Metering verification method and system for electric energy meter group

Abstract

The invention relates to the technical field of intelligent metering of electric energy meters, and discloses a metering verification method and system for an electric energy meter group. The method comprises the steps of selecting a central node in a group through a dynamic scoring algorithm, generating a dynamic measurement reference based on environment information, collecting and detecting deviation data in real time, and carrying out calibration and data consistency processing on abnormal nodes. Compared with the prior art, the technical problem that high-precision automatic calibration cannot be realized by a traditional method in a manual regular calibration mode, especially under a complex power grid environment and a large-scale electric energy meter deployment condition, is solved. According to the invention, the optimal central node is automatically selected, and the metering reference value is dynamically adjusted based on the environmental condition, so that the real-time and adaptive metering calibration of the electric energy meters in the group is realized, the problems of low manual calibration efficiency and inconsistent data are avoided, and the metering efficiency of the electric energy meter group is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent metering of electric energy meters, and in particular relates to a metering calibration method and system for an electric energy meter group. Background Art

[0002] At present, the metering and verification technology of the energy meter group has the problem of insufficient accuracy and automation level. For example, the traditional energy meter verification method usually relies on manual calibration or centralized verification at a fixed period. This method is prone to reduce the calibration accuracy and has high operating costs when the environmental conditions vary greatly, the number of nodes is large, or the communication conditions are complex. The existing technology cannot fully meet the needs of high-precision, real-time, and automated verification of energy meter groups in smart grids. Therefore, there is an urgent need for a metering and verification method for energy meter groups that can achieve high-precision and automated verification under complex environmental conditions and large-scale energy meter groups, so as to improve the accuracy of metering data and the consistency of data within the group, thereby meeting the smart grid's higher requirements for data real-time and reliability. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a measurement and calibration method for a group of electric energy meters, aiming to solve the technical problem that the conventional technology usually relies on manual periodic calibration, especially in complex power grid environments and large-scale electric energy meter deployment conditions, and the traditional method cannot achieve high-precision automatic calibration.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a measurement verification method for an electric energy meter group,

[0005] The metering verification method for an electric energy meter group includes:

[0006] Step S10: Obtain the data of communication stability S, signal strength R and historical measurement error rate E of each member node from the electric energy meter group, and perform weighted scoring on the above three parameters of each member node. The scoring formula is:

[0007]

[0008] α+β+γ=1

[0009] Among them, Score is the score, α, β and γ are dynamic weight coefficients, S exp is the preset standard communication stability, R exp is the preset standard signal strength;

[0010] Arrange all member nodes in ascending order according to the Score, and select the member node with the highest score as the central node;

[0011] Step S20: The central node obtains historical metering data from the electric energy meter group and calculates the historical metering average value H avg ;

[0012] Based on the real-time collected environmental information including temperature data and humidity data, combined with the grid load information and the historical metering average value H avg Generate real-time dynamic benchmark value D ref , dynamic reference value D ref The calculation formula is:

[0013]

[0014] Among them, θ i is the environmental correction factor, X i is the environmental parameter value of the integrated temperature data and humidity data, n is the total number of environmental parameters, and i is the i-th environmental parameter;

[0015] Step S30: The central node receives the real-time metering data D of the member nodes at a fixed collection frequency. node , and real-time measurement data D node Deviation calculation is performed with the dynamic reference value to obtain the deviation value ΔD;

[0016] When the deviation value ΔD exceeds the preset measurement threshold, it will be compared with the real-time measurement data D node The corresponding member node is marked as an abnormal node, and the member node is added to the abnormal detection queue;

[0017] Step S40: The central node sends a calibration instruction to the abnormal nodes in the abnormal detection queue, and the abnormal nodes perform data correction based on the correction coefficient. The correction formula is:

[0018] D new =D current ×K

[0019] Among them, D current is the current measurement value, D new is the corrected measurement value, K is the correction coefficient of the abnormal node, and its value is determined according to the deviation value ΔD;

[0020] Step S50: After the calibration is completed, the correction coefficient of each abnormal node is combined to obtain the comprehensive correction coefficient M. The central node encrypts the comprehensive correction coefficient M to generate calibration data, and sends the calibration data to normal member nodes other than the abnormal nodes. Each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient to achieve consistency of the measurement data within the group.

[0021] Preferably, in step S40, K iis the correction coefficient of the ith abnormal node, and its value is determined according to the deviation value ΔD. The specific formula is:

[0022]

[0023] Wherein, T is a preset reference deviation threshold value, which is used to control the calibration intensity.

