CN115932705A

CN115932705A - Remote error metering method and device for electric energy meter, medium and terminal

Info

Publication number: CN115932705A
Application number: CN202211495233.4A
Authority: CN
Inventors: 李翀; 申洪涛; 郭聚川; 郭荣坤; 李兵; 王浩; 王毅; 杨媛媛
Original assignee: State Grid Corp of China SGCC; Marketing Service Center of State Grid Hebei Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Marketing Service Center of State Grid Hebei Electric Power Co Ltd
Priority date: 2022-11-26
Filing date: 2022-11-26
Publication date: 2023-04-07

Abstract

The invention discloses a method and a device for remote error metering of an electric energy meter, a medium and a terminal, and relates to the field of electric energy metering. The method comprises the following steps: collecting N groups of electric power data of an electric energy meter to be measured, wherein N is a positive integer; inputting N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results; calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results; the number of the electric energy meters to be tested can be one or more. By the method, the accuracy of remotely calculating the metering error of the electric energy meter can be improved, so that the electric energy meter with larger metering error can be found out more accurately.

Description

Remote error metering method and device for electric energy meter, medium and terminal

Technical Field

The disclosure relates to the field of electric power metering, in particular to a method and a device for remote error metering of an electric energy meter, a medium and a terminal.

Background

With the continuous acceleration of urbanization in China, the electricity consumption of residents becomes diversified and complicated, and the error of the electric energy meter needs to be accurately measured.

The prior art document 1 (CN 111046519A) discloses "an application analysis method of an artificial intelligence technology in error diagnosis of an electric energy meter", and the analysis method includes (1) constructing a correlation model of each data in a power consumption information acquisition system; (2) Establishing an error analysis model of the single-phase intelligent meter and the three-phase intelligent meter based on an artificial intelligence technology; (3) And analyzing and verifying the accuracy of the remote error analysis result of the electric energy meter. The disadvantage of the prior art document 1 is that the model accuracy needs to be submitted and the computational power needs to be reduced.

Prior art document 2 (CN 110780259A) discloses "a data cleaning and quality evaluation system based on electric energy meter remote error diagnosis", and the evaluation system includes: the system comprises a data format and integrity checking module, a problem data identification and classification module, an acquisition channel problem positioning module, a metering quantization error detection module, an abnormal value diagnosis module, an acquired data deficiency and error detection module, a suspected false number data automatic identification module through a false number detection algorithm, a deficiency data completion module and a data management result index and evaluation module. The prior art document 2 has the disadvantage that data screening is focused, and improvement on a core algorithm needs to be improved.

Therefore, the conventional means for measuring the error of the electric energy meter has not high enough accuracy.

It is noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

The invention aims to provide a method and a device for remotely measuring errors of an electric energy meter, a medium and a terminal, which improve the accuracy of remotely calculating the measuring errors of the electric energy meter at least to a certain extent.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to one aspect of the present disclosure, there is provided a method for remote error measurement of an electric energy meter, including: collecting N groups of electric power data of an electric energy meter to be measured, wherein N is a positive integer; inputting the N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results; calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results; the number of the electric energy meters to be tested can be one or more.

According to another aspect of the present disclosure, there is provided an electric energy meter remote error metering device, including: an acquisition module: the device is used for collecting N groups of electric power data of the electric energy meter to be measured, wherein N is a positive integer; an acquisition module: the data processing module is used for respectively inputting the N groups of power data into N corresponding data processing models to obtain N groups of data processing results; a calculation module: the error calculation module is used for calculating the error of the electric energy meter to be measured based on the N groups of data processing results; the number of the electric energy meters to be tested can be one or more.

According to still another aspect of the present disclosure, there is provided a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for remote error measurement of an electric energy meter as in the above embodiments.

According to still another aspect of the present disclosure, there is provided a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of remote error metering of an electric energy meter as in the above embodiments.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

in some embodiments of the present disclosure, the following controls are performed, including: collecting N groups of electric power data of an electric energy meter to be measured, wherein N is a positive integer; inputting the N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results; and calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results. According to the technical scheme, through the steps, the accuracy of the metering error of the remote calculation electric energy meter can be improved, so that the electric energy meter with the large metering error can be found out more accurately.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

Fig. 1 schematically illustrates a flow chart of a method for remote error metering of an electric energy meter according to an exemplary embodiment of the present disclosure.

