CN117113833A - Verification method and system for calibration device - Google Patents

Abstract

The invention discloses a verification method and system for a verification device. It obtains verification data of the verification device to be verified, uses a preset electric energy meter error distribution function model to verify the verification data, obtains a verification error result, and compares the error result with the preset value. Comparison is made. If the error result is greater than the first preset value and less than the second preset value, the alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, the early warning process is started and an early warning verification task is generated. The verification device to be verified is verified according to the alarm verification task or the early warning verification task. This method verifies the verification device by linking the abnormal results of the energy meter error distribution function model with the verification task triggering mechanism, which can promptly discover the abnormality of the verification system and improve the accuracy of the verification results. degree and efficiency.

Description

Verification method and system for calibration device

Technical field

The present invention relates to the technical field of electric energy meter calibration, and in particular to a verification method and system for a calibration device.

Background technique

With the rapid growth of electric energy meter calibration business volume, the calibration work of electric energy meters has been transformed from manual calibration to automated assembly line calibration. Calibration devices have the characteristics of highly concentrated distribution, small number of calibration personnel, and greatly improved calibration efficiency. In order to ensure that the standard performance of the energy meter calibration device continues to meet the method requirements, it is particularly important to complete the periodic verification work of the calibration device efficiently and with high quality.

Under the current automated calibration mode, there are a large number of calibration devices, the calibration system operates with high efficiency, and the daily calibration volume is tens of thousands. In order to ensure that the standard performance of each calibration device continues to meet the method requirements during two consecutive value traceability periods, Regular and irregular periodic inspections need to be carried out according to the actual situation. Manual verification during the period is inefficient and has a great impact on daily verification and production. It is difficult to ensure the frequency and timeliness of verification. At the same time, the test process and data processing are affected by human factors and have high risks. If the standard performance of the calibration system is abnormal, it will be difficult to detect, accurately judge and deal with it in time. It will be difficult to recall the affected inspected and installed equipment, and the impact will be severe. It cannot meet the requirements of lean management of metrology calibration production and online risk control.

Although the digital verification method based on the energy meter error distribution function model based on the historical calibration data of the same batch of smart energy meters can solve the problems of interruption of calibration work, low calibration efficiency, and human influence caused by manual wiring inspection during the traditional period, the digital verification method is still This aspect requires a large amount of quantity accumulation and is easily affected by data quality, leading to inaccurate verification results.

Contents of the invention

In order to solve the above technical problems, embodiments of the present invention provide a verification method and system for a calibration device. By linking the abnormal results of the energy meter error distribution function model with the verification task triggering mechanism to verify the calibration device, abnormalities in the calibration system can be discovered in a timely manner. , improve the accuracy and efficiency of verification results.

A first aspect of the embodiment of the present invention provides a verification method for a calibration device, the method includes:

Obtain the calibration data of the calibration device to be verified;

Use the preset electric energy meter error distribution function model to check the calibration data and obtain the verification error results, where the preset electric energy meter error distribution function model is constructed based on historical calibration data;

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, initiate an early warning. process and generate early warning verification tasks;

Verify the device to be verified according to the alarm verification task or early warning verification task, and obtain the first verification result. If the first verification result reaches the preset condition, the verification will end. If the error result is less than the first preset value, the verification will be carried out according to the preset condition. The task is to verify the verification device to be verified and obtain the second verification result. If the second verification result meets the preset conditions, the verification ends.

Implement this embodiment to obtain the calibration data of the calibration device to be verified, use the preset electric energy meter error distribution function model to check the calibration data, obtain the verification error result, compare the error result with the preset value, if the error result is greater than the first If the error result is greater than the second preset value, the alarm process will be started and an alarm verification task will be generated. If the error result is greater than the second preset value, the early warning process will be started and an early warning verification task will be generated. The warning process will be treated according to the alarm verification task or the early warning verification task. The verification and verification device performs verification to obtain the first verification result. If the first verification result reaches the preset condition, the verification is ended. If the error result is less than the first preset value, the verification and verification device to be verified is verified according to the preset verification task to obtain As a result of the second verification, if the second verification result reaches the preset conditions, the verification will end. This method verifies the calibration device by linking the abnormal results of the energy meter error distribution function model with the verification task triggering mechanism, and can detect abnormalities in the calibration system in a timely manner. Improve the accuracy and efficiency of verification results.

