A system and method for adding a measurement error electric quantity to a buckle type three-phase electric energy meter field calibration device

Abstract

The application discloses a kind of for buckle three-phase electric energy meter field verification device's measurement error electric quantity superposition follow-up system and method, comprising: data acquisition module obtains electric parameter data;Error analysis module determines whether there is measurement error;Electric quantity calculation module determines error type when error analysis module determines that there is measurement error;Based on error type, query preset error type-measurement model data table, determine error measurement model;Based on error measurement model and electric parameter data, determine error electric quantity;According to preset correct measurement model and electric parameter data, determine target electric quantity;Superposition follow-up module calculates the difference between target electric quantity and error electric quantity, obtains the electric quantity that needs to be followed up;The electric quantity that needs to be followed up is superimposed to subsequent electric quantity measurement according to preset superposition rule and is followed up.The accuracy of electric quantity follow-up is improved by system, intelligent analysis and accurate follow-up mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric quantity superposition and correction, and in particular to a metering error electric quantity superposition and correction system and method for a buckle type three-phase electric energy meter on-site verification device. BACKGROUND

[0002] When an electric energy meter is found to be non-compliant based on a buckle type three-phase electric energy meter on-site verification device, the old meter often needs to be removed and replaced with a new meter, which takes a long time, and during which the user's electricity consumption cannot be accurately metered. The conventional method is to manually measure the instantaneous power before the electric energy meter stops, record the time of the input current before stopping and the input current after resuming, and estimate the user's electricity consumption. This method is not only tedious to operate manually, but also has a large estimation error and low accuracy when the load changes greatly, and customers do not accept the calculation results of the corrected electric quantity. In the prior art, there is no systematic and intelligent analysis and judgment and accurate correction mechanism for different types of metering errors, which cannot meet the efficient and accurate electric quantity correction requirements. SUMMARY

[0003] The present application aims to at least partially solve one of the above technical problems. To this end, the present application aims to provide a metering error electric quantity superposition and correction system and method for a buckle type three-phase electric energy meter on-site verification device, which is suitable for different types of metering errors and improves the accuracy of electric quantity correction through a systematic and intelligent analysis and judgment and accurate correction mechanism.

[0004] To achieve the above-mentioned purpose, the embodiment of the present application provides a metering error electric quantity superposition and correction system for a buckle type three-phase electric energy meter on-site verification device, comprising:

[0005] A data acquisition module is configured to acquire electric quantity data through the buckle type three-phase electric energy meter on-site verification device.

[0006] An error analysis module is connected to the data acquisition module and is configured to analyze the electric quantity data based on a pre-trained analysis model and determine whether there is a metering error.

[0007] An electric quantity calculation module is connected to the data acquisition module and the error analysis module and is configured to determine the error type when the error analysis module determines that there is a metering error, query a pre-set error type-metering model data table based on the error type, determine an error metering model, determine an error electric quantity based on the error metering model and the electric quantity data, and determine a target electric quantity based on a pre-set correct metering model and the electric quantity data.

[0008] A superposition and correction module is connected to the electric quantity calculation module and is configured to calculate the difference between the target electric quantity and the error electric quantity to obtain a corrected electric quantity, and superimpose the corrected electric quantity into subsequent electric quantity metering according to a pre-set superposition rule for correction.

[0009] According to some embodiments of the present application, the data acquisition module comprises:

[0010] A voltage transformer is used to acquire a voltage signal through the buckle type three-phase electric energy meter field verification device.

[0011] A current transformer is used to acquire a current signal through the buckle type three-phase electric energy meter field verification device.

[0012] An electric parameter acquisition chip is used to acquire power, phase and frequency through the buckle type three-phase electric energy meter field verification device.

[0013] The electric parameter data is determined based on the voltage signal, the current signal, the power, the phase and the frequency.

[0014] According to some embodiments of the present application, the training method of the analysis model comprises:

[0015] A sample data set is obtained; the sample data set comprises historical electric parameter data and a corresponding label, and the label is a measurement normal label or a measurement abnormal label.

[0016] The sample data set is input into the analysis model, and a predicted label is output.

[0017] The predicted label is compared with the labeled label, and the analysis model is iteratively trained according to the comparison result until the predicted label is consistent with the labeled label, thereby obtaining the trained analysis model.