Fig. 2 schematically illustrates a flow chart of a method for remote error metering of an electric energy meter according to another exemplary embodiment of the present disclosure.

Fig. 3 schematically illustrates a flow chart for culling abnormal power data in an exemplary embodiment according to the present disclosure.

Fig. 4 schematically illustrates a flow chart for culling abnormal power data in another exemplary embodiment according to the present disclosure.

FIG. 5 schematically illustrates a flow chart for determining metrology error detection sensitivity in an exemplary embodiment according to the present disclosure.

Fig. 6 schematically shows a block diagram of a remote error metering device for an electric energy meter according to an exemplary embodiment of the present disclosure.

Fig. 7 schematically illustrates a structure diagram of a terminal in an exemplary embodiment according to the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of the present disclosure more apparent, embodiments of the present disclosure will be described in further detail below with reference to the accompanying drawings.

When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

In the description of the present disclosure, it is to be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present disclosure can be understood in specific instances by those of ordinary skill in the art. In addition, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

The method for remote error measurement of an electric energy meter according to the embodiment of the present disclosure will be described in detail below with reference to fig. 1 to 5.

Fig. 1 schematically illustrates a flowchart of a method for remote error metering of an electric energy meter according to an exemplary embodiment of the present disclosure. Referring to fig. 1, the method for remote error measurement of an electric energy meter includes:

s110, collecting N groups of electric power data of the electric energy meter to be measured, wherein N is a positive integer.

In an exemplary embodiment, the N groups of electric power data of the electric energy meter to be measured are collected through an electric energy meter remote error metering system. The electric energy meter remote error metering system is used for executing the electric energy meter remote error metering method, and the manner of collecting the electric power data includes but is not limited to:

1. and the N groups of power data are transmitted to a power terminal in a power line carrier mode, then the power terminal transmits the N groups of power data to a power metering system through a wireless network, and finally the N groups of power data are acquired from the power metering system through the electric energy meter remote error metering system.

2. The electric power data of the electric energy meter is transmitted to the concentrator in a wireless 485 mode, and then the electric power data information of the data information is automatically copied to each meter by the concentrator according to the meter reading task of the management center and stored, and is uniformly delivered to the management center for centralized processing.

3. The electric energy meter is provided with an infrared metering device to monitor N groups of electric power data of the electric energy meter, and the N groups of electric power data are transmitted to the electric energy meter remote error metering system through a wireless network.

And S120, inputting the N groups of power data into the N corresponding data processing models respectively to obtain N groups of data processing results.

In an exemplary embodiment, the electric energy meter remote error metering system inputs N groups of electric power data into N corresponding data processing models respectively to obtain N groups of data processing results.

For example, if the N groups of power data are voltage data, current data, fly-away data, backward travel data, stop travel data, voltage and current loss, overvoltage and overcurrent data, abnormal clock of the electric energy meter, abnormal reverse electric quantity, and the like, a corresponding data processing model is constructed for each group of power data, and each group of power data is input into the corresponding data processing model, so as to obtain N groups of data processing results.

And S130, calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results.

In an exemplary embodiment, the electric energy meter remote error metering system calculates the metering error of the electric energy meter to be measured based on the N groups of data processing results.

For example, the electric energy meter remote error metering system may determine the credibility of the N sets of data processing results by calculating the fitting residual of the N sets of data processing results through a residual analysis model, and use the data processing result with the highest credibility as a criterion for determining the error size of the electric energy meter. The residual error analysis model is a neural network model obtained by training the electric energy meter remote error metering system by using historical electric power data of the electric energy meter.

In the technical scheme provided by the embodiment shown in fig. 1, N groups of data processing results are obtained by acquiring N groups of power data of the electric energy meter to be tested and inputting the N groups of power data into N corresponding data processing models respectively. And calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results. Through the technical scheme of the embodiment shown in fig. 1, the metering error of the electric energy meter is calculated from the electric power data of a plurality of layers, so that the accuracy of remotely calculating the metering error of the electric energy meter can be improved, and the electric energy meter with a large metering error can be found out more accurately.

Illustratively, fig. 2 schematically shows a flow chart of another method for remote error metering of an electric energy meter according to an exemplary embodiment of the present disclosure. Referring to fig. 2, the method shown therein comprises:

s210, collecting N groups of electric power data of the electric energy meter to be measured.

