A method and device for evaluating the reliability of smart electric energy meters

Abstract

The present application provides a reliability evaluation method and device for a smart electric energy meter, the method comprising: obtaining sensitive data characteristics and multiple quality characteristics for evaluating reliability; obtaining multiple test items and corresponding test results corresponding to each quality characteristic; determining a first measurement value of each quality characteristic according to the test results; screening out sensitive test items from multiple test items, and calculating a second measurement value of the sensitive data characteristics according to the test results corresponding to the sensitive test items; calculating a target measurement value of reliability according to the first measurement value of each quality characteristic and the second measurement value of the sensitive data characteristics; performing reliability evaluation on the software of the smart electric energy meter according to the target measurement value of reliability, and obtaining the software reliability level of the smart electric energy meter. By adding the calculation of sensitive data characteristics to the reliability calculation, the sensitive data is prevented from affecting the reliability evaluation result, thereby effectively improving the reliability of the smart electric energy meter software evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of reliability assessment of smart electric energy meters, and in particular to a reliability assessment method and device for smart electric energy meters. Background Art

[0002] Reliability assessment refers to the probability test of software not causing system failure under specified conditions and within a specified time. The probability is a function of system input and system usage, as well as a function of defects in the software.

[0003] At present, the method for testing the software on smart electricity meters is to use black box testing based on test cases to perform functional testing and performance testing, etc., and then count the number of passed test cases as a percentage of the total number of test cases to maximize protocol consistency. However, the scope of the test is not comprehensive enough to ensure the reliability of the smart electricity meter evaluation. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method and device for evaluating the reliability of a smart electric energy meter, so as to improve the problem that the reliability of software on the smart electric energy meter is not accurate.

[0005] In a first aspect, an embodiment of the present application provides a reliability assessment method for a smart electric energy meter, comprising: obtaining sensitive data characteristics and multiple quality characteristics for evaluating reliability, wherein the quality characteristics include at least one measurement indicator; obtaining multiple test items corresponding to each of the quality characteristics and test results corresponding to each of the test items, wherein the test results include whether each of the test items passes or fails; determining a first measurement value of each of the quality characteristics according to the test results; screening out sensitive test items from the multiple test items corresponding to each of the quality characteristics, and calculating a second measurement value of the sensitive data characteristic according to the test results corresponding to the sensitive test items, wherein the sensitive test items are used to test sensitive data, which is predefined data; calculating a target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic; performing reliability assessment on the software of the smart electric energy meter according to the target measurement value of the reliability to obtain the software reliability level of the smart electric energy meter.

[0006] In the embodiment of the present application, by obtaining sensitive data characteristics and multiple quality characteristics for evaluating reliability, as well as multiple test items and corresponding test results corresponding to each quality characteristic, the first measurement value of each quality characteristic can be obtained according to the test results, and then the sensitive test items for testing sensitive data are screened out from these test items, thereby obtaining the second measurement value of the sensitive data, and the target measurement value of reliability can be calculated according to the first measurement value and the second measurement value, and the reliability of the software of the smart energy meter is evaluated by the target measurement value of reliability to obtain the software reliability level. The important influence of sensitive data on reliability evaluation is reflected in the test items, thereby achieving an accurate evaluation of the reliability of the smart energy meter software.

[0007] Further, the calculating the target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic comprises: according to the formula Calculate the target measurement value of the reliability, where V is the target measurement value of the reliability, α is the second measurement value of the sensitive data characteristic, and V i is the first measured value of the i-th quality characteristic, W i is the weight corresponding to the i-th quality characteristic, N is the number of quality characteristics, wherein the weight corresponding to each quality characteristic is obtained by calculating each quality characteristic in advance.

[0008] In the embodiment of the present application, since the first measurement value of each quality characteristic and the second measurement value of the sensitive data characteristic can be determined according to the test results, the weight corresponding to each quality characteristic can be calculated in advance for each quality characteristic, and the weight corresponding to each quality characteristic can be calculated in advance by the formula The target measurement value of reliability is obtained by introducing the second measurement value of the sensitive data characteristic into the formula to calculate the target measurement value of reliability, and the impact of sensitive data on reliability is accurately calculated, so that the target measurement value of reliability can be accurately calculated, which plays a very important role in reliability evaluation.

[0009] Further, the determining of the first measurement value of each of the quality characteristics according to the test results includes: counting the total number of test items corresponding to each of the measurement indicators; counting the number of test items with passed test results among the test items corresponding to each of the measurement indicators; taking the ratio of the number of passed test items to the total number of test items as the intermediate measurement value of each of the measurement indicators; and calculating the value of the first measurement value of each of the quality characteristics according to the formula V i =∑V ij ×W ij Calculate a first measurement value of each of the quality characteristics, where V i is the measured value of the i-th quality characteristic, V ij is the intermediate measurement value corresponding to the jth measurement index in the i-th quality characteristic, Wij The weight corresponding to the jth measurement indicator in the ith quality characteristic, wherein the weight corresponding to each measurement indicator is obtained by pre-calculating each measurement indicator in advance.

[0010] In the embodiment of the present application, since each quality characteristic includes at least one measurement indicator, the weight of each measurement indicator can be obtained by pre-calculation. Therefore, in the calculation process of the first measurement value of each quality characteristic, the total number of test items corresponding to each measurement indicator is first counted, and then the number of test results of these test items is calculated as passed, and the ratio of the number of passed test items to the total number of test items corresponding to the test indicator is used as the intermediate measurement value of each measurement indicator. The intermediate measurement value of each measurement indicator is calculated in turn, and then according to the formula V i =∑V ij ×W ij Calculate the first measurement value of each quality characteristic. The first measurement value of each quality characteristic is obtained by calculating the intermediate measurement value of the measurement indicator. The first measurement value of each quality characteristic is accurately calculated by multiplying the intermediate measurement value of each test indicator in the quality characteristic by the corresponding weight.

