CN110851792A - Staged and layered sampling method for operating intelligent electric energy meter - Google Patents
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Abstract
The invention relates to a staged and layered sampling method for operating an intelligent electric energy meter, which is characterized by acquiring the total amount of the intelligent electric energy meter to be sampled, and sampling the intelligent electric energy meter to be sampled in stages according to a time sequence, wherein the staged sampling comprises a first-stage sampling and a second-stage sampling which are sequentially performed, the first-stage sampling and the second-stage sampling are performed on the intelligent electric energy meter in a layered mode, and the second-stage sampling is performed according to the result of the first-stage sampling. Compared with the prior art, the intelligent electric energy meter real state detection method has the advantages of accurately reflecting the real state of the running intelligent electric energy meter, having less sample amount, saving time and economic cost and having high reliability of sampling results.
Description
Technical Field
The invention relates to the field of intelligent electric energy meters, in particular to a staged and layered sampling method for operating an intelligent electric energy meter.
Background
With the factors of comprehensive construction of the intelligent power grid, transformation and upgrading of the rural power grid and the like, the intelligent electric energy meter is rapidly developed in the market, and the full coverage of the intelligent electric energy meter is basically realized. The application of the metering equipment has the characteristics of large quantity, multiple types, intellectualization and complex operating environment. At present, the number of state network-hung intelligent electric energy meters is nearly 2.2 million, the number of south network-hung intelligent electric energy meters is nearly 5000 ten thousand, and the market of the two large power grid total intelligent electric energy meters and the electricity utilization management system is about 160 million yuan per year in the future. Whether the running state of the metering equipment with huge number is stable and reliable directly relates to the vital interests of common people and the harmony and stability of the society. The trend of strengthening the health state monitoring, life cycle management and operation intensive management of the intelligent electric energy meter is inevitable. With the proposal of the state exchange strategy of the electric energy meter, the operation management mode of the electric energy meter is changed into a fixed period rotation mode, and scientific evaluation decision is carried out according to the operation quality level, so that sufficient technical means are required to analyze the reliability level of the electric energy meter.
The intelligent electric energy meter has the same service life as other products, and if the intelligent electric energy meter is used for a long time, the metering stability of the intelligent electric energy meter is possibly changed, so that the metering performance requirement during the first detection cannot be met. In order to ensure the metering accuracy, according to the verification regulation of the electric energy meter, the operation management mode of the electric energy meter is an expiration rotation system. However, once the electric energy meter has a fault in the grid operation, no active and effective means for monitoring the quality measures of the electric energy meter in the grid operation except for the complaints of residents exists. On the other hand, when the rotation period is reached, the electric energy meter needs to be replaced no matter what the actual metering performance of the electric energy meter in the house of the user is, and then the electric energy meter enters a scrapping process. However, with the improvement of the technical level of the electric energy meter and the improvement of the monitoring level of the operation level of the electric energy meter, the defects of waste of manpower and material resources, inconvenience for energy conservation and environmental protection and the like are more and more obvious in the 'one-cutting' method. Therefore, the operation quality level of the electric energy meter running on the network is required to be mastered, and the most accurate mode is full quality verification. However, with the popularization of one meter per household and the full coverage of the intelligent electric energy meters, the number of the intelligent electric energy meters in operation is huge, and a full verification method is not advisable.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a staged and layered sampling method for operating an intelligent electric energy meter, which can accurately reflect the real state of the intelligent electric energy meter, is time-saving and economical and has high sampling result reliability.
The purpose of the invention can be realized by the following technical scheme:
the method comprises the steps of obtaining the total amount of the intelligent electric energy meter to be sampled, and sampling the intelligent electric energy meter to be sampled in stages according to a time sequence, wherein the stage-by-stage sampling comprises a first stage sampling and a second stage sampling which are sequentially carried out, the first stage sampling and the second stage sampling both carry out stage-by-stage sampling on the intelligent electric energy meter, and the second stage sampling is carried out according to the result of the first stage sampling.
In order to sample the running intelligent electric energy meter according to the sampling principle of 'economy, reasonability, simplicity, feasibility and point-surface combination', a layered sampling method is adopted. The limited sampling samples can truly reflect the operation conditions of the intelligent electric energy meter under different manufacturers and different operation environment conditions. The layered sampling can fully ensure the consistency of the sample structure and the total body, namely, all units in the total body are merged into a plurality of sets which are not crossed and repeated, namely layers, and then the samples are extracted by taking the layers as the units.