In the exemplary embodiment, the specific implementation of S210 is the same as S110, and is not described herein again.

S220, eliminating abnormal data contained in each group of data in the N groups of power data.

In an exemplary embodiment, the electric energy meter remote error metering system eliminates abnormal data contained in each group of data in the N groups of electric power data. The abnormal data refers to power data, of each group of power data of the electric energy meter, of which deviation value from preset standard data is larger than a preset range.

And S230, acquiring the data type of the ith group of power data, wherein i is a positive integer and is not more than N.

In an exemplary embodiment, the electric energy meter remote error metering system obtains the data type of the ith set of power data. Wherein, the data types include but are not limited to: voltage, current, voltage loss and current loss, overvoltage and overcurrent, clock abnormity of the electric energy meter, reverse electric quantity abnormity and the like.

And S240, determining a target data processing model corresponding to the data type based on the data type.

In an exemplary embodiment, the electric energy meter remote error metering system determines a target data processing model corresponding to the data type based on the data type. The target data processing model is one of N data processing models corresponding to the N groups of power data.

And S250, inputting the ith group of power data into the target data processing model to obtain the ith group of data processing result.

In an exemplary embodiment, the electric energy meter remote error metering system inputs the ith group of electric power data into the target data processing model to obtain the ith group of data processing result. The data processing result presentation mode includes but is not limited to: graphically or by means of quantization coding.

S260, screening the data processing results of the N groups by using the genetic algorithm evaluation model to obtain M groups of optimized data processing results, wherein M is a positive integer and M is not more than N.

In an exemplary embodiment, the electric energy meter remote error metering system adopts an error model training strategy based on a genetic algorithm, finds the optimal data processing result in N groups of data processing results by simulating the selection and the genetic mechanism of the nature, and carries out iterative search on the data processing result through operators of selection, intersection and variation. The genetic algorithm sets an optimization target for searching by using residual-based evaluation, and the obtained data processing result is the optimal data processing result.

And S270, calculating the metering error of the electric energy meter to be measured based on the data processing results of the M groups of optimization.

In an exemplary embodiment, the electric energy meter remote error metering system calculates the metering error of the electric energy meter to be measured based on the M groups of optimized data processing results.

For example, the electric energy meter remote error metering system may determine the credibility of the M groups of data processing results by calculating the fitting residual of the M groups of data processing results through a residual analysis model, and use the data processing result with the highest credibility as a criterion for determining the error size of the electric energy meter.

In the technical scheme provided by the embodiment shown in fig. 2, N groups of electric power data of the electric energy meter to be measured are collected, and abnormal data included in each group of data in the N groups of electric power data are removed; the data type of the ith group of power data is acquired, and based on the data type, a target data processing model corresponding to the data type is determined. Inputting the ith group of power data into a target data processing model to obtain an ith group of data processing results, screening the N groups of data processing results by using a genetic algorithm evaluation model to obtain M groups of optimized data processing results, and calculating the metering error of the electric energy meter to be measured based on the M groups of optimized data processing results. Through the steps, the error metering of the electric energy meter closer to the actual situation is realized, the data processing result with higher reliability is screened out from the data processing results of the N groups, and the speed of the error metering of the electric energy meter is improved.

Illustratively, fig. 3 schematically shows a flowchart for culling abnormal power data according to an exemplary embodiment of the present disclosure. Referring to fig. 3, the method shown therein comprises:

s310, obtaining an average value of the ith group of power data, wherein i is a positive integer and is not more than N.

In an exemplary embodiment, the electric energy meter remote error metering system obtains an average value of the ith group of power data, i is a positive integer and i is not greater than N. For example, when the data type of the ith group of power data is current, assuming that the current values are 10A, 12A, 14A, 16A, and 18A, respectively, the average value of the current is 14A.

And S320, calculating the difference value between the target data and the average value in the ith group of power data.

In an exemplary embodiment, the electric energy meter remote error metering system calculates the difference between the target data and the average value in the ith group of power data. Wherein the target data is any one of the ith group of power data.

And S330, determining the target data as abnormal data and removing the abnormal data from the ith group of power data when the difference value is larger than a preset threshold value.

In an exemplary embodiment, in a case that the difference is greater than a preset threshold, the electric energy meter remote error metering system determines the target data as abnormal data, and removes the abnormal data from the ith group of power data. The preset threshold value can be set through deeming, and the ith group of power data can be trained based on a pre-trained abnormal data analysis model to obtain a proper preset threshold value.

