Verification Method and System for a Verification Device

Abstract

The present invention discloses a verification method and system for a verification device, which obtains the verification data of the verification device to be verified, verifies the verification data by using a preset error distribution function model of an electric energy meter, obtains a verification error result, compares the error result with a preset value. If the error result is greater than a first preset value and less than a second preset value, an alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, a pre-alarm process is started and a pre-alarm verification task is generated. The verification device to be verified is verified according to the alarm verification task or the pre-alarm verification task. By linking the abnormal result of the error distribution function model of the electric energy meter with the verification task trigger mechanism, this method can timely detect the abnormality of the verification system and improve the accuracy and efficiency of the verification result.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter verification, and particularly to a verification method and system for a verification device. Background Art

[0002] With the rapid growth of the business volume of electric energy meter verification, the verification work of electric energy meters has changed from manual verification to automatic assembly line verification. The verification device has the characteristics of highly concentrated distribution, a small number of verification personnel, and a greatly improved verification efficiency. In order to ensure that the standard performance of the electric energy meter verification device continuously meets the method requirements, it is particularly important to efficiently and high-qualityly complete the interim verification work of the verification device.

[0003] In the current automatic verification mode, the number of verification devices is huge, the operation efficiency of the verification system is high, and the daily verification quantity is in the tens of thousands. To ensure that the standard performance of each verification device continuously meets the method requirements during the adjacent two value traceability periods, it is necessary to carry out regular and irregular interim verifications according to the actual situation. By manually carrying out the interim verification work, the operation efficiency is low and the impact on daily verification production is large. It is difficult to ensure the frequency and timeliness of the verification. At the same time, the test process and data processing are greatly affected by human factors. If the standard performance of the verification system is abnormal, it is difficult to discover, accurately judge and handle in time. It is difficult to recall the inspected and installed equipment affected, and the impact is bad, which cannot meet the requirements of lean management of metrological verification production and online risk control.

[0004] Although the digital verification method based on the electric energy meter error distribution function model of the historical verification data of the same batch of intelligent electric energy meters can solve the problems such as the interruption of verification work, low verification efficiency, and human influence caused by manual wiring in traditional interim verification, on the one hand, the digital verification method requires a large amount of data accumulation and is easily affected by data quality, resulting in inaccurate verification results. Summary of the Invention

[0005] To solve the above technical problems, an embodiment of the present invention provides a verification method and system for a verification device. By linking the abnormal result of the electric energy meter error distribution function model with the verification task trigger mechanism to verify the verification device, it can timely discover the abnormality of the verification system and improve the accuracy and efficiency of the verification result.

[0006] The first aspect of the embodiment of the present invention provides a verification method for a verification device, and the method includes:

[0007] Obtain the verification data of the verification device to be verified;

[0008] Use a preset electric energy meter error distribution function model to verify the verification data to obtain a verification error result, where the preset electric energy meter error distribution function model is constructed based on historical verification data;

[0009] Compare the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, start the alarm process and generate an alarm verification task. If the error result is greater than the second preset value, start the early warning process and generate an early warning verification task;

[0010] Verify the verification device to be verified according to the alarm verification task or the early warning verification task to obtain a first verification result. If the first verification result meets the preset conditions, end the verification. If the error result is less than the first preset value, verify the verification device to be verified according to the preset verification task to obtain a second verification result. If the second verification result meets the preset conditions, end the verification.

[0011] Implement this embodiment, obtain the verification data of the verification device to be verified, use the preset electric energy meter error distribution function model to verify the verification data, obtain the verification error result, compare the error result with the preset value. If the error result is greater than the first preset value and less than the second preset value, start the alarm process and generate an alarm verification task. If the error result is greater than the second preset value, start the early warning process and generate an early warning verification task. Verify the verification device to be verified according to the alarm verification task or the early warning verification task to obtain a first verification result. If the first verification result meets the preset conditions, end the verification. If the error result is less than the first preset value, verify the verification device to be verified according to the preset verification task to obtain a second verification result. If the second verification result meets the preset conditions, end the verification. This method can timely detect the abnormality of the verification system and improve the accuracy and efficiency of the verification result by linking the abnormal result of the electric energy meter error distribution function model with the verification task trigger mechanism.