[0024] Preferably, in step S50, the correction coefficient of each abnormal node is integrated to obtain a comprehensive correction coefficient M, and the calculation formula is:

[0025]

[0026] Among them, M is the comprehensive correction coefficient, N is the total number of abnormal nodes, K j is the correction coefficient of the jth abnormal node, exp is the index fetching function, and ln is the logarithmic function.

[0027] Preferably, in step S50, each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient, the formula is Z new =Z current ×M, where Z new The value after the normal member node updates its own measurement value, Z current It is the current measurement value of the normal member node.

[0028] Preferably, in step S20, the historical measurement average value H avg The calculation of is done using the arithmetic mean method.

[0029] Preferably, in step S30, the fixed collection frequency is to collect data once every 3 minutes.

[0030] Preferably, in step S50, the process of encrypting the comprehensive correction coefficient M adopts the AES algorithm.

[0031] The present invention also provides a measurement verification system for an electric energy meter group, comprising:

[0032] The scoring module is used to obtain the data of communication stability S, signal strength R and historical measurement error rate E of each member node from the electric energy meter group, and to perform weighted scoring on the above three parameters of each member node. The scoring formula is:

[0033]

[0034] α+β+γ=1

[0035] Among them, Score is the score, α, β and γ are dynamic weight coefficients, S exp is the preset standard communication stability, R exp is the preset standard signal strength;

[0036] Arrange all member nodes in ascending order according to the Score, and select the member node with the highest score as the central node;

[0037] The benchmark generation module is used for the central node to obtain historical measurement data from the electric energy meter group and calculate the historical measurement average value H avg ;

[0038] Based on the real-time collected environmental information including temperature data and humidity data, combined with the grid load information and the historical metering average value H avg Generate real-time dynamic benchmark value D ref , dynamic reference value D ref The calculation formula is:

[0039]

[0040] Among them, θ i is the environmental correction factor, X i is the environmental parameter value of the integrated temperature data and humidity data, n is the total number of environmental parameters, and i is the i-th environmental parameter;

[0041] The data collection and deviation calculation module is used for the central node to receive the real-time metering data of the member nodes at a fixed collection frequency. node , and real-time measurement data D node Deviation calculation is performed with the dynamic reference value to obtain the deviation value ΔD;

[0042] When the deviation value ΔD exceeds the preset measurement threshold, it will be compared with the real-time measurement data D node The corresponding member node is marked as an abnormal node, and the member node is added to the abnormal detection queue;

[0043] The calibration module is used by the central node to send calibration instructions to abnormal nodes in the abnormal detection queue. The abnormal nodes correct the data based on the correction coefficient. The correction formula is:

[0044] D new =D current ×K

[0045] Among them, D current is the current measurement value, D new is the corrected measurement value, K is the correction coefficient of the abnormal node, and its value is determined according to the deviation value ΔD;

[0046] The comprehensive processing module is used to obtain the comprehensive correction coefficient M by integrating the correction coefficient of each abnormal node after the calibration is completed. The central node encrypts the comprehensive correction coefficient M to generate calibration data, and sends the calibration data to normal member nodes other than the abnormal nodes. Each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient to achieve consistency of measurement data within the group.

[0047] The present invention also provides a metering and verification device for an electric energy meter group, comprising a memory, a processor, and a metering and verification program for an electric energy meter group stored in the memory and executable on the processor. When the metering and verification program for an electric energy meter group is executed by the processor, the metering and verification method for an electric energy meter group is implemented.

[0048] The present invention also provides a computer program product, including a metering verification program for an electric energy meter group, wherein the metering verification program for an electric energy meter group implements the metering verification method for an electric energy meter group when the metering verification program for an electric energy meter group is executed by a processor.

[0049] The beneficial effect of the present invention is that compared with the conventional method of relying on manual periodic calibration, especially in complex power grid environments and large-scale deployment of electric energy meters, the traditional method cannot achieve the technical problem of high-precision automatic calibration. The present application realizes real-time and adaptive measurement calibration of electric energy meters in a group by automatically selecting the best central node and dynamically adjusting the measurement reference value based on environmental conditions, thereby avoiding the problems of low manual calibration efficiency and inconsistent data, and significantly improving the measurement efficiency of the electric energy meter group. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 The figure is a flow chart of a first embodiment of a measurement verification method for an electric energy meter group according to the present invention.