[0018] According to some embodiments of the present application, the error analysis module determines the error type, comprising:

[0019] A determination module is used to analyze the electric parameter data based on the analysis model, and determine an abnormal feature.

[0020] An inference module is used to infer in a pre-constructed fault tree according to the abnormal feature based on a fault tree inference algorithm, and locate to a specific error type; the error type comprises current reverse connection, phase sequence error, current transformer ratio error and missing phase voltage.

[0021] According to some embodiments of the present application, the preset superposition rule is proportional superposition; the electric quantity calculation module comprises:

[0022] A prediction module is used to predict the estimated power consumption of each electric quantity settlement period in a subsequent preset time period according to historical electric quantity data and current power grid load prediction data.

[0023] A first superposition module is used to calculate the proportion of the estimated power consumption of each period in the total estimated power consumption as a make-up proportion; and the make-up electric quantity is distributed to each period for superposition according to the make-up proportion.

[0024] According to some embodiments of the present application, the preset superposition rule is a periodical superposition rule; the power calculation module comprises:

[0025] The calculation module is configured to calculate the power value to be superimposed in each period according to the required complementary power and a specified complementary deadline;

[0026] The second superposition module is configured to superimpose the corresponding power value to be superimposed into the power metering of the corresponding period from the start of each power settlement period.

[0027] According to some embodiments of the present application, the uploading module is further configured to:

[0028] The power parameter data, the metering error data and the complementary power data are uploaded to the monitoring center server of the power management department and stored.

[0029] According to some embodiments of the present application, the uploading module comprises:

[0030] The establishment module is configured to scan the power parameter data, the metering error data and the complementary power data to obtain a plurality of keywords; classify and arrange the keywords according to semantics, identify entity keywords and process keywords; each entity keyword corresponds to a semantic node, and each process keyword corresponds to a process relationship between entities; the semantic nodes and the process relationships are established based on the entity keywords and the process keywords to form a semantic graph;

[0031] The division module is configured to divide the semantic graph and the preset data importance weight to obtain a plurality of sub-regions; analyze the attributes of the sub-regions, and divide the sub-regions into static sub-regions and dynamic sub-regions according to the attribute information;

[0032] The packaging module is configured to:

[0033] Pack all the static sub-regions as a data packet to generate a static data packet;

[0034] Pack each dynamic sub-region as a data packet to generate a plurality of dynamic data packets; arrange the dynamic data packets based on the importance of the dynamic sub-regions from large to small to obtain a first queue;

[0035] Add the static data packet to the end of the first queue to generate a second queue;

[0036] The power parameter data, the metering error data and the complementary power data are uploaded to the monitoring center server of the power management department based on the second queue and stored.

[0037] According to some embodiments of the present application, the display module is further configured to visually display the power parameter data, the metering error data and the complementary power data based on a chart.

[0038] According to some embodiments of the present application, the superposition and supplement method of the measurement error electric quantity superposition and supplement system for the buckle type three-phase electric energy meter field verification device as described above comprises:

[0039] Obtaining electric quantity data through the buckle type three-phase electric energy meter field verification device;

[0040] Analyzing the electric quantity data based on the pre-trained analysis model to determine whether there is a measurement error;

[0041] When it is determined that there is a measurement error, determining the error type; querying the preset error type-measurement model data table based on the error type to determine the error measurement model; determining the error electric quantity based on the error measurement model and the electric quantity data; determining the target electric quantity according to the preset correct measurement model and the electric quantity data;

[0042] Calculating the difference between the target electric quantity and the error electric quantity to obtain the electric quantity to be supplemented; and superimposing the electric quantity to be supplemented into the subsequent electric quantity measurement according to the preset superimposition rule for supplement.