In a possible implementation of the first aspect, it is determined whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks, and if there are and each identical alarm verification task or each identical early warning verification task belongs to the same In a line body and device, it is judged whether each of the same alarm verification tasks or early warning verification tasks has been completed. If completed, the verification task is ended;

If there are identical alarm verification tasks or identical early warning verification tasks and they belong to the same line or different devices, then merge the identical alarm verification tasks or early warning verification tasks into one alarm verification task or early warning verification task for verification. .

In a possible implementation of the first aspect, the preset electric energy meter error distribution function model is constructed based on historical calibration data, specifically:

The relative error of the inspected electric energy meter is calculated based on the power data of the inspected electric energy meter, and the relative error of the inspected device is calculated based on the calibration data of the inspected calibration device;

The basic error model is calculated based on the relative error of the electric energy meter being tested and the relative error of the device being tested. The basic error model is:

Y(%)＝X(%)-θ(%)

Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard;

Based on the central limit theorem and Bayesian hierarchical model combined with the basic error model, an energy meter error distribution function model is constructed.

In a possible implementation of the first aspect, a preset electric energy meter error distribution function model is used to check the calibration data to obtain the verification error results, specifically:

Calculate the error based on the kth calibration data of the calibration device to be verified, and obtain the error distribution model;

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

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

In a possible implementation 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 as follows:

According to the alarm verification task or early warning verification task, preset physical verification equipment is used to verify the verification device to be verified.

In a possible implementation of the first aspect, the preset physical verification equipment is preferably a 0.02 level physical verification equipment that is consistent in size with the existing electric energy meter and has high stability.

A second aspect of the embodiment of the present invention provides a verification system for a calibration device, the system including:

The acquisition module is used to obtain the calibration data of the calibration device to be verified;

The first verification module is used to check the calibration data using a preset electric energy meter error distribution function model to obtain verification error results, where the preset electric energy meter error distribution function model is constructed based on historical calibration data;

The second verification module is used to 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, If the default value is set, the early warning process will be started and the early warning verification task will be generated;

The third verification module is used to verify the device to be verified according to the alarm verification task or the early warning verification task, and obtain the first verification result. If the first verification result reaches the preset condition, the verification will be ended. 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 the second verification result is obtained. If the second verification result reaches the preset condition, the verification is ended.

In a possible implementation of the second aspect, the judgment module is used to judge whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If there are multiple identical alarm verification tasks or identical early warning tasks, If the verification tasks belong to the same line body and device, it is judged whether each of the same alarm verification tasks or early warning verification tasks has been completed. If completed, the verification task is ended;

If there are identical alarm verification tasks or identical early warning verification tasks and they belong to the same line or different devices, then merge the identical alarm verification tasks or early warning verification tasks into one alarm verification task or early warning verification task for verification. .

In a possible implementation of the second aspect, the preset electric energy meter error distribution function model is constructed based on historical calibration data, specifically:

The relative error of the inspected electric energy meter is calculated based on the power data of the inspected electric energy meter, and the relative error of the inspected device is calculated based on the calibration data of the inspected calibration device;

The basic error model is calculated based on the relative error of the electric energy meter being tested and the relative error of the device being tested. The basic error model is:

Y(%)＝X(%)-θ(%)

Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard.