[0012] In a possible implementation manner of the first aspect, determine whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If so and each of the identical alarm verification tasks or each of the identical early warning verification tasks belongs to the same line and device, determine whether each of the identical alarm verification tasks or early warning verification tasks has been completed. If completed, end the verification task;

[0013] If so and each of the identical alarm verification tasks or each of the identical early warning verification tasks belongs to the same line but different devices, merge each of the identical alarm verification tasks or early warning verification tasks into one alarm verification task or early warning verification task for verification.

[0014] In a possible implementation manner of the first aspect, the preset electric energy meter error distribution function model is constructed based on historical verification data. Specifically:

[0015] Calculate the relative error of the electric energy meter to be tested based on the power data of the electric energy meter to be tested, and calculate the relative error of the device to be tested based on the verification data of the verification device to be tested;

[0016] The basic error model is calculated based on the relative error of the electricity meter under test and the relative error of the device under test. Among them, the basic error model is as follows:

[0017] Y(%) = X(%) - θ(%)

[0018] Among them, X(%) represents the basic error of the electricity meter under test, θ(%) represents the relative error of the verification device under test, W e represents the indicated electric energy of the device under test, and W0 represents the electric energy measured by the reference standard;

[0019] According to the central limit theorem and the Bayesian hierarchical model, combined with the basic error model, an error distribution function model of the electricity meter is constructed.

[0020] In a possible implementation manner of the first aspect, the preset error distribution function model of the electricity meter is used to verify the verification data, and the verification error result is obtained. Specifically:

[0021] The error calculation is performed according to the k-th verification data of the verification device to be verified, and an error distribution model is obtained;

[0022] The posterior probability distribution is obtained according to Bayes' theorem and each parameter in the error distribution model;

[0023] After sampling the posterior probability distribution using the Gibbs sampling method, the marginal distribution sample of the standard device error is obtained from the sample of the joint distribution, and the verification error result is calculated.

[0024] In a possible implementation manner of the first aspect, the verification device to be verified is verified according to the alarm verification task or the early warning verification task. Specifically:

[0025] According to the alarm verification task or the early warning verification task, a preset physical verification device is used to verify the verification device to be verified.

[0026] In a possible implementation manner of the first aspect, the preset physical verification device is preferably a 0.02-level physical verification device with the same size as the existing electricity meter and high stability.

[0027] A second aspect of the embodiments of the present invention provides a verification system for a verification device, and the system includes:

[0028] An acquisition module, configured to acquire the verification data of the verification device to be verified;

[0029] The first verification module is used to verify the verification data by using a preset error distribution function model of the electricity meter, and obtain a verification error result, where the preset error distribution function model of the electricity meter is constructed based on historical verification data;

[0030] The second verification module is used to compare the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, an alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, a warning process is started and a warning verification task is generated;

[0031] The third verification module is used to verify the verification device to be verified according to the alarm verification task or the warning verification task, and obtain a first verification result. If the first verification result meets the preset conditions, the verification ends. If the error result is less than the first preset value, the verification device to be verified is verified according to the preset verification task, and a second verification result is obtained. If the second verification result meets the preset conditions, the verification ends.

[0032] In a possible implementation manner of the second aspect, the judgment module is used to judge whether there are multiple identical alarm verification tasks or multiple identical warning verification tasks. If so and each of the identical alarm verification tasks or each of the identical warning verification tasks belongs to the same line and device, it is judged whether each of the identical alarm verification tasks or warning verification tasks has been completed. If so, the verification task ends;

[0033] If so and each of the identical alarm verification tasks or each of the identical warning verification tasks belongs to the same line and different devices, each of the identical alarm verification tasks or warning verification tasks is merged into one alarm verification task or warning verification task for verification.

