A method and system for evaluating the service life of an electric energy meter

Abstract

The present invention relates to a method and system for evaluating the service life of an electric energy meter. The method comprises: collecting field fault data of an electric energy meter; adding left-censored data to the field fault data of the electric energy meter to obtain the field fault data of the electric energy meter after the left-censored data is supplemented; determining data related to the life of the electric energy meter according to the calendar time of the field fault data of the electric energy meter after the left-censored data is supplemented, the data related to the life of the electric energy meter including the fault truncation time, life data and the successful truncation time; constructing a left-censored and right-censored electric energy meter life evaluation model according to the data related to the life of the electric energy meter; determining the parameter point estimation and confidence limit of the left-censored and right-censored electric energy meter life evaluation model according to the maximum likelihood estimation and the Fisher information matrix; determining the service life of the electric energy meter according to the parameter point estimation and the confidence limit. The present invention can use the left-censored and right-censored data to determine the reasonable service life of the electric energy meter and formulate a rotation plan for the electric energy meter.

Description

Technical Field

[0001] The present invention relates to the field of life assessment of electric energy meters, and in particular to a method and system for assessing the service life of electric energy meters. Background Art

[0002] Smart energy meters have gradually reached their service life, but the analysis of dismantled data shows that a large number of energy meters did not fail when they were dismantled. It is a waste to scrap energy meters that have not failed, and a scientific evaluation of the service life of energy meters is required in engineering. The collection of field failure data of energy meters has the characteristics of left censoring and right truncation. The existing methods focus on life assessment based on full life data or right censored data, which is not suitable for evaluating the life and reliability of energy meters using field data. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for evaluating the service life of an electric energy meter, which can use left-censored and right-truncated data to determine the reasonable service life of the electric energy meter and formulate an electric energy meter rotation plan.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for evaluating the service life of an electric energy meter, comprising:

[0006] Collect field fault data of electric energy meter;

[0007] Adding the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after the left-censored data is supplemented;

[0008] Determine the data related to the life of the electric energy meter according to the calendar time of the field fault data of the electric energy meter after the left-censored data is padded, wherein the data related to the life of the electric energy meter includes the fault truncation time, the life data and the successful truncation time;

[0009] According to the data related to the life of the electric energy meter, a left-censored and right-truncated electric energy meter life assessment model is constructed;

[0010] Determine the parameter point estimate and confidence limit of the left-censored and right-truncated electric energy meter life assessment model according to the maximum likelihood estimation and the Fisher information matrix;

[0011] The service life of the electric energy meter is determined based on the parameter point estimate and the confidence limit.

[0012] Optionally, the electric energy meter field fault data includes [T 0 ,T 1 The number of single-phase smart energy meters installed on site within the time period n, [T 0 ,T 2 ] The number of faulty energy meters during the period r′, [T 2 ,T3 ] during the period r″ and [T 2 ,T 3 ] The number of faulty energy meters r″ during the installation calendar time τ Z,1 ,τ Z,2 ,…,τ Z,i ,…,τ Z,r ″ and fault calendar time τ F,1 ,τ F,2 ,…,τ F,i ,…,τ F,r ″.

[0013] Optionally, the adding of the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after the left-censored data is supplemented specifically includes:

[0014] The installation time interval of the energy meter [T 0 ,T 1 ] is divided into k groups [T 0,j-1 ,T 0,j ](j=1,2,…,k;T 0,0 =T 0 ,T 0,k =T 1 ), then there are n j Electricity meter, n j Electricity meter in [T 0 ,T 2 The number of faults in ] is recorded as r j ′Taiwan, in [T 2 ,T 3 The number of failures during the period is recorded as r j ″Taiwan, and r j The calendar time of installation of the energy meter is recorded as τ Z,j,1 ,τ Z,j,2 ,…,τ Z,j,i ,…,τ Z,j,rj ″, the fault calendar time is recorded as τ F,j,1 ,τ F,j,2 ,…,τ F,j,i ,…,τ F,j,rj ″, T 3 There is still time j The electric energy meter is working normally, so n=sum(n j ), r′=sum(r j ′), r″=sum(r j ″), s = sum(s j ), n j =r j ′+r j ″+s j ;