[0043] The present application proposes a measurement error electric quantity superposition and supplement system and method for a buckle type three-phase electric energy meter field verification device. The electric quantity calculation module calculates the error electric quantity and the target electric quantity based on the accurate electric quantity data according to the error measurement model and the preset correct measurement model, respectively, can accurately determine the electric quantity deviation caused by the measurement error, provides a reliable data basis for subsequent supplement, and greatly improves the accuracy of electric quantity calculation. The error analysis module analyzes the electric quantity data with the help of the pre-trained analysis model, can quickly and intelligently determine whether there is a measurement error and determine the error type. Compared with manual analysis, the error judgment time is greatly shortened, the accuracy and reliability of the judgment are improved, and the misjudgment caused by human factors is reduced. The superposition and supplement module automatically calculates the difference between the target electric quantity and the error electric quantity to obtain the electric quantity to be supplemented, and automatically superimposes it into the subsequent electric quantity measurement according to the preset superimposition rule for supplement. The entire supplement process does not require a large amount of manual intervention, simplifies the supplement process, and improves the efficiency and accuracy of electric quantity supplement.

[0044] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings.

[0045] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the application, and do not limit the application. In the drawings:

[0047] Figure 1 is a block diagram of a measurement error electric quantity superposition and compensation system for a buckle type three-phase electric energy meter on-site calibration device according to an embodiment of the application;

[0048] Figure 2 is a block diagram of a data acquisition module according to an embodiment of the application;

[0049] Figure 3 is a flowchart of a measurement error electric quantity superposition and compensation method for a buckle type three-phase electric energy meter on-site calibration device according to an embodiment of the application. DETAILED DESCRIPTION

[0050] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to explain and illustrate the application, and do not limit the application.

[0051] As shown in Figure 1 , an embodiment of the application proposes a measurement error electric quantity superposition and compensation system for a buckle type three-phase electric energy meter on-site calibration device, comprising:

[0052] a data acquisition module, configured to acquire electric quantity data through the buckle type three-phase electric energy meter on-site calibration device;

[0053] an error analysis module, connected with the data acquisition module, configured to analyze the electric quantity data based on a pre-trained analysis model, and determine whether there is a measurement error;

[0054] an electric quantity calculation module, connected with the data acquisition module and the error analysis module, configured to determine an error type when the error analysis module determines that there is a measurement error; query a preset error type-measurement model data table based on the error type, determine an error measurement model; determine an error electric quantity based on the error measurement model and the electric quantity data; determine a target electric quantity based on a preset correct measurement model and the electric quantity data;

[0055] a superposition and compensation module, connected with the electric quantity calculation module, configured to calculate a difference between the target electric quantity and the error electric quantity to obtain a compensation electric quantity; and superimpose the compensation electric quantity to subsequent electric quantity measurement according to a preset superposition rule for compensation.

[0056] The working principle and beneficial effects of the above technical solution are as follows: the analysis model is a model obtained based on deep learning. The error types include current reverse connection, phase sequence error, current transformer ratio error, and missing phase voltage. The error power is determined based on the error metering model and the electric parameter data; if the error type is current reverse connection, it is assumed that the A-phase current is reversed. Based on the error metering model, the error total power is calculated: (a-phase power sign error). Wherein, 、 、 are the a-phase voltage, b-phase voltage, and c-phase voltage, respectively; 、 、 are the a-phase current, b-phase current, and c-phase current, respectively; 、 、 are the a-phase phase angle, b-phase phase angle, and c-phase phase angle, respectively. The product of the error total power and the metering time t is taken as the error power. If the error type is phase sequence error, it is assumed that it changes from abc to acb, and the error total power is calculated: a phase angle shift occurs. The product of the error total power and the metering time t is taken as the error power. If the error type is current transformer ratio error, the example is (actual 100 / 5, metering according to 200 / 5), and the error total power is calculated: correct total power*(100 / 5) / (200 / 5), ratio scaling error. The product of the error total power and the metering time t is taken as the error power. If the error type is missing phase voltage, the example is missing b-phase voltage (voltage wire breakage), and the error total power is calculated: missing b-phase power. The product of the error total power and the metering time t is taken as the error power. The target power is determined based on the preset correct metering model and the electric parameter data, that is:

[0057] The correct total power is calculated: The product of the correct total power and the metering time t is taken as the target power.

[0058] The preset superposition rule is proportional superposition or periodic superposition.