[0052] Figure 2 The present invention is a schematic diagram of a device for a measurement and calibration method for an electric energy meter group. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1: Figure 1 , which is a flow chart of a first embodiment of a metering and verification method for a group of electric energy meters according to the present invention, provides a first embodiment of a metering and verification method for a group of electric energy meters according to the present invention.

[0055] In a first embodiment, the metering verification method for an electric energy meter group includes:

[0056] Step S10: Obtain the data of communication stability S, signal strength R and historical measurement error rate E of each member node from the electric energy meter group, and perform weighted scoring on the above three parameters of each member node. The scoring formula is:

[0057]

[0058] α+β+γ=1

[0059] Among them, Score is the score, α, β and γ are dynamic weight coefficients, S exp is the preset standard communication stability, R exp is the preset standard signal strength;

[0060] Arrange all member nodes in ascending order according to the Score, and select the member node with the highest score as the central node;

[0061] It should be noted that the dynamic weight coefficients α, β and γ can be adjusted according to actual needs to balance the impact of communication stability, signal strength and measurement error on the score under different environmental conditions. For example, when the power grid environment is more complex, the weight of communication stability S can be increased to ensure the stability of the central node.

[0062] It is understandable that, when selecting a central node, increasing the priority of member nodes with high scores to become central nodes can effectively improve the overall communication quality and data consistency of the electric energy meter group.

[0063] It should be understood that by setting dynamic weights, the scoring system can adapt to different environments in the power grid, avoiding excessive influence of a single factor on the scoring results, thereby ensuring that the selected central node has more comprehensive stability.

[0064] Step S20: The central node obtains historical metering data from the electric energy meter group and calculates the historical metering average value H avg ;

[0065] Based on the real-time collected environmental information including temperature data and humidity data, combined with the grid load information and the historical metering average value H avg Generate real-time dynamic benchmark value D ref , dynamic reference value D ref The calculation formula is:

[0066]

[0067] Among them, θ i is the environmental correction factor, X i is the environmental parameter value of the integrated temperature data and humidity data, n is the total number of environmental parameters, and i is the i-th environmental parameter;

[0068] It should be noted that the environmental correction factor θ i The setting is based on actual conditions to balance the impact of different environmental parameters on the measurement benchmark. Environmental factors such as temperature and humidity have a certain impact on the measurement accuracy of the electric energy meter. By dynamically adjusting the benchmark value, the error caused by environmental changes can be compensated, thereby improving the accuracy of measurement.

[0069] It can be understood that step S20 not only takes into account the historical measurement data, but also makes adjustments based on real-time environmental conditions, and is able to adapt to complex external environmental changes to ensure the accuracy of the dynamic reference value.

[0070] Step S30: The central node receives the real-time metering data D of the member nodes at a fixed collection frequency. node , and real-time measurement data D node Deviation calculation is performed with the dynamic reference value to obtain the deviation value ΔD;

[0071] When the deviation value ΔD exceeds the preset measurement threshold, it will be compared with the real-time measurement data D node The corresponding member node is marked as an abnormal node, and the member node is added to the abnormal detection queue;

[0072] It should be noted that the calculation of the deviation value ΔD is to evaluate the accuracy of the member node metering data to determine whether the node is in a normal working state. If the deviation value exceeds the threshold, it means that the data of the node may be abnormal.

[0073] It is understandable that by setting the deviation threshold, the metering data of the member nodes can be allowed to deviate from the dynamic baseline value within a certain range, thereby avoiding frequent triggering of abnormal detection due to small fluctuations.

[0074] It should be understood that this step ensures the overall consistency and reliability of the metering data of the electric energy meter group by real-time monitoring of the metering data of each member node, timely discovering and processing abnormal nodes.

[0075] Step S40: The central node sends a calibration instruction to the abnormal nodes in the abnormal detection queue, and the abnormal nodes perform data correction based on the correction coefficient. The correction formula is:

[0076] D new =D current ×K

[0077] Among them, D current is the current measurement value, D new is the corrected measurement value, K is the correction coefficient of the abnormal node, and its value is determined according to the deviation value ΔD;

[0078] It should be noted that the correction coefficient is used to adjust the measurement value according to the degree of deviation of each abnormal node, so as to calibrate the data of the abnormal node to a range close to the dynamic reference value.

[0079] It can be understood that by calibrating the data using the correction coefficient, the measurement error can be effectively reduced, the deviation can be controlled within the allowable range, and the overall measurement accuracy of the electric energy meter group can be improved.