The embodiment shown in fig. 3 provides a technical solution, an average value of the ith group of power data is obtained, a difference value between the target data in the ith group of power data and the average value is calculated, and in case that the difference value is greater than a preset threshold value, the target data is determined as abnormal data, and the abnormal data is removed from the ith group of power data. Through the steps, the validity of the electric power data is improved, and therefore the accuracy and the authenticity of error metering of the electric energy meter can be improved.

Illustratively, fig. 4 schematically shows a flowchart for culling abnormal power data according to another exemplary embodiment of the present disclosure. Referring to fig. 4, the method shown therein includes:

and S410, acquiring box type graphs corresponding to the N groups of electric power data respectively.

In an exemplary embodiment, the electric energy meter remote error metering system acquires N groups of box charts corresponding to electric power data respectively. The box diagram is also called box whisker diagram, box diagram or box diagram, and is a statistical diagram for displaying a group of data dispersion situation data.

And S420, acquiring abnormal data contained in each group of data in the N groups of power data based on the box type diagram, and removing the abnormal data from the ith group of power data.

In an exemplary embodiment, the electric energy meter remote error metering system acquires abnormal data contained in each group of data in the N groups of power data based on a box diagram, and eliminates the abnormal data from the ith group of power data.

According to the technical scheme provided by the embodiment shown in fig. 4, the box charts corresponding to the N groups of power data are acquired, abnormal data contained in each group of data in the N groups of power data are acquired based on the box charts, and the abnormal data are removed from the ith group of power data. Through the steps, the abnormal data can be visually presented, so that the abnormal data can be removed more efficiently, and the accuracy and the authenticity of error metering of the electric energy meter are further improved.

Illustratively, FIG. 5 schematically illustrates a flow chart for determining metrology error detection sensitivity in an exemplary embodiment according to the present disclosure. Referring to fig. 5, the method shown therein includes:

s510, amplifying the numerical values of the N groups of power data by L times in an equal proportion to obtain N groups of simulation power data.

In an exemplary embodiment, the electric energy meter remote error metering system amplifies the numerical values of the N groups of electric power data by L times in an equal proportion to obtain N groups of simulation electric power data.

For example, the voltage value of the voltage type is amplified by 5 times, the current value of the current type is amplified by 5 times, and the number of times the electricity meter flies, falls, and stops is amplified by five times.

S520, inputting the N groups of simulation power data into the N corresponding data processing models respectively to obtain N groups of simulation data processing results.

In an exemplary embodiment, the electric energy meter remote error metering system inputs N groups of simulation electric power data into N corresponding data processing models respectively to obtain N groups of simulation data processing results. And the N groups of simulation power data are the same as data processing models corresponding to the N groups of power data.

S530, calculating the simulation error of the electric energy meter to be tested based on the N groups of simulation data processing results.

In an exemplary embodiment, the electric energy meter remote error metering system calculates the simulation error of the electric energy meter to be measured based on the N sets of simulation data processing results.

S540, calculating an error ratio of the simulation error to the metering error, and determining the detection sensitivity of the metering error based on the error ratio.

In an exemplary embodiment, the electric energy meter remote error metering system calculates an error ratio of the simulation error to the metering error, and determines a detection sensitivity of the metering error based on the error ratio.

In the technical solution provided by the embodiment shown in fig. 5, the numerical values of N sets of power data are amplified by L times in equal proportion to obtain N sets of simulated power data; and respectively inputting the N groups of simulation power data into the N corresponding data processing models to obtain N groups of simulation data processing results. And calculating the simulation error of the electric energy meter to be measured based on the N groups of simulation data processing results, calculating the error ratio of the simulation error to the metering error, and determining the detection sensitivity of the metering error based on the error ratio. Through the steps, the error detection sensitivity of the model to all the electric energy meters can be obtained, so that whether the error measurement of the electric energy meters is accurate or not can be judged based on the detection sensitivity, and the accuracy of the error measurement of the electric energy meters is further improved.

It is to be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to an exemplary embodiment of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules. The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.