[0059] The power calculation module calculates error power and target power according to an error metering model and a preset correct metering model based on accurate electric parameter data, can accurately determine the power deviation caused by metering errors, provides a reliable data basis for subsequent compensation, and greatly improves the accuracy of power calculation. The error analysis module analyzes the electric parameter data with the help of a pre-trained analysis model, can quickly and intelligently judge whether there is a metering error and determine the error type. Compared with manual analysis, the error judgment time is greatly shortened, the accuracy and reliability of judgment are improved, and the misjudgment caused by human factors is reduced. The superimposed compensation module automatically calculates the difference between the target power and the error power to obtain the compensation power, and automatically superimposes it into the subsequent power metering according to the preset superimposed rules for compensation. The entire compensation process does not require a large amount of manual intervention, simplifies the compensation process, and improves the efficiency and accuracy of power compensation.

[0060] As shown in Figure 2 According to some embodiments of the application, the data acquisition module comprises:

[0061] A voltage transformer is used to acquire voltage signals through the buckle type three-phase electric energy meter field verification device.

[0062] A current transformer is used to acquire current signals through the buckle type three-phase electric energy meter field verification device.

[0063] An electric parameter acquisition chip is used to acquire power, phase and frequency through the buckle type three-phase electric energy meter field verification device.

[0064] The electric parameter data is determined based on the voltage signals, current signals, power, phase and frequency.

[0065] The working principle and beneficial effects of the above technical solution are: based on the voltage signals, current signals, power, phase and frequency, the electric parameter data can be accurately determined, and the power metering can be conveniently performed.

[0066] According to some embodiments of the application, the training method of the analysis model comprises:

[0067] Obtain a sample data set; the sample data set includes historical electric parameter data and marks corresponding labels, and the labels are normal metering labels or abnormal metering labels;

[0068] Input the sample data set into the analysis model, and output the predicted label;

[0069] Compare the predicted label with the marked label, and iteratively train the analysis model according to the comparison result, until the predicted label is consistent with the marked label, and the trained analysis model is obtained.

[0070] The working principle and beneficial effects of the technical solution are as follows: a sample data set is acquired; the sample data set includes historical electric parameter data and is labeled with corresponding labels, the labels being measurement normal labels or measurement abnormal labels; the sample data set is input into an analysis model, and a predicted label is output; the predicted label is compared with the labeled label, and the analysis model is iteratively trained according to a comparison result, until the predicted label is consistent with the labeled label, and a trained analysis model is obtained. The accurate analysis model is obtained, and whether there is a measurement error is determined based on the analysis model.

[0071] According to some embodiments of the present application, the error analysis module determines the error type, including:

[0072] The determining module is configured to analyze the electric parameter data based on the analysis model, and determine abnormal features;

[0073] The reasoning module is configured to perform reasoning in a pre-constructed fault tree according to the abnormal features based on a fault tree reasoning algorithm, and locate to a specific error type; the error type includes current reverse connection, phase sequence error, current transformer ratio error, and missing phase voltage.

[0074] The working principle and beneficial effects of the technical solution are as follows: the analysis model models the distribution rule of the electric parameter data under normal working conditions, such as voltage fluctuation range, three-phase current balance degree, and power factor reasonable interval. The analysis model is used to analyze the electric parameter data, and the features that the electric parameter data deviates from the normal range are taken as abnormal features. The fault tree takes the measurement error as a top event, the abnormal feature category (such as power abnormality, phase abnormality, and ratio abnormality) as an intermediate event, and the specific error type (such as current transformer ratio error, A-phase current reverse connection, phase sequence error, and voltage disconnection) as a bottom event. The events in the tree are connected through logical relationships (AND gate and OR gate). The fault tree reasoning algorithm matches the extracted abnormal features with the intermediate events of the fault tree, traces back layer by layer through the logical relationship, excludes the impossible path, and finally locks the unique or most possible bottom event (i.e., the specific error type). The specific error type is accurately determined.

[0075] According to some embodiments of the present application, the preset superposition rule is proportional superposition; and the electric quantity calculation module includes:

[0076] The prediction module is configured to predict the estimated power consumption of each power consumption settlement period in a subsequent preset time period according to the historical electric quantity data and the current power grid load prediction data;

[0077] The first superposition module is configured to calculate the proportion of the estimated power consumption of each period in the total estimated power consumption as a makeup proportion, and distribute the makeup electric quantity to each period for superposition according to the makeup proportion.