[0011] Furthermore, the second measurement value of the sensitive data characteristic is calculated based on the test results corresponding to the sensitive test items, including: obtaining the number of test items whose test results corresponding to the sensitive test items are passed; if the number of passed test items is less than the total number of the sensitive test items, using a preset sensitivity coefficient threshold as the second measurement value of the sensitive data characteristic; if the number of passed test items is equal to the total number of the sensitive test items, setting the second measurement value of the sensitive data characteristic to 1.

[0012] In the embodiment of the present application, in the calculation of the second measurement value of the sensitive data characteristic, since the sensitive test items have been screened out from these test items, the number of test items with a passed test result in the sensitive test items is first obtained. When the number of passed test items is less than the total number of sensitive test items, the preset sensitivity coefficient threshold is used as the second measurement value of the sensitive data characteristic. When all sensitive test items are passed, the second measurement value of the sensitive data characteristic is directly set to 1, which means that no error occurred in the sensitive data during the test, that is, the sensitive data characteristic has no effect on the measurement value of reliability. By considering the passing status of the sensitive test items, the accurate calculation of the sensitive data characteristics is achieved, and the accuracy of the reliability evaluation is guaranteed.

[0013] Furthermore, according to the formula Before calculating the target measurement value of the reliability, the method also includes: obtaining a relationship hierarchy of the reliability, wherein the relationship hierarchy includes: a decision layer, a criterion layer and an indicator layer, the decision layer corresponds to the reliability, the criterion layer corresponds to the sensitive data characteristics and the multiple quality characteristics, and the indicator layer corresponds to the multiple measurement indicators; constructing a judgment matrix based on the relationship hierarchy, wherein the judgment matrix includes a first matrix and a second matrix, the first matrix is ​​a judgment matrix of the criterion layer compared to the decision layer, and the second matrix is ​​a judgment matrix of the indicator layer compared to the criterion layer; performing a consistency check on the first matrix and the second matrix to obtain a check result; if the check result is passed, calculating the quality characteristic weight set corresponding to the first matrix and the measurement indicator weight set corresponding to the second matrix.

[0014] In an embodiment of the present application, before calculating the target measurement value of reliability, the weight set of quality characteristics and the weight set of measurement indicators can be calculated by obtaining the relationship hierarchy of reliability. The relationship hierarchy includes a decision layer, a criterion layer and an indicator layer. The quality characteristic weight set and the measurement indicator weight set are calculated by constructing a judgment matrix. According to the quality characteristic weight set and the measurement indicator weight set, the importance of each quality characteristic in reliability and the measurement indicators therein can be analyzed, thereby realizing the quantitative calculation of weights.

[0015] Furthermore, the consistency check of the first matrix and the second matrix to obtain the check result includes: respectively calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix; calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix to obtain a consistency index; and determining the check result according to the consistency index.

[0016] In an embodiment of the present application, the maximum eigenvalue of the first matrix and the second matrix are calculated respectively, and the maximum eigenvalue is used to obtain a consistency index. The test result can be determined based on the consistency index. Through the test of the consistency index, it is determined that the constructed first matrix and the second matrix are subjected to weight calculation on the premise of passing the consistency check, thereby ensuring the accuracy of the calculation results of the quality characteristic weight set and the measurement indicator weight set.

[0017] Furthermore, the reliability assessment of the software of the smart electric energy meter according to the target measurement value of the reliability includes: obtaining a preset reliability grade classification rule; and grading the target measurement value of the reliability according to the reliability grade classification rule.

[0018] In an embodiment of the present application, by obtaining a preset reliability level classification rule and classifying the target measurement value of the reliability according to the reliability classification rule, the reliability level of the smart electric energy meter can be determined, and the accurate classification of the reliability level is achieved, thereby effectively evaluating the reliability of the smart electric energy meter.

[0019] Furthermore, the obtaining of multiple test items and test results corresponding to each of the quality characteristics includes: obtaining multiple test items corresponding to each of the quality characteristics; and testing the software of the smart electric energy meter according to each of the test items to obtain the test results corresponding to each of the test items.

[0020] In an embodiment of the present application, by obtaining multiple test items corresponding to each quality characteristic, and then testing the software of the smart power meter according to each test item to obtain the test result corresponding to each test item, it is possible to test the software of the smart power meter according to the quality characteristics and obtain the test result corresponding to each quality characteristic, thereby providing a software testing means for reliability assessment.

[0021] Furthermore, the multiple quality characteristics include maturity characteristics, fault tolerance characteristics, easy recovery characteristics and compliance characteristics. The maturity characteristics include demand measurement indicators and logic measurement indicators. The fault tolerance characteristics include failure avoidance measurement indicators and misoperation resistance measurement indicators. The compliance characteristics include coding specification compliance measurement indicators and metrology performance compliance measurement indicators.

[0022] In the embodiment of the present application, the quality characteristics used to evaluate reliability include maturity characteristics, fault tolerance characteristics, easy recovery characteristics and compliance characteristics. Each quality characteristic has a corresponding measurement indicator, thereby determining an indicator system for reliability evaluation. Through the determined standard reliability evaluation system, guidance is provided for the reliability evaluation of smart electricity meter software.

[0023] In a second aspect, an embodiment of the present application provides a reliability assessment device for a smart electric energy meter, the device comprising: an acquisition module, for acquiring sensitive data characteristics and multiple quality characteristics for assessing reliability, the quality characteristics comprising at least one measurement indicator; and also for acquiring multiple test items corresponding to each of the quality characteristics and test results corresponding to each of the test items, wherein the test results include whether each of the test items passes or fails; a quality characteristic calculation module, for determining a first measurement value of each of the quality characteristics according to the test results; a sensitive data characteristic calculation module, for screening out sensitive test items from the multiple test items, and calculating a second measurement value of the sensitive data characteristic according to the test results corresponding to the sensitive test items, wherein the sensitive test items are used to test sensitive data, and the sensitive data is predefined data; a reliability calculation module, for calculating a target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic; a reliability assessment module, for performing reliability assessment on the software of the smart electric energy meter according to the target measurement value of the reliability, and obtaining the software reliability level of the smart electric energy meter.

