A method, system and device for quantitatively evaluating the health state of an electric energy meter

Abstract

This invention provides a method, system, and device for quantitatively assessing the health status of electricity meters, relating to the field of power equipment operation and maintenance management technology. The method includes: collecting multi-dimensional index data of electricity meters and performing outlier detection and standardization; calculating subjective and objective weights using the analytic hierarchy process (AHP) and entropy weight method respectively, and solving for the optimal linear combination coefficient to obtain a combined weight vector; performing comprehensive evaluation using this weight vector to obtain three independent scores; calculating the Kendall coordination coefficient of the evaluation scores, and dynamically adjusting the fusion strategy based on the consistency results to generate a comprehensive health index; finally, performing unsupervised cluster analysis on the comprehensive health index, automatically determining the grading threshold based on the cluster centers, and outputting the health level. This invention, through multi-model collaborative evaluation and a dynamic grading mechanism, can adapt to changes in the power grid operating environment, achieving accurate and robust quantitative assessment of the health status of electricity meters, providing a scientific basis for preventative maintenance.

Description

Technical Field

[0001] This invention relates to the field of power equipment operation and maintenance management technology, specifically to a method, system, and equipment for quantitatively assessing the health status of electricity meters. Background Technology

[0002] With the advancement of smart grid construction, the reliability of electricity meters, as the core terminal for electricity metering, directly affects the safe and stable operation of the power grid. Traditional electricity meter maintenance methods mostly involve "repair after failure" or periodic rotation, lacking real-time and accurate quantitative assessment of the health status of electricity meters throughout their entire lifecycle. Existing assessment methods often suffer from problems such as strong subjectivity of single evaluation models, inconsistent indicator dimensions, and rigid threshold settings, making them difficult to adapt to the complex and ever-changing power grid operating environment. Therefore, there is an urgent need for an electricity meter health status assessment method that can integrate multi-source data, take into account both subjective and objective factors, and adaptively adjust the grading standards. Summary of the Invention

[0003] This application provides a method and system for quantitatively assessing the health status of electricity meters, aiming to solve the problem of high misjudgment rate caused by the single assessment model and fixed threshold in the prior art.

[0004] The first aspect disclosed in this application provides a method for quantitatively assessing the health status of an electricity meter, the method comprising:

[0005] S1. Collect multi-dimensional index data of the metering performance, operating conditions, environmental stress, historical faults, verification data and operation monitoring dimensions of the electricity meter to be evaluated, and standardize the multi-dimensional index data to construct an index standardization matrix; S2. The subjective weight vector of each dimension is determined by the analytic hierarchy process (AHP), the objective weight vector of each dimension is determined by the entropy weight method, the optimal linear combination coefficient is solved, and the subjective weight vector and the objective weight vector are weighted and fused to obtain the combined weight vector. S3. Using the combined weight vector, perform multi-model analysis to obtain corresponding independent evaluation scores; S4. Calculate the Kendall consistency coefficient of the independent assessment scores, judge the consistency of the scores based on the preset consistency threshold, and when the consistency condition is met, weight and merge the independent assessment scores to generate a comprehensive health index. S5. Perform unsupervised cluster analysis on the comprehensive health index, divide the electricity meters into multiple health levels according to the cluster centers, and use the boundary points of the cluster analysis as dynamic grading thresholds to output the health level corresponding to each electricity meter.

[0006] Optionally, the standardization process includes: dividing the multidimensional index data into positive and negative indicators, and performing dimensionless processing on the positive and negative indicators by range normalization and reverse range normalization respectively, and constructing a standardized matrix containing the number of electricity meters to be evaluated and the number of indicators.

[0007] Optionally, the process of determining the combined weight vector in step S2 includes: constructing a judgment matrix using the analytic hierarchy process (AHP), comparing the importance of each dimension indicator pairwise and performing a consistency test to obtain the subjective weight vector; calculating the information entropy of each indicator based on the standardized matrix obtained in step S1, and obtaining the objective weight vector according to the inverse relationship between information entropy and indicator weight; establishing an optimization model with the objective function of minimizing the deviation between the combined weight vector and the subjective weight vector and the objective weight vector, and solving for the optimal linear combination coefficients under the constraint that the sum of the combination coefficients equals 1, and weighting and fusing the subjective weight vector and the objective weight vector to obtain the final combined weight vector.