[0034] In a possible implementation manner of the second aspect, the preset error distribution function model of the electricity meter is constructed based on historical verification data, specifically:

[0035] The relative error of the electricity meter to be tested is calculated based on the power data of the electricity meter to be tested, and the relative error of the testing device to be tested is calculated based on the verification data of the testing device to be tested;

[0036] A basic error model is calculated based on the relative error of the electricity meter to be tested and the relative error of the testing device to be tested, where the basic error model is:

[0037] Y(%) = X(%) - θ(%)

[0038] where X(%) represents the basic error of the electricity meter to be tested, θ(%) represents the relative error of the testing device to be tested, W eIndicates the indicated electrical energy of the device under test, and W0 indicates the electrical energy measured by the reference standard.

[0039] In a possible implementation of the second aspect, a preset electric energy meter error distribution function model is used to verify the verification data, and a verification error result is obtained. Specifically:

[0040] Calculate the error according to the k-th verification data of the verification device to be verified to obtain an error distribution model;

[0041] Obtain the posterior probability distribution according to Bayes' theorem and each parameter in the error distribution model;

[0042] After sampling the posterior probability distribution using the Gibbs sampling method, the marginal distribution sample of the standard device error is obtained from the sample of the joint distribution, and the verification error result is calculated. Brief Description of the Drawings

[0043] Figure 1 : Schematic flowchart of an embodiment of a verification method for a verification device provided by the present invention;

[0044] Figure 2 : Schematic diagram of the working process of a verification system for an embodiment of a verification method for a verification device provided by the present invention;

[0045] Figure 3 : Schematic diagram of the double-layer model structure of the verification data for an embodiment of a verification method for a verification device provided by the present invention;

[0046] Figure 4 : Schematic diagram of the system structure for another embodiment of a verification method for a verification device provided by the present invention. Detailed Embodiments

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1

[0049] Please refer to Figure 1 , which is a schematic flowchart of an embodiment of a verification method for a verification device provided by an embodiment of the present invention, including steps S11 to S14, and the specific steps are as follows:

[0050] S11. Obtain the verification data of the verification device to be verified.

[0051] In this embodiment, the verification data of the device to be verified is obtained, and the verification data includes a plurality of pieces of verification data.

[0052] It should be noted that the verification data of a plurality of devices to be verified is obtained in real time.

[0053] S12. Use a preset error distribution function model of the electric energy meter to verify the verification data, and obtain a verification error result, where the preset error distribution function model of the electric energy meter is constructed based on historical verification data.

[0054] In a preferred embodiment, the preset error distribution function model of the electric energy meter is constructed based on historical verification data, specifically:

[0055] Calculate the relative error of the electric energy meter to be tested according to the power data of the electric energy meter to be tested, and calculate the relative error of the device to be tested according to the verification data of the verification device to be tested;

[0056] Calculate a basic error model according to the relative error of the electric energy meter to be tested and the relative error of the device to be tested, where the basic error model is:

[0057] Y(%) = X(%) - θ(%)

[0058] Where X(%) represents the basic error of the electric energy meter to be tested, θ(%) represents the relative error of the verification device to be tested, W e represents the indicated electric energy of the device to be tested, and W0 represents the electric energy measured by the reference standard;

[0059] Construct an error distribution function model of the electric energy meter by combining the basic error model with the central limit theorem and the Bayesian hierarchical model.

[0060] In a preferred embodiment, use a preset error distribution function model of the electric energy meter to verify the verification data, and obtain a verification error result, specifically:

[0061] Calculate an error according to the k-th verification data of the device to be verified to obtain an error distribution model;

[0062] Obtain a posterior probability distribution according to Bayes' theorem and each parameter in the error distribution model;

[0063] After sampling the posterior probability distribution using the Gibbs sampling method, obtain the marginal distribution sample of the standard device error from the sample of the joint distribution, and calculate the verification error result.