[0078] The working principle and beneficial effects of the technical solution are as follows: according to historical power data and current power grid load prediction data, the estimated power consumption of each power settlement period in a subsequent preset time period is predicted; the proportion of the estimated power consumption of each period in the total estimated power consumption is calculated as a makeup proportion; and the makeup power is distributed to each period according to the makeup proportion for superposition. The future power consumption is estimated in combination with the historical power data and the current power grid load prediction data, so that the distribution of the makeup power can closely match the actual power consumption law of the user, and the distribution of the makeup power can also adapt to the overall operation condition of the power grid, thereby facilitating the smooth distribution of the makeup power and reducing the billing mutation.

[0079] According to some embodiments of the application, the preset superposition rule is a period-by-period superposition rule; and the power calculation module comprises:

[0080] The calculation module is configured to calculate the power value that needs to be superimposed in each period according to the makeup power and a specified makeup deadline;

[0081] The second superposition module is configured to superimpose the corresponding power value that needs to be superimposed into the power metering of the corresponding period from the start of each power settlement period.

[0082] The working principle and beneficial effects of the technical solution are as follows: the makeup power of each period is fixed, the total makeup power, the deadline and the amount of each period can be intuitively understood, and misunderstanding caused by floating proportion is avoided. The makeup power does not abnormally change due to fluctuations in the actual power consumption of the user, and additional impact on the power grid load prediction and the billing system is avoided; the metering resources are planned in advance to ensure that the makeup process is closed in a controllable time.

[0083] According to some embodiments of the application, the uploading module is further configured to:

[0084] The power parameter data, the metering error data and the makeup power data are uploaded to a monitoring center server of a power management department and stored.

[0085] The working principle and beneficial effects of the technical solution are as follows: the monitoring center server can store the power parameter data, the metering error data and the makeup power data, and data traceability can be performed subsequently.

[0086] According to some embodiments of the application, the uploading module comprises:

[0087] The establishment module is configured to scan the power parameter data, the metering error data and the makeup power data to obtain a plurality of keywords; classify and arrange the keywords according to the semantics of the keywords to identify entity keywords and process keywords; each entity keyword corresponds to a semantic node, and each process keyword corresponds to a process relationship between entities; semantic nodes and process relationships are established based on the entity keywords and the process keywords to form a semantic graph.

[0088] a dividing module, configured to divide according to the semantic graph and a preset data importance weight to obtain a plurality of sub-regions; analyze the attributes of the sub-regions, and divide into static sub-regions and dynamic sub-regions according to the attribute information;

[0089] a packaging module, configured to:

[0090] pack all the static sub-regions as a data packet to generate a static data packet;

[0091] pack each dynamic sub-region as a data packet to generate a plurality of dynamic data packets; arrange the dynamic data packets in descending order of importance of the dynamic sub-regions to obtain a first queuing queue;

[0092] add the static data packet to the end of the first queuing queue to generate a second queuing queue;

[0093] upload the electric quantity data, the metering error data and the complementary electric quantity data to a monitoring center server of a power management department based on the second queuing queue, and store them.

[0094] The working principle and beneficial effects of the above technical solution are as follows: the establishing module scans the electric quantity data, the metering error data and the complementary electric quantity data to obtain a plurality of keywords; classifies and organizes according to the semantics of the keywords to identify entity keywords and process keywords; the entity keywords correspond to entity objects, and the process keywords correspond to the association relationship between the entity objects. The semantic nodes and process relationships are established based on the entity keywords and the process keywords to form a semantic graph; the data between the electric quantity data, the metering error data and the complementary electric quantity data is arranged, which is equivalent to a data model. The preset data importance weight is set according to the needs of the receiver. The semantic graph and the preset data importance weight are divided to obtain a plurality of sub-regions; the attributes of the sub-regions are analyzed, the attributes include static attributes and dynamic attributes, and the sub-regions are divided into static sub-regions and dynamic sub-regions according to the attribute information; the static sub-regions represent relatively fixed data and will not change, and the dynamic sub-regions represent data that will change. All the static sub-regions are packed as a data packet to generate a static data packet; each dynamic sub-region is packed as a data packet to generate a plurality of dynamic data packets; the dynamic data packets are arranged in descending order of importance of the dynamic sub-regions to obtain a first queuing queue; the static data packet is added to the end of the first queuing queue to generate a second queuing queue; the electric quantity data, the metering error data and the complementary electric quantity data are uploaded to a monitoring center server of a power management department based on the second queuing queue, and are stored. The data is packaged and uploaded based on the attributes and importance of the data, which improves the efficiency and accuracy of data uploading.