[0008] Optionally, obtaining the subjective weight vector specifically includes: decomposing the electricity meter health assessment problem into a target layer health state, a criterion layer, and a scheme layer; wherein, the target layer represents the health state of the electricity meter, the criterion layer represents the metering performance, operating conditions, environmental stress, historical faults, calibration data, and operational monitoring of the electricity meter, and the scheme layer represents the electricity meter to be assessed; based on the criterion layer, pairwise comparisons are performed, and the judgment matrix is ​​defined as follows: ,in Indicates the first The first indicator is relative to the first The importance of each indicator; if the indicator Comparison Indicators Equally important, If the indicator Comparison Indicators Important, then And so on, and satisfying , The subjective weight vector is obtained by calculating the normalized feature vector. .

[0009] Optionally, obtaining the objective weight vector specifically includes: Based on the normalized matrix elements in Transform into the first The first item under the indicator The proportion of each sample :

[0010] Calculate the first Information entropy of the indicator :

[0011] in, , 0≤ ≤1; Calculate the first Coefficient of difference of the items ; Calculate objective weights :

[0012] Obtain the objective weight vector .

[0013] Optionally, in step S3, using the combined weight vector to perform multi-model analysis to obtain corresponding independent evaluation scores specifically includes: comparing the standardized index data sequence with the preset ideal health state reference sequence, calculating the correlation coefficient of each energy meter to be evaluated and weighting and summing them to obtain the first evaluation score; constructing a weighted normalized decision matrix based on the combined weight vector, determining the positive ideal solution vector and the negative ideal solution vector, calculating the Euclidean distance from each energy meter to be evaluated to the positive ideal solution vector and the negative ideal solution vector, and calculating the relative proximity as the second evaluation score based on the distance ratio; establishing an evaluation index set and a comment set, using the membership function to fuzzify the index data, combining the combined weight vector with the fuzzy operator for synthesis operation, and determining the third evaluation score based on the maximum membership principle.

[0014] Optionally, in step S4, weighting and fusing the independent assessment scores to generate a comprehensive health index includes: when the Kendall coordination coefficient is greater than a preset coordination threshold, using a weighted average method to fuse the independent assessment scores to obtain a comprehensive health index, wherein the weight coefficient of each score is set according to the applicability of the method or historical accuracy; when the Kendall coordination coefficient is less than or equal to the preset coordination threshold, a robustness processing strategy is executed, the robustness processing strategy including at least one of the following: reducing the confidence weight of the fused result, selecting the minimum value among the scores as the comprehensive health index, or triggering a data review mechanism.

[0015] Optionally, step S5, which outputs the health level corresponding to each energy meter, includes: sorting the cluster centers in descending order according to their numerical values, and mapping the sorted cluster centers to a preset health level sequence; calculating a dynamic grading threshold based on the values ​​of two adjacent cluster centers, wherein the dynamic grading threshold is the midpoint value of adjacent cluster centers; and, based on the dynamic grading threshold, dividing the comprehensive health index of each energy meter to be evaluated into the corresponding interval, determining its health level, and outputting the evaluation result.

[0016] The second aspect of this application discloses a quantitative assessment system for the health status of electricity meters, the system comprising: The data acquisition module is used to collect multi-dimensional index data of the electricity meter to be evaluated, including metering performance, operating conditions, environmental stress, historical faults, verification data, and operation monitoring dimensions. The multi-dimensional index data is then standardized to construct an index standardization matrix. The weight calculation module is used to determine the subjective weight vector of each dimension using the analytic hierarchy process, determine the objective weight vector of each dimension using the entropy weight method, solve the optimal linear combination coefficient based on game theory, and weight and fuse the subjective weight vector and the objective weight vector to obtain the combined weight vector. The evaluation and scoring module performs multi-model analysis based on the combined weight vector to obtain corresponding independent evaluation and scoring results. The fusion decision module is used to calculate the Kendall consistency coefficient of independent assessment scores, judge the consistency of the scores based on a preset consistency threshold, and when the consistency condition is met, the independent assessment scores are weighted and fused to generate a comprehensive health index. The hierarchical output module is used to perform unsupervised clustering analysis on the comprehensive health index, divide the electricity meters into multiple health levels according to the cluster centers, and output the health level corresponding to each electricity meter using the boundary points of the clustering analysis as dynamic hierarchical thresholds.

[0017] A third aspect of this application discloses an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the above-described method for quantitatively assessing the health status of an electricity meter.

[0018] The beneficial effects of this invention are as follows: This invention constructs an evaluation system covering multiple dimensions such as metering, operating conditions, and environment through multi-dimensional data collection, comprehensively reflecting the health status of electricity meters. The integration of subjective and objective weights improves the scientific rigor of the weighting process.

[0019] This invention effectively reduces the random error of a single model by using multi-model collaborative evaluation and verifying consistency with Kendall's consistency coefficient.