[0064] In this embodiment, Figure 2As shown in the figure, by using the historical verification data of smart energy meters in the same batch, based on the central limit theorem and Bayesian hierarchical model, an error distribution function model of the energy meter higher than the accuracy level of the verification device is constructed to conduct real-time verification on the verification device. The process of the error distribution function model of the energy meter processing historical verification data is as follows:

[0065] Modeling the basic error source of the verification device. According to the requirements for basic error verification in "JJG 596—2012 Electronic AC Energy Meters", when using the standard meter method to verify the energy meter, the relative error calculation formula of the energy meter under test is:

[0066]

[0067] Among them, m represents the measured number of pulses, and m0 represents the calculated number of pulses. N represents the number of low-frequency or high-frequency pulses of the energy meter under test, C0 represents the (pulse) meter constant of the standard meter, imp / kWh, C L represents the (pulse) meter constant of the energy meter under test, imp / kWh, K I , K U respectively represent the transformation ratios of the current and voltage transformers externally connected to the standard meter.

[0068] When there are no externally connected current and voltage transformers to the standard meter, K I and K U both equal 1, then the calculated number of pulses is Then the relative error of the energy meter under test is Among them, W m is the indicated energy of the energy meter under test, and W e is the indicated energy of the verification device.

[0069] According to the requirements for basic error verification in "JJG 597—2005 Verification Regulation of AC Energy Meter Verification Devices", the relative error calculation formula of the device under test is:

[0070]

[0071] Among them, W e represents the indicated energy of the device under test, and W0 represents the energy measured by the reference standard.

[0072] From the above formulas, it can be seen that the relative error Y (%) of the energy meter under test is the error of the energy meter relative to the verification device; the relative error θ (%) of the device under test is the error of the verification device relative to the measurement of the reference standard. Then the true relative error of the energy meter is the error of the energy meter relative to the measurement of the reference standard, that is Then there is:

[0073]

[0074] It can be obtained that the relationship among the basic error Y (%) of the watt-hour meter verification, the basic error X (%) of the watt-hour meter, and the basic error θ (%) of the verification device is as follows:

[0075] X (%) = θ (%) + Y (%) + 0.01 * θ (%) * Y (%)

[0076] In view of the fact that the value obtained from 0.01 * θ (%) * Y (%) exceeds the precision and has a very small impact on the result, the basic error model of the verification device is:

[0077] Y (%) = X (%) - θ (%)

[0078] Among them, X (%) represents the basic error of the watt-hour meter to be verified, θ (%) represents the relative error of the verification device to be verified, W e represents the indicated electric energy of the device to be verified, and W0 represents the electric energy measured by the reference standard.

[0079] Then, based on the central limit theorem, the verification data of the error distribution function model of the watt-hour meter is accumulated. The central limit theorem states that the mean distribution of independent and identically distributed random variables asymptotically approaches a normal distribution. This theorem is the theoretical basis of mathematical statistics and error analysis, and the specific statement is as follows:

[0080] For n independent and identically distributed random variables, X1, X2, ···, X n , whose expectations and standard deviations are μ and σ respectively, the average value distribution approximately tends to a normal distribution

[0081] According to the central limit theorem, for the verification data of n ordinary smart watt-hour meters of the same production batch, their mean values can be regarded as the verification data of the error distribution function model of the watt-hour meter, and the accuracy level of this error distribution function model of the watt-hour meter is improved by times compared with that of a single ordinary smart watt-hour meter. If n is large enough, the accuracy level of this error distribution function model of the watt-hour meter can reach the accuracy level of the standard watt-hour meter in the physical method and can be used for the verification of the verification device.

[0082] As an example of this embodiment, taking a single-phase smart electricity meter as an example, its accuracy level is usually Class 2, which is about 100 times different from the accuracy level of the standard electricity meter in the physical method. If using the verification data of a single verification device to construct the error distribution function model of the electricity meter, it is necessary to accumulate verification data of electricity meters of the same production batch in the order of 10,000 to meet the requirements of verifying the accuracy of the verification device. However, the verification speed of a single verification device is limited, and the time required for the above-scale data accumulation is relatively long during the verification work. To solve the above problems, this method combines multiple verification devices for verifying smart electricity meters of the same batch, introduces the Bayesian hierarchical model, apportions the required verification data volume, and uniformly constructs the error distribution function model of the electricity meter, thereby greatly reducing the data accumulation time and also realizing the real-time verification of multiple verification devices.