In a possible implementation of the second aspect, a preset electric energy meter error distribution function model is used to check the calibration data to obtain the verification error results, specifically:

Calculate the error based on the kth calibration data of the calibration device to be verified, and obtain the error distribution model;

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

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

Description of drawings

Figure 1: A schematic flow chart of an embodiment of a verification method for a calibration device provided by the present invention;

Figure 2: A schematic diagram of the work flow of the verification system according to one embodiment of the verification method of the verification device provided by the present invention;

Figure 3: A schematic structural diagram of a two-layer model of calibration data for an embodiment of a verification method for a calibration device provided by the present invention;

Figure 4: A schematic system structure diagram of another embodiment of a verification method for a verification device provided by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some 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 making creative efforts fall within the scope of protection of the present invention.

Embodiment 1

Please refer to Figure 1 , which is a schematic flow chart of an embodiment of a verification method for a calibration device provided by an embodiment of the present invention, including steps S11 to S14. The details of each step are as follows:

S11. Obtain the calibration data of the calibration device to be verified.

In this embodiment, the verification data of the verification device to be verified is obtained, and the verification data includes multiple verification data.

It should be noted that the calibration data of multiple calibration devices to be verified are obtained in real time.

S12. Use a preset electric energy meter error distribution function model to check the calibration data to obtain a verification error result, wherein the preset electric energy meter error distribution function model is constructed based on historical calibration data.

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

The relative error of the inspected electric energy meter is calculated based on the power data of the inspected electric energy meter, and the relative error of the inspected device is calculated based on the calibration data of the inspected calibration device;

The basic error model is calculated based on the relative error of the electric energy meter being tested and the relative error of the device being tested. The basic error model is:

Y(%)＝X(%)-θ(%)

Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard;

Based on the central limit theorem and Bayesian hierarchical model combined with the basic error model, an energy meter error distribution function model is constructed.

In a preferred embodiment, the preset electric energy meter error distribution function model is used to check the calibration data to obtain the verification error results, specifically:

Calculate the error based on the kth calibration data of the calibration device to be verified, and obtain the error distribution model;

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

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

In this embodiment, as shown in Figure 2, the same batch of smart electric energy meter historical calibration data is used to construct an electric energy meter error distribution function model that is higher than the accuracy level of the calibration device based on the central limit theorem and Bayesian hierarchical model. The calibration device performs real-time verification. The process of processing historical calibration data by the energy meter error distribution function model is as follows:

Modeling of the basic error sources of the calibration device. According to the requirements for basic error calibration in "JJG 596-2012 Electronic AC Electric Energy Meter", when calibrating the electric energy meter using the standard meter method, the relative error calculation formula of the electric energy meter being tested is:

Among them, m represents the measured pulse number, m ₀ represents the calculated pulse number, N represents the number of low-frequency or high-frequency pulses of the electric energy meter being tested, C ₀ represents the (pulse) instrument constant of the standard meter, imp/kWh, C _L represents the (pulse) instrument constant of the electric energy meter being tested, imp/kWh, K _I , K _U respectively represent the current and voltage transformer ratios connected to the standard meter.

When the standard meter has no external current and voltage transformers, both K _I and K _U are equal to 1, then the calculated number of pulses is Then the relative error of the tested electric energy meter is/> Among them, W _m is the indicated electric energy of the electric energy meter under test, and _We is the indicated electric energy of the calibration device.

According to the requirements for basic error calibration in "JJG 597-2005 Calibration Regulations for AC Electric Energy Meter Calibration Devices", the relative error calculation formula of the device being tested is:

Among them, _We represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard.

It can be seen from the above formula that the relative error Y (%) of the electric energy meter being tested is the error of the electric energy meter relative to the calibration device; the relative error θ (%) of the device being tested is the error measured by the calibration device relative to the reference standard. Then the true relative error of the electric energy meter is the error measured by the electric energy meter relative to the reference standard, that is Then there are:

It can be seen that the relationship between the basic error Y (%) of the electric energy meter calibration, the basic error X (%) of the electric energy meter, and the basic error θ (%) of the calibration device is:

X(%)=θ(%)+Y(%)+0.01*θ(%)*Y(%)

Since the value obtained by 0.01*θ(%)*Y(%) exceeds the accuracy and has a very small impact on the results, the basic error model of the calibration device is:

Y(%)＝X(%)-θ(%)

Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard.