[0020] This invention, through a dynamic grading mechanism, abandons fixed static thresholds and uses clustering to automatically generate grading boundaries, enabling the evaluation criteria to adapt to changes in the overall operation level of the power grid, thereby improving the accuracy and timeliness of early warnings. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of a method for quantitatively assessing the health status of an electricity meter, provided in an embodiment of this application.

[0022] Figure 2 This is a schematic diagram of a quantitative assessment system for the health status of an electricity meter provided in an embodiment of this application. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0024] Example 1, as Figure 1 As shown in the figure, this application provides a method for quantitatively assessing the health status of an electricity meter, the method comprising: S1: Collect multi-dimensional index data of the electricity meter to be evaluated, including metering performance, operating conditions, environmental stress, historical faults, calibration data, and operation monitoring dimensions. Standardize the multi-dimensional index data and construct an index standardization matrix.

[0025] Furthermore, this application also includes the following steps: dividing the multidimensional index data into positive and negative indicators, and performing dimensionless processing on the positive and negative indicators by range normalization and reverse range normalization respectively, and constructing a standardized matrix containing the number of electricity meters to be evaluated and the number of indicators.

[0026] Specifically, to comprehensively reflect the operational health of electricity meters, an evaluation index system comprising six core dimensions was constructed. The data collection targets were a set of electricity meters to be evaluated within a regional smart grid, with each meter serving as a sample point. Key indicator data were extracted from the following six dimensions: (1) Metering performance dimension: This dimension directly reflects the measurement accuracy of the electricity meter. The key indicators extracted include: Basic error: The relative error of the electricity meter's measured value under rated conditions; Starting current: The minimum current value required for the electricity meter to begin continuous metering; Creep time: The time required for the turntable to rotate one revolution under no-load current.

[0027] Among these indicators, the smaller the value, the better the measurement performance, which is a negative indicator.

[0028] (2) Operating Condition Dimension: This dimension reflects the load status of the electricity meter in the actual power grid. The key indicators extracted include: Load factor: The ratio of actual load to rated capacity; Voltage fluctuation range: The maximum deviation of the voltage from the rated value during operation; Current imbalance: The degree of imbalance between the three-phase currents.

[0029] Among them, a moderate load rate is preferred, and the smaller the voltage fluctuation and current imbalance, the better. The direction of the indicators needs to be determined based on the specific threshold.

[0030] (3) Environmental stress dimension: This dimension reflects the impact of the external environment on the lifespan of the electricity meter. The key indicators extracted include: Ambient temperature: Real-time temperature of the installation environment; Humidity: The relative humidity of the installation environment; Vibration frequency: The frequency and amplitude of mechanical vibrations experienced by the equipment.

[0031] Extreme temperatures and severe vibrations are detrimental to equipment and are considered negative indicators.

[0032] (4) Historical Fault Dimension: This dimension is based on the past operation and maintenance records of the electricity meter, and the key indicators extracted include: Past failure count: The total number of failure events that have occurred throughout history; Fault type distribution: the percentage of occurrence frequency of each type of fault (such as system crash, communication interruption, component damage); Mean time to recovery: The average time from the occurrence of a fault to the restoration of operation.

[0033] Among them, the more frequent the failures and the longer the repair time, the worse the health status, all of which are negative indicators.

[0034] (5) Verification data dimension: This dimension reflects the periodic verification status of the electricity meter. The key indicators extracted include: Last verification date: the number of days since the current time; Verification conclusion: The qualitative result of determining whether something is qualified or unqualified is quantified; Error trend: The slope of the evolution of error values ​​between each calibration.

[0035] In particular, excessively long verification intervals or excessively large error trends indicate increased risk.

[0036] (6) Operational Monitoring Dimension: This dimension reflects the digital functional status of the smart meter. Key indicators extracted include: Communication success rate: The percentage of data that is successfully transmitted; Clock deviation: The error value between the internal clock and the standard time; Battery voltage: The remaining voltage value of the backup power supply.

[0037] Among these, higher communication success rate and battery voltage are better, as they are positive indicators; smaller clock deviation is also better.

[0038] Because the units of measurement for each dimension of the indicators are different (such as "ampere", "degree Celsius", "percentage", etc.), and because some indicators have higher values ​​that indicate better health (positive) while others have lower values ​​that indicate better health (negative), direct calculation would lead to the evaluation results being dominated by the units of measurement. Therefore, dimensionless processing is required, as follows: The indicators are divided into positive and negative indicators. The larger the value of the positive indicator, the better the condition of the electricity meter. The smaller the value of the positive indicator, the better the condition of the electricity meter.

[0039] Let the first The first indicator in the The original value on the electricity meter is This indicator is in all The maximum value of each electricity meter is The minimum value is .