[0083] The verification data of smart electricity meters of the same production batch can be grouped based on the verification device they are in to form a two-layer model: The first layer is composed of different verification devices and is the between-group model for describing the errors of verification devices; the second layer is composed of multiple electricity meters to be verified verified by the same verification device and is the within-group model for describing the verification data generated by the same verification device, as Figure 3 shown.

[0084] In the first-layer between-group model, use μ i to represent the error of the i-th verification device, assuming it follows a normal distribution, and its model likelihood is:

[0085]

[0086] Among them, and τ 2 are the expectation and variance of the verification device error distribution respectively.

[0087] In the second-layer within-group model, use Y i,k to represent the k-th verification data of the i-th verification device, and b represents the expectation of the error of the smart electricity meters to be verified in this production batch. The verification data Y i,k , that is, the verification error of the electricity meter to be verified, is the difference between the true error of the electricity meter to be verified and the error of the verification device, and assuming it follows a normal distribution, its model likelihood is:

[0088]

[0089] Among them, σ 2 is the variance of the within-group verification data.

[0090] According to Bayes' theorem, set the conjugate prior distributions of the parameters b, τ 2 , σ 2 as:

[0091]

[0092] Among them, IG represents the inverse Gamma distribution.

[0093] Using Bayes' theorem, the posterior probability distribution is obtained as follows:

[0094]

[0095] In the posterior distribution of the above parameters, μ1, μ2, …, μ m , The posterior distribution of b is a normal distribution, and the posterior distributions of τ 2 and σ 2 are inverse Gamma distributions.

[0096] Based on the above posterior distribution, using the Gibbs sampling method, sample the joint posterior distribution p(μ1, μ2,..., μ m , τ 2 , σ 2 , b|Y), and then directly obtain the marginal distribution samples of the standard device errors μ1, μ2, …, μ m from the samples of the joint distribution, and further obtain statistical information such as the mean and median of this distribution as the obtained verification result.

[0097] S13. Compare the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, start the alarm process and generate an alarm verification task. If the error result is greater than the second preset value, start the early warning process and generate an early warning verification task.

[0098] In a preferred embodiment, it further includes:

[0099] Judge whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If so and each of the identical alarm verification tasks or each of the identical early warning verification tasks belongs to the same line and device, then judge whether each of the identical alarm verification tasks or early warning verification tasks has been completed. If completed, end the verification task;

[0100] If so and each of the identical alarm verification tasks or each of the identical early warning verification tasks belongs to the same line but different devices, then merge each of the identical alarm verification tasks or early warning verification tasks into one alarm verification task or early warning verification task for verification.

[0101] In this embodiment, through the operation of the error distribution function model of the electricity meter, the error value of the intermediate verification of the verification device can be calculated, and then compared with the specified error value. If the maximum allowable error is exceeded, the system will automatically start the alarm process. If it is close to the maximum allowable error, the system will automatically start the early warning process. Both the alarm and early warning processes will automatically generate a to-do task, and then generate a physical verification task. Whether the electricity meter verification device is abnormal is judged through the execution result of this task.

[0102] When the error result of the operation of the electricity meter error distribution function model is close to the maximum allowable error, the system will automatically start the early warning process. The judgment conditions for the start of the early warning can be flexibly set by the system, and the process after the start of the early warning process is the same as the alarm process.

[0103] When the system starts the alarm process, in order to prevent repeated alarms, the system will judge whether there is a process of the same alarm type that has not been processed and ended on this line body. For example, if the same device on the same line body alarms continuously for three days, the system will automatically judge whether the first alarm has been verified and completed. If not, the second alarm will be automatically closed. At the same time, in order to reduce the verification task volume and improve the verification efficiency, the alarm information of the same type for different devices on the same line body will be automatically merged into one alarm information. For example, if the intermediate verification alarms occur for both the No. 1 verification unit and the No. 2 verification unit on the No. 1 line body, the system will automatically merge them into one task and verify the No. 1 verification unit and the No. 2 verification unit on the No. 1 line body at one time.

