CN113642196B - Reliability evaluation method, device and equipment for intelligent electric meter and storage medium - Google Patents
Reliability evaluation method, device and equipment for intelligent electric meter and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a storage medium for evaluating the reliability of an intelligent electric meter, wherein the method comprises the following steps: acquiring test data and field failure data of the intelligent electric meter; performing equivalent processing on the test data to an actual operation environment to obtain equivalent failure data; analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter; based on equivalent failure data and prior distribution of the reliability model parameters of the intelligent electric meter, the posterior expectation of the reliability model parameters is calculated through a data fusion method, and the posterior expectation is used as the estimated value of the reliability model parameters to realize reliability evaluation of the intelligent electric meter. The method and the device have the advantages that the test data covers the whole life cycle and the field data reflects the actual operation condition, and the accuracy of the reliability prediction of the intelligent electric meter is improved.
Description
Technical Field
The invention relates to the field of electric power instrument detection, in particular to a method, a device, equipment and a storage medium for evaluating reliability of an intelligent electric meter.
Background
The reliable operation of the intelligent electric meter is related to the user electricity charge measurement, and the reliability evaluation result has important significance for guiding the overhaul and the alternate work of the intelligent electric meter, so that the improvement of the evaluation accuracy is the core problem of the reliability evaluation of the intelligent electric meter.
One of the traditional methods for evaluating the reliability of the smart meter is to evaluate the reliability of the smart meter based on a reliability prediction manual, and the evaluation result is actually deviated due to the dependence on the prediction manual which is updated slowly. The other method is to combine various intelligent optimization algorithms to analyze and process accelerated life test data or field collected data to evaluate the reliability of the intelligent electric meter, and although the method improves the accuracy of an evaluation result to a certain extent, the method cannot cover the full life cycle due to the fact that accelerated life test stress is different from the actual situation, the samples are limited, the field failure data is truncated to a deep degree, and the evaluation result based on a single data source still has a certain problem.
At present, research on reliability evaluation of the intelligent electric meter by fusing multi-source data is limited, accelerated degradation test data and field detection state data are fused, accelerated life test failure data and performance degradation data are fused, prior distribution usually adopts Bayesian assumption based on the equivalent unknown principle, and the inapplicability of the principle in a high-reliability system is not considered.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for evaluating the reliability of an intelligent ammeter, which are used for taking the advantages of accelerating the life test data to cover the whole life cycle and ensuring that field failure data is close to the actual condition into consideration and improving the accuracy of evaluating the reliability of the intelligent ammeter.
In a first aspect, an embodiment of the present invention provides a method for evaluating reliability of a smart meter, including:
s1, acquiring test data and field failure data of the intelligent electric meter;
s2, carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data;
s3, analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter;
and S4, calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and taking the posterior expectation as the estimated values of the reliability model parameters.
Optionally, the obtaining of the test data of the smart meter in step S1 includes:
carrying out a high-temperature high-humidity accelerated life test, and recording test data of the intelligent ammeter; the test data of the intelligent electric meter comprises but is not limited to total number of test samples, time before failure, fault type, test tail-end cutting time and test application temperature and humidity.
Optionally, the acquiring field failure data in step S1 includes:
analyzing the sorting history information of the disassembled electric meters, selecting the same batch of disassembled electric meter records with the same type, type and model as the test data according to the recorded equipment type, model and arrival batch number, and calculating the failure time of each disassembled electric meter according to the commissioning time and the disassembling time of the batch of disassembled electric meters to obtain field failure data.
Optionally, in step S2, performing equivalent processing on the test data to an actual operating environment, including:
calculating by adopting a Peck model to obtain an acceleration factor based on the test applied temperature and humidity and the actual operating environment temperature and humidity; multiplying the time before failure recorded by the test by an acceleration factor to obtain equivalent failure data equivalent to the actual operating environment;
wherein, a Peck model is adopted to calculate the corresponding acceleration factor by the following calculation formula:
wherein the content of the first and second substances,
、
relative humidity of the actual operating environment and relative humidity applied for the test respectively,
、
respectively the temperature of the actual operating environment and the temperature applied in the test, n is a constant with an empirical value of 3,
empirical values for activation energy of 0.9eV, kBBoltzmann constant.
