CN116106816A - Electric energy meter error calibration method, system, equipment and medium based on 5G module - Google Patents
Electric energy meter error calibration method, system, equipment and medium based on 5G module Download PDFInfo
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Abstract
The invention discloses an electric energy meter error calibration method, a system, equipment and a medium based on a 5G module. And then, after the electric energy meter is electrified on the mounting site for metering work, the actual values of various error influence factors of the electric energy meter in the actual working environment are obtained in real time. And finally, searching the weight value of each error influence factor in a pre-constructed error compensation table according to the actual value, so as to calculate the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value, thereby realizing real-time calibration of the electric energy meter and meeting the high-precision metering requirement of the intelligent Internet of things electric energy meter.
Description
Technical Field
The invention relates to the technical field of electric energy meter calibration, in particular to an electric energy meter error calibration method and system based on a 5G module, electronic equipment and a computer readable storage medium.
Background
After the novel intelligent internet of things electric energy meter is processed and leaves the factory, various electric energy metering values of the novel intelligent internet of things electric energy meter always have larger or smaller precision errors, and subsequent meter calibration work is needed to be carried out so that the errors are controlled within the standard specification requirement range. The current calibration flow is to mount the to-be-calibrated meter in the meter position of the special table verification device, and the table provides stable high-precision parameters such as the rising source voltage current and the phase angle to calibrate the meter error. As metering equipment, the accuracy requirement of the internet of things electric energy meter is extremely high, and the accuracy requirement is generally within +/-0.03% when the platform body detection device is debugged. However, the related components of the actual part of the internet of things meter have processing technology differences, so that the metering error is always in a fluctuation state and is not a stable value, and the real-time calibration cannot be realized due to the change of external environment factors (such as temperature, humidity and the like) and the induction performance change of the internal components of the internet of things meter, the rapid increase of the harmonic content of the metering side, the unavoidable precision degradation condition which occurs along with the increase of the service life and the like after the on-site mounting is electrified, so that the single fixed value calibration performed by the table body detection device cannot meet the requirements of on-site conditions, and thus the high-precision metering requirement cannot be realized.
Disclosure of Invention
The invention provides an electric energy meter error calibration method and system based on a 5G module, electronic equipment and a computer readable storage medium, which are used for solving the technical problem that the high-precision metering requirement of an electric energy meter cannot be met in the existing mode of carrying out single fixed value calibration on a table verification device.
According to one aspect of the invention, there is provided a method for calibrating an error of a 5G module-based electric energy meter, comprising the steps of:
applying different error influence factor variables to the electric energy meter and collecting error value data of the electric energy meter during the meter calibration operation when the electric energy meter mounting table body is electrified, and obtaining weight values of errors caused by the error influence factors so as to generate an error compensation table;
after the electric energy meter is electrified on the mounting site for metering work, acquiring actual values of various error influence factors of the electric energy meter in an actual working environment;
and searching the weight value of each error influence factor in the error compensation table according to the actual value, calculating to obtain the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value.
Further, the process of applying different error influencing factor variables to the electric energy meter and collecting error value data of the electric energy meter to obtain the weight value of the error caused by each error influencing factor so as to generate the error compensation meter comprises the following steps:
applying internal and external typical error influence factor variables with different degrees to the electric energy meter, and recording the metering error value of the electric energy meter under the influence of the variables to form an original data set;
converting the variable data of the error influencing factors in the original data set into offset data, and carrying out forward processing on the offset data;
for each error influencing factor, carrying out N-1 times of segmentation on the variation range of the error influencing factor to obtain N slices;
screening experimental sample data corresponding to each slice from an original data set, constructing a linear relation function set based on the experimental sample data corresponding to each slice, and solving to obtain a weight value corresponding to each slice;
and repeating the slicing and solving processes to obtain the weight value corresponding to each slice of all error influence factors, thereby generating a complete error compensation table.
Further, the forward processing of the offset data specifically includes:
wherein ,
raw offset data, min (x j ) The minimum value of the original offset data representing the jth error influencing factor, max (x j ) Maximum value of original offset data representing jth error influencing factor, x ij And the data after forward processing of the offset of the jth error influence factor in the ith test is represented.