[0040] For positive indicators, the range normalization formula is used to map them to the [0,1] interval:

[0041] in, The value is the standardized value; when When taking the maximum value, =1; when taking the minimum value =0.

[0042] For negative indicators, a reverse range normalization formula is used to transform "the smaller the better" into "the larger the better":

[0043] Under the transformation, the smaller the original value (the better the state). The closer it is to 1, the larger the original value (the worse the state). The closer it is to 0.

[0044] After the above, all Each electricity meter The standardized values ​​under each indicator are arranged and combined to construct... Standardized matrix :

[0045] The matrix This serves as the direct input data for the following steps, eliminating the influence of dimensions and unifying the evaluation direction, thus ensuring the accuracy of multi-source heterogeneous data fusion.

[0046] Furthermore, the following example is given in this embodiment (the following examples are only reference calculation values ​​in this embodiment, and considering the influence of different factors, they do not represent the data in actual use): Suppose three electricity meters are collected for evaluation. =3, raw data across 4 key dimensions =3.

[0047] Indicator Explanation: : Absolute value of basic error, a negative indicator, unit: %.

[0048] Communication success rate, a positive indicator, in percentiles (%).

[0049] Load factor, a positive indicator, unit: %.

[0050] : Ambient temperature difference, a negative indicator, unit: ℃.

[0051] Original data matrix As shown in Table 1.

[0052] Table 1 Original Data Matrix

[0053] according to The calculation yields: : =0.05, =0.25, denominator=0.20; : =88.5, =98.5, denominator=10; : =30.5, =42.0, denominator=11.5; : =60.0, =76, denominator=16.

[0054] Electricity meter A:

[0055] Results: [0.85, 1.00, 0.94, 1.00].

[0056] Electricity meter B:

[0057] Result: [1.00, 0.75, 0.30, 0.75].

[0058] Electricity meter C:

[0059] Results: [0.65, 0.87, 0.78, 0.63].

[0060] Electricity meter D:

[0061] Results: [0.50, 0.35, 1.00, 0.50].

[0062] Standardized matrix As shown in Table 2.

[0063] Table 2 Standardization Matrix

[0064] Based on the above, A performs well, but its temperature performance is slightly worse; B has a temperature warning; C performs moderately; and D has poor communication.

[0065] S2: The subjective weight vectors of each dimension are determined by the analytic hierarchy process (AHP), and the objective weight vectors of each dimension are determined by the entropy weight method. The optimal linear combination coefficients are solved based on game theory. The subjective weight vectors and the objective weight vectors are then weighted and fused to obtain the combined weight vector.

[0066] Furthermore, this application also includes the following steps: the process of determining the combined weight vector includes: A judgment matrix was constructed using the analytic hierarchy process (AHP). The importance of each dimension's indicators was compared pairwise and then subjected to a consistency test to obtain the subjective weight vector. Based on the standardized matrix obtained in step S1, the information entropy of each indicator is calculated, and the objective weight vector is obtained according to the inverse relationship between information entropy and indicator weight. An optimization model is established with the objective function of minimizing the deviation between the combined weight vector and the subjective weight vector and the objective weight vector. Under the constraint that the sum of the combination coefficients is equal to 1, the optimal linear combination coefficients are solved, and the subjective weight vector and the objective weight vector are weighted and fused to obtain the final combined weight vector.

[0067] Furthermore, this application also includes the following steps: decomposing the electricity meter health assessment problem into a target-level health state, a criterion-level, and a scheme-level; wherein, the target-level represents the health state of the electricity meter, the criterion-level represents the metering performance, operating conditions, environmental stress, historical faults, calibration data, and operational monitoring of the electricity meter, and the scheme-level represents the electricity meter to be assessed; based on the criterion-level, pairwise comparisons are performed, defining: The judgment matrix is ,in Indicates the first The first indicator is relative to the first The degree of importance of each indicator; If the indicator Comparison Indicators Equally important, If the indicator Comparison Indicators Important, then And so on, and satisfying , ; The subjective weight vector is obtained by calculating the normalized feature vector. .

[0068] Furthermore, this application also includes the following steps: based on the standardized matrix elements in Transform into the first The first item under the indicator The proportion of each sample :

[0069] like =0, then let , It is a very small positive number.

[0070] Calculate the first Information entropy of the indicator :

[0071] in, , 0≤ ≤1; Calculate the first Coefficient of difference of the items .

[0072] Calculate objective weights :

[0073] Obtain the objective weight vector .

[0074] Based on the aforementioned subjective and objective weight vectors, an objective function is established to minimize the deviation between the combined weight vector and the subjective and objective weight vectors.