Optionally, in step S3, analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the smart meter, including:
performing linear fitting on the field failure data by adopting a least square method to obtain a parameter estimation value of the intelligent electric meter reliability model;
and obtaining prior distribution of the reliability model parameters of the intelligent meter by adopting a Bootstrap method based on the parameter estimation value of the reliability model.
Optionally, the obtaining of the prior distribution of the reliability model parameters of the smart meter by using a Bootstrap method based on the parameter estimation value of the reliability model of the smart meter specifically includes:
repeatedly extracting samples with the capacity being the same as the total number of the disassembled electric meters of the field failure data from the reliability model to obtain a plurality of groups of Bootstrap sub-samples, and performing parameter estimation on each group of Bootstrap sub-samples to obtain prior distribution of the reliability model parameters of the intelligent electric meter.
Optionally, in step S4, calculating a posterior expectation of the reliability model parameter by a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameter of the smart meter, including:
establishing a likelihood function according to the equivalent failure data and the reliability model of the intelligent electric meter:
wherein the content of the first and second substances,
in order to be a function of the likelihood,
、
respectively, an estimated value of a shape parameter and a scale parameter of the reliability model, whereiniAndjrepresents the number of repeated samples;
is as followskThe time to failure of an individual sample,
in order to test the tail-biting time,rthe number of failed samples.
Calculating posterior distribution of the reliability model parameters according to prior distribution and a likelihood function of the reliability model parameters of the intelligent electric meter:
wherein the content of the first and second substances,
for the posterior distribution of the reliability model parameters,
is a prior distribution of reliability model parameters.
Calculating the posterior expectation of the reliability model parameters according to the posterior distribution of the reliability model parameters:
wherein the content of the first and second substances,
、
the method comprises the following steps of respectively obtaining the posterior expectation of the shape parameter and the scale parameter of the reliability model of the intelligent electric meter.
In a second aspect, an embodiment of the present invention further provides an apparatus for evaluating reliability of a smart meter, including:
the data acquisition module is used for acquiring test data and field failure data of the intelligent ammeter;
the equivalent module is used for carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data;
the analysis processing module is used for analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter;
and the data fusion module is used for calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and the posterior expectation is used as the estimated value of the reliability model parameters.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions which can be executed by the processor, and the processor calls the program instructions to execute the reliability evaluation method for the smart meter provided by the embodiment of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores a computer program, and the computer program is implemented, when executed by a processor, to execute the method for evaluating reliability of a smart meter provided in the first aspect.
Compared with the prior art, the method, the device, the equipment and the storage medium for evaluating the reliability of the intelligent electric meter provided by the embodiment of the invention have the following beneficial effects:
according to the invention, accurate reliability model parameter prior information is obtained according to field failure data close to the actual situation by adopting a Bootstrap method, and the problem that Bayesian hypothesis is not suitable for high-reliability equipment is solved. The least square method is adopted when the field failure data is processed to obtain the reliability model parameters, and the problems that the total number of field failure data samples is large, the truncation degree is deep, and the maximum likelihood estimation cannot be adopted for solving are solved. On the basis, the prior distribution and the equivalent failure data are combined, the posterior value of the reliability model parameter of the intelligent electric meter is calculated by adopting a data fusion method, the reliability model parameter estimation value fusing two data sources is obtained, the estimation result is close to the actual situation on site, and the test data is used as the support in the part without the support of the data on site, so that the accuracy of the reliability estimation of the intelligent electric meter is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a reliability evaluation method for an intelligent electric meter according to an embodiment of the present invention;
fig. 2 is a block diagram of a structure of an intelligent electric meter reliability evaluation device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the defects of the prior art pointed out in the background art, the embodiment of the invention provides a method, a device, equipment and a storage medium for evaluating the reliability of an intelligent electric meter, and the method, the device, the equipment and the storage medium for evaluating the reliability of the intelligent electric meter have the advantages of accelerating the service life test data to cover the whole service life cycle and ensuring that the field failure data is close to the actual condition, and improve the accuracy of evaluating the reliability of the intelligent electric meter. The following description and description of various embodiments are presented in conjunction with the following drawings.