In order to judge the quality difference between the electric energy meters produced by different manufacturers and combine the characteristics of the electric energy meters, the method is suitable for selecting a sampling mode combining stage sampling and layered sampling, and mainly considers the factors as follows:
1) the operation of the electric energy meter is a continuous process, the result of one sampling cannot reflect the whole appearance, and the sampling frequency is too high, so that the cost is increased. And setting the full life cycle of the electric energy meter to be 5 years by combining the characteristics of the bathtub curve, and respectively sampling the electric energy meter in the second year and the fourth year of operation, wherein the second year is the early expiration date in the bathtub curve, and the fourth year is the accidental expiration date.
2) The running electric energy meters have the characteristics of large quantity and wide distribution, and the detection of the electric energy meters is destructive and only can adopt a sampling detection mode.
3) The electric energy meters are usually produced in whole batches, and are less influenced by human and objective factors in the production and installation processes, so that the quality conditions of the products in the same batch are always consistent, and samples randomly drawn from the batches can represent the actual quality level of the products in the batch to a certain extent.
4) Considering the difference between the electric energy meters produced by different manufacturers, the quality conditions of the electric energy meters produced by the same manufacturer tend to be consistent, so that the manufacturers have operability as a layering mode.
Further, the hierarchical sampling specifically includes performing hierarchical sampling on the running intelligent electric energy meter based on manufacturers, models, specifications and/or purchasing years.
Further, in the first-stage sampling and the second-stage sampling, the sample size is obtained according to a pre-established electric energy meter sample size calculation model, and the hierarchical sampling is carried out on the intelligent electric energy meter to be sampled based on the sample size to obtain a sampling result.
Further, the electric energy meter sample size calculation model specifically is as follows:
when P is present<When the voltage is equal to the voltage of LQ,
or
When P > LQ, N ═ N,
in the formula, LQ is limit quality which indicates that when the reject ratio of a certain batch of products reaches a certain value, the batch of products are rejected by a sampling scheme with high probability, P is an estimated value of the reject ratio of the intelligent electric energy meter, N is total amount, t is reliability and is α quantiles on two sides of standard normal distribution, d is an absolute error limit, r is a relative error limit, N is sample amount, t is determined by a relevant standard, the absolute error limit d and the relative error limit r are coefficients to be determined in advance, P can be obtained by estimating according to small-scale pre-sampling and estimating two routes by using a previous test result, and LQ can be obtained after analyzing the relevant cost of detection of the intelligent electric energy meter, including the error loss cost of the electric meter, the meter replacement cost and the detection cost.
Furthermore, in the first-stage sampling, obtaining the qualified level and the error level of each layer of intelligent electric energy meter based on the sampling result, and obtaining the comprehensive score. The qualification level of the electric energy meter reflects the qualification rate of the running electric energy meter, and is divided into a pressure test, a creeping test, a word moving test, a starting test, other tests and visual inspection. And carrying out weight assignment on each test qualification rate of the product according to past experience. The error is an important parameter comprehensively reflecting the quality characteristics of the electric energy meter, and the quality level of the electric energy meter produced by each manufacturer can be judged through comparison.
Further, the calculation expression of the qualification level is as follows:
Z＝qwithstand voltage×f1+qDiving motion×f2+qStarting up×f3+qWalk word×f4+qDirect viewing×f5+qOthers×f6
Wherein Z is the qualification level, qWithstand voltageFor the qualification rate of the withstand voltage test, f1As a weight of withstand voltage test, qDiving motionFor creep test pass rate, f2As shunt test weights, qStarting upTo start the test pass rate, f3To initiate trial weights, qWalk wordFor pass rate of the test of writing, f4For test weights of wording, qDirect viewingFor visual test of pass rate, f5To test the weights intuitively, qOthersFor other test yields, f6Other trial weights.
Further, the error level is obtained based on error values of the intelligent electric energy meter at a plurality of detection points, and a calculation expression of the error level is as follows:
CV＝σ/χ
in the formula, CV is an error level, sigma is a standard deviation of errors of all detection points of the intelligent electric energy meter, and χ is an average value of the errors of all the detection points of the intelligent electric energy meter.