Illustratively, fig. 6 schematically shows a block diagram of a remote error metering device for an electric energy meter according to an exemplary embodiment of the present disclosure. Referring to fig. 6, the remote error metering device 600 of the electric energy meter shown in the figure includes: an acquisition module 610, an acquisition module 620, and a calculation module 630, wherein:

the collecting module 610 is configured to: collecting N groups of electric power data of an electric energy meter to be measured, wherein N is a positive integer; the obtaining module 620 is configured to: inputting the N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results; the calculating module 630 is configured to: calculating the error of the electric energy meter to be measured based on the N groups of data processing results; the number of the electric energy meters to be tested can be one or more.

In an exemplary embodiment, based on the foregoing solution, the apparatus further includes a rejecting module, configured to: and eliminating abnormal data contained in each group of data in the N groups of power data.

In an exemplary embodiment, based on the foregoing solution, the culling module is further configured to: acquiring an average value of the ith group of power data, wherein i is a positive integer and is not more than N; calculating a difference between the target data in the ith group of power data and the average value; and determining the target data as the abnormal data and removing the abnormal data from the ith group of power data when the difference is larger than a preset threshold.

In an exemplary embodiment, based on the foregoing solution, the foregoing rejecting module is further configured to: acquiring box graphs corresponding to the N groups of electric power data respectively; and acquiring abnormal data contained in each group of data in the N groups of power data based on the box type diagram, and removing the abnormal data from the ith group of power data.

In an exemplary embodiment, based on the foregoing solution, the obtaining module 620 is further configured to: acquiring the data type of the ith group of power data, wherein i is a positive integer and is not more than N; determining a target data processing model corresponding to the data type based on the data type; and inputting the ith group of power data into the target data processing model to obtain an ith group of data processing result.

In an exemplary embodiment, based on the foregoing solution, the calculating module 630 is further configured to: screening the data processing results of the N groups of data by using a genetic algorithm evaluation model to obtain M groups of optimized data processing results, wherein M is a positive integer and is not more than N; and calculating the metering error of the electric energy meter to be measured based on the data processing results of the M groups of optimization.

In an exemplary embodiment, based on the foregoing solution, the apparatus further includes a monitoring module, configured to: amplifying the numerical values of the N groups of power data by L times in an equal proportion to obtain N groups of simulation power data; inputting the N groups of simulation power data into N corresponding data processing models respectively to obtain N groups of simulation data processing results; calculating the simulation error of the electric energy meter to be tested based on the N groups of simulation data processing results; an error ratio of the simulation error to the measurement error is calculated, and a detection sensitivity of the measurement error is determined based on the error ratio.

It should be noted that, when the remote error metering device for an electric energy meter provided in the foregoing embodiment executes the remote error metering method for an electric energy meter, the division of each function module is only used for illustration, and in practical applications, the function distribution may be completed by different function modules according to needs, that is, the internal structure of the device is divided into different function modules, so as to complete all or part of the functions described above. In addition, the electric energy meter remote error metering device and the electric energy meter remote error metering method embodiment belong to the same concept, and for details not disclosed in the device embodiment of the present disclosure, please refer to the above electric energy meter remote error metering method embodiment of the present disclosure, and details are not repeated here.

The above-mentioned serial numbers of the embodiments of the present disclosure are merely for description and do not represent the merits of the embodiments.

Embodiments of the present disclosure also provide a readable storage medium, on which a computer program is stored, where the program is executed by a processor to implement the steps of any one of the foregoing embodiments of the method. The readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nano-devices (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.

The embodiment of the present disclosure further provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the program, the steps of any of the above-mentioned embodiments of the method are implemented.

Fig. 7 schematically shows a block diagram of a terminal in an exemplary embodiment according to the present disclosure. Referring to fig. 7, a terminal 700 includes: a processor 710 and a memory 720.

In the embodiment of the present disclosure, the processor 710 is a control center of a computer device, and may be a processor of a physical machine or a processor of a virtual machine. Processor 710 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 710 may be implemented in at least one hardware form of DSP (digital signal Processing), FPGA (Field-programmable gate array), PLA (programmable logic array). The processor 710 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state.

In an embodiment of the present disclosure, the processor 710 is specifically configured to:

collecting N groups of electric power data of an electric energy meter to be measured, wherein N is a positive integer; inputting the N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results; calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results; the number of the electric energy meters to be tested can be one or more.

Further, before the N sets of power data are input to N corresponding data processing models, the method further includes: and eliminating abnormal data contained in each group of data in the N groups of power data.