[0024] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a bus, wherein the processor and the memory communicate with each other through the bus;

[0025] The memory stores program instructions that can be executed by the processor, and the processor can execute the method of the first aspect by calling the program instructions.

[0026] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, including:

[0027] The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the method of the first aspect.

[0028] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 A schematic diagram of a process flow of a smart electric energy meter reliability assessment method provided by an embodiment of the present application is shown;

[0031] Figure 2 A schematic diagram of a smart electric energy meter software reliability assessment model at a relational level provided by an embodiment of the present application is shown;

[0032] Figure 3 A schematic diagram of the structure of a smart electric energy meter reliability assessment device provided in an embodiment of the present application is shown;

[0033] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0035] Before introducing the reliability assessment method of a smart electric energy meter provided in an embodiment of the present application, some concepts involved in the embodiment of the present application are first introduced:

[0036] The Analytic Hierarchy Process (AHP) is a method that systematizes complex problems, decomposes them hierarchically from different levels, and uses quantitative calculations to find the context and conduct a comprehensive evaluation. It is mainly used in decision-making problems under uncertain conditions and with multiple evaluation criteria.

[0037] It should be noted that the reliability assessment method of the smart energy meter provided in the embodiment of the present application can be executed by an electronic device, where the electronic device refers to a device terminal having the function of executing a computer program or the above-mentioned server, such as a smart phone, a personal computer, a tablet computer, a personal digital assistant or a mobile Internet device. A server refers to a device that provides computing services through a network, such as an x86 server and a non-x86 server, and the non-x86 server includes a mainframe, a minicomputer and a UNIX server.

[0038] The following introduces application scenarios where the reliability assessment method for smart electric energy meters is applicable. The application scenarios here include but are not limited to: using the reliability assessment method to conduct reliability assessment on software on smart electric energy meters, thereby improving the accuracy of reliability assessment on software on smart electric energy meters, etc.

[0039] See also Figure 1The flowchart of a reliability assessment method for a smart energy meter provided by an embodiment of the present application is shown; the main idea of ​​the reliability assessment method is to add a second measurement value of a sensitive data characteristic in the calculation process of a target measurement value of reliability, thereby reflecting the importance of sensitive data in the reliability assessment and ensuring the accuracy of the reliability assessment result. The above-mentioned reliability assessment method for a smart energy meter may include:

[0040] Step 101: Acquire sensitive data characteristics and a plurality of quality characteristics for evaluating reliability, wherein the quality characteristics include at least one measurement index.

[0041] In a specific implementation, the sensitive data characteristics and multiple quality characteristics may be multiple important factors affecting the reliability of the smart energy meter determined by the reliability evaluation system, and quality requirements are put forward for the functions of the smart energy meter from different aspects, so as to evaluate the reliability of the smart energy meter and reduce the quality problems of the smart energy meter. The above-mentioned sensitive data characteristics and multiple quality characteristics may be obtained by receiving them from other terminal devices, or by obtaining them from a file system, a database or a mobile storage device and pre-stored. Each quality characteristic may correspond to one or more measurement indicators.

[0042] Step 102: Acquire multiple test items corresponding to each of the quality characteristics and a test result corresponding to each of the test items, wherein the test result includes whether each of the test items passes or fails.

[0043] In a specific implementation, each quality characteristic may correspond to multiple test items, each test item may be a test case determined based on the quality characteristic, used to test whether the software of the smart electric energy meter meets the standards in terms of the quality characteristic. The test item may be designed by reading the requirement description or other documents of the electric energy meter software, and may be a test case that can fully cover the software requirements during operation, thereby testing each quality characteristic. The test result may be the test result of the test item running on the software of the smart electric energy meter. If the operation passes, the test result is passed, and if the operation fails, the test result is failed.

[0044] Step 103: Determine a first measurement value of each of the quality characteristics according to the test results.

[0045] In the specific implementation process, the first measurement value of each quality characteristic can be expressed according to the test results. The numerical size of the first measurement value is related to the number of pass items of the test items. The value range is between 0 and 1, including 0 and 1. For example, when all the test items corresponding to the quality characteristic fail, the first measurement value of the quality characteristic is 0. When all the test items corresponding to the quality characteristic pass, the first measurement value of the quality characteristic is 1.

[0046] Step 104: Filter out sensitive test items from the multiple test items corresponding to each of the quality characteristics, and calculate a second measurement value of the sensitive data characteristic according to the test results corresponding to the sensitive test items, wherein the sensitive test items are used to test sensitive data, and the sensitive data is predefined data.

[0047] In the specific implementation process, sensitive data may be data in the smart energy meter that may cause unpredictable damage to users due to leakage, modification, breaking or loss and therefore needs to be protected. For example, data related to settlement in the smart energy meter may become sensitive data, such as: tiered electricity prices, rate electricity prices, settlement date freeze data, etc. Sensitive test items may be test items involving sensitive data.

[0048] Step 105: Calculate the target measurement value of the reliability based on the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic.

[0049] In a specific implementation process, each quality characteristic may have a different first measurement value, and a target measurement value of reliability may be calculated based on the first measurement value of each quality characteristic and the second measurement value of the sensitive data characteristic.

[0050] Step 106: performing reliability evaluation on the software of the smart electric energy meter according to the target measurement value of the reliability to obtain the software reliability level of the smart electric energy meter.