Then the calibration data of the electric energy meter error distribution function model is accumulated based on the central limit theorem. The central limit theorem states that the mean distribution of independent and identically distributed random variables asymptotically approaches the normal distribution. This theorem is the theoretical basis of mathematical statistics and error analysis. It is expressed in detail. as follows:

For n independent and identically distributed random variables, X ₁ , X ₂ ,···,X _n , their expectation and standard deviation are μ and σ respectively, and the mean The distribution approximates a normal distribution/>

According to the central limit theorem, the average value of the calibration data of n pieces of ordinary smart energy meters from the same production batch can be regarded as the calibration data of the energy meter error distribution function model, and the accuracy level of the energy meter error distribution function model is higher than that of a single ordinary smart energy meter. Smart energy meters have improved accuracy levels times. If n is large enough, the accuracy level of the electric energy meter error distribution function model can reach the accuracy level of the standard electric energy meter in the physical method, and can be used for verification of calibration devices.

As an example of this embodiment, take a single-phase smart energy meter as an example. Its accuracy level is usually level 2, which is about 100 times different from the accuracy level of a standard electric energy meter in the physical method. If the calibration data of a single calibration device is used to construct an energy meter error distribution function model, it is necessary to accumulate calibration data of energy meters of the same production batch in the order of 10,000 to meet the verification accuracy requirements of the calibration device. However, the calibration speed of a single calibration device is limited, and the accumulation of data on the above scale requires a long time during the calibration process. In order to solve the above problems, this method jointly certifies multiple calibration devices of the same batch of smart energy meters, introduces the Bayesian hierarchical model, allocates the required amount of calibration data, and uniformly constructs the energy meter error distribution function model, thus significantly It reduces data accumulation time and also enables real-time verification of multiple calibration devices.

The calibration data of smart energy meters of the same production batch can be grouped based on the calibration device where they are located to form a two-layer model: the first layer is composed of different calibration devices, which is an inter-group model describing the error of the calibration device; the second layer is composed of the same calibration device The device calibration consists of multiple inspection tables, which are intra-group models describing the calibration data generated by the same calibration device, as shown in Figure 3.

In the first-level inter-group model, let μ _i represent the error of the i-th calibration device. Assuming that it obeys the normal distribution, the model likelihood is:

in, and τ ² are the expectation and variance of the error distribution of the calibration device respectively.

In the second-level intra-group model, Y _i,k represents the k-th calibration data of the i-th calibration device, and b represents the error expectation of the inspected smart energy meter in this production batch. The calibration data _Yi,k is the calibration error of the meter to be inspected, which is the difference between the real error of the meter to be inspected and the error of the calibration device. Assuming that it obeys the normal distribution, its model likelihood is:

Among them, σ ² is the variance of the test data within the group.

According to Bayes theorem, set parameters The conjugate prior distribution of b,τ ² ,σ ² is:

Among them, IG represents the inverse Gamma distribution.

Using Bayes' theorem, the posterior probability distribution is obtained:

In the posterior distribution of the above parameters, μ ₁ , μ ₂ ,…, μ _m , The posterior distribution of b is the normal distribution, and the posterior distribution of τ ² and σ ² is the inverse Gamma distribution.

Based on the above posterior distribution, using the Gibbs sampling method, the joint posterior distribution p(μ ₁ , μ ₂ ,..., μ _m , _τ ² _{, σ} ² , b | information as a result of the verification obtained.

S13. 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, 2 preset value, the early warning process will be started and the early warning verification task will be generated.

Among the preferred embodiments, it also includes:

Determine whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If they exist and each identical alarm verification task or each identical early warning verification task belongs to the same line body and device, then determine whether each identical alarm Check whether the verification task or early warning verification task has been completed. If completed, the verification task will end;

If there are identical alarm verification tasks or identical early warning verification tasks and they belong to the same line or different devices, then merge the identical alarm verification tasks or early warning verification tasks into one alarm verification task or early warning verification task for verification. .