[0104] It should be noted that the purpose of the early warning process is to discover the problems of the device in advance and prevent problems before they occur; the purpose of the alarm process is to verify the problems of the device. After confirming the device problems, the impact caused by the device problems can be minimized.

[0105] S14. Verify the verification device to be verified according to the alarm verification task or the early warning verification task to obtain the first verification result. If the first verification result meets the preset conditions, the verification ends. If the error result is less than the first preset value, verify the verification device to be verified according to the preset verification task to obtain the second verification result. If the second verification result meets the preset conditions, the verification ends.

[0106] In the preferred embodiment, verifying the verification device to be verified according to the alarm verification task or the early warning verification task specifically includes:

[0107] Verify the verification device to be verified by using the preset physical verification equipment according to the alarm verification task or the early warning verification task.

[0108] In the preferred embodiment, the preset physical verification equipment is preferably a 0.02-level physical verification equipment with the same size as the existing electricity meter and high stability.

[0109] In this embodiment, when an alarm or early warning is generated, according to the alarm verification task or early warning verification task, a preset physical verification device is used to verify the device under verification, and whether the watt-hour meter verification device is abnormal is judged based on the execution result of this task. The preset physical verification device is a 0.02-level physical verification device that is the same size as the existing watt-hour meter and has high stability.

[0110] Each state during the execution of the physical verification task will be synchronized to the watt-hour meter error distribution function model in real time. The distributed function model can discover problems in advance. The physical verification task, as a verification method, realizes the final closed-loop of the alarm. The two complement each other, and ultimately enables the line body to normally carry out daily verification tasks and ensure the accuracy of the verification results, avoiding the recall event of the watt-hour meter due to problems with the verification results.

[0111] The present invention links the abnormal result of the watt-hour meter error distribution function model with the verification task triggering mechanism. When the watt-hour meter error distribution function model discovers an abnormality, the verification task is automatically triggered, and the verification of the verification device is completed through the physical verification device, which can timely discover the abnormality of the verification device.

[0112] Embodiment 2

[0113] Correspondingly, referring to Figure 4 , Figure 4 is a verification system for a verification device provided by the present invention. As shown in the figure, the verification system for the verification device includes:

[0114] An acquisition module 401, configured to acquire the verification data of the device under verification;

[0115] A first verification module 402, configured to verify the verification data by using a preset watt-hour meter error distribution function model to obtain a verification error result, where the preset watt-hour meter error distribution function model is constructed based on historical verification data;

[0116] A second verification module 403, configured to compare the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, an alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, a pre-warning process is started and a pre-warning verification task is generated;

[0117] A third verification module 404, configured to verify the device under verification according to the alarm verification task or the pre-warning verification task to obtain a first verification result. If the first verification result meets the preset condition, the verification ends. If the error result is less than the first preset value, the device under verification is verified according to a preset verification task to obtain a second verification result. If the second verification result meets the preset condition, the verification ends.

[0118] In a preferred embodiment, the determination module 405 is configured to determine whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If so and each of the identical alarm verification tasks or each of the identical early warning verification tasks belongs to the same line and device, it is determined whether each of the identical alarm verification tasks or early warning verification tasks has been completed. If completed, the verification task ends.

[0119] If so and each of the identical alarm verification tasks or each of the identical early warning verification tasks belongs to the same line but different devices, then each of the identical alarm verification tasks or early warning verification tasks is merged into one alarm verification task or early warning verification task for verification.

[0120] In a preferred embodiment, the preset electric energy meter error distribution function model is constructed based on historical verification data, specifically:

[0121] The relative error of the electric energy meter under test is calculated based on the power data of the electric energy meter under test, and the relative error of the verification device under test is calculated based on the verification data of the verification device under test;

[0122] The basic error model is calculated based on the relative error of the electric energy meter under test and the relative error of the verification device under test, where the basic error model is:

[0123] Y(%) = X(%) ― θ(%)

[0124] where X(%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the verification device under test, W e represents the indicated electric energy of the verification device, and W0 represents the electric energy measured by the reference standard.

