CN110796392A - Staged and layered sampling method for continuous batch intelligent electric energy meters - Google Patents
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Abstract
The invention relates to a staged and layered sampling method for continuous batch intelligent electric energy meters, which comprises the steps of obtaining the total amount of the intelligent electric energy meters to be sampled, and sampling the intelligent electric energy meters to be sampled in stages according to a time sequence, wherein the staged sampling comprises a first stage sampling, a second stage sampling and a third stage sampling which are sequentially performed, the first stage sampling is performed on the continuous batch intelligent electric energy meters in a sequential sampling inspection mode, the second stage sampling and the third stage sampling are performed on the continuous batch intelligent electric energy meters in a layered sampling mode, and the third stage sampling is performed on the basis of the results of the second stage sampling. Compared with the prior art, the method and the device are suitable for the characteristics of continuous batch intelligent electric energy meters, can accurately reflect the real state of the running intelligent electric energy meters, have small sample amount, and have the advantages of time saving, economic cost saving and 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 continuous batch intelligent electric energy meters.
Background
With the factors of comprehensive construction of the intelligent power grid, transformation and upgrading of the rural power grid and the like, the market development of the intelligent electric meter is vigorous, and the full coverage of the intelligent electric 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 the electric meters hung on the national grid is nearly 2.2 million, the number of the electric meters hung on the south grid is nearly 5000 ten thousand, and the market scale of the intelligent electric meter and the electricity utilization management system of the two power grids in the future is about 160 million yuan per year. 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 the 'one-time cutting' method in the aspects of causing waste of manpower and material resources, being not beneficial to energy conservation and environmental protection and the like are more and more obvious. 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.
The scheme is a sampling method based on probability statistics, and the working condition of the whole batch of tables is judged through proper sampling inspection, so that the discarding of a large amount of still usable weekly inspection instruments is avoided, and the inspection workload can be reduced.
The scheme of Q/GDW 206 plus 2008 'electric energy meter sampling technical specification' is convenient to retrieve, clear in judgment result and strong in operability, and because the scheme mainly aims to judge whether the product batch is qualified or not, the risk point of a user is not clear enough. GB/T2828.1-2008 adopts an adjustment type counting sampling acceptance method to perform quality acceptance, the standard is mainly used for continuous series of batches, but the method cannot adjust the size of a sample according to the size of the batch and cannot adjust a sampling scheme according to the change of a previous sampling result, so that the sampling detection result cannot accurately reflect the real state of the running intelligent electric energy meter.
In order to ensure the quality level of the operation of the intelligent electric energy meter, the sampling reliability at the acceptance stage of the intelligent electric energy meter needs to be ensured, and if only one sampling method is adopted to carry out sampling detection on the electric energy meter in the whole operation period of the electric energy meter, adjustment cannot be carried out according to the actual condition of the electric energy meter, the economy, the efficiency and the satisfaction of the actual requirement on the electric energy meter verification cannot be both considered, for example, when the electric energy meter is verified, the requirement on the sampling method is higher, and when the electric energy meter is operated, the requirement on the sampling method is changed from low to high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a staged and layered sampling method which is suitable for continuous batch electric energy meters, can accurately reflect the real state of the running intelligent electric energy meter, is time-saving and economical and has high reliability of sampling results.
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, a second stage sampling and a third stage sampling which are sequentially carried out, the first stage sampling is to carry out sequential sampling inspection on the intelligent electric energy meter to be continuously batched, the second stage sampling and the third stage sampling are both to carry out stage-by-stage sampling on the intelligent electric energy meter to be continuously batched, and the third stage sampling is based on the result of the second stage sampling.
Further, the sequential sampling acceptance of the continuous batch of intelligent electric energy meters is specifically implemented by taking a sequential sampling standard GB/T8051-2008 as a basis and combining a transfer rule of the GB/T2828.1 standard on the standard to perform sampling acceptance of the continuous batch of intelligent electric energy meters.
Further, the method is based on the sequentially sampled standard GB/T8051-2008, and optimizes the transfer rule of the GB/T2828.1 standard on the standard, and specifically, according to the sequentially sampled standard GB/T8051-2008, continuous batches of intelligent electric energy meters are sampled in batches, and based on the sampling result of the batch in each batch, the next batch is subjected to relaxation, tightening or normal sampling through the transfer rule of the GB/T2828.1 standard.