In this embodiment, through the operation of the energy meter error distribution function model, the error value of the calibration device during the verification can be calculated, and then compared with the specified error value. If the maximum allowable error is exceeded, the system will automatically start an alarm process. If When approaching the maximum allowable error, the system will automatically start the early warning process. Both the alarm and early warning processes will automatically generate an agent task, and then generate a physical verification task. The execution results of this task will be used to determine whether the energy meter calibration device is abnormal.

When the error result calculated by the energy 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 starting the early warning can be flexibly set. The process after the early warning process is started is consistent with the alarm process.

When the system starts the alarm process, in order to prevent repeated alarms, the system will determine whether there is an unprocessed process of the same alarm type on the line body. For example, if the same device on the same line body has alarmed for three consecutive days, the system will automatically determine whether the first alarm has been processed. The verification is completed. If it is not completed, the second alarm will be automatically closed. At the same time, in order to reduce the workload of verification and improve verification efficiency, the same type of alarm information from different devices on the same line body will be automatically merged into one alarm message. For example, the verification information during inspection occurred in both the No. 1 verification unit and the No. 2 verification unit of Line 1. Alarm, the system will automatically merge it into one task to check the No. 1 verification unit and the No. 2 verification unit of Line 1 at one time.

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

S14. Verify the device to be verified and calibrated according to the alarm verification task or early warning verification task, and obtain the first verification result. If the first verification result reaches the preset condition, the verification will end. If the error result is less than the first preset value, the verification will be completed according to the preset value. Set up a verification task to verify the verification device to be verified and obtain the second verification result. If the second verification result meets the preset conditions, the verification will be ended.

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

According to the alarm verification task or early warning verification task, preset physical verification equipment is used to verify the verification device to be verified.

In a preferred embodiment, the preset physical verification equipment is preferably a 0.02 level physical verification equipment that is consistent in size with the existing electric energy meter and has high stability.

In this embodiment, when an alarm or early warning occurs, according to the alarm verification task or the early warning verification task, the preset physical verification equipment is used to verify the verification device to be verified, and whether the energy meter verification device is abnormal is determined based on the execution result of the task. The preset physical verification equipment is a 0.02 level physical verification equipment that is consistent in size with the existing electric energy meter and has high stability.

Each state during the execution of the physical verification task will be synchronized in real time to the energy meter error distribution function model. The distributed function model can detect problems in advance. The physical verification task serves as a verification method to achieve the final closed loop of the alarm. The two complement each other and finally reach the line. The body can carry out daily calibration tasks normally and ensure the accuracy of calibration results, so as to avoid the recall of electric energy meters due to problems with calibration results.

The invention links the abnormal results of the electric energy meter error distribution function model with the verification task triggering mechanism. When the electric energy meter error distribution function model finds an abnormality, the verification task is automatically triggered. The verification work of the calibration device is completed through the physical verification equipment, and the abnormality of the calibration device can be discovered in time. .

Embodiment 2

Correspondingly, refer to Figure 4, which is a verification system of a verification device provided by the present invention. As shown in the figure, the verification system of the verification device includes:

The acquisition module 401 is used to obtain the calibration data of the calibration device to be verified;

The first verification module 402 is used to check the calibration data using a preset electric energy meter error distribution function model to obtain verification error results, where the preset electric energy meter error distribution function model is constructed based on historical calibration data;

The second verification module 403 is used to 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 first preset value, 2 preset values, the early warning process will be started and the early warning verification task will be generated;

The third verification module 404 is used to verify the verification device to be verified according to the alarm verification task or the early warning verification task, and obtain the first verification result. If the first verification result reaches the preset condition, the verification is ended. If the error result is less than the first predetermined condition, If the value is set, the verification device to be verified will be verified according to the preset verification task, and the second verification result will be obtained. If the second verification result meets the preset conditions, the verification will be ended.