Further, the removing abnormal data included in each group of data in the N groups of power data includes: acquiring an average value of the ith group of power data, wherein i is a positive integer and is not more than N; calculating a difference between the target data in the ith group of power data and the average value; and determining the target data as the abnormal data and removing the abnormal data from the ith group of power data when the difference is larger than a preset threshold.

Further, the removing abnormal data included in each group of data in the N groups of power data includes: acquiring box graphs corresponding to the N groups of electric power data respectively; and acquiring abnormal data contained in each group of data in the N groups of power data based on the box type diagram, and removing the abnormal data from the ith group of power data.

Further, the inputting the N groups of power data into N corresponding data processing models to obtain N groups of data processing results includes: acquiring the data type of the ith group of power data, wherein i is a positive integer and is not more than N; determining a target data processing model corresponding to the data type based on the data type; and inputting the ith group of power data into the target data processing model to obtain an ith group of data processing result.

Further, the calculating the metering error of the electric energy meter to be measured based on the N sets of data processing results includes: screening the N groups of data processing results by using a genetic algorithm evaluation model to obtain M groups of optimized data processing results, wherein M is a positive integer and is not more than N; and calculating the metering error of the electric energy meter to be measured based on the data processing result of the M groups of optimization.

Further, after the error of the electric energy meter to be measured is calculated based on the N groups of data processing results, the method further includes: amplifying the numerical values of the N groups of power data by L times in an equal proportion to obtain N groups of simulation power data; inputting the N groups of simulation power data into N corresponding data processing models respectively to obtain N groups of simulation data processing results; calculating the simulation error of the electric energy meter to be tested based on the N groups of simulation data processing results; an error ratio of the simulation error to the measurement error is calculated, and a detection sensitivity of the measurement error is determined based on the error ratio.

Memory 720 may include one or more readable storage media, which may be non-transitory. Memory 720 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments of the present disclosure, a non-transitory readable storage medium in memory 720 is used to store at least one instruction for execution by processor 710 to implement a method in embodiments of the present disclosure.

In some embodiments, the terminal 700 further comprises: a peripheral interface 730 and at least one peripheral. Processor 710, memory 720 and peripheral interface 730 may be connected by buses or signal lines. Various peripheral devices may be connected to peripheral interface 730 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a display 740, a camera 750, and an audio circuit 760.

Peripheral interface 730 may be used to connect at least one peripheral associated with an I/O (Input/Output) to processor 710 and memory 720. In some embodiments of the present disclosure, processor 710, memory 720, and peripheral interface 730 are integrated on the same chip or circuit board; in some other embodiments of the present disclosure, any one or both of processor 710, memory 720, and peripherals interface 730 may be implemented on separate chips or circuit boards. The embodiments of the present disclosure are not particularly limited in this regard.

The display screen 740 is used to display a UI (user interface). The UI may include graphics, text, icons, video, and any combination thereof. When display screen 740 is a touch display screen, display screen 740 also has the ability to capture touch signals on or over the surface of display screen 740. The touch signal may be input to the processor 710 as a control signal for processing. At this point, the display 740 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments of the present disclosure, the display 740 may be one, providing a front panel of the terminal 700; in other embodiments of the present disclosure, the display 740 may be at least two, respectively disposed on different surfaces of the terminal 700 or in a folding design; in still other embodiments of the present disclosure, the display 740 may be a flexible display disposed on a curved surface or a folded surface of the terminal 700. Even more, the display 740 may be configured in a non-rectangular irregular pattern, i.e., a shaped screen. The display 740 can be made of LCD (liquid crystal display), OLED (organic light-emitting diode), and the like.

The camera 750 is used to capture images or video. Optionally, the camera 750 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of a terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and the rear cameras are any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so as to implement a background blurring function by fusing the main camera and the depth-of-field camera, implement panoramic shooting and a VR (virtual reality) shooting function by fusing the main camera and the wide-angle camera, or implement other fusion shooting functions. In some embodiments of the present disclosure, camera 750 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.

Audio circuitry 760 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals and inputting the electric signals to the processor 710 for processing. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different portions of the terminal 700. The microphone may also be an array microphone or an omni-directional acquisition microphone.

A power supply 770 is used to supply power to the various components in terminal 700. The power source 770 may be alternating current, direct current, disposable or rechargeable. When the power supply 770 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.