[0075] Specifically, let the combined weight vector be... ,in , , The goal is to minimize and The sum of squared deviations and with The sum of the squares of the deviations:

[0076] Construct a system of linear equations:

[0077] get , The final combined weight vector is calculated. .

[0078] S3: Using the combined weight vector, perform multi-model analysis to obtain corresponding independent evaluation scores.

[0079] Furthermore, this application also includes the following steps: comparing the standardized indicator data sequence with a preset ideal health state reference sequence, calculating the correlation coefficient of each energy meter to be evaluated and weighting and summing them to obtain a first evaluation score; constructing a weighted normalized decision matrix based on the combined weight vector, determining the positive ideal solution vector and the negative ideal solution vector, calculating the Euclidean distance from each energy meter to be evaluated to the positive ideal solution vector and the negative ideal solution vector, and calculating the relative proximity as a second evaluation score based on the distance ratio; establishing an evaluation indicator set and a comment set, using a membership function to fuzzify the indicator data, combining the combined weight vector with a fuzzy operator for synthesis operation, and determining a third evaluation score based on the maximum membership principle.

[0080] Specifically, the first assessment score was obtained as follows: Constructing an ideal health state sequence ,in Take the standardized matrix The first in The maximum value of the column; the first The index sequence of each electricity meter is as follows For each indicator Calculate the absolute difference between the comparison sequence and the reference sequence. Set the resolution coefficient. Calculate the correlation coefficient: ; Use the combined weight vector obtained in step S2 to combine the weight vector Calculate the first Grey relational analysis of individual electricity meters: This is the first assessment score, and the closer the value is to 1, the better the health status.

[0081] The specific scores obtained from the second assessment are as follows: Standardized matrix Each element is multiplied by its corresponding combined weight. The weighted matrix is ​​obtained. ,in ; where, positive ideal solution vector All indicators are at their maximum values; negative ideal solution vector. All indicators are at their minimum values; Distance from an electricity meter to the ideal solution ;No. Distance from an electricity meter to the negative ideal solution Second assessment score This value ranges from 0 to 1. The closer it is to 1, the closer it is to the ideal solution and the better the health status.

[0082] The specific scores obtained in the third assessment are as follows: Establish a factor set and a comment set. A collection of comments corresponding to 6 dimensions. Let the grading be {Excellent, Good, Average, Poor, Poor}, corresponding to scores [1, 0.8, 0.6, 0.4, 0.2] respectively. Based on the actual values ​​of each indicator... The probability of an indicator belonging to each rating level is calculated using a preset membership function, forming a single-factor rating matrix. ; Using combined weight vectors With fuzzy relation matrix Perform composition operation ,in For the first Fuzzy comprehensive evaluation vector for an electricity meter For the collection of comments The corresponding number of levels. Selected based on the principle of maximum membership. The component with the highest median score corresponds to the rating level, which receives the third assessment score. .

[0083] Furthermore, the following examples are provided for the three evaluation scores in this embodiment: Set the combined weight vector The evaluation focuses on measurement while also considering communication and the environment. The specific scores for the three evaluation methods are shown in Table 3.

[0084] Table 3 Three Assessment Scores

[0085] Based on the above, it can be seen that A>C>B>D.

[0086] S4: Calculate the Kendall consistency coefficient of the independent assessment scores, judge the consistency of the scores based on the preset consistency threshold, and when the consistency condition is met, weight and merge the independent assessment scores to generate a comprehensive health index.

[0087] Furthermore, this application also includes the following steps: when the Kendall reconciliation coefficient is greater than a preset reconciliation threshold, the independent assessment scores are fused using a weighted average method to obtain a comprehensive health index, wherein the weight coefficient of each score is set according to the applicability of the method or historical accuracy; when the Kendall reconciliation coefficient is less than or equal to the preset reconciliation threshold, a robustness processing strategy is executed, the robustness processing strategy including at least one of the following: reducing the confidence weight of the fused result, selecting the minimum value among the score results as the comprehensive health index, or triggering a data review mechanism.

[0088] Specifically, based on the three evaluation scores mentioned above, let there be a total of One electricity meter to be evaluated, with three evaluation scores as follows: , , Each vector is The scores for each method are ranked (if scores are the same, the average rank is taken) to obtain the rank of each energy meter among the three methods. , , Calculate the rank sum of each electricity meter. ,in The number of methods; calculate the sum of squared deviations of the rank sum. ,in For all The average value; calculate the maximum possible sum of squared deviations. Kendall's Coordination Factor ,in The value range is [0,1]. This indicates that the rankings given by the three assessment scores are completely consistent; This indicates a complete inconsistency.

[0089] Set a preset coordination threshold (e.g., a value of 0.7), if If it is determined to be "highly consistent", it will proceed to the regular fusion process; if The case was determined to be "disagreement exists" and entered the robustness handling process.