In the preferred embodiment, the determination module 405 is used to determine whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If there are multiple identical alarm verification tasks or each identical early warning verification task, they belong to the same In the line body and device, it is judged whether each of the same alarm verification tasks or early warning verification tasks has been completed. If completed, the verification task is ended;

If there are identical alarm verification tasks or identical early warning verification tasks and they belong to the same line or different devices, then merge the identical alarm verification tasks or early warning verification tasks into one alarm verification task or early warning verification task for verification. .

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

The relative error of the inspected electric energy meter is calculated based on the power data of the inspected electric energy meter, and the relative error of the inspected device is calculated based on the calibration data of the inspected calibration device;

The basic error model is calculated based on the relative error of the electric energy meter being tested and the relative error of the device being tested. The basic error model is:

Y(%)＝X(%)-θ(%)

Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard.

In a preferred embodiment, the preset electric energy meter error distribution function model is used to check the calibration data to obtain the verification error results, specifically:

Calculate the error based on the kth calibration data of the calibration device to be verified, and obtain the error distribution model;

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

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

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

According to the alarm verification task or early warning verification task, preset physical verification equipment is used to verify the verification device to be verified.

In a preferred embodiment, the preset physical verification equipment is preferably a 0.02 level physical verification equipment that is consistent in size with the existing electric energy meter and has high stability.

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

The present invention obtains the calibration data of the calibration device to be verified, uses the preset electric energy meter error distribution function model to check the calibration data, obtains the verification error result, and compares the error result with the preset value. If the error result is greater than the first preset value and is less than the second preset value, then the alarm process is started and an alarm verification task is generated. If the error result is greater than the second preset value, the early warning process is started and an early warning verification task is generated. The device is to be inspected and calibrated according to the alarm verification task or the early warning verification task. Carry out verification and obtain the first verification result. If the first verification result reaches the preset condition, the verification will be ended. If the error result is less than the first preset value, the verification device to be verified will be verified according to the preset verification task and the second verification will be obtained. As a result, if the second verification result reaches the preset conditions, the verification will be terminated. This method verifies the verification device by linking the abnormal results of the energy meter error distribution function model with the verification task triggering mechanism, which can promptly discover the abnormality of the verification system and improve the verification results. Accuracy and efficiency.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials, or features are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

In addition, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

The above-mentioned specific embodiments further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above-mentioned are only specific embodiments of the present invention and are not intended to limit the scope of the present invention. . It is particularly pointed out that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims (10)

