operation, the score of each evaluation index item in the multi-level evaluation system tree is calculated. Weight Calculation Module: Used to calculate the expert weights of each evaluation indicator based on the FAHP algorithm, and to calculate the objective weights of each evaluation indicator based on the IEWM algorithm and the scores of each evaluation indicator. Weighting combination module: Used to calculate dynamic combination coefficients based on expert weights and objective weights. l Based on WGM- l Calculate the combined subjective and objective weights of each evaluation indicator; Element Item Score Module: Used to calculate the score of each evaluation element item in the element layer based on the scores of each evaluation indicator item and the subjective and objective combination weights of each evaluation indicator item. Maturity Score Module: Used to progressively calculate the weight of each evaluation element item in the element layer, the score of each evaluation dimension item in the dimension layer, the weight of each evaluation dimension item in the dimension layer, and the score of the evaluation target item in the target layer. The resulting evaluation target item score is the maturity score of the substation digital twin system. Maturity Level Determination Module: Based on predefined maturity levels, nonlinear score ranges, and typical indicators of level compliance constraints, combined with the maturity score of the substation digital twin system, the module determines the current maturity level of the substation digital twin system.

[0078] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A maturity evaluation method for substation digital twin systems based on a multi-level evaluation system tree, characterized in that, Includes the following steps: A multi-level evaluation system tree for assessing the maturity of a digital twin system for substations is constructed, including an objective layer, a dimension layer, an element layer, and an indicator layer. Based on real-world data collected during substation operation, the scores of each evaluation indicator item in the multi-level evaluation system tree are calculated. The expert weights of each evaluation indicator are calculated based on the FAHP algorithm, and the objective weights of each evaluation indicator are calculated based on the IEWM algorithm and the scores of each evaluation indicator. Calculate the dynamic combination coefficient based on expert weights and objective weights. λ Based on WGM- λ Calculate the combined subjective and objective weights of each evaluation indicator; Based on the scores of each evaluation indicator and the weights of the subjective and objective combinations of each evaluation indicator, the scores of each evaluation element in the element layer are calculated by weighting. The weights of each evaluation element item in the element layer, the scores of each evaluation dimension item in the dimension layer, the weights of each evaluation dimension item in the dimension layer, and the scores of the evaluation target items in the target layer are calculated step by step. The resulting evaluation target item scores are the maturity scores of the substation digital twin system. Based on predefined maturity levels, nonlinear score ranges, and typical indicators of level compliance constraints, combined with the maturity score of the substation digital twin system, the current maturity level of the substation digital twin system is determined.

2. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, It also includes using visualized radar charts and obstacle degree models to identify the core shortcomings of the substation digital twin system and prioritize their improvement.

3. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, The dimension layer includes six dimensions: functional completeness, operational reliability, user-friendliness, expansion flexibility, migration compatibility, and security protection. The number of evaluation element items in the element layer is greater than the number of evaluation dimension items. The number of evaluation indicator items in the indicator layer is greater than the number of evaluation element items.

4. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, It also includes classifying each evaluation indicator item in the indicator layer into indicator categories and corresponding objective quantitative scoring methods. There are five indicator categories: tiered scoring, proportion attainment, upper limit threshold, lower limit threshold, and subjective scoring. The specific objective quantitative scoring methods are as follows: The tiered scoring system is used to measure indicators related to the functionality of a system. It employs a tiered scoring method by classifying different levels of functionality implementation. ； In the formula, s i For the first i The score for each evaluation indicator ranges from [0, 6]. v Indicates the defined number of ladder levels and v ≥2, x For the actual capabilities achieved by the system, T j For the first j Step threshold, I (·) is an indicator function, indicating the achievement of the first... j The level is 1 if it is set to 1, otherwise it is 0. The percentage achievement category is used for indicators related to statistical scale and quantity. It compares the actual completion rate with the target value to obtain a standardized score. ； In the formula, x This indicates the actual quantity reached by the current system. T For the total target quantity, The penalty coefficient and ∈(0,1]; Upper limit thresholds are used for indicators with the characteristic of "the smaller the better". They employ nonlinear penalties for exceeding the limit. Some indicators are composite indicators with multiple parameters. The "weakest link effect" is introduced to determine the indicator score more quickly and rigorously. ； In the formula, k Calculate the total number of parameters covered for this indicator item. x j For the first j The measured value of each parameter T maxj This is the upper limit threshold allowed for this parameter. j Considering the tolerance range of measurement error for this parameter and j >0, unit same x j , The attenuation coefficient and ≥1, controlling the non-linear penalty intensity of upper limit threshold-type index scores; The lower threshold is designed for metrics exhibiting the "the larger the better" characteristic, and a Sigmoid function is used to transition rewards to the threshold value; a bottleneck effect is introduced to address multi-parameter metrics. In the formula, T minj For the first j The lower limit threshold allowed for each parameter; For subjective rating categories that address user experience metrics that are difficult to quantify fully, standardized rating cards were designed for each metric, and the final score was obtained by averaging multiple ratings. In the formula, q This indicates the total number of raters. r j Indicates the first j The scores given by each evaluator.

5. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, The expert subjective weights calculated based on the FAHP algorithm include the following steps: Construct a triangular fuzzy judgment matrix A: For each level, construct... n × n A dimensional judgment matrix. n The number of factors at the same level; the judgment and assignment of indicator importance refer to the triangular fuzzy number corresponding to Saaty's 1-9 scale, satisfying the following conditions. a ij =1 / a ji >0, i,j =1,2,…, n : In the formula, a ij Indicates the first i The first indicator is relative to the first j The importance of each indicator is determined using triangular fuzzy numbers, representing the value range as [...]. l ij , u ij The most likely value is... m ij And satisfying 0 < l ij ≤ m ij ≤ u ij ,and a ji The corresponding value range is [1 / u ij , 1 / l ij The most likely value is 1 / m ij ; Calculate the fuzzy weight vector w i : In the formula, ( l i , m i , u i ) represents the first digit of the discriminant matrix A. i Take the geometric mean of all fuzzy numbers in the row, and use it as the first fuzzy number. i Fuzzy weight vector of each indicator; Calculate the fuzzy weight normalized vector : In the formula, , , They represent the first i The lower bound, most likely value, and upper bound of the weight values ​​for each indicator; The weighted average method is used to defuzzify and determine the subjective weight vector. w FAHP : 。 6. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, Based on the IEWM algorithm and the objective weights of the calculation index layer, the specific steps include: right m Group scoring vector s I Normalization is performed: ； In the formula, x ij For the first i The first group of score vectors j The normalized scores of each evaluation indicator i =1,2,…, m , j =1,2,…, n , ε =0.0001 is used to correct for cases where the denominator is 0; Calculate the weight of evaluation index items z ij : ； Calculate information entropy e j : when e j When the value approaches 1, the original objective weight calculation is not robust to changes in entropy. Therefore, the objective weight calculation is optimized by introducing the mean information entropy. To obtain the objective weight vector w IEWM : In the formula, d j Indicates the first j The larger the coefficient of difference for each evaluation indicator, the higher the objective weight of that indicator.

7. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, Calculate dynamic combination coefficients λ Based on WGM- λ The calculation of the subjective and objective combined weights of the indicator layer includes the following steps: The Pearson correlation coefficient method was used to calculate the correlation between subjective and objective weights. r , r ∈[-1,1]: in, w FAHP Represents the subjective weight vector; Determine the dynamic coefficients of subjective and objective weights based on the correlation coefficient. value, ∈[0.5,1]: ； WGM- λ Calculate and normalize the combined weights to obtain the combined weight vector of the indicator layer. w I : 。 8. The method for evaluating the maturity of a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, Evaluation Element Score Vector s E The calculation is as follows: ； in, w I Represents the combined weight vector. s I This represents the rating vector.

9. The maturity evaluation method for a substation digital twin system based on a multi-level evaluation system tree as described in claim 1, characterized in that, Weights of each evaluation element in the element layer w E Scores for each evaluation dimension item in the dimension layer s C Weights of each evaluation dimension item in the dimension layer w C The evaluation score S of the target item in the target layer T The calculation formula is as follows: 。 10. A maturity evaluation system for substation digital twin systems based on a multi-level evaluation system tree, characterized in that, include: Multi-level evaluation system tree construction and evaluation index item scoring module: Used to construct a multi-level evaluation system tree specifically for evaluating the maturity of substation digital twin systems, including target layer, dimension layer, element layer and index layer. Combined with real data collected from substation operation, the score of each evaluation index item in the multi-level evaluation system tree is calculated. Weight Calculation Module: Used to calculate the expert weights of each evaluation indicator based on the FAHP algorithm, and to calculate the objective weights of each evaluation indicator based on the IEWM algorithm and the scores of each evaluation indicator. Weighting combination module: Used to calculate dynamic combination coefficients based on expert weights and objective weights. λ Based on WGM- λ Calculate the combined subjective and objective weights of each evaluation indicator; Element Item Score Module: Used to calculate the score of each evaluation element item in the element layer based on the scores of each evaluation indicator item and the subjective and objective combination weights of each evaluation indicator item. Maturity Score Module: Used to progressively calculate the weight of each evaluation element item in the element layer, the score of each evaluation dimension item in the dimension layer, the weight of each evaluation dimension item in the dimension layer, and the score of the evaluation target item in the target layer. The resulting evaluation target item score is the maturity score of the substation digital twin system. Maturity Level Determination Module: Based on predefined maturity levels, nonlinear score ranges, and typical indicators of level compliance constraints, combined with the maturity score of the substation digital twin system, the module determines the current maturity level of the substation digital twin system.

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