Further, in the second stage sampling, a raman distribution method is adopted for hierarchical sampling, and the expression of the raman distribution method is as follows:
in the formula, nhThe sampling number of the h layer of the intelligent electric energy meter is N, N is the sample size, NhIs the total number of h layers of the intelligent electric energy meter, ShAnd h is the comprehensive score of the first stage sampling of the h layer of the intelligent electric energy meter, wherein h is 1, 2, 3 … …, k.
Furthermore, in the first-stage sampling, hierarchical sampling is performed by adopting an equal proportion distribution method, and in the second-stage sampling, hierarchical sampling is performed by adopting an unequal proportion distribution method. In the first stage, the sample size distribution is mainly based on equal proportion distribution, the sample capacity selected from each layer is in direct proportion to the total size of the layer, and when the difference between the layers is great and the homogeneity exists in the layers, the sample variance is reduced; the second stage performs unequal proportion distribution according to the variance size, and can improve the precision or reduce the inspection cost by increasing the sampling ratio in the layer with higher variance or lower cost.
Furthermore, the first-stage sampling is carried out in the second year of the operation of the intelligent electric energy meter, and the second-stage sampling is carried out in the fourth year of the operation of the intelligent electric energy meter. The operation of the electric energy meter is a continuous process, the result of one sampling cannot reflect the whole appearance, and the cost is increased when the sampling frequency is too high. And setting the full life cycle of the electric energy meter to be 5 years by combining the characteristics of the bathtub curve, and respectively sampling the electric energy meter in the second year and the fourth year of operation, wherein the second year is the early expiration date in the bathtub curve, and the fourth year is the accidental expiration date.
Compared with the prior art, the invention has the following advantages:
(1) the invention operates the staged and layered sampling method of the intelligent electric energy meter, the quality of the intelligent electric energy meter is controlled in two stages, in the second stage sampling, the adaptive sampling adjustment is carried out according to the result of the first stage sampling, and the reliability of the random sampling result is ensured; sampling detection is carried out on the intelligent electric energy meter in a layered mode in each stage of sampling, the quality conditions of products on the same layer are always consistent, time and economy are saved, and the reliability of random sampling results is guaranteed.
(2) The invention relates to a staged and layered sampling method for operating an intelligent electric energy meter, wherein in the first-stage sampling and the second-stage sampling, the operating intelligent electric energy meter is subjected to layered sampling based on manufacturers, models, specifications and/or purchasing years, so that the quality conditions of each layer of intelligent electric energy meter tend to be consistent, for example, the quality conditions of the electric energy meters produced by various manufacturers tend to be consistent due to the difference among the electric energy meters produced by various manufacturers, and the quality conditions of the electric energy meters produced by the same manufacturer tend to be consistent, therefore, the manufacturers are layered, and the quality conditions of the intelligent electric energy meters tend to be consistent.
(3) In the second stage of sampling, the hierarchical sampling is carried out based on the comprehensive scores of the first stage of sampling to each layer of intelligent electric energy meters by adopting the internal man distribution method, the difference between each layer of intelligent electric energy meters and the reject ratio of the intelligent electric energy meters after running for several years are fully considered, the adaptive sampling adjustment is carried out, and the reliability of the sampling result is ensured on the basis of saving time and economic cost.
(4) The invention relates to a staged and layered sampling method for operating an intelligent electric energy meter, which adopts random sampling in two-stage sampling, wherein the electric energy meter is usually produced in whole batch and is less influenced by human and objective factors in the production and installation processes, so the quality conditions of the same batch of products are always consistent, and the samples randomly extracted from the electric energy meter can represent the actual quality level of the batch of products to a certain extent.
(5) The invention relates to a staged and layered sampling method for operating an intelligent electric energy meter, which considers that the operation of the electric energy meter is a continuous process, the result of one-time sampling cannot reflect the whole appearance, and the sampling frequency is too high to increase the cost, so that the whole life cycle of the electric energy meter is set to 5 years by combining the characteristics of a bathtub curve, the sampling is needed to be carried out in the second year and the fourth year of the operation of the electric energy meter, namely the second year is the early expiration date in the bathtub curve, and the fourth year is the accidental expiration date.