[0090] Specifically, a high degree of consistency indicates that the assessment results are reliable and convergent. In this case, a weighted average method is used to generate a comprehensive health index. ,in, , , The first Three evaluation scores for each electricity meter , , These are the weighting coefficients, and their sum must be 1. In practice, the three weights can be equal. If backtesting based on historical data shows that TOPSIS has higher accuracy under specific operating conditions, it can be assigned a higher weight, such as... .

[0091] Specifically, when disagreements exist, it indicates significant differences in assessment results, increasing the risk of randomness or bias in a single assessment. In such cases, instead of directly outputting a high-confidence average, one or more of the following strategies are adopted to ensure the robustness of the assessment: (1) Minimum value principle: Select the minimum value among the three scores as the comprehensive health index, i.e. .

[0092] (2) The weighted average is still calculated, but in the subsequent hierarchical clustering, the sample is marked as "low confidence" and a manual review is required in the output report, or its health level is automatically downgraded by one level.

[0093] (3) The system pauses the automatic output of the final report, marks the relevant electricity meter data as "to be manually reviewed", and pushes it to the operation and maintenance terminal for secondary diagnosis.

[0094] S5: Perform unsupervised cluster analysis on the comprehensive health index, divide the electricity meters into multiple health levels according to the cluster centers, and use the boundary points of the cluster analysis as dynamic grading thresholds to output the health level corresponding to each electricity meter.

[0095] Furthermore, this application also includes the following steps: sorting the cluster centers in descending order according to their numerical values, and mapping the sorted cluster centers sequentially to a preset health level sequence; calculating a dynamic grading threshold based on the values ​​of two adjacent cluster centers, wherein the dynamic grading threshold is the midpoint value of adjacent cluster centers; and dividing the comprehensive health index of each energy meter to be evaluated into corresponding intervals according to the dynamic grading threshold, determining its health level, and outputting the evaluation result.

[0096] Specifically, based on the comprehensive health index in step S4 , construct a set ,in The total number of electricity meters; the number of clusters is set according to actual management needs. , For example, if it is necessary to divide it into five levels: "healthy, sub-healthy, attention, abnormal, and severe", then set... =5, perform initialization and iteration: Initialization: From the dataset Random selection 10 sample points were used as the initial cluster centers. , , ..., ; Allocation: Calculate each sample point The Euclidean distance to each cluster center is used to assign the cluster to the nearest cluster. ; Update: Recalculate each cluster The mean of all sample points in the cluster is used as the new cluster center. ; Convergence criterion: Repeat the "assign" and "update" steps until the change in cluster centers is less than the preset precision. Or the maximum number of iterations may be reached, at which point the clustering results become stable.

[0097] After clustering is completed, the numerical values ​​of the cluster centers are used to establish a correspondence with the health level, and the hierarchical boundaries are generated, specifically as follows: (1) Sort in descending order: Sort the converged... The cluster centers are sorted in descending order of their numerical values, denoted as _____. > >…> .

[0098] (2) Grade mapping: Establish mapping rules to map the sorted cluster centers to a predefined health grade sequence: Corresponding to the highest level (e.g., "healthy"); The next highest level (e.g., "sub-health"); ... The corresponding lowest level (e.g., "severe").

[0099] Furthermore, the midpoint between two adjacent cluster centers is the dynamic grading threshold for that level. Level and First Level threshold This forms a set of dynamic thresholds. .

[0100] Based on dynamic threshold set Determine the range to which each electricity meter belongs, and output the final health level and assessment report: like It is classified as Level 1 (Healthy). like It was determined to be Level 2 (sub-health). ... like Determined as a level (serious).

[0101] Furthermore, the following example is given in this embodiment: The comprehensive health index data of 10 electricity meters were selected for analysis. Assume a comprehensive health index set has already been obtained. and set This corresponds to five health levels.

[0102] Five initial centers are randomly selected. After several iterations, convergence is achieved, resulting in five cluster centers. Since the data itself exhibits five distinct clustering characteristics, the cluster centers will be very close to the mean of each group of data. Cluster 1 - High Value Group: Contains {0.98, 0.96}, mean ≈0.97; Cluster 2 - Higher Value Group: Contains {0.85, 0.82}, mean ≈0.835; Cluster 3 - Medium value group: Contains {0.75, 0.68}, mean ≈0.715; Cluster 4 - Lower Value Group: Contains {0.45, 0.30}, mean ≈0.375; Cluster 5 - Low Value Group: Contains {0.15, 0.05}, mean ≈0.10; Final set of cluster centers: μ =[0.97,0.835,0.715,0.375,0.10].

