CN111598387A - Method and system for determining quality of electric energy meter in multiple dimensions - Google Patents
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Abstract
The invention discloses a method and a system for determining the quality of an electric energy meter based on multiple dimensions, wherein the method comprises the following steps: determining the weight of the secondary evaluation index corresponding to each primary evaluation index; respectively determining the score value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index; determining the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method; and determining the quality score of the electric energy meter of each equipment set according to the score value of the primary evaluation index corresponding to the electric energy meter of each equipment set and the optimal weight of each primary evaluation index. The method adopts a mode of combining a subjective weighting method and an objective weighting method, reasonably calculates the weight of each evaluation index, and evaluates the quality of the electric energy meter of one equipment set in multiple directions, thereby providing scientific, objective, fair and fair data support for the screening of the electric energy meters.
Description
Technical Field
The present invention relates to the field of electric energy meter quality assessment technology, and more particularly, to a method and system for determining the quality of an electric energy meter in multiple dimensions.
Background
With the integration and application of information communication new technologies such as 'cloud moving intelligence' and the like and metering technologies, the influence and image of electric power metering are improved on the basis of data resource services of an intelligent electric energy meter and a power utilization information acquisition system, the quality strong network and high-quality development of a company are better served, and a modern service system of the company centering on customers is comprehensively supported. The existing electric energy meter quality evaluation system integrated by equipment of each company in each province at present has the problems that the evaluation indexes are not comprehensive, the subjective preference is too strong according to the experience of an evaluator, and the like, and the fairness of the evaluation result is influenced.
Therefore, how to more comprehensively, reasonably and accurately evaluate the quality of the electric energy meter of the equipment set is a key problem to be solved in the electric power metering industry.
Disclosure of Invention
The invention provides a method set system for determining the quality of an electric energy meter based on multiple dimensions, which aims to solve the problem of how to determine the quality of the electric energy meter.
In order to solve the above problem, according to an aspect of the present invention, there is provided a method for determining quality of an electric energy meter based on multiple dimensions, the method comprising:
for each primary evaluation index of the electric energy meter quality evaluation indexes comprising a plurality of secondary evaluation indexes, respectively determining the weight of the secondary evaluation index corresponding to each primary evaluation index by using an analytic hierarchy process;
respectively determining the score value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index;
respectively determining the weight of each primary evaluation index corresponding to different objective weight methods, and determining the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method;
and determining the quality score of the electric energy meter of each equipment set according to the score value of the primary evaluation index corresponding to the electric energy meter of each equipment set and the optimal weight of each primary evaluation index.
Preferably, the first-level evaluation index of the electric energy meter quality evaluation indexes comprises: equipment set scale, product specification consistency, product diversity, trend stability and operation reliability; the secondary evaluation indexes corresponding to the equipment set scale include: the quantity of electric energy meters provided by the equipment set; the secondary evaluation indexes corresponding to the product specification consistency comprise: failure rates of electric energy meters of different specifications; the secondary evaluation indexes corresponding to the product diversity comprise: the number of the product gauges; the secondary evaluation indexes corresponding to the trend stability include: the failure rate of electric energy meters with different meter ages; the secondary evaluation indexes corresponding to the operational reliability include: and (4) disassembling the metering failure rate and the non-metering failure rate of the meter.
Preferably, wherein the method further comprises:
and carrying out data verification and data cleaning on the set information of each equipment set and the fault data of the electric energy meter.
Preferably, the determining a score value of each primary evaluation index corresponding to the electric energy meter of each device set according to the set information of each device set, the electric energy meter fault data, and the weight of each secondary evaluation index includes:
for any equipment set, determining the scale score value of the equipment set according to the quantity of electric energy meters provided by the equipment set;
determining the difference degree of the electric energy meters of different product specifications corresponding to the equipment set according to the fault rate of the electric energy meters of different product specifications corresponding to the equipment set, and determining the product specification consistency scoring value according to the difference degree of the electric energy meters of different product specifications;
determining product diversity score values according to the number of the product gauges corresponding to the equipment set and the fault rate among the product gauges;
determining a trend stability scoring value according to the fault rates of the electric energy meters with different meter ages corresponding to the equipment set and the weight corresponding to the fault rate of the electric energy meter with each meter age;
and determining an operation reliability score value according to the fault metering rate, the non-fault metering rate, the weight of the fault metering rate and the weight of the non-fault metering rate corresponding to the equipment set.
Preferably, the determining the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method includes:
and determining the optimal weight of each primary evaluation index by using an included angle measurement method according to the weight of each primary evaluation index corresponding to each objective weight method.
Preferably, wherein the objective weighting method comprises: at least two of an entropy weight method, a correlation coefficient method, and a coefficient of variation method.