1. A verification method for a calibration device, characterized by including: Obtain the calibration data of the calibration device to be verified; Check the calibration data using a preset electric energy meter error distribution function model to obtain a verification error result, wherein the preset electric energy meter error distribution function model is constructed based on historical calibration 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 the alarm process and generate an alarm verification task. If the error result is greater than the second preset value, If set to a value, the early warning process will be started and the early warning verification task will be generated; The verification device to be verified is verified according to the alarm verification task or early warning verification task to obtain a first verification result. If the first verification result reaches the preset condition, the verification is ended. If the error result is less than the first If the preset value is set, the verification device to be verified will be verified according to the preset verification task, and the second verification result will be obtained. If the second verification result reaches the preset condition, the verification will be ended. 2. The verification method of a verification device according to claim 1, further comprising: Determine whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If they exist and each of the same alarm verification tasks or each of the same early warning verification tasks belong to the same line and device, then determine Whether each of the same alarm verification tasks or early warning verification tasks has been completed, and if completed, the verification task ends; If there are and the same alarm verification tasks or the same early warning verification tasks belong to the same line and different devices, then merge the same alarm verification tasks or early warning verification tasks into one alarm. Verification tasks or the early warning verification tasks are carried out. 3. The verification method of a calibration device according to claim 1, characterized in that the preset electric energy meter error distribution function model is constructed based on historical calibration data, specifically: The relative error of the inspected electric energy meter is calculated based on the power data of the inspected electric energy meter, and the relative error of the inspected device is calculated based on the calibration data of the inspected calibration device; A basic error model is calculated based on the relative error of the tested electric energy meter and the relative error of the tested device, where the basic error model is: Y(%)＝X(%)-θ(%) Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard; The electric energy meter error distribution function model is constructed based on the central limit theorem and the Bayesian hierarchical model combined with the basic error model. 4. The verification method of a verification device according to claim 1, characterized in that the verification data is verified using a preset electric energy meter error distribution function model to obtain verification error results, specifically: Perform error calculation based on the kth calibration data of the calibration device to be verified to obtain an error distribution model; Obtain the posterior probability distribution according to Bayes' theorem and each parameter in the error distribution model; After using the Gibbs sampling method to sample the posterior probability distribution, the marginal distribution sample of the standard device error is obtained from the joint distribution sample, and the verification error result is calculated. 5. The verification method of a verification device according to claim 1, characterized in that the verification of the verification device to be verified according to the alarm verification task or early warning verification task is specifically: According to the alarm verification task or the early warning verification task, the preset physical verification equipment is used to verify the verification device to be verified. 6. The verification method of a calibration device according to claim 5, wherein the preset physical verification equipment is preferably a 0.02 level physical verification equipment that is consistent in size with the existing electric energy meter and has high stability. 7. A verification system for calibration equipment, characterized by including: The acquisition module is used to obtain the calibration data of the calibration device to be verified; The first verification module is used to check the calibration data using a preset electric energy meter error distribution function model to obtain a verification error result, wherein the preset electric energy meter error distribution function model is constructed based on historical calibration data; The second verification module is used to 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 first preset value and less than the second preset value, If the error result is greater than the second preset value, the early warning process will be started and an early warning verification task will be generated; The third verification module is used to verify the verification device to be verified according to the alarm verification task or early warning verification task, and obtain the first verification result. If the first verification result reaches the preset condition, the verification is ended. If If the error result is less than the first preset value, the verification device to be verified will be verified according to the preset verification task to obtain a second verification result. If the second verification result reaches the preset condition, the verification will end. 8. The verification system of a verification device according to claim 7, further comprising: The judgment module is used to judge whether there are multiple identical alarm verification tasks or multiple identical early warning verification tasks. If there are multiple identical alarm verification tasks or each of the same early warning verification tasks, they belong to the same line and In the device, it is judged whether each of the same alarm verification tasks or early warning verification tasks has been completed, and if completed, the verification task is ended; If there are and the same alarm verification tasks or the same early warning verification tasks belong to the same line and different devices, then merge the same alarm verification tasks or early warning verification tasks into one alarm. Verification tasks or the early warning verification tasks are carried out. 9. A verification system for a calibration device as claimed in claim 7, characterized in that the preset electric energy meter error distribution function model is constructed based on historical calibration data, specifically: The relative error of the inspected electric energy meter is calculated based on the power data of the inspected electric energy meter, and the relative error of the inspected device is calculated based on the calibration data of the inspected calibration device; A basic error model is calculated based on the relative error of the tested electric energy meter and the relative error of the tested device, where the basic error model is: Y(%)＝X(%)-θ(%) Among them, X (%) represents the basic error of the electric energy meter under test, θ(%) represents the relative error of the calibration device being tested,/> W _e represents the indicated electric energy of the device under test, and W ₀ represents the electric energy measured by the reference standard; The electric energy meter error distribution function model is constructed based on the central limit theorem and the Bayesian hierarchical model combined with the basic error model. 10. The verification system of a verification device according to claim 7, characterized in that the verification data is verified using a preset electric energy meter error distribution function model to obtain verification error results, specifically: Perform error calculation based on the kth calibration data of the calibration device to be verified to obtain an error distribution model; Obtain the posterior probability distribution according to Bayes' theorem and each parameter in the error distribution model; After using the Gibbs sampling method to sample the posterior probability distribution, the marginal distribution sample of the standard device error is obtained from the joint distribution sample, and the verification error result is calculated.