[0015] Determine the installation time of the electric energy meter in the jth group: Take the jth group [T 0,j-1 ,T 0,j ] midpoint T M,j As the group n j The calendar time when the electric energy meter was installed;

[0016] Determine the number of electric energy meters n in the jth group j for

[0017] n j =n×r j ″ / r″;

[0018] Determine the electric energy meter in the jth group in [T M,j ,T 2 ] The number of faults within r j 'for

[0019] r j ′=r j ″×(T 2 –T M,j ) / (T 3 –T 2 ).

[0020] Optionally, determining the data related to the life of the electric energy meter according to the calendar time of the electric energy meter field fault data after the left-censored data is padded specifically includes:

[0021] For the jth group in [T M,j ,T 2 ] j ′ faulty energy meter, set the calendar time T 2 Subtract the installation calendar time of group j to obtain the failure censoring time t r′,j ;

[0022] For the jth group in [T 2 ,T 3 ] j ″ A faulty electric energy meter, subtract the installation calendar time from the fault calendar time to get the life data t r″,j,i =τ F,j,i – τ Z,j,i ;

[0023] For the jth group in T 3 The electric energy meter that continues to operate normally after the calendar time T 2 Subtract the installation calendar time of group j to obtain the successful test t s,j .

[0024] Optionally, constructing a left-censored and right-truncated electric energy meter life assessment model based on the electric energy meter life-related data specifically includes:

[0025] According to the data related to the life of the energy meter and the likelihood function, a left-censored and right-truncated energy meter life assessment model is constructed:

[0026]

[0027] Where β is the shape parameter of the Weibull distribution, η is the scale parameter of the Weibull distribution, and t r′,j is the fault censoring time, t r″,j,i is the observed lifespan data, t s,j is the successful censoring time, r′ is [T 0 ,T 2 ], r″ is the number of faulty energy meters during the period [T 2 ,T 3 ]The number of electricity meter failures during the period.

[0028] Optionally, determining the service life of the electric energy meter according to the parameter point estimation and the confidence limit specifically includes:

[0029] Based on the parameter point estimate and the confidence limits, the formula Determine the service life of the energy meter;

[0030] Among them, R SM =1-F YZ , R SM is the reliability of the energy meter, F YZ The largest proportion of faulty energy meters, is the lower confidence limit of the characteristic lifetime, is the point estimate of the shape parameter, t SM,L It is the service life of the energy meter.

[0031] An electric energy meter service life assessment system comprises:

[0032] Electric energy meter field fault data collection module, used to collect electric energy meter field fault data;

[0033] A left-censored data filling module, used for adding the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after filling the left-censored data;

[0034] A module for determining data related to the life of an electric energy meter, for determining data related to the life of an electric energy meter according to the calendar time of the electric energy meter field fault data after the left-censored data is padded, wherein the data related to the life of the electric energy meter includes a fault truncation time, life data, and a successful truncation time;

[0035] A left-censored and right-truncated electric energy meter life assessment model construction module is used to construct a left-censored and right-truncated electric energy meter life assessment model based on the electric energy meter life related data;

[0036] A parameter point estimation and confidence limit determination module, used to determine the parameter point estimation and confidence limit of the left-censored and right-truncated electric energy meter life assessment model according to the maximum likelihood estimation and the Fisher information matrix;

[0037] The electric energy meter service life determination module is used to determine the service life of the electric energy meter according to the parameter point estimation and the confidence limit.

[0038] Optionally, the collected electric energy table field fault data includes [T 0 ,T 1 The number of single-phase smart energy meters installed on site within the time period n, [T 0 ,T 2 ] The number of faulty energy meters during the period r′, [T 2 ,T 3 ] during the period r″ and [T 2 ,T 3 ] The number of faulty energy meters r″ during the installation calendar time τ Z,1 ,τ Z,2 ,…,τ Z,i ,…,τ Z,r″ and fault calendar time τ F,1 ,τ F,2 ,…,τ F,i ,…,τ F,r″ .