According to another aspect of the invention, there is provided a system for determining the quality of an electric energy meter based on multiple dimensions, the system comprising:
the weight determining unit of the secondary evaluation indexes is used for determining the weight of each secondary evaluation index corresponding to each primary evaluation index by utilizing an analytic hierarchy process for each primary evaluation index of the quality evaluation indexes of the electric energy meter, wherein the primary evaluation indexes comprise a plurality of secondary evaluation indexes;
the primary evaluation index scoring value determining unit is used for respectively determining the scoring value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index;
the optimal weight determining unit of the first-level evaluation indexes is used for respectively determining the weight of each first-level evaluation index corresponding to different objective weight methods and determining the optimal weight of each first-level evaluation index according to the weight of each first-level evaluation index corresponding to each objective weight method;
and the electric energy meter quality scoring unit is used for determining the electric energy meter quality scoring of each equipment set according to the scoring value of the primary evaluation index corresponding to the electric energy meter of each equipment set and the optimal weight of each primary evaluation index.
Preferably, the first-level evaluation index of the electric energy meter quality evaluation indexes comprises: equipment set scale, product specification consistency, product diversity, trend stability and operation reliability; the secondary evaluation indexes corresponding to the equipment set scale include: the quantity of electric energy meters provided by the equipment set; the secondary evaluation indexes corresponding to the product specification consistency comprise: failure rates of electric energy meters of different specifications; the secondary evaluation indexes corresponding to the product diversity comprise: the number of the product gauges; the secondary evaluation indexes corresponding to the trend stability include: the failure rate of electric energy meters with different meter ages; the secondary evaluation indexes corresponding to the operational reliability include: and (4) disassembling the metering failure rate and the non-metering failure rate of the meter.
Preferably, wherein the system further comprises:
and the data processing unit is used for carrying out data verification and data cleaning on the set information of each equipment set and the fault data of the electric energy meter.
Preferably, the determining unit of the grade values of the primary evaluation indexes determines the grade value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index, and includes:
for any equipment set, determining the scale score value of the equipment set according to the quantity of electric energy meters provided by the equipment set;
determining the difference degree of the electric energy meters of different product specifications corresponding to the equipment set according to the fault rate of the electric energy meters of different product specifications corresponding to the equipment set, and determining the product specification consistency scoring value according to the difference degree of the electric energy meters of different product specifications;
determining product diversity score values according to the number of the product gauges corresponding to the equipment set and the fault rate among the product gauges;
determining a trend stability scoring value according to the fault rates of the electric energy meters with different meter ages corresponding to the equipment set and the weight corresponding to the fault rate of the electric energy meter with each meter age;
and determining an operation reliability score value according to the fault metering rate, the non-fault metering rate, the weight of the fault metering rate and the weight of the non-fault metering rate corresponding to the equipment set.
Preferably, the optimal weight determining unit for the primary evaluation indexes determines the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method, and includes:
and determining the optimal weight of each primary evaluation index by using an included angle measurement method according to the weight of each primary evaluation index corresponding to each objective weight method.
Preferably, wherein the objective weighting method comprises: at least two of an entropy weight method, a correlation coefficient method, and a coefficient of variation method.
The invention provides a method and a system for determining the quality of an electric energy meter based on multiple dimensions, wherein the scale of an equipment set, the consistency of a product specification, the product diversity, the trend stability and the operation reliability are selected as evaluation indexes of the electric energy meter of the equipment set, the weight of each evaluation index is reasonably calculated in a mode of combining a subjective weight method and an objective weight method, and the quality of the electric energy meter of one equipment set is evaluated in multiple directions; wherein, the subjective weight method adopts an analytic hierarchy process to calculate the weight of the secondary evaluation index; the method has the advantages that the weights of the primary evaluation indexes are determined by different objective weight methods, the weight values of the primary evaluation indexes are further optimized by an included angle measurement method, reasonable evaluation index weights are obtained finally, the limitations of the subjective weight method and the objective weight method can be avoided, the product quality of the electric energy meter can be analyzed through electric energy meter fault data, and scientific, objective, fair and fair data support is provided for electric energy meter screening.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow diagram of a method 100 for determining the quality of an electric energy meter based on multiple dimensions according to an embodiment of the invention;
FIG. 2 is a diagram of an analysis concept for determining the quality of an electric energy meter according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a system 300 for determining the quality of an electric energy meter based on multiple dimensions according to an embodiment of the invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Fig. 1 is a flow diagram of a method 100 for determining the quality of an electric energy meter based on multiple dimensions according to an embodiment of the invention. As shown in fig. 1, in the method for determining the quality of an electric energy meter based on multiple dimensions provided by the embodiment of the present invention, the scale of an equipment set, the consistency of a product specification, the product diversity, the trend stability and the operation reliability are selected as evaluation indexes of the electric energy meter of the equipment set, and the weight of each evaluation index is reasonably calculated by adopting a combination manner of a subjective weight method and an objective weight method, so as to evaluate the quality of the electric energy meter of one equipment set in multiple directions; wherein, the subjective weight method adopts an analytic hierarchy process to calculate the weight of the secondary evaluation index; the method has the advantages that the weights of the primary evaluation indexes are determined by different objective weight methods, the weight values of the primary evaluation indexes are further optimized by an included angle measurement method, reasonable evaluation index weights are obtained finally, the limitations of the subjective weight method and the objective weight method can be avoided, the product quality of the electric energy meter can be analyzed through electric energy meter fault data, and scientific, objective, fair and fair data support is provided for electric energy meter screening. The method 100 for determining the quality of the electric energy meter based on multiple dimensions provided by the embodiment of the invention starts from step 101, and for each primary evaluation index of the quality evaluation indexes of the electric energy meter, which comprises a plurality of secondary evaluation indexes, in step 101, the weight of the secondary evaluation index corresponding to each primary evaluation index is respectively determined by using an analytic hierarchy process.