[0051] In the specific implementation process, the software reliability level may include multiple levels, such as failing, passing, good and excellent. Each reliability level may correspond to a different target measurement value. The reliability target measurement value may be used to evaluate the software reliability of the smart energy meter and obtain the software reliability level.

[0052] Based on the above embodiment, the step of calculating the target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic includes:

[0053] According to the formula Calculate the target measurement value of the reliability, where V is the target measurement value of the reliability, α is the second measurement value of the sensitive data characteristic, and V i is the first measured value of the i-th quality characteristic, W i is the weight corresponding to the i-th quality characteristic, N is the number of quality characteristics, wherein the weight corresponding to each quality characteristic is obtained by calculating each quality characteristic in advance.

[0054] In a specific implementation, a weighted sum is performed based on the first measurement value of each quality characteristic and the weight corresponding to each quality characteristic. The weight corresponding to each quality characteristic can be obtained from a pre-analysis of the importance of each quality characteristic and multiplied by the second measurement value of the sensitive characteristic to calculate the target measurement value of reliability.

[0055] Based on the above embodiment, determining the first measurement value of each quality characteristic according to the test result includes:

[0056] Counting the total number of test items corresponding to each of the measurement indicators;

[0057] Counting the number of test items with passed test results among the test items corresponding to each of the measurement indicators;

[0058] Taking the ratio of the number of the passed test items to the total number of the test items as the intermediate measurement value of each of the measurement indicators;

[0059] According to the formula V i =∑V ij ×W ij Calculate a first measurement value of each of the quality characteristics, where V i is the measured value of the i-th quality characteristic, V ij is the intermediate measurement value corresponding to the jth measurement index in the i-th quality characteristic, W ij The weight corresponding to the jth measurement indicator in the ith quality characteristic, wherein the weight corresponding to each measurement indicator is obtained by pre-calculating each measurement indicator in advance.

[0060] In the specific implementation process, the importance of each measurement indicator in each quality characteristic is analyzed to obtain the weight corresponding to each measurement indicator. The intermediate measurement value and the corresponding weight of each measurement indicator are obtained to better calculate the first measurement value of each quality characteristic. For example, the quality characteristic used to evaluate reliability can be a mature characteristic, which can correspond to a demand test indicator and a logic test indicator. The demand test indicator can include multiple test items, such as electric energy metering, demand measurement, clock, rate and time period, etc.

[0061] Based on the above embodiment, the step of calculating the second measurement value of the sensitive data characteristic according to the test result corresponding to the sensitive test item includes:

[0062] Obtain the number of test items whose test results corresponding to the sensitive test items are passed;

[0063] If the number of the passed test items is less than the total number of the sensitive test items, a preset sensitivity coefficient threshold is used as a second measurement value of the sensitive data characteristic;

[0064] If the number of passed test items is equal to the total number of sensitive test items, the second measurement value of the sensitive data characteristic is set to 1.

[0065] In the specific implementation process, the above text has screened out test items about sensitive data from the test items corresponding to each quality characteristic as sensitive test items. Here, the number of passed items in all sensitive test items can be directly counted. The preset sensitivity coefficient threshold can be set according to the importance of the sensitive data. For example, it can be set to 0.5, thereby indicating the importance of the sensitive data characteristics. When there are failed test items in the sensitive test items, the second measurement value of the sensitive data characteristic test corresponding to the smart electricity meter is 0.5.

[0066] Based on the above embodiment, according to the formula Prior to calculating the target measurement value of the reliability, the method further comprises:

[0067] Acquire a relationship hierarchy of the reliability, wherein the relationship hierarchy includes: a decision layer, a criterion layer, and an indicator layer, the decision layer corresponds to the reliability, the criterion layer corresponds to the sensitive data characteristics and the multiple quality characteristics, and the indicator layer corresponds to the multiple measurement indicators;

[0068] Constructing a judgment matrix based on the relationship hierarchy, wherein the judgment matrix includes a first matrix and a second matrix, the first matrix is ​​a judgment matrix of the criterion layer compared to the decision layer, and the second matrix is ​​a judgment matrix of the indicator layer compared to the criterion layer;

[0069] Performing a consistency check on the first matrix and the second matrix to obtain a check result;

[0070] If the inspection result is passed, a quality characteristic weight set corresponding to the first matrix and a measurement index weight set corresponding to the second matrix are calculated.

[0071] In the specific implementation process, the relationship level of reliability is obtained. The relationship level can be a reliability assessment model established according to the reliability assessment requirements. The required quality characteristics and measurement indicators are obtained according to the reliability assessment requirements. The corresponding relationship level can be determined by the analytic hierarchy process AHP. The relationship level includes: decision layer (also known as decision target layer), criterion layer and indicator layer, among which the decision layer can include: smart electric energy meter software reliability, the criterion layer can include multiple quality characteristics and sensitive data characteristics, and the indicator layer can include multiple measurement indicators. It can be understood that each of the above measurement indicators includes many test items and measurement sub-items. Taking the maturity of demand test as an example, the measurement items corresponding to the above demand test indicators can include: electric energy metering and demand measurement, etc.; the measurement sub-items of electric energy metering include: active constant, reactive constant and electric energy zeroing, etc.

[0072] Based on the above embodiment, the step of performing a consistency check on the first matrix and the second matrix to obtain a check result includes:

[0073] respectively calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix;

[0074] Calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix to obtain a consistency index;

[0075] The inspection result is determined according to the consistency index.

[0076] In the specific implementation process, the corresponding weight set can only be calculated after the consistency check of the first matrix and the second matrix. The weight distribution must be checked for consistency, that is, the general consistency index CI and the random consistency index CR are calculated. When n>2 and CR<0.1, the consistency check is passed. The index values ​​of the 1st to 5th order matrices are shown in Table 1.