[0039] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0040] The field fault data of electric energy meters has the characteristics of left censoring and right truncation. The existing methods are all for full life data and right truncated life data, which cannot be used to calculate the life and reliability of electric energy meters with left censoring and right truncation characteristics. The present invention can use the field fault data of left censoring and right truncation to construct an electric energy meter life and reliability assessment model to assess the field service life and remaining life of the electric energy meter, which is used to determine the reasonable service life of the electric energy meter and formulate the electric energy meter rotation plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0042] Figure 1 This is a flow chart of the method for evaluating the service life of an electric energy meter according to the present invention;

[0043] Figure 2 It is a schematic diagram of left-censored and right-truncated fault data of the electric energy performance field of the present invention;

[0044] Figure 3 This is a schematic diagram of the life conversion of the electric energy meter of the present invention;

[0045] Figure 4 This is a structural diagram of the electric energy meter service life assessment system of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The purpose of the present invention is to provide a method and system for evaluating the service life of an electric energy meter, which can use left-censored and right-truncated data to determine the reasonable service life of the electric energy meter and formulate an electric energy meter rotation plan.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Left censored data: After the electric energy meter is put into operation, at t 1 The fault data of the electric energy meter starts to be collected at time t 1 Before the time point, the specific failure time data of the electric energy meter cannot be known, and this type of data is left-censored data.

[0050] Right censored data: After the electric energy meter is put into operation, at t 1 The fault data of the electric energy meter starts to be collected at t 2 The observation cut-off time (t 1 <t 2 ), it is impossible to know t 2 The fault data of the electric energy meter after time, this type is right-truncated data.

[0051] Figure 1 This is a flow chart of the method for evaluating the service life of an electric energy meter according to the present invention. Figure 1 As shown, the present invention provides a method for evaluating the service life of an electric energy meter, the method comprising:

[0052] Step 101: Collecting field fault data of the electric energy meter.

[0053] The electric energy meter field fault data includes [T 0 ,T 1 The number of single-phase smart energy meters installed on site within the time period n, [T 0 ,T 2 ] The number of faulty energy meters during the period r′, [T 2 ,T 3 ] during the period r″ and [T 2 ,T 3 ] The number of faulty energy meters r″ during the installation calendar time τ Z,1 ,τ Z,2 ,…,τ Z,i ,…,τ Z,r″ and the fault calendar time τ F,1 ,τ F,2 ,…,τ F,i ,…,τ F,r″ . Figure 2 It is a schematic diagram of left-censored and right-truncated fault data of the electric energy performance field of the present invention.

[0054] Step 102: Add the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after the left-censored data is supplemented.

[0055] 1) Set the installation time interval of the energy meter [T 0 ,T 1 ] is divided into k groups [T 0,j-1 ,T 0,j ](j=1,2,…,k;T 0,0 =T 0 ,T 0,k =T 1 ), then there are n j Electricity meter, n j Electricity meter in [T 0 ,T 2 The number of faults in ] is recorded as r j ′Taiwan, in [T 2 ,T 3 The number of failures during the period is recorded as r j ″Taiwan, and r j The calendar time of installation of the energy meter is recorded as τ Z,j,1 ,τ Z,j,2 ,…,τ Z,j,i ,…,τ Z,j,rj″ , the fault calendar time is recorded as τ F,j,1 ,τ F,j,2 ,…,τ F,j,i ,…,τ F,j,rj″ , T 3 There is still time jThe electric energy meter is working normally, so n=sum(n j ), r′=sum(r j ′), r″=sum(r j ″), s = sum(s j ), n j =r j ′+r j ″+s j .

[0056] 2) Determine the installation time of the electric energy meter in the jth group: Take the jth group [T 0,j-1 ,T 0,j ] midpoint T M,j As the group n j Calendar time of installation of electricity meters.