Further, relaxation, tightening or normal sampling is performed by adjusting the production square risk index in the standard GB/T8051-2008 of sequential sampling. The tightening sampling is strictest, and the acceptance rate is lowest when the risk quality of the producing party is the same, so that the risk of the using party is protected; the relaxed sampling is the loosest, and the acceptance rate is the highest when the risk quality of a user is the same, so that the production side is encouraged to improve the production quality.
Further, the hierarchical sampling specifically includes performing hierarchical sampling on the continuous batches of the intelligent electric energy meters based on manufacturers, models, specifications and/or purchasing years.
Further, in the second-stage sampling and the third-stage sampling, a sample amount is obtained according to a pre-established electric energy meter sampling sample amount calculation model, and the intelligent electric energy meter to be sampled is subjected to layered sampling based on the sample amount to obtain a sampling result.
Further, the electric energy meter sampling inspection sample size calculation model is obtained based on American national standard ANSIC 12.1-1995.
Furthermore, in the second-stage sampling, hierarchical sampling is performed by adopting an equal proportion distribution method, and in the third-stage sampling, hierarchical sampling is performed by adopting an unequal proportion distribution method. In the second 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 third 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, in the second-stage sampling, the method also comprises the step of 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.
Furthermore, in the third stage of sampling, a Raman distribution method is adopted for hierarchical sampling.
Compared with the prior art, the invention has the following advantages:
(1) the quality control method of the continuous batch intelligent electric energy meter controls the quality of the continuous batch intelligent electric energy meter in three stages according to the sampling principle of economy, reasonability, simplicity and feasibility and point-surface combination, the first stage carries out random sampling acceptance inspection on the continuous batch intelligent electric energy meter through sequential sampling, and the sampling inspection sample volume of each batch is adjusted along with the change of the sampling result; in the third stage sampling, adaptive sampling adjustment is carried out according to the result of the second stage sampling, and the reliability of the random sampling result is ensured; sampling detection is carried out on the intelligent electric energy meters in each stage according to batches or layers, the quality conditions of the same batch of products are always consistent, time and economy are saved, and the reliability of random sampling results is guaranteed.
(2) The quality control method of the continuous batch intelligent electric energy meter performs sequential sampling acceptance on the continuous batch intelligent electric energy meter in the first stage sampling, and has the following advantages: first, the tests for each batch are more stringent and normative in the records; secondly, the average sample size of the test is obviously reduced, so that the test time can be reduced, and the economic cost is reduced; thirdly, strict adjustment of the scheme not only strictly controls the quality, but also protects the benefit of a user when the quality is poor and encourages a producer when the quality is good. Compared with the common adjustment type sampling scheme, the method has the advantages that in the strict adjustment process, the receiving number and the rejection number are changed, and the sampling inspection sample size is adjusted along with the change of the production quality, so that the total average sample size is reduced, and the time and the economic cost are saved.
(4) In the second stage sampling and the third stage sampling, the continuous batch of intelligent electric energy meters are sampled in a layering way based on manufacturers, models, specifications and/or purchasing years, so that the quality conditions of each layer of intelligent electric energy meters tend to be consistent, for example, the quality conditions of the electric energy meters produced by various manufacturers tend to be consistent if the electric energy meters produced by the same manufacturer are different, and the quality conditions of the electric energy meters produced by the same manufacturer tend to be consistent, so that the manufacturers are layered, and the quality conditions of the intelligent electric energy meters tend to be consistent.
(5) In the third-stage sampling, the comprehensive score of each layer of intelligent electric energy meters is sampled on the basis of the second-stage sampling, the internal Manman distribution method is adopted for sampling in a layering manner, the difference among the intelligent electric energy meters on each layer 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.
(6) The quality control method of the continuous batch intelligent electric energy meter adopts random sampling in the sampling of three stages, 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.
(7) The quality control method of the continuous batch intelligent electric energy meter 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, so that the cost is increased, therefore, the full life cycle of the electric energy meter is set to be 5 years by combining the characteristic 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 failure period in the bathtub curve, and the fourth year is the occasional failure period.
