Method and system for evaluating quality of electric energy metering device based on HPLC communication unit

Abstract

The application discloses a kind of based on HPLC communication unit's electric energy metering equipment quality evaluation method and system, the method steps include: S01.determine the multiple evaluation indexes for evaluating the quality of electric energy metering equipment;S02.HPLC communication unit's field data are acquired, and the corresponding each evaluation index is calculated respectively;S03.KICA model is used to each evaluation index respectively and principal component calculation is carried out, as the individual weight of each index;S04.CIM model is used to each evaluation index respectively and RAW score calculation is carried out, and the action weight of each index is obtained;S05.combination evaluation index weight is obtained according to individual weight, action weight, to realize the performance evaluation of HPLC communication unit thing is connected.The application has the advantages that implementation method is simple, cost is low, evaluation precision is high and safe and reliable.

Description

Technical Field

[0001] This invention relates to the field of power metering equipment technology, and in particular to a method and system for quality assessment of power metering equipment based on an HPLC communication unit. Background Technology

[0002] Electricity metering refers to the accurate measurement of consumed electrical energy, and it is a crucial link in power production, marketing, and the safe operation of the power grid. Electricity metering devices, as the most important equipment in the electricity metering process, are installed in various aspects of power generation, supply, and consumption to measure power generation, plant consumption, supply, and sales. Therefore, any malfunction or damage to electricity metering equipment can lead to measurement errors, which in turn can cause problems such as power grid operational safety and dispatching deviations. Thus, evaluating the quality of electricity metering equipment is of great significance for ensuring the safe and stable operation of the power grid.

[0003] For the quality assessment of electricity metering equipment, existing technologies typically calculate indicators such as operational reliability and failure rate based on electricity metering data, and then determine the quality of the electricity metering equipment based on one or a combination of these calculated indicators. However, due to the complexity of the operating status of electricity metering equipment, the quality assessment method using a single indicator has low accuracy and is difficult to accurately characterize the quality status of the electricity metering equipment. The method using a combination of multiple indicators usually employs a simple weighting approach, where the weighting coefficients are typically set based on experience. The setting of these weighting coefficients directly affects the final assessment result, and this experience-based approach relies heavily on human expertise, making it difficult to accurately determine the weighting coefficients for different indicators, thus maintaining low assessment accuracy.

[0004] The aforementioned problems can be addressed by employing both subjective and objective weighting methods. Subjective weighting methods primarily include expert surveys, the Analytic Hierarchy Process (AHP), the binomial coefficient method, and chain ratio scoring. Objective weighting methods mainly include principal component analysis, entropy method, multi-objective programming, and deviation and mean square error methods. However, subjective weighting methods, due to their inherent subjectivity and the significant influence of decision-makers' subjective opinions, still exhibit relatively high uncertainty. Objective weighting methods, on the other hand, often neglect prior information such as knowledge and experience, may lead to excessive bias. Some practitioners have proposed combining subjective and objective weighting methods to leverage their advantages. However, this approach typically only considers the individual importance of each indicator, neglecting its impact on the overall reliability of on-site operations. Given the complex operating conditions of electricity metering equipment, directly applying a combination of subjective and objective weighting methods still results in low evaluation accuracy. Summary of the Invention

[0005] The technical problem to be solved by this invention is: in view of the technical problems existing in the prior art, this invention provides a method and system for quality assessment of power metering equipment based on HPLC communication unit that is simple to implement, low in cost, high in evaluation accuracy and safe and reliable.

[0006] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0007] A method for quality assessment of power metering equipment based on an HPLC communication unit, comprising the following steps:

[0008] S01. Determine multiple evaluation indicators for assessing the quality of electricity metering equipment;

[0009] S02. Acquire the field data from the HPLC communication unit, and calculate the corresponding evaluation indicators based on the acquired field data;

[0010] S03. For each evaluation index obtained in step S02, principal component calculation is performed using the KICA (Kernel Independent Component Analysis) model, and the calculation results are used as the individual weights of each evaluation index.

[0011] S04. For each evaluation index obtained in step S02, RAW scores are calculated using the CIM (Component Importance Measure) model to obtain the weight of each evaluation index.

[0012] S05. Based on the individual weights and the effect weights, the combined evaluation index weights are obtained to achieve the IoT performance evaluation of the HPLC communication unit.