[0077]

[0078] CR=CI / RI

[0079] Where: CI is the general consistency index, λ max is the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix, CR is the random consistency index, and RI is the average consistency index.

[0080] Table 1

[0081] n 1 2 3 4 5 RI 0 0 0.52 0.89 1.12

[0082] Based on the above embodiment, the reliability evaluation of the software of the smart electric energy meter according to the target measurement value of the reliability includes:

[0083] Obtaining preset reliability level classification rules;

[0084] The target measurement value of the reliability is graded according to the reliability grade classification rule.

[0085] In the specific implementation process, the reliability level classification rules can be divided according to the target measurement value of reliability. The software reliability level can be divided into multiple levels, each level corresponds to an interval range, and the level is determined according to the size of the target measurement value of reliability. For example: if the target measurement value of reliability is 0.92, the level of reliability assessment of the electric energy meter software is excellent, and the measurement value interval of the target measurement value V of reliability is 0.90≤V≤1, then the software reliability level can be determined to be excellent.

[0086] Based on the above embodiment, the step of obtaining a plurality of test items and test results corresponding to each of the quality characteristics includes:

[0087] Obtaining multiple test items corresponding to each of the quality characteristics;

[0088] The software of the smart electric energy meter is tested according to each of the test items to obtain a test result corresponding to each test item.

[0089] In the specific implementation process, each quality characteristic can correspond to multiple test items. The test items can be compiled on a preset software testing system, and then the test results of each test item can be obtained based on the pass status of the test items running on the software testing system.

[0090] Based on the above embodiment, the multiple quality characteristics include maturity characteristics, fault tolerance characteristics, easy recovery characteristics and compliance characteristics. The maturity characteristics include demand measurement indicators and logic measurement indicators. The fault tolerance characteristics include failure avoidance measurement indicators and misoperation resistance measurement indicators. The compliance characteristics include coding specification compliance measurement indicators and metering performance compliance measurement indicators.

[0091] In the specific implementation process, the quality characteristics used to evaluate reliability may include maturity characteristics, fault tolerance characteristics, easy recovery characteristics and compliance characteristics. These quality characteristics can be used to establish a reliability evaluation model for the smart electricity meter. When performing reliability evaluation, appropriate quality characteristics will be selected according to the evaluation requirements of the software to determine the evaluation model. The embodiment of the present application does not specifically limit the types and quantities of quality characteristics.

[0092] Down Figure 2A schematic diagram of a relational hierarchy of a smart power meter software reliability assessment model provided by an embodiment of the present application is shown; in a specific practice, the relational hierarchy of the reliability assessment model can be determined, wherein the decision layer may include: smart power meter software reliability A, the indicators of the criterion layer may include: sensitive data characteristics B, maturity characteristics C1, fault tolerance characteristics C2, easy recovery characteristics C3, compliance characteristics C4 and other characteristics, and the indicator layer may include: demand measurement indicators D 11 , logical measurement index D 12 , Failure avoidance measurement index D 21 , Misoperation resistance measurement index D 22 , Coding Standard Compliance Measurement Indicator D 41 and metrological performance compliance measurement indicator D 42 It is understandable that each measurement indicator may include multiple measurement items and measurement sub-items, such as the failure avoidance measurement indicator D 21 The measurement items include occasional device read and write failures, battery undervoltage, and external module read and write failures. The occasional device read and write failures include metering chip abnormalities, clock abnormalities, and memory abnormalities.

[0093] The importance ranking assumption of the above criterion layer is: maturity characteristic C1>easy recovery characteristic C3>fault tolerance characteristic C2>compliance characteristic C4, then the first matrix of the criterion layer compared with the decision layer is calculated as shown in Table 2 below.

[0094] Table 2

[0095] A / B <![CDATA[C1]]> <![CDATA[C2]]> <![CDATA[C3]]> <![CDATA[C4]]> <![CDATA[C1]]> 1 5 3 7 <![CDATA[C2]]> 1 / 5 1 1 / 5 3 <![CDATA[C3]]> 1 / 3 5 1 7 <![CDATA[C4]]> 1 / 7 1 / 3 1 / 7 1

[0096] The above indicator layer importance ranking assumption is: Demand measurement indicator D 11 >Logical measurement index D 12 , failure avoidance measurement index D 21 <Measurement index D for protection against misoperation 22 , Coding Standard Compliance Measurement Index D 41 <Metrological performance compliance measurement index D 42 , then the second matrix of the indicator layer compared to the criterion layer can be calculated as follows:

[0097] The first matrix in the second matrix, namely the indicator layer D 11 , D 12 The judgment matrix compared with the criterion layer C1 is shown in Table 3 below.

[0098] Table 3

[0099] <![CDATA[C1]]> <![CDATA[D 11 ]]> <![CDATA[D 12 ]]> <![CDATA[D 11 ]]> 1 3 <![CDATA[D 12 ]]> 1 / 3 1

[0100] The second matrix in the second matrix, namely the indicator layer D 21 , D 22Compared with the judgment matrix of the criterion layer C2, the following table 4 shows.

[0101] Table 4

[0102] <![CDATA[C2]]> <![CDATA[D 21 ]]> <![CDATA[D 22 ]]> <![CDATA[D 21 ]]> 1 1 / 2 <![CDATA[D 22 ]]> 2 1

[0103] The third matrix in the second matrix, namely the indicator layer C 41 , C 42 The judgment matrix compared with the criterion layer C4 is shown in Table 5 below.

[0104] Table 5

[0105] <![CDATA[C4]]> <![CDATA[D 41 ]]> <![CDATA[D 42 ]]> <![CDATA[D 41 ]]> 1 1 / 3 <![CDATA[D 42 ]]> 3 1

[0106] A consistency check is performed on the first matrix and the second matrix to obtain a test result. When the test result is calculated as passed, a set of indicator weights corresponding to the judgment matrix is ​​calculated.