Drawings
FIG. 1 is a schematic flow chart of a staged and stratified sampling method for continuous batch intelligent electric energy meters according to the present invention;
FIG. 2 is a schematic diagram of a first stage sampling method according to the present invention;
FIG. 3 is a schematic diagram of sampling characteristics for three adjustment schemes based on SPRT;
FIG. 4 is a flow chart of the second stage sampling and the third stage sampling of the staged and hierarchical sampling method of 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
As shown in fig. 1, the embodiment is a staged and layered sampling method for a continuous batch of intelligent electric energy meters, and the method specifically includes acquiring the total amount of the intelligent electric energy meters to be sampled, and performing staged sampling on the intelligent electric energy meters to be sampled according to a time sequence, where the staged sampling includes a first-stage sampling, a second-stage sampling and a third-stage sampling which are performed in sequence, the first-stage sampling is performed by performing sequential sampling acceptance on the continuous batch of intelligent electric energy meters, the second-stage sampling and the third-stage sampling both perform layered sampling on the continuous batch of intelligent electric energy meters, and the third-stage sampling performs sampling based on a result of the second-stage sampling.
The following is a detailed description of the sampling of each stage:
1. first stage sampling
The sequential sampling acceptance of the continuous batch of intelligent electric energy meters is specifically that the continuous batch of intelligent electric energy meters are sampled in batches according to a sequential sampling standard GB/T8051-2008, a production square risk index in the sequential sampling standard GB/T8051-2008 is adjusted through a transfer rule of the GB/T2828.1 standard in each batch based on a batch sampling result, the next batch is relaxed, tightened or normally sampled, and the continuous batch of intelligent electric energy meters are sampled and accepted.
1.1 basic principle and procedure of sequential sampling
SPRT (sequential Proavailability Ratio test), sequential Probability Ratio test. It is a branch of mathematical statistics, named from a similar work published in 1947 by abraham wald, and the subject of its study is the so-called "sequential sampling scheme", and how to use the samples obtained by this sampling scheme for statistical inference. The sequential sampling scheme is that a small amount of samples are firstly extracted without specifying the total sampling number (observation or experiment times) in sampling, and then the sampling is stopped or sampling and extraction are continued according to the result, and the sampling is stopped. On the contrary, a sampling scheme in which the number of samples is determined in advance is referred to as a fixed sampling scheme.
For studies on the aspect of the SPRT sampling theory, sequential sampling proposed by Wald was performed domestically according to ISO 8422: 2006 sets out corresponding domestic standard, namely GB/T805l-2008 count sequential sampling inspection scheme. In counting sequential sampling inspection, sample products are randomly drawn and inspected one by one, the number of rejects (or number of rejects) is inspected with the cumulative number, and after each unit product inspection, the cumulative number is compared to the acceptance criteria to evaluate whether there is sufficient information to make a decision on the inspection lot at this stage of inspection. At some stage of the inspection, if the cumulative number indicates that the risk of receiving a batch of unsatisfactory quality level is sufficiently low, the batch is received and the inspection is terminated. On the other hand, if the cumulative number indicates that the risk of rejecting a satisfactory quality level batch is sufficiently low, the batch is not received and the test is terminated. If the above decision cannot be made based on the accumulated number, another unit of product is continuously drawn for inspection until there is sufficient information to make a decision to accept or not accept the batch.
And (3) a checking step:
1) determining values for QCR and QPR
The GB/T805l-2008 count sequential sampling inspection scheme standard gives preference values for 28 QPR (Producer Risk Mass) in the range of 0.020 (%) to 0.10 (%) and preference values for 23 QCR (user Risk Mass) in the range of 0.200 (%) to 31.5 (%), respectively, and the project is only used under the limits of α ≦ 0.05, β ≦ 0.10 for selected QCR and QPR preference values.
2) The parameters hA (intercept of receiving line), hR (intercept of rejecting line), g (slope of receiving line and rejecting line for each stage of test, the receiving and non-receiving criteria of batch are determined by hA, hR and g. GB/T805l-2008 counting sequential sampling test scheme. Standard Table 1 and Table 2 give a set of parameters corresponding to QCR and QPR, production formula risk α less than or equal to 0.05 and usage formula risk β less than or equal to 0.10. Table 1 in GB/T805l-2008 counting sequential sampling test scheme is suitable for percent test of unqualified products, and Table 2 is suitable for test of unqualified products per hundred units.