[0125] In a preferred embodiment, the preset electric energy meter error distribution function model is used to verify the verification data, and the verification error result is obtained, specifically:

[0126] Error calculation is performed based on the k-th verification data of the verification device to be verified to obtain an error distribution model;

[0127] The posterior probability distribution is obtained based on Bayes' theorem and each parameter in the error distribution model;

[0128] After sampling the posterior probability distribution using the Gibbs sampling method, the marginal distribution sample of the standard device error is obtained from the sample of the joint distribution, and the verification error result is calculated.

[0129] In a preferred embodiment, the verification device to be verified is verified according to the alarm verification task or early warning verification task, specifically:

[0130] According to the alarm verification task or the early warning verification task, a preset physical verification device is used to verify the device under verification.

[0131] In a preferred embodiment, the preset physical verification device is preferably a 0.02-level physical verification device that is the same size as the existing electricity meter and has high stability.

[0132] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0133] The present invention obtains the verification data of the device under verification, verifies the verification data using a preset electricity meter error distribution function model to obtain a verification error result, compares the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, an alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, an early warning process is started and an early warning verification task is generated. The device under verification is verified according to the alarm verification task or the early warning verification task to obtain a first verification result. If the first verification result meets the preset conditions, the verification ends. If the error result is less than the first preset value, the device under verification is verified according to a preset verification task to obtain a second verification result. If the second verification result meets the preset conditions, the verification ends. This method verifies the verification device by linking the abnormal result of the electricity meter error distribution function model with the verification task trigger mechanism, can timely detect abnormalities in the verification system, and improve the accuracy and efficiency of the verification results.

[0134] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0135] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present disclosure, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0136] The specific embodiments described above have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A verification method for a calibration device, characterized in that: Including: Obtain the verification data of the device to be verified and calibrated; Verify the verification data by using a preset error distribution function model of the electricity meter to obtain a verification error result, where the preset error distribution function model of the electricity meter is constructed based on historical verification data; Compare the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, start an alarm process and generate an alarm verification task. If the error result is greater than the second preset value, start a warning process and generate a warning verification task; Verify the device to be verified and calibrated according to the alarm verification task or the warning verification task to obtain a first verification result. If the first verification result meets the preset conditions, end the verification. If the error result is less than the first preset value, verify the device to be verified and calibrated according to a preset verification task to obtain a second verification result. If the second verification result meets the preset conditions, end the verification; Verifying the device to be verified and calibrated according to the alarm verification task or the warning verification task specifically includes: Verifying the device to be verified and calibrated by using a preset physical verification device according to the alarm verification task or the warning verification task; Each state during the verification of the device to be verified and calibrated by using a preset physical verification device will be synchronized to the preset error distribution function model of the electricity meter in real time.

2. The verification method of a verification device as described in claim 1, wherein, It also includes: Judge whether there are multiple identical alarm verification tasks or multiple identical warning verification tasks. If so and each of the identical alarm verification tasks or each of the identical warning verification tasks belongs to the same line and device, judge whether each of the identical alarm verification tasks or warning verification tasks has been completed. If completed, end the verification task; If so and each of the identical alarm verification tasks or each of the identical warning verification tasks belongs to the same line but different devices, merge each of the identical alarm verification tasks or warning verification tasks into one alarm verification task or warning verification task for verification.

3. The verification method of a verification device as claimed in claim 1, characterized in that, The preset error distribution function model of the electricity meter is constructed based on historical verification data, specifically: Calculate the relative error of the electricity meter to be tested based on the power data of the electricity meter to be tested, and calculate the relative error of the device to be tested based on the verification data of the device to be tested; Calculate a basic error model based on the relative error of the electricity meter to be tested and the relative error of the device to be tested, where the basic error model is: Y(%) = X(%) - θ(%) Among them, X (%) represents the basic error of the electric energy meter under inspection, θ(%) represents the relative error of the device under test. W e represents the indicated electrical energy of the device under test, and W0 represents the electrical energy measured by the reference standard; Construct an error distribution function model of the electricity meter by combining the basic error model with the central limit theorem and the Bayesian hierarchical model.