Drawings
Fig. 1 is a flow chart of a staged and hierarchical sampling method for operating an intelligent electric energy meter according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
The embodiment relates to a staged and layered sampling method for operating an intelligent electric energy meter, which specifically comprises the steps of obtaining the total amount of the intelligent electric energy meter to be sampled, and carrying out staged sampling on the intelligent electric energy meter to be sampled according to a time sequence, wherein the staged sampling comprises a first-stage sampling and a second-stage sampling which are sequentially carried out, the first-stage sampling and the second-stage sampling are carried out on the intelligent electric energy meter in a layered mode, and the second-stage sampling is carried out according to the result of the first-stage sampling.
The hierarchical sampling specifically includes hierarchical sampling of the running intelligent electric energy meter based on the manufacturer, the model, the specification and/or the purchasing year, and in this embodiment, hierarchical sampling is performed based on the manufacturer. In the first-stage sampling and the second-stage sampling, the sample size is obtained according to a pre-established electric energy meter sample size calculation model, and the intelligent electric energy meter to be sampled is subjected to layered sampling based on the sample size to obtain a sampling result.
And performing first-stage sampling in the second year of operation of the intelligent electric energy meter, and performing second-stage sampling in the fourth year of operation of the intelligent electric energy meter. In the first stage of sampling, an equal proportion distribution method is adopted for layered sampling, and based on the sampling result, the qualification level and the error level of each layer of intelligent electric energy meter are obtained, and the comprehensive score is obtained; in the second stage of sampling, hierarchical sampling is carried out by adopting an unequal proportion distribution method, and hierarchical sampling is carried out by adopting a Raman distribution method.
The following is a detailed description:
1. construction of models
By referring to American national standard ANSIC12.1-1995, a power meter sample quantity calculation model is obtained according to two conditions of the intelligent power meter failure rate P:
when P < ═ LQ, there are:
(considering absolute error limits)
Or
(considering relative error limits)
When P > LQ, consider to carry out the full inspection acceptance to the electric energy meter.
Wherein LQ is the limit quality, which means that when the reject ratio of a certain batch of products reaches a certain value, the batch of products are rejected with high probability by a sampling scheme, P represents the estimated value of the reject ratio of the intelligent electric energy meter, N represents the total amount, t represents the reliability, which is the double side α quantiles of standard normal distribution, d and r represent the precision, which are represented by absolute error limit and relative error limit, and N represents the sample size.
2. Solving of models
In the sample quantity calculation model, except that the overall N is easy to know and the relevant standard of the critical value t is specified, the overall fraction defective P, the limit quality LQ, the absolute error limit d and the relative error limit r are all waiting coefficients and need to be determined in advance.
(1) Determination of the overall reject ratio P
Since the true value of P is not available before the sampling test is performed, the estimated value of P can be obtained by two ways, namely, estimation according to small-scale pre-sampling and estimation by using the previous test result. U.S. standard ANSIC12.1-1995 uses the cumulative mean failure rate y% calculated from the last (or cumulative) spot test results as an estimate of P.
(2) Determination of the limiting mass LQ
The relevant cost detected by the intelligent electric energy meter, including the error loss cost, the meter replacement cost and the detection cost of the intelligent electric energy meter, can be obtained after analysis, and 6% is taken as the value of LQ.
(3) Determination of the absolute error limit d and the relative error limit r
Since the absolute error and the relative error are two expressions of the sampling precision requirement, the sampling precision is related to the sampling cost on one hand, and is also closely related to the cost of benefit loss caused by the sampling error on the other hand.
3. Distribution of layers at stages of sample size
(1) Distribution of sample size in layers of the first stage
For the combination of the layered sampling and the staged sampling, the whole life cycle of the electric energy meter is divided into two stages, and then the layered sampling is adopted for each stage. For the hierarchical sampling, when the total sample size is constant, the problem of how much sample size should be allocated to each layer needs to be studied, because the variance of the estimation quantity is related to not only the variance of each layer but also the sample size allocated to each layer when the overall estimation is carried out.
In the first stage, the sample size distribution is mainly based on equal proportion distribution, the sample capacity selected from each layer is in direct proportion to the total size of the layer, and when the difference between the layers is great and the homogeneity exists in the layers, the sample variance is reduced; the second stage performs unequal proportion distribution according to the variance size, and can improve the precision or reduce the inspection cost by increasing the sampling ratio in the layer with higher variance or lower cost.