[0013] Furthermore, the evaluation indicators in step S01 include any combination of the following: operational stability (OS), single data acquisition time (OAD), after-sales service satisfaction (CSR), phase recognition execution rate (PRR), single data acquisition success rate (OASR), full data acquisition completeness rate (DCR), full inspection pass rate (QRGI), on-time delivery rate (TOS), and power outage event reporting accuracy (POWP).

[0014] Furthermore, in step S03, when using the KICA model to calculate principal components, the input data is mapped to the implicit feature space F: Φ: x∈R. N →Φ(x)∈F, and then perform ICA in F to generate a set of nonlinear features of the input data, to obtain the whitened data obtained by whitening the input data X in the feature space F, and obtain the individual weights of the evaluation index by solving the independent elements in the data.

[0015] Furthermore, in step S04, when using the CIM model to calculate the RAW score, the components of the HPLC communication unit system are defined as element e. j，k The risk of each component is defined as the risk of element e when the HPLC communication unit system fails. j，k The probability of simultaneous occurrence is defined as the overall likelihood of a failure occurring in the HPLC communication unit system.

[0016] Furthermore, the calculation expression for the overall system failure risk is as follows:

[0017]

[0018] Among them, R i For the failure risk of component i in the HPLC communication unit system, R s The overall failure risk of the system is given by n, where n is the number of components in the HPLC communication unit system.

[0019] Furthermore, the overall system failure risk is calculated by multiplying the comprehensive likelihoods of a failure occurring in the system when the corresponding elements in each of the evaluation indicators appear. The calculation expression is as follows:

[0020]

[0021] In the formula, 1-h(p(t) i )) indicates fault t i The risk that appears in the system, h() represents the system stability in a specific state, p(t) i ) indicates fault t i The probability of appearing in the system, D(i,j) represents the data in the i-th row and j-th column of the database matrix, f j Let j represent the j-th evaluation indicator, and n be the number of indicators.

[0022] Furthermore, the calculation expression for the RAW score in step S04 is as follows:

[0023]

[0024] Where i and j are the i-th row and j-th column in the database, and k and l represent the position of element e. j，k When it appears, it corresponds to the l-th range interval of the k-th indicator.

[0025] Furthermore, in step S05, the optimal combination coefficient is calculated using a linear weighted model, and the individual weights and effect weights are fused using the optimal combination coefficients to obtain the combined evaluation index weights. The calculation expression is as follows:

[0026] c i =ai +(1-)b i

[0027] In the formula, is the action preference coefficient, ∈ [0, 1]; 1- is the individual preference coefficient; c i a represents the weight obtained after combining the i-th indicator; i b i These represent the influence weight and individual weight corresponding to the i-th evaluation index, respectively.

[0028] A quality assessment system for power metering equipment based on an HPLC communication unit, comprising:

[0029] The indicator determination module is used to determine various evaluation indicators for assessing the quality of electricity metering equipment;

[0030] The index calculation module is used to acquire field data from the HPLC communication unit and calculate the corresponding evaluation indexes based on the acquired field data.

[0031] The individual weight calculation module is used to perform principal component calculation on each of the evaluation indicators calculated by the indicator calculation module using the KICA model, and to use the calculation results as the individual weights of each evaluation indicator.

[0032] The role weight calculation module is used to calculate the RAW score of each evaluation index obtained by the index calculation module using the CIM model to obtain the role weight of each evaluation index.

[0033] The combined weight calculation module is used to obtain the combined evaluation index weight based on the individual weights and the effect weights, so as to realize the IoT performance evaluation of the HPLC communication unit.

[0034] A computer system includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0035] Compared with the prior art, the advantages of the present invention are as follows:

[0036] 1. This invention analyzes the field data of the power metering equipment in the HPLC communication unit, calculates the weight coefficients of factors affecting the quality of the power metering equipment, and evaluates the importance of each indicator to the power metering equipment. It can effectively evaluate the operating performance level of the HPLC and reflect the actual operating status of the power metering equipment, thereby effectively realizing the status assessment of the power metering equipment, so as to provide reasonable and effective maintenance strategies for the power metering equipment and realize the screening of power metering equipment.