[0057] 3) Determine the number of electric energy meters n in the jth group j :The design life of the energy meter exceeds 10 years. In a shorter operating time (such as 3 years), the failure rate of the energy meter can be considered to be approximately unchanged. Based on this, the energy meter in the jth group is 2 ,T 3 ] The number of faults within r j ″ can be considered to be only related to the number of electric energy meters in this group n j The number of electric energy meters in the jth group is n j for

[0058] n j =n×r j ″ / r″ (1)

[0059] 4) Determine the number of electric energy meters in the jth group in [T M,j ,T 2 ] The number of faults within r j ′: Since the failure rate of electric energy meters is low, the electric energy meters in the jth group are M,j ,T 2 ] The number of faults within r j ′ and in [T 2 ,T 3 ] The number of faults within r j ″ is only related to the length of the two time intervals. Based on this, it can be obtained that the electric energy meter in the jth group is [T 1 ,T 2 ] is the estimated number of faults within j ′,

[0060] r j ′=r j ″×(T 2 –T M,j ) / (T 3 –T2 ) (2)

[0061] Step 103: Determine the data related to the life of the electric energy meter according to the calendar time of the field fault data of the electric energy meter after the left-censored data is padded, wherein the data related to the life of the electric energy meter includes the fault truncation time, the life data and the successful truncation time, specifically including:

[0062] For the jth group in [T M,j ,T 2 ] j ′ faulty energy meter, set the calendar time T 2 Subtract the installation calendar time of group j to obtain the failure censoring time t r′,j ;

[0063] For the jth group in [T 2 ,T 3 ] j ″ A faulty electric energy meter, subtract the installation calendar time from the fault calendar time to get the life data t r″,j,i =τ F,j,i – τ Z,j,i ;

[0064] For the jth group in T 3 The electric energy meter that continues to operate normally after the calendar time T 2 Subtract the installation calendar time of group j to obtain the successful test t s,j .

[0065] The acquired electric energy meter field data is calendar time data, which needs to be converted into life data in days. Figure 3 It is a schematic diagram of the life conversion of the electric energy meter of the present invention.

[0066] Step 104: constructing a left-censored and right-truncated life assessment model for the electric energy meter based on the electric energy meter life related data, specifically including:

[0067] According to the data related to the life of the energy meter and the likelihood function, a left-censored and right-truncated energy meter life assessment model is constructed:

[0068]

[0069]

[0070] Where β is the shape parameter of the Weibull distribution, η is the scale parameter of the Weibull distribution, and t r′,j is the fault censoring time, t r″,j,i is the observed lifespan data, t s,j is the successful censoring time, r′ is [T 0 ,T2 ], r″ is the number of faulty energy meters during the period [T 2 ,T 3 ]The number of electricity meter failures during the period.

[0071] Assume that the life of the energy meter is a random variable that obeys the Weibull distribution, and its probability density function, reliability function and failure rate function are:

[0072]

[0073]

[0074]

[0075] Where β and η are the shape parameter and scale parameter of Weibull distribution. j ′ fault truncation time t r′,j , r j ″Lifetime observation data t r″,j,i , and s j The successful censoring time of each trial is t s,j After that, the likelihood function can be calculated,

[0076]

[0077] Taking the logarithm of both sides constructs the log-likelihood function,

[0078]

[0079] The log-likelihood function can be further simplified as,

[0080]

[0081] Take partial derivatives of the parameters β and η on both sides and set them equal to 0 to establish the log-likelihood equation.

[0082]

[0083]

[0084] Using iteration, the maximum likelihood estimates of the parameters β and η can be calculated and

[0085] Step 105: Determine the parameter point estimate and confidence limit of the left-censored and right-truncated electric energy meter life assessment model according to the maximum likelihood estimation and the Fisher information matrix.