[0103] Sort the cluster centers in descending order: =0.97; =0.835; =0.715; =0.375; =0.10.

[0104] Mapping level: -healthy, -Sub-health -Notice, -abnormal, -serious.

[0105] Calculate the midpoint of adjacent centers as the dividing line: threshold - Healthy / Sub-health boundary: (0.97+0.835) / 2=0.9025; threshold - Sub-health / Pay attention to the boundary: (0.835+0.715) / 2=0.775; threshold - Note / Abnormal boundary: (0.715+0.375) / 2=0.545; threshold - Abnormal / Critical Boundary: (0.375 + 0.10) / 2 = 0.2375; Dynamic threshold set: T ={0.9025,0.775,0.545,0.2375}.

[0106] The final output is shown in Table 4.

[0107] Table 4 Health Level Output

[0108] Example 2, as Figure 2 As shown, based on the same inventive concept as the quantitative assessment method for the health status of an electricity meter in Embodiment 1 above, this application also provides a dynamic allocation system for computing resources of a power distribution smart fusion terminal. The system includes: a data acquisition module, used to collect multi-dimensional index data under the dimensions of metering performance, operating conditions, environmental stress, historical faults, verification data and operation monitoring of the electricity meter to be evaluated, and to standardize the multi-dimensional index data to construct an index standardization matrix; The weight calculation module is used to determine the subjective weight vector of each dimension using the analytic hierarchy process, determine the objective weight vector of each dimension using the entropy weight method, solve the optimal linear combination coefficient based on game theory, and weight and fuse the subjective weight vector and the objective weight vector to obtain the combined weight vector. The evaluation and scoring module performs multi-model analysis based on the combined weight vector to obtain corresponding independent evaluation and scoring results. The fusion decision module is used to calculate the Kendall consistency coefficient of independent assessment scores, judge the consistency of the scores based on a preset consistency threshold, and when the consistency condition is met, the independent assessment scores are weighted and fused to generate a comprehensive health index. The hierarchical output module is used to perform unsupervised clustering analysis on the comprehensive health index, divide the electricity meters into multiple health levels according to the cluster centers, and output the health level corresponding to each electricity meter using the boundary points of the clustering analysis as dynamic hierarchical thresholds.

[0109] In Embodiment 3, based on the same inventive concept as the method for quantitatively assessing the health status of an electricity meter in Embodiment 1, this application also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that can be executed by the at least one processor.

Claims

1. A method for quantitatively assessing the health status of an electricity meter, characterized in that, The method includes: S1. Collect multi-dimensional index data of the metering performance, operating conditions, environmental stress, historical faults, verification data and operation monitoring dimensions of the electricity meter to be evaluated, and standardize the multi-dimensional index data to construct an index standardization matrix; S2. The subjective weight vector of each dimension is determined by the analytic hierarchy process (AHP), the objective weight vector of each dimension is determined by the entropy weight method, the optimal linear combination coefficient is solved, and the subjective weight vector and the objective weight vector are weighted and fused to obtain the combined weight vector. S3. Using the combined weight vector, perform multi-model analysis to obtain corresponding independent evaluation scores; S4. Calculate the Kendall consistency coefficient of the independent assessment scores, judge the consistency of the scores based on the preset consistency threshold, and when the consistency condition is met, weight and merge the independent assessment scores to generate a comprehensive health index. S5. Perform unsupervised clustering analysis on the comprehensive health index, divide the electricity meters into multiple health levels according to the cluster centers, and use the boundary points of the clustering analysis as dynamic grading thresholds to output the health level corresponding to each electricity meter. The process of determining the combined weight vector in step S2 includes: A judgment matrix was constructed using the analytic hierarchy process (AHP). The importance of each dimension's indicators was compared pairwise and then subjected to a consistency test to obtain the subjective weight vector. Based on the standardized matrix obtained in step S1, the information entropy of each indicator is calculated, and the objective weight vector is obtained according to the inverse relationship between information entropy and indicator weight. An optimization model is established with the objective function of minimizing the deviation between the combined weight vector and the subjective weight vector and the objective weight vector. Under the constraint that the sum of the combination coefficients is equal to 1, the optimal linear combination coefficients are solved, and the subjective weight vector and the objective weight vector are weighted and fused to obtain the final combined weight vector. The subjective weight vector is obtained by including: The problem of energy meter health assessment is decomposed into target layer health status, criterion layer and scheme layer. The target layer represents the health status of the energy meter, the criterion layer represents the metering performance, operating conditions, environmental stress, historical faults, calibration data and operation monitoring of the energy meter, and the scheme layer represents the energy meter to be assessed. Based on pairwise comparisons at the criterion level, the following definition is made: The judgment matrix is ,in Indicates the first The first indicator is relative to the first The degree of importance of each indicator; If the indicator Comparison Indicators Equally important, If the indicator Comparison Indicators Important, then And so on, and satisfying , ; The subjective weight vector is obtained by calculating the normalized feature vector. .