[0037] 2. When analyzing field data from an HPLC communication unit, this invention first calculates multiple evaluation indicators, then uses the KICA model to process each indicator to solve for principal components. The calculation results serve as the individual weights for each indicator. Simultaneously, the CIM model is used to calculate each indicator to determine its impact weight, thereby measuring the influence of each indicator in the HPLC communication unit system on the overall risk of the system. The indicators are weighted according to their importance. Finally, by fusing the individual weights and impact weights, a combined evaluation index is obtained to construct the KICA-CIM combined weighting model for energy metering assessment. Based on this model, accurate and reasonable weights can be assigned to each indicator, which not only minimizes the error caused by single weighting but also eliminates the need to rely on experience for weight setting, maximizing the accuracy of combined weighting and thus greatly improving the accuracy and reliability of energy metering equipment quality status assessment. Attached Figure Description

[0038] Figure 1 This is a schematic diagram illustrating the implementation process of the power metering equipment quality assessment method based on the HPLC communication unit in this embodiment.

[0039] Figure 2 This is a schematic diagram of the process for implementing quality assessment of power metering equipment based on an HPLC communication unit in a specific application embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram showing the results obtained by calculating the individual weight coefficients of each indicator in a specific application embodiment of the present invention.

[0041] Figure 4 This is a schematic diagram showing the results obtained by calculating the weighting coefficients of each indicator in a specific application embodiment of the present invention.

[0042] Figure 5 This is a schematic diagram illustrating the result of calculating the optimal combination weights in a specific application embodiment of the present invention. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0044] The HPLC (High-Speed ​​Power Line Communication) communication unit is a communication unit in energy metering equipment that uses high-speed power line communication technology to transmit data. In other words, the IoT performance status of the HPLC communication unit reflects the quality status of the energy metering equipment. This invention analyzes the field data of the energy metering equipment at the HPLC communication unit, calculates the weighting coefficients of factors affecting the quality of the energy metering equipment, and assesses the importance of each indicator to the energy metering equipment. Based on these weighting coefficients, the operating performance level of the HPLC can be effectively evaluated, reflecting the actual operating status of the equipment. This allows for the assessment of the energy metering equipment's condition, facilitating the provision of effective maintenance strategies and enabling subsequent screening of energy metering equipment.

[0045] This invention analyzes field data from an HPLC communication unit by first calculating multiple evaluation indicators (features). Then, the KICA model is used to process each indicator (nonlinear feature) to solve for principal components. The calculation results serve as the individual weights for each indicator (feature). Simultaneously, the CIM model is used to calculate the impact weight of each indicator (feature) to measure its influence on the overall risk of the HPLC communication unit system. Importance weights are assigned to the indicators. Finally, by fusing individual weights and impact weights, a combined evaluation indicator is obtained, constructing the KICA-CIM combined weighting model for energy metering assessment. This model allows for precise and reasonable weighting of each indicator (feature), minimizing errors caused by single weighting and eliminating reliance on experience for weight setting. This maximizes the accuracy of combined weighting, significantly improving the accuracy and reliability of energy metering equipment quality status assessment and facilitating reasonable maintenance strategies for energy metering equipment.

[0046] like Figure 1 As shown, the steps of the energy metering equipment quality assessment method based on the HPLC communication unit in this embodiment include:

[0047] S01. Indicator Determination: Determine various evaluation indicators for assessing the quality of electricity metering equipment.

[0048] To accurately reflect the on-site operation of energy metering equipment and evaluate the operational quality of the HPLC communication unit (chip), this embodiment specifically uses the following nine indicators: operational stability (OS), single acquisition time (OAD), customer satisfaction rate (CSR), phase recognition execution rate (PRR), single acquisition success rate (OASR), full data acquisition integrity rate (DCR), full inspection pass rate (QRGI), on-time delivery rate (TOS), and power outage event reporting accuracy rate (POWP). This establishes a universal indicator system for performance evaluation of energy metering equipment in local IoT scenarios. The single acquisition success rate is the success rate of the communication unit or chip of the evaluated CCO / STA in acquiring the single energy reading within a specified time range (d). The single acquisition time (OAD) is the time efficiency during which the communication unit or chip of the CCO / STA achieves the success rate of acquiring the single frozen energy reading of the energy meter through the main station, reaching a preset threshold.

[0049] Understandably, in addition to the nine indicators mentioned above, other evaluation indicators can be used according to actual needs. The selection and combination of evaluation indicators can be configured and adjusted according to actual needs.

[0050] S02. Index Calculation: Obtain the field data from the HPLC communication unit and calculate the corresponding evaluation indexes based on the obtained field data.