Further, for n error influencing factors, n groups of experimental sample data corresponding to each slice are screened from the original data set, and an expression of a linear relation function group constructed based on the n groups of experimental sample data corresponding to each slice is as follows:
wherein ,Yi Represents the electric energy meter measurement error value, x in the ith experimental sample i，n Offset value, a, representing the nth error influencing factor in the ith experimental sample n And a weight value representing an nth error influencing factor.
Further, after solving to obtain the weight value corresponding to each slice, the method further comprises the following steps:
and (3) carrying out weight learning on the weight value corresponding to each slice by adopting a machine learning algorithm to obtain the weight value after optimizing each slice.
Further, the process of obtaining the optimized weight value of each slice by performing weight learning on the weight value corresponding to each slice by using a machine learning algorithm specifically comprises the following steps:
the learning step length epsilon is preset, and n groups of experimental sample data corresponding to each slice are used as base points for gradual learning, so that:
Y n+1 ＝(a 1 +a j1 ε)x n+1,1 +(a 2 +a j2 ε)x n+1,2 +...+(a n +a jn ε)x n+1,n
wherein ,Yn+1 Indicating the metering error of the electric energy meter in the (n+1) th experimental sampleValue of a jn The jth learning step number of the weight coefficient of the nth error influence factor in the (n+1) th experimental sample is represented;
bringing the weight value of n error influence factors in the n+1th experimental sample into the n+2th experimental sample, and continuing learning to enable the weight value to be obtained:
Y n+2 ＝[a 1 +(a k1 +a j1 )ε]x n+2,1 +[a 2 +(a k2 +a j2 )ε]x n+2,2 +...
+[(a n +(a kn +a jn )ε]x n+2,n
wherein ,akn A kth learning step number of a weight coefficient representing an nth error influence factor in an nth+2th experiment sample;
repeatedly executing the process until the weight learning of all experimental sample data corresponding to each slice is completed;
and introducing a pheromone to search a global optimal solution, so as to obtain an optimal weight value corresponding to each slice.
Further, the process of introducing the pheromone to find the global optimal solution so as to obtain the optimal weight value corresponding to each slice specifically comprises the following steps:
introduction of pheromone beta j ＝∑a ij For the learning step length sum of the weight coefficient of the jth error influence factor in each iteration process, after all experimental samples corresponding to each slice are iterated, the total learning step length value beta= Σbetais used j And taking the weight value corresponding to the minimum as the optimal weight value corresponding to the slice.
In addition, the invention also provides an electric energy meter error calibration system based on the 5G module, which comprises:
the weight value calculation module is used for applying different error influence factor variables to the electric energy meter and collecting error value data of the electric energy meter during the process of powering on the electric energy meter mounting table body to perform meter calibration operation, so as to obtain weight values of errors caused by the error influence factors and generate an error compensation table;
the data acquisition module is used for acquiring actual values of various error influence factors of the electric energy meter in an actual working environment after the electric energy meter is electrified on the mounting site for metering work;
and the real-time calibration module is used for searching the weight value of each error influence factor in the error compensation table according to the actual value, calculating the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value.
In addition, the invention also provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used for executing the steps of the method by calling the computer program stored in the memory.
In addition, the invention also provides a computer readable storage medium for storing a computer program for performing error calibration of a 5G module-based electric energy meter, characterized in that the computer program performs the steps of the method as described above when running on a computer.
The invention has the following effects:
according to the electric energy meter error calibration method based on the 5G module, different error influence factor variables are applied to the electric energy meter to be calibrated and error value data of the electric energy meter are collected during the process of powering on the electric energy meter mounting table body to be calibrated, so that the weight value of each error influence variable to the electric energy meter error value can be calculated, and an error compensation table is generated in advance. And then, after the electric energy meter is electrified on the mounting site for metering work, the actual values of various error influence factors of the electric energy meter in the actual working environment are obtained in real time. And finally, searching a weight value of each error influence factor in a pre-constructed error compensation table according to the actual value, so as to calculate and obtain a current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value. According to the electric energy meter error calibration method based on the 5G module, the weight value of each error influence variable on the electric energy meter error value is obtained in advance, and the error compensation meter is constructed, so that the real-time metering error value of the electric energy meter can be calculated only by looking up a table according to the actual value of each error influence factor when calibration is carried out, the real-time calibration of the electric energy meter can be realized, and the high-precision metering requirement of the intelligent internet of things electric energy meter can be met.