[0080] It should be understood that the value of the correction factor is based on the deviation value. The larger the deviation value, the larger the adjustment of the correction factor may be to ensure that the measurement data can be restored to the normal range after one or more calibrations.

[0081] Step S50: After the calibration is completed, the correction coefficient of each abnormal node is combined to obtain the comprehensive correction coefficient M. The central node encrypts the comprehensive correction coefficient M to generate calibration data, and sends the calibration data to normal member nodes other than the abnormal nodes. Each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient to achieve consistency of the measurement data within the group.

[0082] It should be noted that the comprehensive correction coefficient M is an average correction factor calculated based on the correction conditions of all abnormal nodes, which is used to ensure that the data of all nodes in the group remain consistent after calibration.

[0083] It should be understood that by broadcasting the comprehensive correction coefficient to all normal member nodes and letting these nodes adjust the metering values ​​synchronously, the metering data of the entire electric energy meter group can be kept consistent, avoiding data inconsistency caused by calibration of individual nodes. The encrypted transmission of calibration data is to ensure the security of data during the distribution process, prevent unauthorized access or tampering, and thus ensure the reliability of calibration results and data consistency.

[0084] In addition, the metering verification system for a group of electric energy meters provided by the present invention adopts a metering verification method for a group of electric energy meters in the above embodiment, which can solve a technical problem of metering verification for a group of electric energy meters. Compared with the prior art, the beneficial effects of the metering verification system for a group of electric energy meters provided by the present invention are the same as the beneficial effects of the metering verification method for a group of electric energy meters provided by the above embodiment, and other technical features of the metering verification system for a group of electric energy meters are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0085] The present invention provides a measurement and verification device for an electric energy meter group, please refer to Figure 2, a metering and verification device for a group of electric energy meters comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a metering and verification method for a group of electric energy meters in the above-mentioned embodiment 1. A metering and verification device for a group of electric energy meters in an embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A metering and verification device for a group of electric energy meters is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention. A metering and verification device for a group of electric energy meters may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of a metering and verification device for a group of electric energy meters are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow a metering and verification device for a group of electric energy meters to communicate with other devices wirelessly or by wire to exchange data. Although a metering and verification device for a group of electric energy meters having various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0086] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for measuring and verifying a group of electric energy meters are implemented. The computer program product provided by the present invention can solve a technical problem of measuring and verifying a group of electric energy meters. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for measuring and verifying a group of electric energy meters provided by the above-mentioned embodiment, which will not be described in detail here.

[0087] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0088] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0089] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A measurement verification method for an electric energy meter group, characterized in that: The metering verification method for an electric energy meter group includes: Step S10: Obtain the data of communication stability S, signal strength R and historical measurement error rate E of each member node from the electric energy meter group, and perform weighted scoring on the above three parameters of each member node. The scoring formula is: α+β+γ=1 Among them, Score is the score, α, β and γ are dynamic weight coefficients, S exp is the preset standard communication stability, R exp is the preset standard signal strength; Arrange all member nodes in ascending order according to the Score, and select the member node with the highest score as the central node; Step S20: The central node obtains historical metering data from the electric energy meter group and calculates the historical metering average value H avg ; Based on the real-time collected environmental information including temperature data and humidity data, combined with the grid load information and the historical metering average value H avg Generate real-time dynamic benchmark value D ref , dynamic reference value D ref The calculation formula is: Among them, θ i is the environmental correction factor, X i is the environmental parameter value of the integrated temperature data and humidity data, n is the total number of environmental parameters, and i is the i-th environmental parameter; Step S30: The central node receives the real-time metering data D of the member nodes at a fixed collection frequency. node , and real-time measurement data D node Deviation calculation is performed with the dynamic reference value to obtain the deviation value ΔD; When the deviation value ΔD exceeds the preset measurement threshold, it will be compared with the real-time measurement data D node The corresponding member node is marked as an abnormal node, and the member node is added to the abnormal detection queue; Step S40: The central node sends a calibration instruction to the abnormal nodes in the abnormal detection queue, and the abnormal nodes perform data correction based on the correction coefficient. The correction formula is: D new =D current ×K Among them, D current is the current measurement value, D new is the corrected measurement value, K is the correction coefficient of the abnormal node, and its value is determined according to the deviation value ΔD; Step S50: After the calibration is completed, the correction coefficient of each abnormal node is combined to obtain the comprehensive correction coefficient M. The central node encrypts the comprehensive correction coefficient M to generate calibration data, and sends the calibration data to normal member nodes other than the abnormal nodes. Each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient to achieve consistency of the measurement data within the group.