Fig. 1 is a schematic flow chart of a method for evaluating reliability of a smart meter according to an embodiment of the present invention, and as shown in fig. 1, the method for evaluating reliability of a smart meter according to an embodiment of the present invention includes, but is not limited to, the following steps:
and step S1, acquiring test data and field failure data of the intelligent electric meter.
In one embodiment, the method for acquiring the test data of the smart meter comprises the following steps: determining and recording the number of samples for the high-temperature high-humidity accelerated life test, the temperature and humidity applied in the test and the tail-cutting time of the test, and according to the part 311 of electric measurement equipment credibility of GB/T17215.9311-2017: carrying out a high-temperature high-humidity accelerated life test in a temperature and humidity accelerated reliability test, and recording test data of the intelligent ammeter; the test data of the intelligent electric meter comprises but is not limited to total number of test samples, time before failure, fault type, test tail-end cutting time and test application temperature and humidity.
In one embodiment, the method for acquiring field failure data comprises the following steps: analyzing the sorting history information of the disassembled electric meters, selecting the same batch of disassembled electric meter records with the same type, type and model as the test data according to the recorded equipment type, model and arrival batch number, and calculating the failure time of each disassembled electric meter according to the commissioning time and the disassembling time of the batch of disassembled electric meters to obtain field failure data.
And step S2, carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data.
In one embodiment, performing equivalent processing on the test data to an actual operating environment to obtain equivalent failure data specifically includes:
calculating by adopting a Peck model to obtain an acceleration factor based on the test applied temperature and humidity and the actual operating environment temperature and humidity; multiplying the time before failure recorded by the test by an acceleration factor to obtain equivalent failure data equivalent to the actual operating environment;
wherein, a Peck model is adopted to calculate the corresponding acceleration factor by the following calculation formula:
wherein the content of the first and second substances,
、
relative humidity of the actual operating environment and relative humidity applied for the test respectively,
、
respectively the temperature of the actual operating environment and the temperature applied in the test, n is a constant with an empirical value of 3,
empirical values for activation energy of 0.9eV, kBBoltzmann constant.
And step S3, analyzing and processing the field failure data to obtain prior distribution of the reliability model parameters of the intelligent electric meter.
In one embodiment, step S3 specifically includes the following steps:
firstly, sequencing field failure data in an ascending order and recording corresponding serial numbers, and calculating the empirical failure probability of a failure sample by adopting an approximate median rank according to the batch number:
wherein the content of the first and second substances,F(t k ) To be ordered tokThe probability of an empirical failure of the individual data,kis a serial number, and N is the batch number.
Then, performing linear fitting on the field failure data by adopting a least square method to obtain a coefficient of a regression equation; the intelligent electric meter reliability model is Weibull distribution, the intelligent electric meter reliability model after linearization is combined with a calculation formula for solving regression equation coefficients, and the shape parameters of the intelligent electric meter reliability model are obtained
And scale parameter
。
Wherein the content of the first and second substances,
、
the shape parameter and the scale parameter of the reliability model. And:
and finally, obtaining the prior distribution of the reliability model parameters of the intelligent meter by adopting a Bootstrap method based on the parameter estimation value of the reliability model. Specifically, samples with the capacity being the same as the total number of the detached electric meters of the field failure data are repeatedly extracted from the intelligent electric meter reliability model to obtain a plurality of groups of Bootstrap sub-samples, and the reliability model parameter estimation value of the sampling samples is obtained by adopting the least square method. Repeating the process for 10 times to obtain 10 groups of values of the reliability model parameters of the intelligent electric meter and the prior distribution of the values.
And step S4, calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and taking the posterior expectation as the estimated values of the reliability model parameters.
In one embodiment, step S4 specifically includes the following steps:
establishing a likelihood function according to the equivalent failure data and the reliability model of the intelligent electric meter; the equivalent failure data refers to failure data which is obtained by converting the test data and is equivalent to the actual operation environment. The established likelihood function is:
wherein the content of the first and second substances,
in order to be a function of the likelihood,
、
respectively, an estimated value of a shape parameter and a scale parameter of the reliability model, whereiniAndjrepresents the number of repeated samples;
is as followskThe time to failure of an individual sample,
in order to test the tail-biting time,rthe number of failed samples.