[0051] The specific calculation methods for the above indicators are as follows:

[0052] 1) Operational Stability (OS)

[0053] By analyzing the operational fault conditions of each CCO / STA communication unit or chip, the operational stability of the evaluated CCO / STA communication unit or chip is statistically analyzed within a specified period. The calculation expression is as follows:

[0054]

[0055]

[0056] Where f(1) = 1, f(2) = 0.9, f(3) = 0.6, f(4) = 0.4, f(5) = 0.2, and f(n > 5) = 0.1. R stab To evaluate the operational stability of the CCO / STA communication unit or chip, N fault N represents the number of faulty CCO / STA communication units or chips being evaluated, and N represents the total number of operational CCO / STA communication units or chips being evaluated. iLet f(i) be the number of CCO / STA communication unit or chip product failures evaluated in year i, n be the number of operating years, f(i) be the failure factor in year i, and 1-f(i) be the allowable failure factor in year i.

[0057] 2) One-time Acquisition Duration (OAD) is calculated using the following formula:

[0058]

[0059] In the formula, T acc This refers to the time frame during which the communication unit or chip manufacturer of CCO / STA can successfully collect the energy readings of primary energy meters within a designated market through the main station, achieving a preset percentage.

[0060] When the evaluation object is a CCO communication unit, T min T represents the minimum time taken for all evaluated CCO communication units to achieve a successful acquisition rate of a preset threshold; the shorter the time, the higher the efficiency. i This refers to the acquisition time when the success rate of a single acquisition by each evaluated CCO communication unit reaches a preset threshold. When T... acc The same principle applies when considering the success rate of a single data acquisition using the CCO chip.

[0061] When the evaluation object is the STA communication unit, T min T represents the minimum time taken for all evaluated STA communication units to achieve a successful acquisition rate of a preset threshold, with shorter time indicating higher efficiency; i This refers to the acquisition time required for each evaluated STA communication unit to achieve a preset threshold for single-acquisition success rate. When T... acc The same principle applies when calculating the success rate of a single data acquisition using the STA chip.

[0062] 3) Customer Satisfaction Rate (CSR)

[0063] This indicator is derived from consumer ratings and is used to statistically analyze the after-sales service performance of the manufacturer being evaluated over a specified period, using R... sa express.

[0064] 4) The formula for calculating the Phase Recognition Rate (PRR) is:

[0065]

[0066] In the formula, R phase The phase recognition execution rate of the STA communication unit being evaluated on a certain day. N succS represents the number of successful phase identification operations performed by the evaluated STA communication unit. total The number of all operational communication units for the evaluated STA communication unit; when R phase The same principle applies when calculating the phase recognition execution rate of the STA chip.

[0067] 6) The formula for calculating the One-time Acquisition Success Rate (OASR) is:

[0068]

[0069] In the formula, R acc The success rate of acquiring a single energy reading within a specified time range d for the communication unit or chip of the evaluated CCO / STA.

[0070] 7) The formula for calculating the Data Completeness Rate (DCR) is:

[0071]

[0072]

[0073]

[0074] In the formula, R v The completeness rate of data acquisition for all data items of the communication unit or chip of the CCO / STA being evaluated within a specified time range d.

[0075] 7) The formula for calculating the Qualified Rate of General Inspection (QRGI) is as follows:

[0076]

[0077] In the formula, R total The pass rate of the full inspection test for the CCO / STA communication unit or chip being evaluated. succ To evaluate the number of CCO / STA communication units or chips that pass full inspection, S total This refers to the total number of communication units and chips delivered for the CCO / STA communication unit or chip being evaluated.

[0078] 8) The formula for calculating Timeliness of Supply (ToS) is:

[0079]

[0080] In the formula, R in-timeTo assess the cumulative on-time delivery rate of the communication unit or chip of the CCO / STA within a specified period, M i N represents the number of communication units or chips of the CCO / STA in the i-th batch that has completed batch registration according to the delivery notification within the specified time period; i This refers to the number of communication units or chips required for the i-th batch of CCO / STA within a specified time period.

[0081] 9) Power Outage Warning Performance (POWP)

[0082]

[0083] In the formula, R st The accuracy of evaluating the STA communication unit or chip during power outage events within a specified time range d. M i For the power outage event that the evaluated STA communication unit judged to be invalid on day i, N i S represents the power-on event of the STA communication unit being evaluated on day i; i Power outage incident for service provider.

[0084] After obtaining the field data of the HPLC communication unit, the values ​​of nine evaluation indicators, excluding TOS and CSR, can be calculated according to the above formulas (1) to (11), thus constructing a universal index system for performance evaluation of local IoT scenarios of power metering equipment.