2. The method for quantitatively assessing the health status of an electricity meter as described in claim 1, characterized in that, The standardization process includes: dividing the multidimensional index data into positive and negative indicators, and performing dimensionless processing on the positive and negative indicators using range normalization and reverse range normalization respectively, and constructing a standardized matrix containing the number of electricity meters to be evaluated and the number of indicators.

3. The method for quantitatively assessing the health status of an electricity meter as described in claim 1, characterized in that, Obtaining the objective weight vector specifically includes: Based on the normalized matrix elements in Transform into the first The first item under the indicator The proportion of each sample : Calculate the first Information entropy of the indicator : in, , 0≤ ≤1; Calculate the first Coefficient of difference of the items ; Calculate objective weights : Obtain the objective weight vector .

4. The method for quantitatively assessing the health status of an electricity meter as described in claim 1, characterized in that, In step S3, using the combined weight vector, multi-model analysis is performed to obtain corresponding independent evaluation scores, specifically including: The standardized indicator data sequence is compared with the preset ideal health state reference sequence, the correlation coefficient of each energy meter to be evaluated is calculated and weighted summed to obtain the first evaluation score; Based on the combined weight vector, a weighted normalized decision matrix is ​​constructed to determine the positive ideal solution vector and the negative ideal solution vector. The Euclidean distance from each energy meter to be evaluated to the positive ideal solution vector and the negative ideal solution vector is calculated. The relative proximity is calculated based on the distance ratio as the second evaluation score. An evaluation index set and a comment set are established. The index data are fuzzified using a membership function. The combined weight vector is then synthesized using a fuzzy operator. The third evaluation score is determined based on the principle of maximum membership.

5. The method for quantitatively assessing the health status of an electricity meter as described in claim 4, characterized in that, In step S4, the independent assessment scores are weighted and fused to generate a comprehensive health index, including: When the Kendall coordination coefficient is greater than the preset coordination threshold, the weighted average method is used to fuse the independent evaluation scores to obtain a comprehensive health index, wherein the weight coefficient of each score is set according to the applicability of the method or historical accuracy. When the Kendall coordination coefficient is less than or equal to a preset coordination threshold, a robustness processing strategy is executed. The robustness processing strategy includes at least one of the following: reducing the confidence weight of the fusion result, selecting the minimum value in the scoring result as the comprehensive health index, or triggering a data review mechanism.

6. The method for quantitatively assessing the health status of an electricity meter as described in claim 3, characterized in that, Step S5 outputs the health level corresponding to each energy meter, including: The cluster centers are sorted in descending order based on their numerical values, and then mapped to a preset health level sequence. The dynamic grading threshold is calculated based on the numerical values ​​of two adjacent cluster centers, where the dynamic grading threshold is the midpoint value of the adjacent cluster centers; Based on the dynamic grading threshold, the comprehensive health index of each energy meter to be evaluated is divided into the corresponding interval, the health level of the meter is determined, and the evaluation result is output.

7. A quantitative assessment system for the health status of an electricity meter, characterized in that, The system is used to implement the method for quantitatively assessing the health status of an electricity meter according to any one of claims 1-6, the system comprising: The data acquisition module is used to collect multi-dimensional index data of the electricity meter to be evaluated, including metering performance, operating conditions, environmental stress, historical faults, verification data, and operation monitoring dimensions. The multi-dimensional index data is then standardized to construct an index standardization matrix. The weight calculation module is used to determine the subjective weight vector of each dimension using the analytic hierarchy process, determine the objective weight vector of each dimension using the entropy weight method, solve the optimal linear combination coefficient based on game theory, and weight and fuse the subjective weight vector and the objective weight vector to obtain the combined weight vector. The evaluation and scoring module performs multi-model analysis based on the combined weight vector to obtain corresponding independent evaluation and scoring results. The fusion decision module is used to calculate the Kendall consistency coefficient of independent assessment scores, judge the consistency of the scores based on a preset consistency threshold, and when the consistency condition is met, the independent assessment scores are weighted and fused to generate a comprehensive health index. The hierarchical output module is used to perform unsupervised clustering analysis on the comprehensive health index, divide the electricity meters into multiple health levels according to the cluster centers, and output the health level corresponding to each electricity meter using the boundary points of the clustering analysis as dynamic hierarchical thresholds.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a method for quantitatively assessing the health status of an electricity meter as described in any one of claims 1-6.

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