[0095] According to some embodiments of the present invention, it further includes: a display module for visually displaying electrical parameter data, metering error data, and supplementary power data based on charts.

[0096] The working principle and beneficial effects of the above technical solution are: it facilitates a clear and accurate understanding of electrical parameter data, metering error data, and supplementary power data.

[0097] like Figure 3 As shown, according to some embodiments of the present invention, the superposition and compensation method for the metering error superposition and compensation system for the snap-on three-phase energy meter field verification device as described above includes steps S1-S4:

[0098] S1. Obtain electrical parameter data through a snap-on three-phase power meter field verification device;

[0099] S2. Analyze the electrical parameter data based on the pre-trained analysis model to determine whether there are measurement errors;

[0100] S3. When a measurement error is confirmed, determine the error type; query the preset error type-measurement model data table based on the error type to determine the erroneous measurement model; determine the erroneous electricity amount based on the erroneous measurement model and electrical parameter data; determine the target electricity amount based on the preset correct measurement model and electrical parameter data.

[0101] S4. Calculate the difference between the target power and the erroneous power to obtain the power that needs to be supplemented; add the power that needs to be supplemented to the subsequent power metering according to the preset superposition rules.

[0102] The working principle and beneficial effects of the above technical solution are as follows: Based on accurate electrical parameter data, the erroneous electricity amount and the target electricity amount are calculated according to both the error measurement model and the preset correct measurement model. This accurately determines the electricity deviation caused by measurement errors, providing a reliable data foundation for subsequent compensation and greatly improving the accuracy of electricity calculation. By analyzing the electrical parameter data using a pre-trained analysis model, it is possible to quickly and intelligently determine whether measurement errors exist and identify the type of error. Compared to manual analysis, this significantly shortens the error judgment time, improves the accuracy and reliability of the judgment, and reduces misjudgments caused by human factors. The difference between the target electricity amount and the erroneous electricity amount is automatically calculated to obtain the electricity amount that needs to be compensated, and this amount is automatically added to subsequent electricity measurements according to preset superposition rules. The entire compensation process requires minimal manual intervention, simplifying the compensation process and improving the efficiency and accuracy of electricity compensation.

[0103] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A system for adding and compensating measurement error electric quantity for a field calibration device of a buckle type three-phase electric energy meter, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire electric quantity data through a buckle type three-phase electric energy meter field verification device; an error analysis module is connected with the data acquisition module and is configured to analyze the electric quantity data based on a pre-trained analysis model, and determine whether there is a measurement error; an electric quantity calculation module is connected with the data acquisition module and the error analysis module, and is configured to determine an error type when the error analysis module determines that there is a measurement error; query a preset error type-measurement model data table based on the error type to determine an error measurement model; and determine an error electric quantity based on the error measurement model and the electric quantity data; determine a target electric quantity based on the preset correct measurement model and the electric quantity data; a superposition and follow-up module is connected with the electric quantity calculation module, and is configured to calculate a difference between the target electric quantity and the error electric quantity to obtain a follow-up electric quantity; the follow-up electric quantity is superimposed on subsequent electric quantity measurement according to a preset superposition rule for follow-up; a training method of the analysis model comprises the following steps: acquire a sample data set; the sample data set comprises historical electric quantity data and a corresponding label, and the label is a normal measurement label or an abnormal measurement label; input the sample data set into the analysis model to output a predicted label; compare the predicted label with the labeled label, and iteratively train the analysis model according to a comparison result until the predicted label is consistent with the labeled label, thereby obtaining a trained analysis model; the error analysis module determines the error type, which comprises the following steps: a determination module is configured to analyze the electric quantity data based on the analysis model to determine an abnormal feature; an inference module is configured to infer in a pre-constructed fault tree according to the abnormal feature based on a fault tree inference algorithm to locate a specific error type; the error type includes current reverse connection, phase sequence error, current transformer ratio error, and missing phase voltage.