Calculating posterior distribution of the reliability model parameters according to prior distribution and a likelihood function of the reliability model parameters of the intelligent electric meter:
wherein the content of the first and second substances,
for the posterior distribution of the reliability model parameters,
is a prior distribution of reliability model parameters.
Calculating the posterior expectation of the reliability model parameters according to the posterior distribution of the reliability model parameters:
wherein the content of the first and second substances,
、
the method comprises the following steps of respectively obtaining the posterior expectation of the shape parameter and the scale parameter of the reliability model of the intelligent electric meter.
And the posterior expectation of the reliability model parameters is used as the reliability model parameter estimation value, so that the reliability of the intelligent ammeter is evaluated.
According to the invention, accurate reliability model parameter prior information is obtained by adopting a Bootstrap method according to field failure data close to the actual situation, and the problem that Bayesian hypothesis is not suitable for high-reliability equipment is solved. When the field failure data is processed to obtain the reliability model parameters, a least square method is adopted, and the problems that the total number of field failure data samples is large, the truncation degree is deep, and the maximum likelihood estimation cannot be adopted for solving are solved. On the basis, the prior distribution and the equivalent failure data are combined, the posterior value of the reliability model parameter of the intelligent electric meter is calculated by adopting a data fusion method, the reliability model parameter estimation value fusing two data sources is obtained, the estimation result is close to the actual situation on site, and the test data is used as the support in the part without the support of the data on site, so that the accuracy of the reliability estimation of the intelligent electric meter is improved.
Fig. 2 is a structural block diagram of the device for evaluating reliability of a smart meter according to the embodiment of the present invention, and the device for evaluating reliability of a smart meter according to the embodiment of the present invention is used for executing the method for evaluating reliability of a smart meter according to the foregoing embodiments. Referring to fig. 2, the apparatus includes:
the data acquisition module 201 is used for acquiring test data and field failure data of the intelligent ammeter;
the equivalence module 202 is used for performing equivalence processing on the test data to an actual operation environment to obtain equivalent failure data;
the analysis processing module 203 is used for analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter;
and the data fusion module 204 is used for calculating the posterior expectation of the reliability model parameters by a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and using the posterior expectation as the estimated values of the reliability model parameters.
It can be understood that the device for evaluating reliability of an intelligent electric meter provided by the present invention corresponds to the method for evaluating reliability of an intelligent electric meter provided by the foregoing embodiment, and the related technical features of the system for evaluating reliability of an intelligent electric meter may refer to the related technical features of the method for evaluating reliability of an intelligent electric meter, which are not described herein again.
In one embodiment, an embodiment of the present invention provides an electronic device, as shown in fig. 3, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. The processor 301 may call the logic instructions in the memory 303 to execute the steps of the method for evaluating reliability of a smart meter according to the embodiments, including: s1, acquiring test data and field failure data of the intelligent electric meter; s2, carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data; s3, analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter; and S4, calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and taking the posterior expectation as the estimated values of the reliability model parameters.
In an embodiment, the embodiment of the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the steps of the method for evaluating reliability of a smart meter provided in the above embodiments, for example, the method includes: s1, acquiring test data and field failure data of the intelligent electric meter; s2, carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data; s3, analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter; and S4, calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and taking the posterior expectation as the estimated values of the reliability model parameters.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. 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 computer, 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A reliability assessment method for a smart meter is characterized by comprising the following steps:
s1, acquiring test data and field failure data of the intelligent electric meter; wherein, acquire smart electric meter test data, include: carrying out a high-temperature high-humidity accelerated life test, and recording test data of the intelligent ammeter; the test data of the intelligent ammeter comprises but is not limited to the total number of test samples, the time before failure, the fault type, the test tail-ending time and the test applied temperature and humidity;
s2, carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data;
s3, analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter;
and S4, calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and taking the posterior expectation as the estimated values of the reliability model parameters.