The equal proportion distribution method refers to the sample capacity n extracted from each layer when all units of the sample are distributed in each layerkAccounts for all the units NhIs equal, equivalent to the proportion of the sample volume N to the total volume N, i.e.:
The proportion of the sample size of each layer to the total sample size is as follows:
for stratified sampling, this time the overall mean
The unbiased estimate of (c) is:
(2) distribution of sample size in the second stage layers
And the sample size is distributed in each layer in the second stage in an unequal proportion mode mainly according to the variance. Common unequal proportion distribution modes include optimal distribution and Raman distribution.
a. Optimal allocation
In the hierarchical random sampling, how to distribute the sample size to each layer enables the variance of the estimated quantity to be minimum under the condition of the total cost to be given, or the total cost to be minimum under the condition of the given estimated quantity variance, and the sample size distribution which can meet the sample size is the optimal distribution.
The linear function of the sampling cost is:
wherein c is the total cost, c0For a base cost, chThe cost per unit sample in the h-th layer.
The optimal allocation mode is as follows:
b. neumann distribution
During the electric energy meter sampling inspection process, the inspection cost of the electric energy meters in different layers is basically the same, namely chC, the optimal allocation is simplified as:
wherein N is the total amount of samples, NhIs the total number of h-th layers, ShIs the composite score of the first spot inspection of the h-th layer, nhIs the number of samples of the h-th layer.
4. Indoor inspection and data analysis
(1) The qualification level of the electric energy meter reflects the qualification rate of the running electric energy meter, and is divided into a pressure test, a diving test, a character moving test, a starting test, other tests and visual inspection. The qualification rates of the products in the prior tests are weighted and assigned according to the past experience, as shown in Table 1
Table 1 schematic of the experimental weight assignments
And then calculating to obtain the qualified level Z of each manufacturer by a weighting method, namely
Z＝qWithstand voltage×f1+qDiving motion×f2+qStarting up×f3+qWalk word×f4+qDirect viewing×f5+qOthers×f6
Wherein Z is the qualification level, qWithstand voltageFor the qualification rate of the withstand voltage test, f1As a weight of withstand voltage test, qDiving motionFor the shunt test qualification rate, f2As shunt test weights, qStarting upTo start the test pass rate, f3To initiate trial weights, qWalk wordFor pass rate of the test of writing, f4For test weights of wording, qDirect viewingFor visual test of pass rate, f5For visual testWeight, qOthersFor other test yields, f6Other trial weights. Other tests include component testing, parameter setting testing, communication response delay testing, and the like.
The closer Z is to 1, the higher the qualification level of the manufacturer. By comparing the sizes of the manufacturers Z, the high or low degree of the cold can be evaluated.
(2) The error is an important parameter comprehensively reflecting the quality characteristics of the electric energy meter, and the quality level of the electric energy meter produced by each factory can be judged through comparison. Considering the inconsistency of the error conditions of the electric energy meters under different powers, respectively selects
Lower sum of
The total 8 detection points (the detection points can be increased or decreased according to actual conditions) are used for analyzing errors among household electrical energy meters of different factories and household electrical appliances. The detection points are shown in Table 2.
TABLE 2 error detection points
The standard deviation coefficient is a statistic for measuring the variation degree of each observed value. Due to the different levels between different manufacturers, it is appropriate to use the standard deviation coefficients for comparison. The coefficient of standard deviation is the ratio of the standard deviation to the mean, i.e.
CV＝σ/χ
In the formula, CV is an error level, sigma is a standard deviation of errors of all detection points of the intelligent electric energy meter, and χ is an average value of the errors of all the detection points of the intelligent electric energy meter.
The method comprises the steps of firstly calculating the average value of the error values of the electric energy meters produced by each manufacturer under each detection point, then giving different weights to each detection point according to actual requirements, calculating the weighted average value of the error values of the electric energy meters of each manufacturer and the standard deviation of the error values, and finally calculating the standard deviation coefficient of the error values of the electric energy meters of each manufacturer. The larger the standard deviation coefficient is, the larger the quality fluctuation of the electric energy meter of the manufacturer is, and the worse the quality stability is.