Preferably, the first-level evaluation index of the electric energy meter quality evaluation indexes comprises: equipment set scale, product specification consistency, product diversity, trend stability and operation reliability; the secondary evaluation indexes corresponding to the equipment set scale include: the quantity of electric energy meters provided by the equipment set; the secondary evaluation indexes corresponding to the product specification consistency comprise: failure rates of electric energy meters of different specifications; the secondary evaluation indexes corresponding to the product diversity comprise: the number of the product gauges; the secondary evaluation indexes corresponding to the trend stability include: the failure rate of electric energy meters with different meter ages; the secondary evaluation indexes corresponding to the operational reliability include: and (4) disassembling the metering failure rate and the non-metering failure rate of the meter.
In the implementation mode of the invention, the indexes of equipment set scale, product specification consistency, product diversity, trend stability and operation reliability are selected to comprehensively evaluate the quality of the electric energy meters of the equipment set. Wherein, an equipment set corresponds to an electric energy meter equipment set. The equipment set scale refers to the production delivery capacity and relative quality condition of the equipment set, and the larger the supply quantity is, the stronger the comprehensive strength and risk bearing capacity of the equipment set is indirectly indicated; the consistency of the product specification refers to the professional technical ability of equipment set, and the better the consistency is, the stronger the quality control ability is in the production process of the equipment set product, and the more stable the product quality is; the product diversity refers to the number of product specifications provided by the equipment set; the trend stability refers to the change trend of the failure rate of the product along with the age, and the lower the failure rate of the product along with the increase of the service life, the better the product quality is, and the more stable the product running quality is; the operation reliability is to measure the reliability of the product in operation, and the reliability of the product operation is calculated by using the failure rate of the metering type failure and the non-metering type failure. The indexes can evaluate the running quality of the electric energy meter of one equipment set in multiple directions, and the equipment set is objectively and comprehensively evaluated comprehensively. The electric energy meter evaluation indexes of the equipment set are shown in table 1.
TABLE 1 evaluation data and extraction index
The failure rates of metering type failures and non-metering type failures represent the operational reliability of the electric energy meter. According to expert experience, the metering performance of the electric energy meter is the root of representing the product quality, the importance of the metering fault is greater than that of the non-metering fault, and an analytic hierarchy process is suitable for calculating the secondary index weight of the operational reliability.
The trend stability represents the stability degree of the electric energy meter running along with the increase of the meter age, according to expert experience, according to different importance of fault rates of the electric energy meters with different meter ages, the smaller the meter age is and the higher the fault rate is, the worse the running quality of the electric energy meter is, the fault rate weight sequence of the meter age is determined as follows: and the trend stability secondary index weight is calculated by adopting an analytic hierarchy process in the processes of 1 st year, 2 nd year, 3 rd year, 4 th year, 5 th year, 6 th year, 7 th year and 8 th year.
Therefore, in the embodiment of the present invention, the weight of each secondary evaluation index in the trend stability and the operational reliability is determined using the analytic hierarchy process. In the embodiment of the invention, the weight of each secondary evaluation index in the determined operational reliability is shown in table 2; the weight of each secondary rating measure in the trend stability is shown in table 3.
TABLE 2 operational reliability index weights
TABLE 3 trend stability index weights
In step 102, according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index, the score value of each primary evaluation index corresponding to the electric energy meter of each equipment set is respectively determined.
Preferably, wherein the method further comprises:
and carrying out data verification and data cleaning on the set information of each equipment set and the fault data of the electric energy meter.
Preferably, the determining a score value of each primary evaluation index corresponding to the electric energy meter of each device set according to the set information of each device set, the electric energy meter fault data, and the weight of each secondary evaluation index includes:
for any equipment set, determining the scale score value of the equipment set according to the quantity of electric energy meters provided by the equipment set;
determining the difference degree of the electric energy meters of different product specifications corresponding to the equipment set according to the fault rate of the electric energy meters of different product specifications corresponding to the equipment set, and determining the product specification consistency scoring value according to the difference degree of the electric energy meters of different product specifications;
determining product diversity score values according to the number of the product gauges corresponding to the equipment set and the fault rate among the product gauges;
determining a trend stability scoring value according to the fault rates of the electric energy meters with different meter ages corresponding to the equipment set and the weight corresponding to the fault rate of the electric energy meter with each meter age;
and determining an operation reliability score value according to the fault metering rate, the non-fault metering rate, the weight of the fault metering rate and the weight of the non-fault metering rate corresponding to the equipment set.