2. A measurement and verification method for an electric energy meter group as claimed in claim 1, characterized in that: In step S40, K i is the correction coefficient of the ith abnormal node, and its value is determined according to the deviation value ΔD. The specific formula is: Wherein, T is a preset reference deviation threshold value, which is used to control the calibration intensity.

3. A measurement and calibration method for an electric energy meter group as claimed in claim 1, characterized in that: In step S50, the correction coefficient of each abnormal node is integrated to obtain a comprehensive correction coefficient M, and the calculation formula is: Among them, M is the comprehensive correction coefficient, N is the total number of abnormal nodes, K j is the correction coefficient of the jth abnormal node, exp is the index fetching function, and ln is the logarithmic function.

4. A measurement and verification method for an electric energy meter group as claimed in claim 1, characterized in that: In step S50, each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient. The formula is Z new =Z current ×M, where Z new The value after the normal member node updates its own measurement value, Z cueeent It is the current measurement value of the normal member node.

5. A measurement and verification method for an electric energy meter group as claimed in claim 4, characterized in that: In step S20, the historical measurement average value H avg The calculation of is done using the arithmetic mean method.

6. A measurement and calibration method for an electric energy meter group as claimed in claim 1, characterized in that: In step S30, the fixed collection frequency is to collect data once every 3 minutes.

7. A measurement and verification method for an electric energy meter group as claimed in claim 1, characterized in that: In step S50, the comprehensive correction coefficient M is encrypted using the AES algorithm.

8. A measurement verification system for an electric energy meter group, characterized in that: The metering and verification system for an electric energy meter group comprises: The scoring module is used to obtain the data of communication stability S, signal strength R and historical measurement error rate E of each member node from the electric energy meter group, and to perform weighted scoring on the above three parameters of each member node. The scoring formula is: α+β+γ=1 Among them, Score is the score, α, β and γ are dynamic weight coefficients, S wxp is the preset standard communication stability, R exp is the preset standard signal strength; Arrange all member nodes in ascending order according to the Score, and select the member node with the highest score as the central node; The benchmark generation module is used for the central node to obtain historical measurement data from the electric energy meter group and calculate the historical measurement average value H avg ; Based on the real-time collected environmental information including temperature data and humidity data, combined with the grid load information and the historical metering average value H avg Generate real-time dynamic benchmark value D ref , dynamic reference value D ref The calculation formula is: Among them, θ i is the environmental correction factor, X i is the environmental parameter value of the integrated temperature data and humidity data, n is the total number of environmental parameters, and i is the i-th environmental parameter; The data collection and deviation calculation module is used for the central node to receive the real-time metering data of the member nodes at a fixed collection frequency. node , and the real-time measurement data D node Deviation calculation is performed with the dynamic reference value to obtain the deviation value ΔD; When the deviation value ΔD exceeds the preset measurement threshold, it will be compared with the real-time measurement data D node The corresponding member node is marked as an abnormal node, and the member node is added to the abnormal detection queue; The calibration module is used by the central node to send calibration instructions to abnormal nodes in the abnormal detection queue. The abnormal nodes correct the data based on the correction coefficient. The correction formula is: D new =D current ×K Among them, D current is the current measurement value, D new is the corrected measurement value, K is the correction coefficient of the abnormal node, and its value is determined according to the deviation value ΔD; The comprehensive processing module is used to obtain the comprehensive correction coefficient M by integrating the correction coefficient of each abnormal node after the calibration is completed. The central node encrypts the comprehensive correction coefficient M to generate calibration data, and sends the calibration data to normal member nodes other than the abnormal nodes. Each normal member node decrypts the data packet and updates its own measurement value according to the comprehensive correction coefficient to achieve consistency of measurement data within the group.

9. A measurement and verification device for an electric energy meter group, characterized in that: The metering and verification equipment for the electric energy meter group includes: A memory, a processor, and a metering verification program for an electric energy meter group stored in the memory and executable on the processor, wherein the metering verification program for an electric energy meter group implements the metering verification method for an electric energy meter group described in any one of claims 1 to 7 when executed by the processor.

10. A computer program product, characterized in that The computer program product comprises a metering verification program for a group of electric energy meters, and when the metering verification program for a group of electric energy meters is executed by a processor, the metering verification method for a group of electric energy meters described in any one of claims 1 to 7 is implemented.

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