[0086] The distribution parameters are solved by iterative techniques, and the estimated parameters are asymptotically normal. The estimated variance can be obtained by inverting the Fisher information matrix. The Fisher information matrix is

[0087]

[0088] The second-order derivative of the distribution parameter is

[0089]

[0090]

[0091]

[0092] The variance of the parameter estimate is

[0093]

[0094] Then the approximate confidence intervals for the shape parameter and scale parameter with confidence level γ are

[0095]

[0096]

[0097] After obtaining the point estimates and confidence limits of the distribution parameters, the point estimates and confidence limits of reliability measures such as the reliability, MTBF, failure rate, and reliable life of the electricity meter can also be calculated.

[0098] Given the confidence level γ, the point estimate and lower confidence limit of the reliability of the electric energy meter are:

[0099]

[0100]

[0101] The point estimate and lower confidence limit of the MTBF of the energy meter are

[0102]

[0103]

[0104] The point estimate and lower confidence limit of the reliable life of the energy meter are

[0105]

[0106]

[0107] Step 106: Determine the service life of the electric energy meter according to the parameter point estimate and the confidence limit, specifically including:

[0108] Based on the parameter point estimate and the confidence limits, the formula Determine the service life of the energy meter;

[0109] Among them, R SM =1-F YZ , R SM is the reliability of the energy meter, F YZ The largest proportion of faulty energy meters, is the lower confidence limit of the characteristic lifetime, is the point estimate of the shape parameter, t SM,L It is the service life of the energy meter.

[0110] The power grid company can comprehensively weigh the cost of replacing the non-faulty energy meters and the energy metering loss caused by the degradation of the energy metering performance, and determine the acceptable maximum proportion of faulty energy meters F. YZ As a threshold, if the proportion of faulty electric energy meters in the batch electric energy meter exceeds a given threshold F YZ , then batches of electric energy meters can be rotated. Then the threshold value can be used to determine the reliability R of the electric energy meter when the service life is reached. SM =1-F YZ , service life of electric energy meter t SM The lower confidence limit of the reliable life can be calculated.

[0111]

[0112] Remaining life of electric energy meter SY for

[0113] t SY =t SM –t C (25)

[0114] At this point, the prediction of the service life and remaining life of the energy meter is completed. SY , then it can be replaced, t C The running time of the energy meter.

[0115] Figure 4 This is a structural diagram of the electric energy meter service life assessment system of the present invention. Figure 4 As shown, a system for evaluating the service life of an electric energy meter includes:

[0116] The electric energy meter field fault data collection module 201 is used to collect the electric energy meter field fault data. The collected electric energy meter field fault data includes: 0 ,T 1 The number of single-phase smart energy meters installed on site within the time period n, [T 0 ,T 2 ] The number of faulty energy meters during the period r′, [T 2 ,T3 ] during the period r″ and [T 2 ,T 3 ] The number of faulty energy meters r″ during the installation calendar time τ Z,1 ,τ Z,2 ,…,τ Z,i ,…,τ Z,r″ and the fault calendar time τ F,1 ,τ F,2 ,…,τ F,i ,…,τ F,r″ .

[0117] The left-censored data filling module 202 is used to add the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after filling the left-censored data.

[0118] The module 203 for determining data related to the life of the electric energy meter is used to determine data related to the life of the electric energy meter according to the calendar time of the electric energy meter field fault data after the left-censored data is padded, and the data related to the life of the electric energy meter includes fault truncation time, life data and successful truncation time.

[0119] The left-censored and right-truncated electric energy meter life assessment model construction module 204 is used to construct a left-censored and right-truncated electric energy meter life assessment model according to the electric energy meter life related data.

[0120] The parameter point estimation and confidence limit determination module 205 is used to determine the parameter point estimation and confidence limit of the left-censored and right-truncated electric energy meter life assessment model according to the maximum likelihood estimation and the Fisher information matrix.

[0121] The electric energy meter service life determination module 206 is used to determine the service life of the electric energy meter according to the parameter point estimation and the confidence limit.