3) Finding the truncation value
The truncated accumulated sample size nt and the truncated received number Act of the sequential sampling scheme are given in standard table 1 and table 2 of GB/T805l-2008 count sequential sampling test scheme along with the parameters hA, hR, and g.
4) Sampling
A sample product should be randomly drawn from the test batch and tested one by one in the order of the draws.
5) Counting
For the test of percent defective, if the sample product is defective, the count d is 1, otherwise the count d is 0.
For the test of the number of failures per hundred units of product, d is the number of failures found on the sample product. The cumulative number D is the sum of the counts D from the first to the last sample product (i.e., ncum) tested.
1.2 first stage sampling scheme design
1.2.1 basic idea of design
As shown in FIG. 2, the sequential sampling scheme is used as an adaptive normal scheme (N) based on SPRT, the receiving value A and the rejection value R are calculated according to the steps through determined α, β, QCR and QPR, then the receiving number, the rejection number Ac and the rejection number Re. are determined, sampled according to the steps, checked one by one and recorded in a numerical table after counting according to rules, meanwhile, in combination with the transfer rules of the adaptive sampling, when the production quality is poor, the risk of the producer needs to be increased, the risk index of the producer needs to be increased as compared with the risk index of the normal scheme lambda 1 and lambda 1>1, a tightening scheme (T) is obtained, when the table lookup can obtain the values of hR and hA, so that the rejection field is increased, the receiving field is decreased, and the benefit of the user is protected, and when the production quality is good, the risk of the producer needs to be decreased, the risk index of the production risk index needs to be increased as compared with lambda 2 of the normal scheme and lambda 2<1, a widening scheme (R), and the value of the hR and hA are increased, so that the receiving field is encouraged to be increased, and the production field is decreased.
1.2.2 first stage sampling scheme
1) Application scope
The scheme takes the production side risk quality and the use side risk quality as retrieval points, and is used when the production side risk α is less than or equal to 0.05 and the production side risk β is less than or equal to 0.10.
The scheme is applicable to the following objects: end products, parts, raw materials, work in process, stock products, and the like.
The scheme is suitable for counting inspection of the discrete individuals. The method is used for the condition that the defective rate (or defective percentage) or the number of unqualified products per unit (or the number of unqualified products per hundred units) is taken as the batch quality index.
The scheme is based on the assumption that the generation of unqualified products is random and statistically independent, only a certain product is considered to be a qualified product or an unqualified product, and the condition of multiple unqualified products is not considered.
2) Parameters in a sampling scheme
α production side Risk index
β user side Risk indices
Acceptable upper quality limit for AQL
L probability of reception
A received value
Ac acceptance number
Number of receptions at Act tail truncation
Ac0 corresponds to the received number of samples
d counting
Cumulative number of D
g cumulative number of received and rejected lines
Intercept of hA receiver line
Intercept of hR wire-receiving rejection line
n0 corresponds to the sample size of a sampling scheme
Cumulative sample size of ncum
R rejection value
Number of Re rejected
Re0 corresponds to the number of rejects of a sample
Number of rejections at Ret tail-cut
Note: ret ═ 1 (Act)
QPR production Fang Risk quality (expressed as percent reject or number of rejects per hundred units)
QCR use side Risk quality (expressed as percent reject or number of rejects per hundred units product)
3) Selection, preparation, and determination of statistical methods
The scheme adopts a numerical method in GB/T8051. Because of its accuracy, disputes between receipt and non-receipt can be avoided in marginal cases and can be used to record the verification result.
The main tool of the numerical method is the receiving table, and in the process of preparing the receiving table, for each ncum value, when the accumulated sample size is smaller than the truncated sample size, the receiving value a is given by formula 1:
A＝(g×ncum)-hA (1)
the acceptance number Ac is obtained by rounding down the acceptance value a. For each ncum value, the rejection value R is given by equation 8.2:
R＝(g×ncum)+hR (2)
the rejection number Re is obtained by rounding up the rejection value R.
If the value a obtained by equation (1) is negative, the cumulative sample size is too small to make a determination of acceptance of the test batch. In contrast, for the percent defective test, if the R value obtained by equation (8.2) is greater than the cumulative sample size, the cumulative sample size is too small to make a reject determination for the test batch. If the rejection number Re is greater than the truncated rejection number Ret, then the application should replace Re with Ret.
A and R calculated in formula (1) and formula (2) should be the same as the number of digits after the decimal point of g.