[0107] The maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix are calculated to obtain the consistency index. The test result is determined according to the consistency index. According to the calculation formula of AHP, the consistency of the judgment matrix is ​​calculated, and the random consistency index of the judgment matrix is ​​0.0854, 0.0000, and 0.0000 respectively, and the consistency is passed.

[0108] If the test result is passed, the first indicator weight set corresponding to the first matrix and the second indicator weight set corresponding to the second matrix are calculated. The first indicator weight set is the quality characteristic weight set of the criterion layer compared with the decision layer, and the second indicator weight set is the measurement indicator weight set of the indicator layer compared with the criterion layer.

[0109] When the test result is passed, the first matrix of the criterion layer is calculated compared with the first matrix of the decision layer, and the first indicator weight set corresponding to the first matrix is ​​obtained as shown in Table 6 below.

[0110] Table 6

[0111] Criteria Layer Criteria layer weight <![CDATA[Mature characteristic C1]]> 0.5437 <![CDATA[Fault tolerance feature C2]]> 0.0975 <![CDATA[Easy recovery feature C3]]> 0.3109 <![CDATA[Compliance characteristic C4]]> 0.0479

[0112] When the test result is passed, the second matrix of the indicator layer is calculated compared with the criterion layer, and the second indicator weight set corresponding to the second matrix is ​​obtained as shown in Table 7 below.

[0113] Table 7

[0114]

[0115] The above-mentioned preset software testing system can be a reliability evaluation system on the smart electric energy meter. The measurement value of the measurement element calculated by the reliability evaluation system on the smart electric energy meter is obtained. The calculation results are shown in Table 8 below.

[0116] Table 8

[0117]

[0118]

[0119] Optionally, in the specific calculation process, the second measurement value of the sensitive data characteristic can also be calculated according to the test results of the sensitive test items. The preset sensitivity coefficient threshold is 0.5. The second measurement value of the sensitive data reliability is shown in Table 9 below.

[0120] Table 9

[0121]

[0122] It should be noted that, in the sensitive data reliability assessment results, if there are items that fail the test, the second measurement value is 0.5, and only if all the test items pass, the second measurement value is 1.

[0123] The first measurement value and the second measurement value are weighted and summed according to the quality characteristic weight set and the measurement index weight set to obtain the first measurement value of the corresponding quality characteristic. The first measurement value of the quality characteristic is shown in Table 10 below.

[0124] Table 10

[0125]

[0126]

[0127] The first measurement value of the quality characteristic is weighted and summed according to the quality characteristic weight set to obtain the target measurement value of reliability. The target measurement value of the reliability of the smart electric energy meter software is V=(0.5437×0.9105)+(0.0975×0.8727)+(0.3109×0.8889)+(0.0479×0.7926)=0.8945.

[0128] The reliability of the software of the smart energy meter is evaluated according to the target measurement value of reliability. The target measurement value of reliability can be graded according to the reliability grade classification rules in the following table, and the software reliability grade of the smart energy meter is good. Among them, the reliability grade classification rules of the preset software test system are as shown in Table 11.

[0129] Table 11

[0130] grade Measurement value range excellent 0.90≤V≤1 good 0.80≤V＜0.90 qualified 0.60≤V＜0.80 Failure 0≤V＜0.60

[0131] Reliability assessment is performed based on the target measurement value of reliability. For example, the reliability of the software of the smart electric energy meter can be assessed based on the first measurement value of the quality characteristic and the second measurement value of the sensitive data characteristic. The reliability scores (V=0.5×0.8945=0.44725) corresponding to the test data and the sensitive data are graded according to the reliability grade classification rule, and the software reliability grade of the smart electric energy meter is unqualified.

[0132] Adding the measurement value of the reliability of sensitive data in the reliability assessment process can more accurately assess the reliability of the smart energy meter, thereby improving the accuracy of the reliability assessment of the software on the smart energy meter.

[0133] See also Figure 3 The structure diagram of the smart energy meter reliability assessment device provided by the embodiment of the present application is shown. The embodiment of the present application provides a smart energy meter reliability assessment device 300, including:

[0134] The acquisition module 301 is used to acquire sensitive data characteristics and multiple quality characteristics for evaluating reliability, wherein the quality characteristics include at least one measurement indicator; and is also used to acquire multiple test items corresponding to each of the quality characteristics and a test result corresponding to each of the test items, wherein the test result includes whether each of the test items passes or fails;

[0135] A quality characteristic calculation module 302, configured to determine a first measurement value of each of the quality characteristics according to the test results;

[0136] A sensitive data characteristic calculation module 303, configured to filter out a sensitive test item from the plurality of test items, and calculate a second measurement value of the sensitive data characteristic according to a test result corresponding to the sensitive test item, wherein the sensitive test item is used to test sensitive data, and the sensitive data is predefined data;

[0137] A reliability calculation module 304, configured to calculate a target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic;

[0138] The reliability evaluation module 305 is used to perform reliability evaluation on the software of the smart electric energy meter according to the target measurement value of the reliability, so as to obtain the software reliability level of the smart electric energy meter.

[0139] Based on the above embodiment, the reliability calculation module 304 is specifically used for:

[0140] According to the formula Calculate the target measurement value of the reliability, where V is the target measurement value of the reliability, α is the second measurement value of the sensitive data characteristic, and V iis the first measured value of the i-th quality characteristic, W i is the weight corresponding to the i-th quality characteristic, N is the number of quality characteristics, wherein the weight corresponding to each quality characteristic is obtained by calculating each quality characteristic in advance.