In the implementation mode of the invention, firstly, the acquired collection information and the electric energy meter fault data are checked and cleaned, and the grade value of each primary evaluation index is determined according to the processed data.
Wherein, for the first-order evaluation index: and the equipment set scale is represented by the supply quantity of the secondary evaluation index, and quantification is performed according to the supply quantity so as to obtain the equipment set scale score value.
For the first-order evaluation index: and the product specification consistency is reflected by the fault rate of the secondary index product specification and the number of the product specifications, the fault rate of the produced electric energy meters with different product specifications is calculated for each equipment set, the difference degree of the different product specifications is calculated, and quantification is performed according to the determined difference degree to obtain the product specification consistency rating value.
For the first-order evaluation index: product diversity, considering the number of product specifications produced by equipment sets and the consistency of fault rates among the product specifications, wherein the equipment sets with more types of product specifications and high consistency of fault rates have higher scores, and only considering the consistency can cause the equipment sets with fewer part of product specifications to have higher scores, so that the indexes of product diversity are increased, and the influence of the product specifications is considered; on the other hand, the more types of specifications the equipment set allows to produce, the higher the in-process control capability of the equipment set is. And quantifying according to the product specification number to determine the product diversity score value.
For the first-order evaluation index: the trend stability is reflected by secondary evaluation index meter age fault rates, the 1 st year fault rate, the 2 nd year fault rate, … … and the 8 th year fault rate of the electric energy meter are respectively calculated, the stability of the equipment set is analyzed through the expression of the fault rates of the electric energy meters in different ages, and quantification is carried out according to the fault rates and corresponding weights of the electric energy meters in different meter ages so as to determine a trend stability score value.
For the first-order evaluation index: and the operation reliability is embodied by measuring the fault rate and the non-measuring fault rate through secondary evaluation indexes, the reliability of the equipment set comprises the measuring reliability and the non-measuring reliability, and quantification is carried out according to the measuring fault rate and the non-measuring fault rate of the disassembled list so as to determine the operation reliability score value.
In step 103, the weight of each primary evaluation index corresponding to different objective weighting methods is determined, and the optimal weight of each primary evaluation index is determined according to the weight of each primary evaluation index corresponding to each objective weighting method.
Preferably, the determining the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method includes:
and determining the optimal weight of each primary evaluation index by using an included angle measurement method according to the weight of each primary evaluation index corresponding to each objective weight method.
Preferably, wherein the objective weighting method comprises: at least two of an entropy weight method, a correlation coefficient method, and a coefficient of variation method.
In the embodiment of the invention, the quality of the electric energy meter is evaluated from five aspects of trend stability, operation reliability, quality and specification consistency, equipment set scale and product diversity, but the five indexes have different importance, and the evaluation is not objective if the weight is formulated manually. If the quality of the electric energy meter needs to be evaluated fairly and reasonably, the index weight value can be automatically generated according to the data rule. Therefore, in an embodiment of the present invention, the objective weighting method includes: entropy weight method, correlation coefficient method and coefficient of variation method.
Entropy is a physical concept of thermodynamics and is a measure of the degree of disorder or chaos of a system, and a larger entropy indicates that the system is more disordered (i.e. carries less information), and a smaller entropy indicates that the system is more ordered (i.e. carries more information). In informatics, entropy is used to characterize the degree of disorder of the system and the degree of dispersion of the data. The information entropy is the measurement of the information disorder degree, the larger the information entropy is, the higher the information disorder degree is, and the smaller the utility value of the information is; conversely, the smaller the entropy of the information is, the smaller the disorder degree of the information is, and the larger the utility value of the information is. In the comprehensive evaluation, the information entropy can be used for evaluating the order degree of the obtained system information and the utility value of the information, the larger the information entropy of the index is, the less the information provided by the index in the comprehensive evaluation is, and the smaller the information entropy is, the more the information provided by the index in the comprehensive evaluation is.
The formula for calculating the information entropy value is as follows:
wherein i is a condition index, j is a calculation target index, j takes a value of 1,2, … … Q, and Q is the number of the target indexes; hjThe information entropy value of the index target index j under the condition of i; f. ofijIs the probability of i, j occurring simultaneously.
The entropy weight value calculation formula is as follows:
wherein j is a calculation target element, and the value of j is 1,2, … … Q; hjThe information entropy value of the element j is obtained, and Q is the value number of the element j; suppose fijWhen equal to 0, fijlnfij0; k is a constant, and the value of k is as follows:
k＝1-lnp，fijlnfij＝0，
in the embodiment of the invention, the entropy weight method carries out objective quantification according to the information quantity of an evaluation object, the importance degree of the index is judged according to the dimension of the information quantity, the more important the index carrying the larger the information quantity is, the larger the objective weight corresponding to the index is, and the problem of unbalanced information carrying quantity of the evaluation index is solved. In the evaluation, the weight is determined according to the information quantity carried by the five indexes of supply quantity, product specification consistency, trend stability, operation reliability and product diversity through the size of the index information entropy, and when the difference of the representation on each index of the equipment set is not large, the difference of the equipment set evaluation can be more visually represented.