Even if each sample product is qualified until now, when the minimum accumulated sample amount is less than hA/g, the receiving judgment can not be made; even if each sample product has failed so far, and the minimum cumulative sample amount is equal to or less than hR/(1-g), no determination of non-acceptance can be made.
Finally, the necessary values are recorded, and the preparation of the reception table is completed.
The judgment rule of the scheme is as follows: after each sample product is tested, the values are recorded in a prepared acceptance table.
a) For the cumulative sample size (ncum), if the cumulative number D is less than or equal to the corresponding received number Ac, the batch is received and the test is terminated.
b) For the cumulative sample size (ncum), if the cumulative number D is greater than or equal to the corresponding rejection number Re, the batch is not received and the test is terminated.
c) If neither a) nor b) is satisfied, the next product is continuously extracted for inspection.
When the accumulated sample size reaches the truncated sample size nt, the truncated received count Act and the truncated rejected count Ret ═ t (Act +1) are used in rules a) and b), respectively.
4) SPRT-based Normal case (N)
And according to the steps of the sequential sampling scheme, determining that the risk indexes of a producer and a consumer of a test batch are α and β respectively, the risk quality of the producer and the risk quality of the consumer are QPR and QCR respectively, checking the tail-truncation accumulated sample amount to be nt according to table 1 in GB/T8051, checking the tail-truncation receiving number to be Act and parameters hA, hR and g, randomly extracting a sample product from the test batch and testing one by one.
Let the acceptance value of the ith sample product be recorded as Ai and the rejection value as Ri. According to the formula (8.1), a1 ═ g-hA, similarly a2 ═ 2g-hA … … Ai ═ g × ncum) -hA; from equation (8.2), R1 ═ g + hR, similarly R2 ═ 2g + hR, and Ri ═ g × ncum) + hR. Ac and Re were determined according to the rules.
The values in the table are observed and determined according to the decision rule set forth in the principle to determine whether to terminate the test.
When the numerical method is used, the reception and rejection values are calculated from ncum ═ 1 to nt-1, and then rounded up to obtain reception and rejection numbers (Ac and Re), respectively, and if the reception number is larger than the truncated reception number Act, Ac is replaced with Act, and if the rejection number is larger than the truncated rejection number Ret, Re is replaced with Ret, where Ret ═ (Act + 1).
5) Tightening scheme (T) based on SPRT
The design of the tightening scheme is to increase the risk of the production method on the basis of the normal scheme, namely increase α, so that α (T) is equal to λ 1 α (λ 1>1), but α (T) is required to be less than 0.05, and QCR (T) is necessarily reduced, the QPR is reduced by one step according to the table 1 in the GB/T805l-2008 counting sequential sampling scheme standard when the tightening is specified in the scheme, so that nt (T), Act (T), hA (T), hR (T) and g (T) which are searched according to the GB/T8051 table 1 have new values, and the new Ac (T) and Re (T) are obtained by calculation according to the formulas (8.1) and (8.2).
6) SPRT-based relaxation schemes (R)
The design of the relaxation scheme is the same as the principle of the tightening scheme, and on the basis of the normal scheme, the risk of a production party is reduced, namely α is reduced, so that α (R) ═ λ 2 α (λ 2<1) and QCR (T) becomes large, the QPR during the relaxation is increased by one step according to the table 1 in the standard GB/T805l-2008 counting sequential sampling inspection scheme, so that new nt (R), Act (R), hA (R), hR (R) and g (R) are found during the relaxation, and Ac (R) and Re (R) are calculated.
7) Transfer method
The transfer rules between the three schemes of normal, relaxed and tightened are mainly combined with GB/T2828.1. The function is as follows:
a protection of the user can be enhanced by shifting to a tightening check or suspending the sampling check once the quality is found to be deteriorated.
b once the quality reaches a certain good level, the quality can be transferred to the relaxation test after the consent of the responsible department, and the extraction of the sample volume is reduced, thus not only encouraging the producer, but also reducing the cost of the sampling test.
(1) Normal to tightened (N → T)
The sampling inspection is generally started from a normal inspection, under the normal inspection scheme, 2 inspection batches accumulated in 5 continuous batches or less than 5 continuous batches are rejected, and then the next batch is switched to an obturation scheme for sampling.