[0085] S03. Individual weight calculation: For each evaluation index obtained in step S02, principal component calculation is performed using the KICA model, and the calculation results are used as the individual weights of each evaluation index.

[0086] This step specifically utilizes the KICA model based on nine indicators to calculate principal components of the standardized input data sample set, thereby measuring the information effectiveness of each feature. The classic ICA model is as follows:

[0087] X = AY (12)

[0088] In the formula, A is the matrix to be determined, X is the observed signal, and Y is the independent component.

[0089] ICA primarily calculates the inverse matrix of A; this embodiment, however, uses... To estimate the weight, This is an estimate of Y. To further reduce the redundancy of the input data, W is whitened to ensure that X is uncorrelated. The whitened data I is then represented as:

[0090]

[0091] In the formula, Λ and V are the eigenvalue matrix and eigenvector matrix of the covariance matrix of X, respectively.

[0092] ICA, as a linearization method, is only applicable to data redundancy reduction and feature extraction in linear models, and cannot be applied to linear models themselves. Since the index data obtained in step S02 is a non-linear relationship, this embodiment introduces KICA to process the non-linear features. The specific steps include: mapping the input evaluation index data (input data) to the implicit feature space F: Φ: x∈R N →Φ(x)∈F; then ICA is performed in F to generate a set of nonlinear features of the input data. Similarly, the input evaluation index data (input data) X will be whitened in the feature space F, and the whitening matrix is: Among them Λ φ V Φ It is the covariance matrix The eigenvalue matrix and eigenvector matrix in i. Then the whitened data can be obtained.

[0093]

[0094] In the formula, K(X, s)=[k(x1, s), k(x1, s),...k(x1, s)] T Here, k is the kernel function, and α is the eigenvector matrix of K. Then, by solving for the independent elements in the data, the individual weights of each indicator can be calculated.

[0095] S04. Calculation of Role Weights: For each evaluation index obtained in step S02, the RAW score is calculated using the CIM model to obtain the role weight of each evaluation index.

[0096] Component Criticality Analysis (CIM) can be used to measure the degree of influence of each component in a system on the overall risk increase or decrease trend and magnitude of the system. Therefore, CIM can also be used to identify elements that have a significant impact on the occurrence of system failures. This embodiment calculates the Risk Growth RAW score for each feature based on the CIM model to measure the change trend and magnitude of the overall operational status assessment results when this indicator is taken into account, i.e., when the indicator is in effect. RAW is an important metric in CIM. As a boundary metric, RAW can provide the change in the overall system risk level that a certain basic factor may cause, i.e., the potential increase in risk associated with the occurrence of an event. Its mathematical expression is:

[0097]

[0098] in, R0 represents the increase in overall system risk when component i is guaranteed to generate risk, and R0 represents the current overall system risk. This value quantifies the factors that would increase system risk if we assume that the relevant basic events are completely unreliable. Therefore, RAW is suitable for the detection and maintenance of system stability.

[0099] When calculating the Risk Achievement Worth (RAW) score based on the CIM calculation model, the components of the HPCL communication unit system are specifically redefined as element e. j，k The corresponding component risk is defined as element e when a communication unit system failure occurs. j，k The probability of simultaneous occurrence defines the overall system failure risk as the comprehensive likelihood that a failure will occur in the system. This embodiment redefines RAW as the probability that element e... j，k When an occurrence inevitably has an impact, the corresponding fault t i The relative increase in overall system risk can be expressed mathematically as follows:

[0100]

[0101] In the formula, 1-h(0 k ,p(t i )) indicates that when element e j，k When determined, the risk of a fault ti occurring in the system. h() represents the system stability under a specific state, O k Represents element e j，k This has affected the stability of the system, p(t) i ) represents the probability that fault ti occurs in the system. 1-h(p(t) i )) indicates fault t i Risks that may arise in the system.

[0102] According to the theory of system risk structure, the overall risk of a system depends on the relative positions and composition of its components. The most basic system structures are series and parallel. In a series system, the failure of any component will lead to the failure of the entire system. In the HPLC communication unit system, all components are independent of each other. In this embodiment, the overall failure risk of the system is calculated using the following formula:

[0103]

[0104] In the formula: R i For the failure risk of component i, R s This is to mitigate the risk of overall system failure.