Calculating the primary evaluation index data scores of different equipment sets according to an entropy weight method calculation formula, wherein the information entropy of the supply quantity is minimum, the information quantity provided is maximum when the entropy is small according to the definition of the entropy, and the weight is maximum when the utility value of the information is larger; similarly, the operation reliability is compared with the minimum information quantity provided by other indexes, the entropy value is maximum, and the weight is minimum. The weight of five indexes of equipment set scale, product specification consistency, trend stability, operation reliability and product diversity obtained by an entropy method is respectively 0.56, 0.04, 0.03, 0.01 and 0.36. The entropy weight method evaluation shows that the information carried by the equipment set scale index is larger.
The correlation analysis refers to the analysis of two or more variable elements with correlation, so as to measure the degree of closeness of correlation of the two variable elements. Certain connection or probability is required to exist between elements of the correlation so as to carry out correlation analysis. The internal correlation degree among the indexes is calculated by using the Pearson correlation coefficient, and the weight is reversely deduced by the correlation coefficient among the indexes, so that the influence of the internal information repeatability among the indexes is reduced; the larger the repeatability of the information among the indexes is, the larger the correlation degree is, so that the correlation coefficient is larger and the weight is smaller; conversely, the smaller the repeatability of the information between the indexes is, the smaller the correlation degree is, so that the smaller the correlation coefficient is, the larger the weight is.
The correlation coefficient calculation formula is as follows:
wherein x and y are target vectors, and the values of x and y are 1,2 and … … Q; pxy is the correlation coefficient of the vector x, y;
and
are respectively vector: d'x＝(d′1x，d′2x，......d′px)TAnd d'y＝(d′1y，d′2y，......d′py)TAverage value of (a).
The correlation coefficient weight calculation formula is as follows:
wherein y is a weight calculation object and takes the values of 1,2, … … and Q; rhoxyThe correlation coefficient of the object x and y, and Wy is the correlation coefficient weight of y; the smaller the coefficient of variation, the smaller the weight.
In the embodiment of the present invention, the correlation coefficient method determines each index weight according to the correlation of the evaluation target, and solves the problem of information duplication between evaluation indexes. In this evaluation, two correlation coefficients for each index are shown in table 4.
TABLE 4 index correlation coefficient Table
Through analyzing the correlation coefficient, pairwise correlation exists among the equipment set scale, the trend stability and the operation reliability, the product specification consistency and other indexes have no close relation, the weight of each index is obtained through calculating the average level of the correlation coefficient, and the weights of the equipment set scale, the product specification consistency, the trend stability, the operation reliability and the product diversity are 0.09, 0.61, 0.10, 0.11 and 0.09 respectively. The result shows that the standard consistency index is more independent in the evaluation of the correlation coefficient method.
The coefficient of variation method is to directly use the information contained in each index to obtain the weight of the index through calculation. In an evaluation index system, indexes with larger index value difference, namely indexes which are difficult to realize, can reflect the difference of evaluated units. The larger the variation degree is, the stronger the contrast intensity of the strain relative to other indexes is; the smaller the variation degree, the smaller the contrast intensity relative to other indexes; the weight is in direct proportion to the coefficient of variation, and the larger the coefficient of variation is, the larger the weight is; conversely, the smaller the coefficient of variation, the smaller the weight.
The coefficient of variation calculation is as follows:
the coefficient of variation weight calculation formula is as follows:
wherein, VjIs the coefficient of variation of j; d'jIs vector d'j＝(d′1j，d′2j，......d′pj)TAverage value of (d); d'ijIs the matrix product of vectors i, j; j is the target object;
is the coefficient of variation weight of j.
The coefficient of variation method determines the weight of each index according to the difference of information of an evaluation object, the problem of information contrast between evaluation indexes is solved, and the index with large coefficient of variation is more effective in carrying information in comprehensive evaluation.
In the embodiment of the invention, the index weight is objectively determined according to the comparison strength among five indexes, namely trend stability, running reliability, product specification consistency, supply quantity and product diversity, the variation coefficient of the supply quantity is the largest and the variation degree of the running reliability is the smallest through analysis, the variation coefficient values of the five indexes are respectively calculated, and the index weights are respectively 0.46, 0.10, 0.07, 0.04 and 0.33 through the variation coefficient values. It is shown that the information carried by the supply quantity index is more effective in the evaluation of the coefficient of variation method.
Each algorithm has own limitation, but each algorithm also has own emphasis point, and the three weight calculation methods have mutual correction effect. Therefore, a combined weight representing the three weights is calculated from the weight values obtained by the three objective weight methods. In the embodiment of the invention, after the weight of each primary evaluation index corresponding to different objective weight algorithms is obtained, the optimal weight of each primary evaluation index is determined by using an included angle measurement method. The weights of the primary evaluation indexes corresponding to the different algorithms are shown in table 5.
TABLE 5 weight table of first-class evaluation indexes
In step 104, the electric energy meter quality score of each equipment set is determined according to the score value of the primary evaluation index corresponding to the electric energy meter of each equipment set and the optimal weight of each primary evaluation index.