(2) Tighten to normal (T → N)
When a stringent test is used, if 5 consecutive tests are received, the next test resumes normal testing.
(3) Normal to relaxed (N → R)
When the normal protocol is adopted, it is possible to move to the relaxation check protocol, ① production stable, ② the current score of the transition is at least 30, ③ the department responsible for the department considers the relaxation check to be advisable, with respect to "score of transition", specified in GB/T2828.1:
the transfer score should be calculated at the beginning of the normal test unless otherwise specified by the responsible department. The rule of the scheme is the same as the sampling scheme, and the rule is as follows:
① when the number of receptions is equal to or greater than 2, if the batch is received after the AQL is tightened by one step, then a score of 3 is added to the transition score, otherwise the transition score is reset to 0.
② when the number of receptions is 0 or 1, if the batch is received, add 2 points to the transfer score, otherwise reset the transfer score to 0.
(4) Relax to normal (R → N)
While the relaxation check is being performed, the next lot of spot checks should revert to the normal check scheme if any of the following occurs with the initial check, ① the lot of relaxation checks rejected, ② production instability or stoppage, and ③ the quality department deems it necessary to revert to the normal check.
(5) Tighten to pause (T → S)
When a continuous series of batches under stringent inspection is used, the inspection should be temporarily stopped if the cumulative number of rejected batches reaches 5.
(6) Pause to tighten (S → T)
After sampling is suspended, the sampling inspection can not be resumed until the producer takes action to improve the quality of the product or service and receives approval from the governing body. When the sampling is resumed, the first batch should start with a stringent sampling protocol.
8) Application of transfer method
According to the design principle of GB/T2828.1-2003, l (aql) > 0.90 should be set when designing a normal solution, l (aql) > 0.81 should be set when designing a tightening solution, and l (aql) > 0.95 should be set when designing an relaxation solution, that is, theoretically, under the normal solution, the production risk α is 0.1 and, under the tightening solution, the production risk α (T) > 0.19, so in the tightening solution, λ 1 is α (T)/α is 1.9 and under the relaxation solution, the production risk α (R) ≦ 0.05 and, therefore, λ 2 is α (R)/α is 0.5.
In summary, both the tightening and relaxing schemes are derived from the normal scheme.
As shown in fig. 3, the three adjustment schemes based on SPRT are exactly the same as those described in design, the strictest scheme is adopted, and the acceptance rate is lowest when the risk quality of the production party is the same, so that the risk of the user is protected; the relaxation scheme is the loosest, and the acceptance rate is the highest when the risk quality of a user is the same, so that a producer is encouraged to improve the production quality.
1.3 application of sampling scheme
The batch size during the electric energy meter inspection is not equal at 950-.
The production side and the use side agree together that the QPR (i.e. AQL) is 1.0 (%) at the time of normal examination, the QCR is 8.0 (%) α -0.02, and β -0.1.
The protocol recorded the first 10 batches in a test run for the product, following the calculation and recording process, the test started with the normal protocol.
From the given parameters, table 1 in GB/T805l-2008 yields hA ═ 1.058, hR ═ 1.046, g ═ 0.0341, nt ═ 77, and Act ═ 2.
The following formula is calculated: a ═ g × ncum-hA ═ 0.0341 × ncum-1.058
R＝g×ncum+hR＝0.0341×ncum+1.046
According to the calculation mode of numerical method, the receiving and rejecting values in the sample are calculated from ncum-1 to nt-1-76 to obtain the following receiving table:
table 1.1 first lot inspection receiving table
From table 1.1, the first batch started with the normal protocol and was judged to be accepted, and the next batch continued to use the normal protocol. According to the transfer rule, the receiving number in the batch is 1, the batch is judged to be receiving, and 2 points are added to the transfer score, so that the current score is 2 points.
TABLE 1.2 second batch of test receipt tables
From table 1.2, the second batch was judged as rejected and the next batch continued to use the normal protocol. According to the transfer rule, the batch is judged to be rejected, the transfer score is reset to 0, so the current score is 0.
TABLE 1.3 third batch test receipt List
From Table 1.3, the third batch was judged to be rejected, two in less than 5 batches were rejected, and the next batch required the use of a tightening scheme. According to the transfer rule, the batch rejects, the transfer score is 0, so the current score is 0.