[0105] To solve the overall system failure risk, it is necessary to analyze the logical relationships between all environmental features in the system to determine the system's risk structure. In a real failure record, considering that the occurrence of the failure requires the appearance of all corresponding elements in all environmental features, that is, even if the corresponding element in any feature does not appear, it will no longer be completely consistent with the environmental state in the record, and the failure may not occur. Combining the characteristics of a series structure system, it can be seen that the various environmental features are in a series relationship. Assuming that all environmental features are independent of each other, the overall system failure risk can be solved by the product of the risks of the corresponding elements in all features. This embodiment utilizes the above characteristics to solve the overall system failure risk 1-h(p(t) in equation (15). i Define the database matrix D(i,j) to represent the data of the database on that day, f j The representative indicator features are arranged in a series, and it is assumed that all environmental features are independent of each other. The overall failure risk of the system is calculated by multiplying the comprehensive likelihoods of a failure occurring in the system when the corresponding elements in each feature appear. Its mathematical expression is as follows:

[0106]

[0107] In the formula, the database matrix D(i,j) represents the data of the database on that day, f j The representative indicator characteristic, |...|, represents the number of fault records in the annual input database D that simultaneously meet all the included conditions.

[0108] Furthermore, the calculation expression for RAW is obtained as follows:

[0109]

[0110] In the formula, i and j are the i-th row and j-th column of the database, n is the number of indicators, and k and l represent the values ​​of elements e. j，k When it appears, it corresponds to the l-th range interval of the k-th indicator.

[0111] S05. Combined weight calculation: The combined evaluation index weights are obtained based on the individual weights and the effect weights, so as to realize the IoT performance evaluation of the HPLC communication unit.

[0112] To find the minimum deviation between each weight and the optimal weight, this embodiment first sets the action preference coefficient to 0.5 to balance the weights of the two types of weights. Then, it uses a linear weighted model to calculate the optimal combination coefficient and integrates the individual action weights to obtain the combined evaluation index weights, which are expressed as follows:

[0113] c i =εa i +(1-ε)b i (19)

[0114] In the formula, ε is the action preference coefficient, ε∈[0,1]; 1-ε is the individual preference coefficient; c i a represents the weight obtained after combining the i-th indicator; i b i These represent the influence weight and individual weight corresponding to the i-th indicator, respectively.

[0115] Considering that the individual weights and effect weights of each indicator are not directly related and there are certain differences between different characteristics, using a single weight measurement method would ignore other influencing factors and affect the evaluation results. Combined weights can be between individual weights and effect weights. This invention establishes an HPLC weight calculation model based on multiple indicators, and uses KICA and CIM to calculate the individual weights and effect weights on the weight model to obtain weight coefficients. Then, by finding the optimal combination coefficient of individual weights and effect weights, the optimal combination weight is formed, which solves the problem of unreasonable manual weight setting. This effectively ensures that the metering of power devices is more reasonable and effective, and also optimizes the on-site safety performance of power metering equipment to meet the needs of multiple power services, thereby evaluating the IoT performance of HPLC communication units.

[0116] The following example, using a specific application embodiment of the present invention to implement the quality assessment of power metering equipment based on an HPLC communication unit, further illustrates the present invention. Figure 2 As shown, the detailed steps for implementing the quality assessment of power metering equipment based on the HPLC communication unit in this embodiment are as follows:

[0117] Step 001: Based on the characteristics of typical customer-side metering equipment local IoT scenarios, nine evaluation indicators are selected: Operating Stability (OS), One-Time Acquisition Duration (OAD), Customer Satisfaction Rate (CSR), Phase Recognition Execution Rate (PRR), One-Time Acquisition Success Rate (OASR), Full Data Acquisition Completeness Rate (DCR), Full Inspection Test Pass Rate (QRGI), On-Time Delivery Rate (TOS), and Power Outage Event Reporting Accuracy Rate (POWP). These nine indicators are used to construct a universal indicator system for performance evaluation of typical customer-side metering equipment local IoT scenarios.

[0118] Step 002: After acquiring the field data from the HPLC communication unit, calculate the values ​​of each evaluation index for the quality of the aforementioned power metering equipment. The sample data is collected in days, and the simulation verification uses a ten-fold cross-validation method, with 75% of the data samples used as the training set, 10% as the validation set, and 15% as the test set.

[0119] Step 003: Based on the calculated evaluation indicators, principal component calculation is performed on the standardized input data sample set using the KICA model to measure the information effectiveness of each feature.