In the embodiment of the invention, for any equipment set, the electric energy meter quality score corresponding to the equipment set is determined according to the score of the primary evaluation index corresponding to the electric energy meter of the equipment set and the weight of the primary evaluation index. After the quality scores of the electric energy meters corresponding to each device set are obtained, ranking can be performed, and ranking results are shown in table 6.
TABLE 6 device set ranking table
Fig. 2 is a diagram of an analysis concept for determining the quality of an electric energy meter according to an embodiment of the present invention. As shown in fig. 2, in the embodiment of the present invention, the analyzing concept for determining the quality of the electric energy meter includes: performing data inspection and data cleaning on the acquired fault data and equipment set information; determining the grade value of the primary evaluation index according to the processed data and the weight of the secondary evaluation index; determining the weight of the primary evaluation index by using different objective analysis methods, and performing combined optimization to determine the optimal weight of the primary evaluation index; and determining the quality of the electric energy meters of each equipment set according to the optimal weight of the primary evaluation index and the score of the primary evaluation index, and sequencing the quality of the electric energy meters of the equipment sets. And determining the grade value of the corresponding primary index according to the secondary index data and the weight of the secondary index determined by using the analytic hierarchy process.
In the embodiment of the invention, the equipment set information of 2016, 2017 and 2018 supply quantity ranking TOP25 is extracted, and compared with the name discovery that each equipment set is in 'comprehensive ranking based on multi-dimensional equipment set power meter quality', 20 equipment sets in the equipment set of 2016 supply quantity TOP25 are in a comprehensive ranking TOP25 list, 19 equipment sets in the equipment set of 2017 supply quantity TOP25 are in a comprehensive ranking TOP25 list, and 19 equipment sets in the equipment set of 2018 supply quantity TOP25 are in a comprehensive ranking TOP25 list, and the matching degrees of the equipment sets are respectively 80%, 76% and 76%. The ranked goodness of fit for different tops of the device set is shown in table 7. The device set rank difference values are shown in table 8.
Table 7 device set ranking fit table
| Year \ goodness of fit | top25 goodness of fit | top20 goodness of fit | top15 goodness of fit | top10 goodness of fit | top5 goodness of fit |
| 2016 | 80％ | 75％ | 80％ | 50％ | 60％ |
| 2017 | 76％ | 70％ | 73％ | 40％ | 40％ |
| 2018 | 76％ | 65％ | 67％ | 40％ | 40％ |
| General assembly | 76％ | 75％ | 73％ | 40％ | 40％ |
Table 8 device set ranking difference value table
The new ranking of partial device sets is found to be beyond the range of top25 through comparison, wherein the difference of the device sets with the number 22243 is large, the original ranking is 6 th, the new ranking is 29, the trend stability of the device sets is relatively poor through analysis (64 th), the product diversity is ranked backwards (48 th), but the product specification consistency (17 th) and the supply quantity are relatively advanced (23 th), and therefore 29 bits are ranked in the new comprehensive ranking.
The rank of a part of the equipment sets in the original top25 is found to be reduced seriously by comparison, wherein the rank of the equipment set with the number 22760 is reduced most obviously, the supply quantity (48 th) and the product diversity (56 th) of the equipment set are found to be ranked last in the top25 by analysis, meanwhile, the trend stability (77 th) of the equipment set is poor, only the product consistency (28 th) is ranked slightly better, and therefore, the new comprehensive rank is ranked 49.
The ranking of the partial device set in the original top25 is obviously increased through comparison, wherein the ranking of the device set with the number 32243 is most prominently increased, the trend stability and the operation reliability of the device set are found to be not obviously insufficient in the top25 through analysis, meanwhile, the supply quantity (9 th name) and the product diversity (5 th name) of the device set are arranged within the top10 names, so the position is increased in the new ranking, and the 7 th rank is comprehensively ranked.
The method for determining the quality of the electric energy meter based on the multiple dimensions designs an index selection method for comprehensive evaluation of the quality of the electric energy meter of a multi-dimensional equipment set, comprehensively evaluates the equipment set according to supply quantity, product specification consistency, trend stability and operation reliability, reflects comprehensive evaluation indexes of equipment set products under different environments and different operation conditions, and is beneficial to comprehensively analyzing the quality of the electric energy meter of the equipment set; index weight algorithm selection of comprehensive evaluation of the quality of the multi-dimensional equipment set electric energy meter is designed, and finally a combined weight optimization model is selected, so that scientific, objective, fair and comprehensive analysis of the quality of the equipment set electric energy meter is facilitated. When the user selects enough intelligent electric energy meters, the quality comprehensive ranking of the equipment set products can be referred to, an electric energy meter bidding strategy is formulated, when the comprehensive ranking of one equipment set is advanced, a plurality of packages can be considered to be paid in the equipment set, the material department can limit the equipment set bidding with poor ranking, the equipment set bidding with good ranking is encouraged, effective data basis is provided for the purchasing work of the user, the material supply quality is guaranteed, the purchasing cost is reduced, and the cost reduction and efficiency improvement effects are achieved for enterprises.