The terminal block diagrams shown in the embodiments of the present disclosure do not constitute limitations on terminal 700, and terminal 700 may include more or fewer components than those shown, or may combine some components, or adopt a different arrangement of components.

In the present disclosure, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or order; the term "plurality" means two or more unless expressly limited otherwise. The terms "mounted," "connected," "fixed," and the like are to be construed broadly, and for example, "connected" may be a fixed connection, a removable connection, or an integral connection; "coupled" may be direct or indirect through an intermediary. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.

In the description of the present disclosure, it is to be understood that the terms "upper", "lower", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present disclosure and simplifying the description, but do not indicate or imply that the referred device or unit must have a specific direction, be configured and operated in a specific orientation, and thus, should not be construed as limiting the present disclosure.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present disclosure, and all the changes or substitutions should be covered within the scope of the present disclosure. Accordingly, equivalents may be resorted to as falling within the scope of the disclosure as claimed.

Claims

1. A method for remote error metering of an electric energy meter, the method comprising:

collecting N groups of electric power data of an electric energy meter to be measured, wherein N is a positive integer;

inputting the N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results;

calculating the metering error of the electric energy meter to be measured based on the N groups of data processing results;

the number of the electric energy meters to be tested can be one or more.

2. The method for remote error measurement of an electric energy meter according to claim 1, wherein before the inputting the N sets of power data into N corresponding data processing models, further comprises:

and eliminating abnormal data contained in each group of data in the N groups of power data.

3. The method for remote error measurement of an electric energy meter according to claim 2, wherein the removing abnormal data included in each of the N groups of electric power data comprises:

acquiring an average value of the ith group of power data, wherein i is a positive integer and is not more than N;

calculating a difference value between the target data in the ith group of power data and the average value;

and determining the target data as the abnormal data and removing the abnormal data from the ith group of power data when the difference value is larger than a preset threshold value.

4. The method for remote error measurement of an electric energy meter according to claim 1, wherein the removing abnormal data included in each of the N groups of electric power data comprises:

acquiring box type graphs corresponding to the N groups of power data respectively;

based on the box type graph, abnormal data contained in each group of data in the N groups of power data are obtained, and the abnormal data are removed from the ith group of power data.

5. The method for remote error measurement of an electric energy meter according to claim 1, wherein the step of inputting the N groups of electric power data into N corresponding data processing models respectively to obtain N groups of data processing results comprises:

acquiring the data type of the ith group of power data, wherein i is a positive integer and is not more than N;

determining a target data processing model corresponding to the data type based on the data type;

and inputting the ith group of power data into the target data processing model to obtain an ith group of data processing result.

6. The method for remote error metering of an electric energy meter according to claim 1, wherein the calculating of the metering error of the electric energy meter to be measured based on the N groups of data processing results comprises:

screening the N groups of data processing results by using a genetic algorithm evaluation model to obtain M groups of optimized data processing results, wherein M is a positive integer and is not more than N;

and calculating the metering error of the electric energy meter to be measured based on the data processing result of the M groups of optimization.

7. The method for remote error measurement of an electric energy meter according to any one of claims 1 to 6, wherein after the error of the electric energy meter under test is calculated based on the N sets of data processing results, the method further comprises:

amplifying the numerical values of the N groups of power data by L times in an equal proportion to obtain N groups of simulation power data, wherein L is a positive number;

inputting the N groups of simulation power data into N corresponding data processing models respectively to obtain N groups of simulation data processing results;

calculating the simulation error of the electric energy meter to be tested based on the N groups of simulation data processing results;

an error ratio of the simulation error to the metrology error is calculated, and a sensitivity of detection of the metrology error is determined based on the error ratio.

8. A remote error metering device of an electric energy meter is characterized by comprising:

an acquisition module: the device is used for collecting N groups of electric power data of the electric energy meter to be measured, wherein N is a positive integer;

an acquisition module: the data processing module is used for inputting the N groups of power data into N corresponding data processing models respectively to obtain N groups of data processing results;

a calculation module: the error of the electric energy meter to be tested is calculated based on the N groups of data processing results;

wherein, the electric energy meter to be tested can be one or more.

9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method for remote error metering of an electric energy meter according to any one of claims 1 to 7 when executing the computer program.

10. A readable storage medium on which a computer program is stored, which computer program, when being executed by a processor, carries out a method for remote error metering of an electric energy meter according to any one of claims 1 to 7.