[0141] Based on the above embodiment, the quality characteristic calculation module 302 is specifically used for:

[0142] Counting the total number of test items corresponding to each of the measurement indicators;

[0143] Counting the number of test items with passed test results among the test items corresponding to each of the measurement indicators;

[0144] Taking the ratio of the number of the passed test items to the total number of the test items as the intermediate measurement value of each of the measurement indicators;

[0145] According to the formula V i =∑V ij ×W ij Calculate a first measurement value of each of the quality characteristics, where V i is the measured value of the i-th quality characteristic, V ij is the intermediate measurement value corresponding to the jth measurement index in the i-th quality characteristic, W ij The weight corresponding to the jth measurement indicator in the ith quality characteristic, wherein the weight corresponding to each measurement indicator is obtained by pre-calculating each measurement indicator in advance.

[0146] Based on the above embodiment, the sensitive data characteristic calculation module 303 is specifically used for:

[0147] Obtain the number of test items whose test results corresponding to the sensitive test items are passed;

[0148] If the number of the passed test items is less than the total number of the sensitive test items, a preset sensitivity coefficient threshold is used as a second measurement value of the sensitive data characteristic;

[0149] If the number of passed test items is equal to the total number of sensitive test items, the second measurement value of the sensitive data characteristic is set to 1.

[0150] Based on the above embodiment, the device further includes a relationship level calculation module, which is used to:

[0151] Acquire a relationship hierarchy of the reliability, wherein the relationship hierarchy includes: a decision layer, a criterion layer, and an indicator layer, the decision layer corresponds to the reliability, the criterion layer corresponds to the sensitive data characteristics and the multiple quality characteristics, and the indicator layer corresponds to the multiple measurement indicators;

[0152] Constructing a judgment matrix based on the relationship hierarchy, wherein the judgment matrix includes a first matrix and a second matrix, the first matrix is ​​a judgment matrix of the criterion layer compared to the decision layer, and the second matrix is ​​a judgment matrix of the indicator layer compared to the criterion layer;

[0153] Performing a consistency check on the first matrix and the second matrix to obtain a check result;

[0154] If the inspection result is passed, a quality characteristic weight set corresponding to the first matrix and a measurement index weight set corresponding to the second matrix are calculated.

[0155] Based on the above embodiment, the relationship level calculation module is specifically used for:

[0156] respectively calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix;

[0157] Calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix to obtain a consistency index;

[0158] The inspection result is determined according to the consistency index.

[0159] Based on the above embodiment, the reliability evaluation module 305 is specifically used for:

[0160] Obtaining preset reliability level classification rules;

[0161] The target measurement value of the reliability is graded according to the reliability grade classification rule.

[0162] Based on the above embodiment, the acquisition module 301 is specifically used for:

[0163] Obtaining multiple test items corresponding to each of the quality characteristics;

[0164] The software of the smart electric energy meter is tested according to each of the test items to obtain a test result corresponding to each test item.

[0165] Based on the above embodiment, the multiple quality characteristics include maturity characteristics, fault tolerance characteristics, easy recovery characteristics and compliance characteristics. The maturity characteristics include demand measurement indicators and logic measurement indicators. The fault tolerance characteristics include failure avoidance measurement indicators and misoperation resistance measurement indicators. The compliance characteristics include coding specification compliance measurement indicators and metering performance compliance measurement indicators.

[0166] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application, such as Figure 4As shown, the electronic device includes: a processor (processor) 401, a memory (memory) 402 and a bus 403; wherein,

[0167] The processor 401 and the memory 402 interact with each other via the bus 403;

[0168] The processor 401 is used to call the program instructions in the memory 402 to execute the methods provided by the above-mentioned method embodiments, for example, including: initiating a to-be-developed instruction through the PC end, and sending the to-be-developed instruction to the virtual bus coordinator, wherein the PC end runs a development environment of smart electric energy meter software; determining through the virtual bus coordinator a data interaction channel for transmitting the to-be-developed instruction from the PC end to a target functional module of the smart electric energy meter, wherein the smart electric energy meter includes multiple functional modules, and the target functional module is one of the multiple functional modules; transmitting the to-be-developed instruction to the target functional module of the smart electric energy meter end through the data interaction channel, so as to realize running and debugging of the target functional module according to the to-be-developed instruction.

[0169] The processor 401 may be an integrated circuit chip with signal processing capabilities. The processor 401 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It may implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0170] The memory 402 may include, but is not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc.

[0171] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0172] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the methods provided by the above-mentioned method embodiments.

[0173] In the embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or interactive connection shown or discussed can be through some interactive interfaces, and the indirect coupling or interactive connection of devices or units can be electrical, mechanical or other forms.

[0174] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0176] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0177] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A reliability assessment method for a smart electric energy meter, characterized in that: include: Acquire sensitive data characteristics and multiple quality characteristics for evaluating reliability, wherein the quality characteristics include at least one measurement index; the multiple quality characteristics include maturity characteristics, fault tolerance characteristics, easy recovery characteristics, and compliance characteristics, the maturity characteristics include demand measurement indicators and logic measurement indicators, the fault tolerance characteristics include failure avoidance measurement indicators and misoperation resistance measurement indicators, and the compliance characteristics include coding specification compliance measurement indicators and metering performance compliance measurement indicators; the sensitive data characteristics include data related to settlement in the smart electric energy meter; Acquire a plurality of test items corresponding to each of the quality characteristics and a test result corresponding to each of the test items, wherein the test result includes whether each of the test items passes or fails; Determine a first measurement value of each of the quality characteristics according to the test results; the first measurement value is positively correlated with the number of passes of the test item; Screening out sensitive test items from a plurality of test items corresponding to each of the quality characteristics, and calculating a second measurement value of the sensitive data characteristic according to a test result corresponding to the sensitive test item, wherein the sensitive test item is used to test sensitive data, and the sensitive data is predefined data; Calculating a target measure of reliability based on the first measure of each of the quality characteristics and the second measure of the sensitive data characteristic; Performing reliability evaluation on the software of the smart electric energy meter according to the target measurement value of the reliability to obtain the software reliability level of the smart electric energy meter; The calculating the second measurement value of the sensitive data characteristic according to the test result corresponding to the sensitive test item includes: Obtain the number of test items whose test results corresponding to the sensitive test items are passed; If the number of the passed test items is less than the total number of the sensitive test items, a preset sensitivity coefficient threshold is used as a second measurement value of the sensitive data characteristic; If the number of the passed test items is equal to the total number of the sensitive test items, setting the second measurement value of the sensitive data characteristic to 1; The step of calculating the target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic comprises: According to the formula Calculate the target measurement value of the reliability, where V is the target measurement value of the reliability, α is the second measurement value of the sensitive data characteristic, and V i is the first measured value of the i-th quality characteristic, W i is the weight corresponding to the i-th quality characteristic, N is the number of quality characteristics, wherein the weight corresponding to each quality characteristic is obtained by calculating each quality characteristic in advance.