According to the rule established in the design of the scheme, when the QCR is reduced by one step during the tightening, we find that hA is 1.181, hR is 1.309, g is 0.0288, nt is 127, and Act is 3 according to table 1 in GB/T8051. The following formula is calculated:
A＝g×ncum-hA＝0.0288×ncum-1.181
R＝g×ncum+hR＝0.0288×ncum+1.309
according to the numerical method, the receiving and rejecting values in the current sample are calculated from ncum-1 to nt-1 (equal to 126). The following reception table was obtained:
TABLE 1.4 fourth batch of test acceptance sheet
From table 1.4, the fourth lot was accepted and the next lot continued to use the tightening scheme. According to the transition rule, the number of receptions in the batch is equal to 2, and after one stage of obtention, the table lookup yields hA-1.389, hR-1.591, g-0.0251, nt-189, and Act-4. The formula shows that Ac is 0 from ncum 55 to 95 and 1 from ncum 96 to 126. Re is 2 from ncum 2 to 126, so according to the above table, the batch is not received after being tightened one stage, the transfer score is 0, so the current score is 0.
TABLE 1.5 fifth batch test acceptance sheet
From table 1.5, the fifth batch was judged as received and the next batch continued to use the tightening scheme. The number of receptions in the batch is 1 and it is judged to be reception, so the score is added by 2 according to the transfer rule, so the current score is 2.
TABLE 1.6 sixth batch of test receipt tables
From table 1.6, the sixth batch was judged as received and the next batch continued to use the tightening protocol. The number of receptions in the batch is 1 and judged as reception, and the transfer score is increased by 2 points according to the transfer rule, so that the current score is 4 points.
TABLE 1.7 seventh batch test receipt List
From table 1.7, the seventh batch was accepted and the next batch continued to use the tightening scheme. The number of receptions in the batch is 2, and according to the calculation of each parameter after the first stage of obturation and the actual unqualified record of the batch, the batch can be judged not to be received after the first stage of obturation, and the transfer rule is reset to 0, so that the current score is 0.
TABLE 1.8 eighth batch test receipt form
From table 1.8, if the eighth batch is received, five consecutive batches are received, and the next batch can use the normal scheme according to the transfer rule. The number of receptions in the batch is 0 and judged to be reception, and the score of the transfer is added by 2 according to the transfer rule, so that the current score is 2.
TABLE 1.9 ninth batch test receipt form
From table 1.9, the ninth batch is receiving, the number of receiving in the batch is 1, and the transfer score is added by 2 according to the transfer rule, so that the current score is 4, and the next batch continues to use the normal scheme.
TABLE 1.10 tenth batch test receipt form
From table 1.10, the tenth batch is receiving, the number of receiving in the batch is 0, and 2 points are added to the transfer score according to the transfer rule, so that the current score is 6 points, and the next batch continues to use the normal scheme.
2. Second stage sampling and third stage sampling
And the second-stage sampling and the third-stage sampling are performed on the intelligent electric energy meter in a layered mode, and the third-stage sampling is performed according to the result of the second-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 second-stage sampling and the third-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 sampling in the second stage in the second year of the operation of the intelligent electric energy meter, and sampling in the third stage in the fourth year of the operation of the intelligent electric energy meter. In the second 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 third stage of sampling, hierarchical sampling is carried out by adopting an unequal proportion distribution method, and hierarchical sampling is carried out by adopting an internal Manchester distribution method.
The following is a detailed description:
2.1 construction of the model
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:
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.
2.2 solving of the model
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.
2.3 distribution of layers at stages of sample size
(1) Distribution of sample size in the second stage layers
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 second 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 third 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.:
or fh＝f(h＝1，2，3……，k)
The proportion of the sample size of each layer to the total sample size is as follows:
for stratified sampling, this time the overall meanThe unbiased estimate of (c) is:
(2) distribution of sample size in third stage of each layer
And the sample size is distributed in each layer in the third 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 sample size, NhIs the total number of h-th layers, ShIs the comprehensive score of the first sampling inspection of the h-th layer, nhIs the number of samples of the h-th layer.
2.4, 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 according to the past experience, and the weights are shown in the table 2.1
TABLE 2.1 Experimental weight assignment schematic
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 testing of the weights, 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 the weight of comprehensively reflecting the quality characteristics of the electric energy meterThe quality level of the electric energy meters produced by each factory can be judged by comparing the parameters. 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 2.2.