Fig. 3 is a schematic diagram of a system 300 for determining the quality of an electric energy meter based on multiple dimensions according to an embodiment of the invention. As shown in fig. 3, a system 300 for determining the quality of an electric energy meter based on multiple dimensions according to an embodiment of the present invention includes: a secondary evaluation index weight determination unit 301, a primary evaluation index score value determination unit 302, a primary evaluation index optimal weight determination unit 303, and an electric energy meter quality score unit 304.
Preferably, the weight determining unit 301 of the secondary evaluation index is configured to, for each primary evaluation index of the quality evaluation indexes of the electric energy meter, which includes a plurality of secondary evaluation indexes, respectively determine the weight of the secondary evaluation index corresponding to each primary evaluation index by using an analytic hierarchy process.
Preferably, the first-level evaluation index of the electric energy meter quality evaluation indexes comprises: equipment set scale, product specification consistency, product diversity, trend stability and operation reliability; the secondary evaluation indexes corresponding to the equipment set scale include: the quantity of electric energy meters provided by the equipment set; the secondary evaluation indexes corresponding to the product specification consistency comprise: failure rates of electric energy meters of different specifications; the secondary evaluation indexes corresponding to the product diversity comprise: the number of the product gauges; the secondary evaluation indexes corresponding to the trend stability include: the failure rate of electric energy meters with different meter ages; the secondary evaluation indexes corresponding to the operational reliability include: and (4) disassembling the metering failure rate and the non-metering failure rate of the meter.
Preferably, the score value determination unit 302 of the primary evaluation index is configured to determine a score value of each primary evaluation index corresponding to the electric energy meter of each device set according to the set information of each device set, the electric energy meter fault data, and the weight of each secondary evaluation index.
Preferably, wherein the system further comprises: and the data processing unit is used for carrying out data verification and data cleaning on the set information of each equipment set and the fault data of the electric energy meter.
Preferably, the determining unit 302 of the first-level evaluation index score value determines the score value of each first-level evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each second-level evaluation index, and includes:
for any equipment set, determining the scale score value of the equipment set according to the quantity of electric energy meters provided by the equipment set;
determining the difference degree of the electric energy meters of different product specifications corresponding to the equipment set according to the fault rate of the electric energy meters of different product specifications corresponding to the equipment set, and determining the product specification consistency scoring value according to the difference degree of the electric energy meters of different product specifications;
determining product diversity score values according to the number of the product gauges corresponding to the equipment set and the fault rate among the product gauges;
determining a trend stability scoring value according to the fault rates of the electric energy meters with different meter ages corresponding to the equipment set and the weight corresponding to the fault rate of the electric energy meter with each meter age;
and determining an operation reliability score value according to the fault metering rate, the non-fault metering rate, the weight of the fault metering rate and the weight of the non-fault metering rate corresponding to the equipment set.
Preferably, the optimal weight determining unit 303 of the primary evaluation indexes is configured to determine the weight of each primary evaluation index corresponding to different objective weight methods, and determine the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method.
Preferably, wherein the objective weighting method comprises: at least two of an entropy weight method, a correlation coefficient method, and a coefficient of variation method.
Preferably, the optimal weight determining unit 303 for the primary evaluation indexes determines the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method, and includes:
and determining the optimal weight of each primary evaluation index by using an included angle measurement method according to the weight of each primary evaluation index corresponding to each objective weight method.
Preferably, the electric energy meter quality scoring unit 304 is configured to determine the electric energy meter quality score of each device set according to the score value of the primary evaluation index corresponding to the electric energy meter of each device set and the optimal weight of each primary evaluation index.
The system 300 for determining the quality of the electric energy meter based on multiple dimensions according to the embodiment of the present invention corresponds to the method 100 for determining the quality of the electric energy meter based on multiple dimensions according to another embodiment of the present invention, and is not described herein again.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. A method for determining the quality of an electric energy meter based on multiple dimensions, the method comprising:
for each primary evaluation index of the electric energy meter quality evaluation indexes comprising a plurality of secondary evaluation indexes, respectively determining the weight of the secondary evaluation index corresponding to each primary evaluation index by using an analytic hierarchy process;
respectively determining the score value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index;
respectively determining the weight of each primary evaluation index corresponding to different objective weight methods, and determining the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method;
and determining the quality score of the electric energy meter of each equipment set according to the score value of the primary evaluation index corresponding to the electric energy meter of each equipment set and the optimal weight of each primary evaluation index.
2. The method of claim 1, wherein the first-level evaluation indicators of the electric energy meter quality evaluation indicators comprise: equipment set scale, product specification consistency, product diversity, trend stability and operation reliability; the secondary evaluation indexes corresponding to the equipment set scale include: the quantity of electric energy meters provided by the equipment set; the secondary evaluation indexes corresponding to the product specification consistency comprise: failure rates of electric energy meters of different specifications; the secondary evaluation indexes corresponding to the product diversity comprise: the number of the product gauges; the secondary evaluation indexes corresponding to the trend stability include: the failure rate of electric energy meters with different meter ages; the secondary evaluation indexes corresponding to the operational reliability include: and (4) disassembling the metering failure rate and the non-metering failure rate of the meter.