[0122] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0123] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for evaluating the service life of an electric energy meter, characterized in that: include: Collect field fault data of electric energy meter; Adding the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after the left-censored data is supplemented; Determine the data related to the life of the electric energy meter according to the calendar time of the field fault data of the electric energy meter after the left-censored data is padded, wherein the data related to the life of the electric energy meter includes the fault truncation time, the life data and the successful truncation time; According to the data related to the life of the electric energy meter, a left-censored and right-truncated electric energy meter life assessment model is constructed; Determine the parameter point estimate and confidence limit of the left-censored and right-truncated electric energy meter life assessment model according to the maximum likelihood estimation and the Fisher information matrix; Determining the service life of the electric energy meter based on the parameter point estimate and the confidence limit; The electric energy meter on-site fault data includes the number of single-phase smart electric energy meters installed on-site during [T0, T1], the number of electric energy meter failures r′ during [T0, T2], the number of electric energy meter failures r″ during [T2, T3], and the installation calendar time τ of the number of electric energy meter failures r″ during [T2, T3]. Z,1 ,τ Z,2 ,…,τ Z,i ,…,τ Z,r″ and fault calendar time τ F,1 ,τ F,2 ,…,τ F,i ,…,τ F,r″ ; The adding of the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after the left-censored data is supplemented specifically includes: The energy meter installation time interval [T0, T1] is divided into k groups [T 0,j-1 ,T 0,j ], where j = 1, 2, ..., k; T 0,0 =T0,T 0,k =T1, then there are n j Electricity meter, n j The number of faults of the energy meter in [T0, T2] is recorded as r j ′, the number of failures during the period [T2, T3] is recorded as r j ″Taiwan, and r j The calendar time of installation of the energy meter is recorded as τ Z,j,1 ,τ Z,j,2 ,…,τ Z,j,i ,…,τ Z,j,rj″ , the fault calendar time is recorded as τ F,j,1 ,τ F,j,2 ,…,τ F,j,i ,…,τ F,j,rj″ , there is still s at T3 j The electric energy meter is working normally, so n=sum(n j ), r′=sum(r j ′), r″=sum(r j ″), s = sum(s j ), n j =r j ′+r j ″+s j ; Determine the installation time of the electric energy meter in the jth group: Take the jth group [T 0,j-1 ,T 0,j ] midpoint T M,j As the group n j The calendar time of installation of the electric energy meter; Determine the number of electric energy meters n in the jth group j for n j =n×r j ″ / r″; Determine the electric energy meter in the jth group in [T M,j ,T2] the number of faults within r j 'for r j ′=r j ″×(T2-T M,j ) / (T3-T2); The determining of the data related to the life of the electric energy meter according to the calendar time of the field fault data of the electric energy meter after the left-censored data is supplemented specifically includes: For the jth group in [T M,j ,T2] j ′ faulty energy meter, subtract the installation calendar time of the jth group from the calendar time T2 to obtain the fault truncation time t r′,j ; For the jth group r in [T2,T3] j ″ A faulty electric energy meter, subtract the installation calendar time from the fault calendar time to get the life data t r″,j,i =τ F,j,i -τ Z,j,i ; For the electric energy meters in the jth group that continue to operate normally after T3, the calendar time T3 minus the installation calendar time of the jth group is subtracted to obtain the successful test truncation time. s,j ; The constructing of a left-censored and right-truncated life assessment model for the electric energy meter according to the electric energy meter life related data specifically includes: According to the data related to the life of the energy meter and the likelihood function, a left-censored and right-truncated energy meter life assessment model is constructed: Where β is the shape parameter of Weibull distribution, η is the scale parameter of Weibull distribution, F(·) is the unreliability function, F(·) = 1-R(·), and R(·) is the reliability function.

2. The method for evaluating the service life of an electric energy meter according to claim 1, characterized in that: The determining the service life of the electric energy meter according to the parameter point estimation and the confidence limit specifically includes: Based on the parameter point estimate and the confidence limits, the formula Determine the service life of the energy meter; Among them, R SM =1-F YZ , R SM is the reliability of the energy meter, F YZ The largest proportion of faulty energy meters, is the lower confidence limit of the characteristic lifetime, is the shape parameter point estimate, t SM,L It is the service life of the energy meter.