2. The metrology error electrical quantity superposition chasing-up system for the on-site calibration device of the buckle type three-phase electric energy meter according to claim 1, characterized in that, The data acquisition module comprises: a voltage transformer configured to acquire a voltage signal through the buckle type three-phase electric energy meter field verification device; a current transformer configured to acquire a current signal through the buckle type three-phase electric energy meter field verification device; an electric quantity acquisition chip configured to acquire power, phase and frequency through the buckle type three-phase electric energy meter field verification device; the electric quantity data is determined based on the voltage signal, the current signal, the power, the phase and the frequency.

3. The metrology error electrical quantity superposition chasing-up system for the on-site calibration device of the buckle type three-phase electric energy meter according to claim 1, characterized in that, The preset superposition rule is proportional superposition; The electric quantity calculation module comprises: a prediction module configured to predict an estimated electric quantity of each electric quantity settlement period in a subsequent preset time period based on historical electric quantity data and current power grid load prediction data; a first superposition module configured to calculate a proportion of each period estimated electric quantity in total estimated electric quantity as a follow-up proportion; and distribute the follow-up electric quantity to each period for superposition according to the follow-up proportion.

4. The metrology error electrical quantity superposition chasing-up system for the on-site calibration device of the buckle type three-phase electric energy meter according to claim 1, characterized in that, The preset superposition rule is a period superposition rule; The electric quantity calculation module comprises: a calculation module configured to calculate an electric quantity value to be superimposed in each period based on the follow-up electric quantity and a specified follow-up period; a second superposition module configured to superimpose the corresponding electric quantity value to be superimposed into the electric quantity measurement of the corresponding period from the beginning of each electric quantity settlement period.

5. The metrology error electrical quantity superposition chasing-up system for the on-site calibration device of the buckle type three-phase electric energy meter according to claim 1, characterized in that, Further comprising: an uploading module configured to: The electric parameter data, the metering error data and the complementary electric quantity data are uploaded to a monitoring center server of a power management department and stored.

6. The metrology error electrical quantity superposition chasing-up system for the on-site calibration device of the buckle type three-phase electric energy meter according to claim 5, characterized in that, The uploading module comprises: The establishing module is configured to scan the electric parameter data, the metering error data and the complementary electric quantity data to obtain a plurality of keywords, classify and arrange the keywords according to semantics, identify entity keywords and process keywords, correspond each entity keyword to a semantic node, correspond each process keyword to a process relationship between entities, establish semantic nodes and process relationships based on the entity keywords and the process keywords, and form a semantic graph. The dividing module is configured to divide the semantic graph and a preset data importance weight to obtain a plurality of sub-regions, analyze attributes of the sub-regions, and divide the sub-regions into static sub-regions and dynamic sub-regions according to attribute information. The packing module is configured to: pack all the static sub-regions as a data packet to generate a static data packet; pack each dynamic sub-region as a data packet to generate a plurality of dynamic data packets, arrange the dynamic data packets based on importance of the dynamic sub-regions from large to small to obtain a first queue, and add the static data packet to the end of the first queue to generate a second queue. The uploading module is configured to upload the electric parameter data, the metering error data and the complementary electric quantity data to the monitoring center server of the power management department based on the second queue and store the electric parameter data, the metering error data and the complementary electric quantity data. The display module is configured to visually display the electric parameter data, the metering error data and the complementary electric quantity data based on a graph.

7. The metrology error electrical quantity superposition chasing-up system for the on-site calibration device of the buckle type three-phase electric energy meter according to claim 1, characterized in that, The method comprises: obtaining the electric parameter data through a buckle type three-phase electric energy meter field verification device; 8. The superposition and correction method of the measurement error electric quantity superposition and correction system for the on-site calibration device of the buckle type three-phase electric energy meter according to any one of claims 1-7, characterized in that, analyzing the electric parameter data based on a pre-trained analysis model to determine whether there is a metering error; determining an error type when it is determined that there is a metering error, querying a preset error type-metering model data table based on the error type to determine an error metering model, determining an error electric quantity based on the error metering model and the electric parameter data; determining a target electric quantity based on a preset correct metering model and the electric parameter data; calculating a difference between the target electric quantity and the error electric quantity to obtain a complementary electric quantity; stacking the complementary electric quantity to subsequent electric quantity metering according to a preset stacking rule for complementary. ​ ​ ​

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