3. The method of claim 1, further comprising:
and carrying out data verification and data cleaning on the set information of each equipment set and the fault data of the electric energy meter.
4. The method according to claim 2, wherein the determining the score value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index comprises:
for any equipment set, determining the scale score value of the equipment set according to the quantity of electric energy meters provided by the equipment set;
determining the difference degree of the electric energy meters of different product specifications corresponding to the equipment set according to the fault rate of the electric energy meters of different product specifications corresponding to the equipment set, and determining the product specification consistency scoring value according to the difference degree of the electric energy meters of different product specifications;
determining product diversity score values according to the number of the product gauges corresponding to the equipment set and the fault rate among the product gauges;
determining a trend stability scoring value according to the fault rates of the electric energy meters with different meter ages corresponding to the equipment set and the weight corresponding to the fault rate of the electric energy meter with each meter age;
and determining an operation reliability score value according to the fault metering rate, the non-fault metering rate, the weight of the fault metering rate and the weight of the non-fault metering rate corresponding to the equipment set.
5. The method according to claim 1, wherein the determining the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method comprises:
and determining the optimal weight of each primary evaluation index by using an included angle measurement method according to the weight of each primary evaluation index corresponding to each objective weight method.
6. The method according to claim 1, wherein the objective weighting method comprises: at least two of an entropy weight method, a correlation coefficient method, and a coefficient of variation method.
7. A system for determining the quality of an electric energy meter based on multiple dimensions, the system comprising:
the weight determining unit of the secondary evaluation indexes is used for determining the weight of each secondary evaluation index corresponding to each primary evaluation index by utilizing an analytic hierarchy process for each primary evaluation index of the quality evaluation indexes of the electric energy meter, wherein the primary evaluation indexes comprise a plurality of secondary evaluation indexes;
the primary evaluation index scoring value determining unit is used for respectively determining the scoring value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index;
the optimal weight determining unit of the first-level evaluation indexes is used for respectively determining the weight of each first-level evaluation index corresponding to different objective weight methods and determining the optimal weight of each first-level evaluation index according to the weight of each first-level evaluation index corresponding to each objective weight method;
and the electric energy meter quality scoring unit is used for determining the electric energy meter quality scoring of each equipment set according to the scoring value of the primary evaluation index corresponding to the electric energy meter of each equipment set and the optimal weight of each primary evaluation index.
8. The system of claim 7, wherein the first-level evaluation indicators of the electric energy meter quality evaluation indicators comprise: equipment set scale, product specification consistency, product diversity, trend stability and operation reliability; the secondary evaluation indexes corresponding to the equipment set scale include: the quantity of electric energy meters provided by the equipment set; the secondary evaluation indexes corresponding to the product specification consistency comprise: failure rates of electric energy meters of different specifications; the secondary evaluation indexes corresponding to the product diversity comprise: the number of the product gauges; the secondary evaluation indexes corresponding to the trend stability include: the failure rate of electric energy meters with different meter ages; the secondary evaluation indexes corresponding to the operational reliability include: and (4) disassembling the metering failure rate and the non-metering failure rate of the meter.
9. The system of claim 7, further comprising:
and the data processing unit is used for carrying out data verification and data cleaning on the set information of each equipment set and the fault data of the electric energy meter.
10. The system according to claim 8, wherein the primary evaluation index score value determining unit determines the score value of each primary evaluation index corresponding to the electric energy meter of each equipment set according to the set information of each equipment set, the electric energy meter fault data and the weight of each secondary evaluation index, and includes:
for any equipment set, determining the scale score value of the equipment set according to the quantity of electric energy meters provided by the equipment set;
determining the difference degree of the electric energy meters of different product specifications corresponding to the equipment set according to the fault rate of the electric energy meters of different product specifications corresponding to the equipment set, and determining the product specification consistency scoring value according to the difference degree of the electric energy meters of different product specifications;
determining product diversity score values according to the number of the product gauges corresponding to the equipment set and the fault rate among the product gauges;
determining a trend stability scoring value according to the fault rates of the electric energy meters with different meter ages corresponding to the equipment set and the weight corresponding to the fault rate of the electric energy meter with each meter age;
and determining an operation reliability score value according to the fault metering rate, the non-fault metering rate, the weight of the fault metering rate and the weight of the non-fault metering rate corresponding to the equipment set.
11. The system according to claim 7, wherein the optimal weight determination unit for the primary evaluation indexes determines the optimal weight of each primary evaluation index according to the weight of each primary evaluation index corresponding to each objective weight method, and includes:
and determining the optimal weight of each primary evaluation index by using an included angle measurement method according to the weight of each primary evaluation index corresponding to each objective weight method.
12. The system according to claim 7, wherein the objective weighting method comprises: at least two of an entropy weight method, a correlation coefficient method, and a coefficient of variation method.
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