TABLE 2.2 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. 4, the sampling inspection scheme of the intelligent 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.
2.5 detailed description of the invention
2.5.1 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 2.3
TABLE 2.3 installation of three-phase electric energy meter for 2010 manufacturer in a certain city
2.5.2 second-time electric energy meter sampling inspection sample size confirmation
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 size from each manufacturer is shown in Table 2.4
TABLE 2.4 sample size distribution from the first run of each manufacturer
The qualification rate of each indoor test is shown in table 2.5.
TABLE 2.5 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 2.6 mean value of electric energy meter of each manufacturer at each detection point
Calculated from table 2.6: 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 is referred to as table 2.7.
TABLE 2.7 Standard of acceptable and error level division
| 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
2.5.3, determination of sample size of sampling test of third electric energy meter
And performing a third sampling inspection in the fourth year of the operation of the electric energy meter, and determining the sample amount of each layer in a Raman distribution mode. The total amount N from the three-phase electric energy meter is 65000. Determining the average sample damage rate according to the first sample inspection result as follows:
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 2.8.
TABLE 2.8 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 by specifically 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, a second stage sampling and a third stage sampling which are sequentially performed, the first stage sampling is sequential sampling acceptance of the intelligent electric energy meter to be continuously batched, the second stage sampling and the third stage sampling are both used for sampling the intelligent electric energy meter to be continuously batched in layers, and the third stage sampling is based on the result of the second stage sampling.
2. The staged and layered sampling method for continuous batch intelligent electric energy meters according to claim 1, wherein the sequential sampling acceptance of the continuous batch intelligent electric energy meters is specifically based on a sequential sampling standard GB/T8051-2008, and combines a transfer rule of the GB/T2828.1 standard on the standard to perform sampling acceptance on the continuous batch intelligent electric energy meters.
3. The staged and hierarchical sampling method for continuous batch intelligent electric energy meters according to claim 2, characterized in that the method is based on a sequential sampling standard GB/T8051-2008, and optimizes a transfer rule combining the GB/T2828.1 standard on the standard, specifically, the continuous batch intelligent electric energy meters are sampled in batches according to the sequential sampling standard GB/T8051-2008, and the next batch is widened, tightened or normally sampled in each batch according to the transfer rule of the GB/T2828.1 standard based on the result of sampling the batch.
4. The staged and stratified sampling method for continuous batch of intelligent electric energy meters as claimed in claim 3, wherein the relaxation, tightening or normal sampling is performed by adjusting the production square risk index in the standard GB/T8051-2008 for sequential sampling.
5. The staged and stratified sampling method for continuous batch of intelligent electric energy meters as claimed in claim 1, wherein the stratified sampling is carried out on the continuous batch of intelligent electric energy meters based on manufacturer, model, specification and/or purchasing year.
6. The staged and layered sampling method for continuous batch of intelligent electric energy meters as claimed in claim 1, wherein in the second stage sampling and the third stage sampling, the sample size is obtained according to a pre-established electric energy meter sampling detection sample size calculation model, and based on the sample size, the intelligent electric energy meter to be sampled is hierarchically sampled to obtain a sampling result.
7. The staged and stratified sampling method for continuous batch intelligent electric energy meters as claimed in claim 6, wherein the electric energy meter sampling inspection sample size calculation model is obtained based on American national Standard ANSIC 12.1-1995.
8. The staged and stratified sampling method for continuous batch intelligent electric energy meters as claimed in claim 1, wherein in the second stage sampling, stratified sampling is performed by using an equal proportion distribution method, and in the third stage sampling, stratified sampling is performed by using an unequal proportion distribution method.
9. The staged and stratified sampling method for continuous batch of intelligent electric energy meters as claimed in claim 1, wherein the second 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.
10. The staged and stratified sampling method for continuous batch intelligent electric energy meters as claimed in claim 9, wherein in the third stage of sampling, stratified sampling is performed by using a Raman distribution method.
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| CN113281695A (en) * | 2021-04-29 | 2021-08-20 | 国网福建省电力有限公司营销服务中心 | Automatic sampling system of automatic verification assembly line of electric energy meter |
| CN113281695B (en) * | 2021-04-29 | 2024-01-23 | 国网福建省电力有限公司营销服务中心 | Automatic sampling system of automatic verification assembly line of electric energy meter |
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