2. The method according to claim 1, characterized in that: Determining a first measurement value of each of the quality characteristics according to the test results comprises: Counting the total number of test items corresponding to each of the measurement indicators; Counting the number of test items with passed test results among the test items corresponding to each of the measurement indicators; Taking the ratio of the number of the passed test items to the total number of the test items as the intermediate measurement value of each of the measurement indicators; According to the formula V i =∑V ij ×W ij Calculate a first measurement value of each of the quality characteristics, where V i is the measured value of the i-th quality characteristic, V ij is the intermediate measurement value corresponding to the jth measurement index in the i-th quality characteristic, W ij The weight corresponding to the jth measurement indicator in the ith quality characteristic, wherein the weight corresponding to each measurement indicator is obtained by pre-calculating each measurement indicator in advance.

3. The method according to claim 1, characterized in that According to the formula Prior to calculating the target measurement value of the reliability, the method further comprises: Acquire a relationship hierarchy of the reliability, wherein the relationship hierarchy includes: a decision layer, a criterion layer, and an indicator layer, the decision layer corresponds to the reliability, the criterion layer corresponds to the sensitive data characteristics and the multiple quality characteristics, and the indicator layer corresponds to the multiple measurement indicators; Constructing a judgment matrix based on the relationship hierarchy, wherein the judgment matrix includes a first matrix and a second matrix, the first matrix is ​​a judgment matrix of the criterion layer compared to the decision layer, and the second matrix is ​​a judgment matrix of the indicator layer compared to the criterion layer; Performing a consistency check on the first matrix and the second matrix to obtain a check result; If the inspection result is passed, a quality characteristic weight set corresponding to the first matrix and a measurement index weight set corresponding to the second matrix are calculated.

4. The method according to claim 3, characterized in that: The performing consistency check on the first matrix and the second matrix to obtain a check result includes: respectively calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix; Calculating the maximum eigenvalue of the first matrix and the maximum eigenvalue of the second matrix to obtain a consistency index; The inspection result is determined according to the consistency index.

5. The method according to claim 1, characterized in that: The reliability evaluation of the software of the smart electric energy meter according to the target measurement value of the reliability includes: Obtaining preset reliability level classification rules; The target measurement value of the reliability is graded according to the reliability grade classification rule.

6. The method according to claim 1, characterized in that The obtaining of multiple test items and test results corresponding to each of the quality characteristics includes: Obtaining multiple test items corresponding to each of the quality characteristics; The software of the smart electric energy meter is tested according to each of the test items to obtain a test result corresponding to each test item.

7. A reliability assessment device for a smart electric energy meter, characterized in that: include: An acquisition module, configured to acquire sensitive data characteristics and a plurality of quality characteristics for evaluating reliability, wherein the quality characteristics include at least one measurement indicator; It is also used to obtain multiple test items corresponding to each of the quality characteristics and test results corresponding to each of the test items, wherein the test results include whether each of the test items is passed or not; the multiple quality characteristics include maturity characteristics, fault tolerance characteristics, easy recovery characteristics and compliance characteristics, the maturity characteristics include demand measurement indicators and logic measurement indicators, the fault tolerance characteristics include failure avoidance measurement indicators and misoperation resistance measurement indicators, the compliance characteristics include coding specification compliance measurement indicators and metering performance compliance measurement indicators; the sensitive data characteristics include data related to settlement in the smart electric energy meter; A quality characteristic calculation module, used to determine a first measurement value of each of the quality characteristics according to the test results; the first measurement value is positively correlated with the number of passes of the test item; a sensitive data characteristic calculation module, configured to screen out a sensitive test item from the plurality of test items, and calculate a second measurement value of the sensitive data characteristic according to a test result corresponding to the sensitive test item, wherein the sensitive test item is used to test sensitive data, and the sensitive data is predefined data; A reliability calculation module, configured to calculate a target measurement value of the reliability according to the first measurement value of each of the quality characteristics and the second measurement value of the sensitive data characteristic; A reliability evaluation module, used to perform reliability evaluation on the software of the smart electric energy meter according to the target measurement value of the reliability, and obtain the software reliability level of the smart electric energy meter; A sensitive data characteristic calculation module, specifically used to obtain the number of test items whose test results corresponding to the sensitive test items are passed; if the number of passed test items is less than the total number of sensitive test items, a preset sensitivity coefficient threshold is used as the second measurement value of the sensitive data characteristic; if the number of passed test items is equal to the total number of sensitive test items, the second measurement value of the sensitive data characteristic is set to 1; The reliability calculation module is specifically used to calculate the reliability of the product according to the formula Calculate the target measurement value of the reliability, where V is the target measurement value of the reliability, α is the second measurement value of the sensitive data characteristic, and V i is the first measured value of the i-th quality characteristic, W i is the weight corresponding to the i-th quality characteristic, N is the number of quality characteristics, wherein the weight corresponding to each quality characteristic is obtained by calculating each quality characteristic in advance.

Images