4. A verification method for a verification device as claimed in claim 1, characterized in that, The verification of the verification data by using the preset error distribution function model of the electricity meter to obtain a verification error result specifically includes: Perform error calculation based on the k-th verification data of the device to be verified and calibrated to obtain an error distribution model; Obtain the posterior probability distribution according to Bayes' theorem and each parameter in the error distribution model; After sampling the posterior probability distribution using the Gibbs sampling method, the marginal distribution sample of the standard device error is obtained from the samples of the joint distribution, and the verification error result is calculated.

5. The verification method of a verification device according to claim 1, characterized in that, The preset physical verification device is a 0.02-level physical verification device with the same size as the existing electricity meter and high stability.

6. A verification system for a verification device, characterized in that, It includes: An acquisition module for acquiring the verification data of the device to be verified. A first verification module for verifying the verification data using a preset electricity meter error distribution function model to obtain a verification error result, where the preset electricity meter error distribution function model is constructed based on historical verification data. A second verification module for comparing the error result with a preset value. If the error result is greater than the first preset value and less than the second preset value, an alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, a warning process is started and a warning verification task is generated. A third verification module for verifying the device to be verified according to the alarm verification task or the warning verification task to obtain a first verification result. If the first verification result meets the preset conditions, the verification ends. If the error result is less than the first preset value, the device to be verified is verified according to a preset verification task to obtain a second verification result. If the second verification result meets the preset conditions, the verification ends. Verifying the device to be verified according to the alarm verification task or the warning verification task specifically means: Verifying the device to be verified using a preset physical verification device according to the alarm verification task or the warning verification task. Each state during the verification of the device to be verified using the preset physical verification device will be synchronized to the preset electricity meter error distribution function model in real time.

7. The verification system of a verification device according to claim 6, characterized in that It further includes: A judgment module for judging whether there are multiple identical alarm verification tasks or multiple identical warning verification tasks. If there are and each of the identical alarm verification tasks or each of the identical warning verification tasks belongs to the same line and device, it is judged whether each of the identical alarm verification tasks or warning verification tasks has been completed. If completed, the verification task ends. If there are and each of the identical alarm verification tasks or each of the identical warning verification tasks belongs to the same line but different devices, each of the identical alarm verification tasks or warning verification tasks is merged into one alarm verification task or warning verification task for verification.

8. The verification system of a verification device as described in claim 6, characterized in that, The preset electricity meter error distribution function model is constructed based on historical verification data, specifically: Calculating the relative error of the electricity meter to be tested based on the power data of the electricity meter to be tested, and calculating the relative error of the device to be tested based on the verification data of the device to be tested. Calculating a basic error model based on the relative error of the electricity meter to be tested and the relative error of the device to be tested, where the basic error model is: Y(%) = X(%) - θ(%) Wherein, X(%) represents the basic error of the electricity meter under test, θ(%) represents the relative error of the calibration device under test, W e represents the indicated electric energy of the device under test, and W0 represents the electric energy measured by the reference standard; Constructing an electricity meter error distribution function model by combining the basic error model with the central limit theorem and the Bayesian hierarchical model.

9. The verification system of a calibration device according to claim 6, characterized in that: Verifying the verification data by using the preset error distribution function model of the watt-hour meter to obtain a verification error result, specifically: Calculating an error to obtain an error distribution model according to the k-th verification data of the verification device to be verified; Obtaining a posterior probability distribution according to Bayes' theorem and each parameter in the error distribution model; After sampling the posterior probability distribution by using the Gibbs sampling method, obtaining a marginal distribution sample of the standard device error from the samples of the joint distribution, and calculating to obtain the verification error result.

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