[0120] like Figure 3 As shown, this embodiment selects datasets for operational stability, single data acquisition time, after-sales service satisfaction, phase recognition execution rate, single data acquisition success rate, full data acquisition completeness rate, full inspection pass rate, on-time delivery rate, and power outage event reporting accuracy. Based on KICA, the individual weight coefficient 'a' for each indicator is determined. i For a i = [0.110, 0.150, 0.121, 0.120, 0.072, 0.081, 0.171, 0.090, 0.062].

[0121] Step 004: Solve the RAW scores of each evaluation indicator (feature) according to the CIM model, and calculate the role weight of each indicator to measure the trend and magnitude of the overall operation status assessment results when each indicator is taken into account, that is, when each indicator plays its role.

[0122] like Figure 4 As shown, this embodiment considers the proportion of each indicator's influence on the overall model, and uses the data of a certain day as an example to solve for the weight coefficient b of each indicator on that day. i = [0.140, 0.090, 0.079, 0.100, 0.142, 0.153, 0.101, 0.110, 0.082].

[0123] Step 005: Obtain the optimal combined weight by combining individual weights and effect weights using subjective preference coefficients.

[0124] like Figure 5 As shown, this embodiment first uses independent component analysis to analyze historical data to obtain the individual weights of each indicator. In the evaluation, the individual weights of each state variable are calculated using local criticality based on the experimental data itself. The initial individual weights obtained by independent component analysis are then corrected to determine the impact weights. Finally, the optimal combination coefficients are calculated using a linear weighted model to obtain the combination weight c. i = [0.125, 0.120, 0.100, 0.115, 0.107, 0.117, 0.136, 0.100, 0.072]. From Figure 5 It is understood that the combined weight of the present invention is between the individual weight and the effect weight, which can avoid the one-sidedness of a single evaluation perspective; among the various indicators, the combined weight of OAD, QRGI and OS is relatively large, which means that it can have a greater impact on the evaluation effect of HPLC communication unit operation.

[0125] Step 006: To verify the fitness and simulation accuracy of the model of this invention, the evaluation accuracy results of this invention are compared with those of traditional single weighting methods, as shown in Table 1. Simultaneously, the evaluation accuracy results of this invention are compared with those of traditional combined weighting methods, as shown in Table 2. Table 1 shows that this invention can overcome the shortcomings of single evaluation methods in terms of the correlation between indicators. By comparing it with other traditional combined weighting methods (EIW, EW, AHP, and PCA, respectively, expert weighting method, entropy weighting method, analytic hierarchy process, and principal component analysis method) in Table 2, it can be verified that this invention has high evaluation accuracy and is suitable for the current scenario.

[0126] Table 1 Internal Comparison of Evaluation Models

[0127]

[0128] Table 2 External Comparison of Evaluation Models

[0129]

[0130]

[0131] This embodiment of the power metering equipment quality assessment system based on the HPLC communication unit includes:

[0132] The indicator determination module is used to determine various evaluation indicators for assessing the quality of electricity metering equipment;

[0133] The index calculation module is used to acquire field data from the HPLC communication unit and calculate the corresponding evaluation indexes based on the acquired field data.

[0134] The individual weight calculation module is used to perform principal component calculation on each of the evaluation indicators calculated by the indicator calculation module using the KICA model, and to use the calculation results as the individual weights of each evaluation indicator.

[0135] The role weight calculation module is used to calculate the RAW score of each evaluation index obtained by the index calculation module using the CIM model to obtain the role weight of each evaluation index.

[0136] The combined weight calculation module is used to obtain the combined evaluation index weight based on the individual weights and the effect weights, so as to realize the IoT performance evaluation of the HPLC communication unit.

[0137] The energy metering equipment quality assessment system based on the HPLC communication unit in this embodiment corresponds one-to-one with the energy metering equipment quality assessment method based on the HPLC communication unit described above, and will not be described in detail here.

[0138] This embodiment also provides a computer system, including a processor and a memory, the memory for storing computer programs, and the processor for executing the computer programs to perform the methods described above.