3. A system for evaluating the service life of an electric energy meter, characterized in that: include: Electric energy meter field fault data collection module, used to collect electric energy meter field fault data; A left-censored data filling module, used for adding the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after filling the left-censored data; A module for determining data related to the life of an electric energy meter, for determining data related to the life of an electric energy meter according to the calendar time of the electric energy meter field fault data after the left-censored data is padded, wherein the data related to the life of the electric energy meter includes a fault truncation time, life data, and a successful truncation time; A left-censored and right-truncated electric energy meter life assessment model construction module is used to construct a left-censored and right-truncated electric energy meter life assessment model based on the electric energy meter life related data; A parameter point estimation and confidence limit determination module, used to determine the parameter point estimation and confidence limit of the left-censored and right-truncated electric energy meter life assessment model according to the maximum likelihood estimation and the Fisher information matrix; An electric energy meter service life determination module, used to determine the service life of the electric energy meter based on the parameter point estimation and the confidence limit; The electric energy meter on-site fault data includes the number of single-phase smart electric energy meters installed on-site during [T0, T1], the number of electric energy meter failures r′ during [T0, T2], the number of electric energy meter failures r″ during [T2, T3], and the installation calendar time τ of the number of electric energy meter failures r″ during [T2, T3]. Z,1 ,τ Z,2 ,…,τ Z,i ,…,τ Z,r″ and fault calendar time τ F,1 ,τ F,2 ,…,τ F,i ,…,τ F,r″ ; The adding of the left-censored data to the electric energy meter field fault data to obtain the electric energy meter field fault data after the left-censored data is supplemented specifically includes: The energy meter installation time interval [T0, T1] is divided into k groups [T 0,j-1 ,T 0,j ], where j = 1, 2, ..., k; T 0,0 =T0,T 0,k =T1, then there are n j Electricity meter, n j The number of faults of the energy meter in [T0, T2] is recorded as r j ′, the number of failures during the period [T2, T3] is recorded as r j ″Taiwan, and r j The calendar time of installation of the energy meter is recorded as τ Z,j,1 ,τ Z,j,2 ,…,τ Z,j,i ,…,τ Z,j,rj″ , the fault calendar time is recorded as τ F,j,1 ,τ F,j,2 ,…,τ F,j,i ,…,τ F,j,rj″ , there is still s at T3 j The electric energy meter is working normally, so n=sum(n j ), r′=sum(r j ′), r″=sum(r j ″), s = sum(s j ), n j =r j ′+r j ″+s j ; Determine the installation time of the electric energy meter in the jth group: Take the jth group [T 0,j-1 ,T 0,j ] midpoint T M,j As the group n j The calendar time when the electric energy meter was installed; Determine the number of electric energy meters n in the jth group j for n j =n×r j ″ / r″; Determine the electric energy meter in the jth group in [T M,j ,T2] the number of faults within r j 'for r j ′=r j ″×(T2-T M,j ) / (T3-T2); The determining of the data related to the life of the electric energy meter according to the calendar time of the field fault data of the electric energy meter after the left-censored data is supplemented specifically includes: For the jth group in [T M,j ,T2] j ′ faulty energy meter, subtract the installation calendar time of the jth group from the calendar time T2 to obtain the fault truncation time t r′,j ; For the jth group r in [T2,T3] j ″ A faulty electric energy meter, subtract the installation calendar time from the fault calendar time to get the life data t r″,j,i =τ F,j,i -τ Z,j,i ; For the electric energy meters in the jth group that continue to operate normally after T3, the calendar time T3 minus the installation calendar time of the jth group is subtracted to obtain the successful test truncation time. s,j ; The constructing of a left-censored and right-truncated life assessment model for the electric energy meter according to the electric energy meter life related data specifically includes: According to the data related to the life of the energy meter and the likelihood function, a left-censored and right-truncated energy meter life assessment model is constructed: Where β is the shape parameter of Weibull distribution, η is the scale parameter of Weibull distribution, F(·) is the unreliability function, F(·) = 1-R(·), and R(·) is the reliability function.

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