As shown in FIG. 1, the sampling verification scheme for operating an electric energy meter is as follows:
(1) determining a period of inspection;
(2) determining a sampling method and the total amount according to a related sampling statistical theory and by combining limiting conditions such as precision, cost and the like;
(3) layering the electric energy meter to be sampled and inspected according to manufacturer, model, specification, purchasing year and the like;
(4) determining the quantity of electric energy meters to be sampled and inspected for each group of sampling population, namely determining the distribution of the sample quantity in each layer;
(5) sampling in stages, namely if the whole life cycle of the electric energy meter is 5 years, the first inspection is carried out in the 1 st year; in the 2 nd year, sampling in equal proportion according to the proportion of the electric energy meter unit; in the 3 rd year, the electric energy meter operates; in the 4 th year, unequal proportion sampling is carried out by using the result of sampling in the second year;
(6) sampling each group of intelligent electric energy meters according to the determined sample amount, and then carrying out performance detection on each sampled electric energy meter according to a verification standard;
(7) and deducing the population according to the detection result of the sample, and judging whether the population is qualified or not.
5. Detailed description of the preferred embodiments
5.1 Total sample size
The sampling detection method proposed by the research is verified by taking a three-phase electric energy meter installed in 2010 of a certain city as an example. The distribution of the electric energy meters of each manufacturer is shown in table 3
TABLE 3 installation situation of three-phase electric energy meter of 2010 manufacturer in a certain city
5.2, confirmation of sample size in first sampling test of electric energy meter
According to the estimation of the detection cost and the loss cost of the intelligent electric energy meter and by referring to the sampling inspection method in appendix A of national standard JB/T5007-2002 'reliability requirement and assessment method for intelligent electric energy meters', the limit quality LQ is determined to be 6%. According to past experience, the reject ratio of a three-phase electronic electric energy meter in a certain market is between 0.1% and 0.3%, so that the sampling average damage rate P set by the example is 0.3%, and the operating electric energy meter in the area is determined to be detected in a sampling inspection mode because P < LQ.
Taking the confidence level 1-a as 0.95, namely t as 1.96; the accuracy requirement controls the absolute error d to be 0.004: calculating the total number of samples to be extracted by using a sample size model:
the sampling ratio in each layer is:
therefore, the distribution of the sample amount extracted from each manufacturer is shown in Table 4
TABLE 4 distribution of the sample size first extracted by each manufacturer
The qualification rates of the various indoor tests are shown in table 5.
TABLE 5 qualified horizontal distribution of the manufacturers
All the test items were 0.15 except for the visual inspection weighted 0.25, Z for the acceptable level aloneA＝0.9990，ZB0.9991, manufacturer B is superior to manufacturer a.
Table 6 mean value of electric energy meter of each manufacturer at each detection point
Calculated from table 6: the standard deviation of manufacturer A is 0.0739, and the standard deviation coefficient is 0.49205;
the manufacturer B standard deviation was 0.0846, and the coefficient of standard deviation was 0.74178.
And (4) giving scores to the electric energy meters of the two manufacturers by comprehensively considering the qualified level and the error level, wherein the specific standard refers to the table 7.
TABLE 7 Standard of percent of pass and error
| Item scoring | Qualification level | Level of error |
| 5 | (99.9％，100％] | (0，0.46) |
| 4 | (99.8％，99.9％] | (0.47，0.62) |
| 3 | (99.7％，99.8％] | (0.63，0.78) |
| 2 | (99.6％，99.7％] | (0.79，0.93) |
| 1 | (-∞，99.6％] | (0.93，+∞) |
The qualification level is weighted to 0.4 and the error level is weighted to 0.6 according to experience, so the comprehensive scores of the two manufacturers are as follows:
SA＝4×0.4+4×0.6＝4.0
SB＝5×0.4+3×0.6＝3.8
5.3, determination of sample size of sampling test of secondary electric energy meter
And performing second sampling inspection in the fourth year of the operation of the electric energy meter, and determining the sample amount of each layer in a Neumann distribution mode. The total amount N from the three-phase electric energy meter is 65000. Determining a sample average failure rate of
And (3) determining the limit quality LQ to be 6% by referring to a sampling inspection method of national standard JB/T50070-2002 appendix A. And determining that P is less than LQ according to the result of the first sampling, and detecting the operating electric energy meter of the region in a sampling inspection mode.
Taking the confidence level 1-a as 0.95, namely t as 1.96; the precision requires that the absolute error d is controlled to be 0.004; calculating the total number of samples to be extracted by using a sample size model:
according to the formula of endoman assignment:
the distribution of sample size between layers is shown in table 8.