2. The method for evaluating the reliability of the intelligent electric meter according to claim 1, wherein the step S1 of acquiring field failure data comprises the following steps:
analyzing the sorting history information of the disassembled electric meters, selecting the same batch of disassembled electric meter records with the same type, type and model as the test data according to the recorded equipment type, model and arrival batch number, and calculating the failure time of each disassembled electric meter according to the commissioning time and the disassembling time of the batch of disassembled electric meters to obtain field failure data.
3. The method for evaluating reliability of a smart meter according to claim 1, wherein in step S2, the equivalent processing of the test data to an actual operation environment includes:
calculating by adopting a Peck model to obtain an acceleration factor based on the test applied temperature and humidity and the actual operating environment temperature and humidity; multiplying the time before failure recorded by the test by an acceleration factor to obtain equivalent failure data equivalent to the actual operating environment;
wherein, a Peck model is adopted to calculate the corresponding acceleration factor by the following calculation formula:
wherein the content of the first and second substances,
、
relative humidity of the actual operating environment and relative humidity applied for the test respectively,
、
respectively the temperature of the actual operating environment and the temperature applied in the test, n is a constant with an empirical value of 3,
empirical values for activation energy of 0.9eV, kBBoltzmann constant.
4. The method for evaluating the reliability of the smart meter according to claim 1, wherein in step S3, analyzing and processing the field failure data to obtain the prior distribution of the reliability model parameters of the smart meter comprises:
performing linear fitting on the field failure data by adopting a least square method to obtain a parameter estimation value of the intelligent electric meter reliability model;
and obtaining prior distribution of the reliability model parameters of the intelligent meter by adopting a Bootstrap method based on the parameter estimation value of the reliability model.
5. The method for evaluating the reliability of the smart meter according to claim 4, wherein the prior distribution of the parameters of the reliability model of the smart meter is obtained by adopting a Bootstrap method based on the parameter estimation value of the reliability model of the smart meter, and the method specifically comprises the following steps:
repeatedly extracting samples with the capacity being the same as the total number of the disassembled electric meters of the field failure data from the reliability model to obtain a plurality of groups of Bootstrap sub-samples, and performing parameter estimation on each group of Bootstrap sub-samples to obtain prior distribution of the reliability model parameters of the intelligent electric meter.
6. The method for evaluating the reliability of the smart meter according to claim 4, wherein in step S4, calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the smart meter comprises:
establishing a likelihood function according to the equivalent failure data and the reliability model of the intelligent electric meter:
wherein the content of the first and second substances,
in order to be a function of the likelihood,
、
respectively, an estimated value of a shape parameter and a scale parameter of the reliability model, whereiniAndjrepresents the number of repeated samples;
is as followskThe time to failure of an individual sample,
in order to test the tail-biting time,ras the number of failed samples；
Calculating posterior distribution of the reliability model parameters according to prior distribution and a likelihood function of the reliability model parameters of the intelligent electric meter:
wherein, g
For the posterior distribution of the reliability model parameters,
is a prior distribution of reliability model parameters;
calculating the posterior expectation of the reliability model parameters according to the posterior distribution of the reliability model parameters:
wherein the content of the first and second substances,
、
the method comprises the following steps of respectively obtaining the posterior expectation of the shape parameter and the scale parameter of the reliability model of the intelligent electric meter.
7. An intelligent electric meter reliability assessment device is characterized by comprising:
the data acquisition module is used for acquiring test data and field failure data of the intelligent ammeter;
the equivalent module is used for carrying out equivalent processing on the test data to an actual operation environment to obtain equivalent failure data;
the analysis processing module is used for analyzing and processing the field failure data to obtain prior distribution of reliability model parameters of the intelligent electric meter;
and the data fusion module is used for calculating the posterior expectation of the reliability model parameters through a data fusion method based on the equivalent failure data and the prior distribution of the reliability model parameters of the intelligent electric meter, and the posterior expectation is used as the estimated value of the reliability model parameters.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for evaluating reliability of a smart meter according to any one of claims 1 to 6 when executing the program.
9. A non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for reliability assessment of a smart meter according to any one of claims 1 to 6.
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