[0139] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for quality assessment of power metering equipment based on an HPLC communication unit, characterized in that the steps include... include: S01. Determine multiple evaluation indicators for assessing the quality of electricity metering equipment; S02. Acquire the field data from the HPLC communication unit, and calculate the corresponding evaluation indicators based on the acquired field data; S03. For each evaluation index obtained in step S02, principal component calculation is performed using the KICA model, and the calculation results are used as the individual weights of each evaluation index. S04. For each evaluation index calculated in step S02, RAW scores are calculated using the CIM model to correct the individual weights of each evaluation index, thereby obtaining the role weight of each evaluation index. S05. Based on the individual weights and the effect weights, the combined evaluation index weights are obtained to achieve the IoT performance evaluation of the HPLC communication unit; In step S04, when calculating the RAW score using the CIM model, the components of the HPLC communication unit system are defined as elements. The risk of each component is defined as the risk that occurs when the HPLC communication unit system fails. The probability of simultaneous occurrence is defined as the overall likelihood of a failure occurring in the HPLC communication unit system.

2. The method for quality assessment of power metering equipment based on an HPLC communication unit according to claim 1, characterized in that, The evaluation indicators in step S01 include any combination of the following: operational stability (OS), single data acquisition time (OAD), customer satisfaction rate (CSR), phase recognition execution rate (PRR), single data acquisition success rate (OASR), full data acquisition completeness rate (DCR), full inspection pass rate (QRGI), on-time delivery rate (TOS), and power outage event reporting accuracy rate (POWP).

3. The method for quality assessment of power metering equipment based on an HPLC communication unit according to claim 1, characterized in that, In step S03, when using the KICA model to calculate principal components, the input evaluation index data is mapped to the implicit feature space F: Then F The process involves performing ICA to generate a set of nonlinear characteristics of the input evaluation index data, thus obtaining the input evaluation index data. In feature space The whitened data obtained by whitening is used to obtain the individual weights of the evaluation index by solving the independent elements in the data.

4. The method for quality assessment of power metering equipment based on an HPLC communication unit according to claim 1, characterized in that, The formula for calculating the overall failure risk of the system is as follows: in, For the failure risk of component i in the HPLC communication unit system, The overall failure risk of the system is given by n, where n is the number of components in the HPLC communication unit system.

5. The method for quality assessment of power metering equipment based on an HPLC communication unit according to claim 1, characterized in that, The overall system failure risk is calculated by multiplying the comprehensive likelihoods of a failure occurring in the system when the corresponding elements in each of the evaluation indicators appear. The calculation expression is as follows: In the formula, Indicates a fault Risks that appear in the system This indicates the system stability under a specific state. Indicates a fault The probability of appearing in the system. Represents the first in the database matrix Line 1 The data in the column, Represents the j-th evaluation index, The number of indicators.

6. The method for quality assessment of power metering equipment based on an HPLC communication unit according to claim 5, characterized in that, The formula for calculating the RAW score in step S04 is as follows: in, The respective databases of the first Line 1 List, Represents the element When it appears The first indicator corresponding to the A range interval.

7. The method for quality assessment of power metering equipment based on an HPLC communication unit according to any one of claims 1 to 6, characterized in that, In step S05, the optimal combination coefficient is calculated using a linear weighted model, and the individual weights and effect weights are fused using the optimal combination coefficients to obtain the combined evaluation index weights. The calculation expression is as follows: In the formula, This is the effect preference coefficient. ; Individual preference coefficient; Representing the The weights obtained by combining the indicators; , Representing the first The role weight and individual weight corresponding to each evaluation indicator.

8. A quality assessment system for power metering equipment based on an HPLC communication unit, characterized in that, include: The indicator determination module is used to determine various evaluation indicators for assessing the quality of electricity metering equipment; The index calculation module is used to acquire field data from the HPLC communication unit and calculate the corresponding evaluation indexes based on the acquired field data. The individual weight calculation module is used to perform principal component calculation on each of the evaluation indicators calculated by the indicator calculation module using the KICA model, and to use the calculation results as the individual weights of each evaluation indicator. The role weight calculation module is used to calculate the RAW score of each evaluation index obtained by the index calculation module using the CIM model to correct the individual weight of each evaluation index, so as to obtain the role weight of each evaluation index. The combined weight calculation module is used to obtain the combined evaluation index weight based on the individual weights and the effect weights, so as to realize the IoT performance evaluation of the HPLC communication unit; When using the CIM model to calculate RAW scores in the weighting calculation module, the components of the HPLC communication unit system are defined as elements. The risk of each component is defined as the risk that occurs when the HPLC communication unit system fails. The probability of simultaneous occurrence is defined as the overall likelihood of a failure occurring in the HPLC communication unit system.

9. A computer system comprising a processor and a memory, the memory being used to store computer programs, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 7.

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