TABLE 8 sample size distribution from the second run of each manufacturer
After sampling at each stage, detecting the extracted intelligent electric energy meter, and if the detected unqualified number d is less than or equal to the receiving number Ac, judging that the batch is qualified; if the number d of the unqualified products at the detection position is larger than or equal to the rejection number Re, the batch is judged to be unqualified, and the acceptance number Ac and the rejection number Re are preset.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. The method is characterized in that the method specifically comprises the steps of obtaining the total amount of the intelligent electric energy meter to be sampled, and sampling the intelligent electric energy meter to be sampled in stages according to a time sequence, wherein the stage-by-stage sampling comprises a first-stage sampling and a second-stage sampling which are sequentially performed, the first-stage sampling and the second-stage sampling both perform stage-by-stage sampling on the intelligent electric energy meter, and the second-stage sampling is performed according to the result of the first-stage sampling.
2. The staged and stratified sampling method for operating an intelligent electric energy meter according to claim 1, wherein the stratified sampling is to hierarchically sample the operating intelligent electric energy meter based on a manufacturer, a model, a specification and/or a purchasing year.
3. The staged and stratified sampling method for operating an intelligent electric energy meter according to claim 1, wherein in the first stage sampling and the second stage sampling, the sample size is obtained according to a pre-established electric energy meter sample size calculation model, and based on the sample size, the stratified sampling is performed on the intelligent electric energy meter to be sampled, so as to obtain the sampling result.
4. The staged and hierarchical sampling method for operating an intelligent electric energy meter according to claim 3, wherein the electric energy meter sample size calculation model is specifically:
when P is less than or equal to LQ,
or
When P > LQ, N ═ N
In the formula, LQ is limit quality, P is an estimated value of the failure rate of the intelligent electric energy meter, N is total amount, t is reliability and is the double side α quantile of standard normal distribution, d is absolute error limit, r is relative error limit, and N is sample size.
5. The staged and stratified sampling method for operating an intelligent electric energy meter as claimed in claim 1, wherein the first stage of sampling further comprises obtaining the qualification level and the error level of each intelligent electric energy meter layer based on the sampling result, and obtaining the comprehensive score.
6. The staged and stratified sampling method for operating an intelligent electric energy meter according to claim 5, wherein the calculation expression of the qualification level is as follows:
Z＝qwithstand voltage×f1+qDiving motion×f2+qStarting up×f3+qWalk word×f4+qDirect viewing×f5+qOthers×f6
Wherein Z is the qualification level, qWithstand voltageFor the qualification rate of the withstand voltage test, f1As a weight of withstand voltage test, qDiving motionFor creep test pass rate, f2As shunt test weights, qStarting upTo start the test pass rate, f3To initiate trial weights, qWalk wordFor pass rate of the test of writing, f4For test weights of wording, qDirect viewingFor visual test of pass rate, f5For visual testing of the weights, qOthersFor other test yields, f6Other trial weights.
7. The staged and stratified sampling method for operating an intelligent electric energy meter as claimed in claim 5, wherein the error level is obtained based on the error values of the intelligent electric energy meter at a plurality of detection points, and the calculation expression of the error level is:
CV＝σ/χ
in the formula, CV is an error level, sigma is a standard deviation of errors of all detection points of the intelligent electric energy meter, and χ is an average value of the errors of all the detection points of the intelligent electric energy meter.
8. The staged and stratified sampling method for operating an intelligent electric energy meter according to claim 5, wherein in the second-stage sampling, stratified sampling is performed by adopting a Raman distribution method, and the expression of the Raman distribution method is as follows:
in the formula, nhThe sampling number of the h layer of the intelligent electric energy meter is N, N is the sample size, NhIs the total number of h layers of the intelligent electric energy meter, ShAnd h is the comprehensive score of the first stage sampling of the h layer of the intelligent electric energy meter, wherein h is 1, 2, 3 … …, k.
9. The staged and stratified sampling method for operating an intelligent electric energy meter according to claim 1, wherein in the first stage sampling, stratified sampling is performed by using an equal proportion distribution method, and in the second stage sampling, stratified sampling is performed by using an unequal proportion distribution method.
10. The staged and stratified sampling method for operating an intelligent power meter as claimed in claim 1, wherein the first stage sampling is performed in the second year of operation of the intelligent power meter, and the second stage sampling is performed in the fourth year of operation of the intelligent power meter.
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