In addition, the electric energy meter error calibration system based on the 5G module has the advantages.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The present invention will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a flow chart of a method for calibrating an error of a 5G module-based electric energy meter according to a preferred embodiment of the present invention.
Fig. 2 is a schematic flow chart of step S1 in fig. 1.
Fig. 3 is another sub-flowchart of step S1 in fig. 1.
Fig. 4 is a schematic block diagram of a 5G block-based error calibration system for an electric energy meter according to a preferred embodiment of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawing figures, but the invention can be practiced in a number of different ways, as defined and covered below.
As shown in fig. 1, a preferred embodiment of the present invention provides a method for calibrating an error of an electric energy meter based on a 5G module, which includes the following steps:
step S1: applying different error influence factor variables to the electric energy meter and collecting error value data of the electric energy meter during the meter calibration operation when the electric energy meter mounting table body is electrified, and obtaining weight values of errors caused by the error influence factors so as to generate an error compensation table;
step S2: after the electric energy meter is electrified on the mounting site for metering work, acquiring actual values of various error influence factors of the electric energy meter in an actual working environment;
step S3: and searching the weight value of each error influence factor in the error compensation table according to the actual value, calculating to obtain the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value.
It can be appreciated that in the electric energy meter error calibration method based on the 5G module of the present embodiment, during the meter calibration operation performed by powering up the mounting table body of the electric energy meter to be calibrated, different error influencing factor variables are applied to the electric energy meter to be calibrated, and error value data of the electric energy meter are collected, so that a weight value of each error influencing variable for the error value of the electric energy meter can be calculated, thereby generating an error compensation meter in advance. And then, after the electric energy meter is electrified on the mounting site for metering work, the actual values of various error influence factors of the electric energy meter in the actual working environment are obtained in real time. And finally, searching a weight value of each error influence factor in a pre-constructed error compensation table according to the actual value, so as to calculate and obtain a current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value. According to the electric energy meter error calibration method based on the 5G module, the weight value of each error influence variable on the electric energy meter error value is obtained in advance, and the error compensation meter is constructed, so that the real-time metering error value of the electric energy meter can be calculated only by looking up a table according to the actual value of each error influence factor when calibration is carried out, the real-time calibration of the electric energy meter can be realized, and the high-precision metering requirement of the intelligent internet of things electric energy meter can be met.
It can be understood that in the step S1, the electric energy meter is firstly mounted in the epitope of the special table body verification device for performing the meter calibration operation. As shown in fig. 2, the process of applying different error influencing factor variables to the electric energy meter and collecting error value data of the electric energy meter to obtain a weight value of an error caused by each error influencing factor to generate an error compensation meter includes the following steps:
step S11: applying internal and external typical error influence factor variables with different degrees to the electric energy meter, recording the metering error value of the electric energy meter under the influence of the variables, and constructing an original data set;
step S12: converting the variable data of the error influencing factors in the original data set into offset data, and carrying out forward processing on the offset data;
step S13: for each error influencing factor, carrying out N-1 times of segmentation on the variation range of the error influencing factor to obtain N slices;
step S14: screening experimental sample data corresponding to each slice from an original data set, constructing a linear relation function set based on the experimental sample data corresponding to each slice, and solving to obtain a weight value corresponding to each slice;
step S15: and repeating the slicing and solving processes to obtain the weight value corresponding to each slice of all error influence factors, thereby generating a complete error compensation table.
Specifically, during the operation of calibrating the electric energy meter by powering on the electric energy meter mounting table body, internal and external typical error influence factor variables with different degrees, such as voltage harmonic waves, current harmonic waves, temperature, humidity and the like, are actively applied to the electric energy meter, and the measured error value data of the electric energy meter under the influence of the variables are recorded to form an original data set.
Then, the error influencing factor variable data in the original data set is converted into offset data, for example, voltage harmonic distortion data is converted into voltage harmonic total distortion rate THD u Converting current harmonic distortion data into current harmonic total distortion rate THD i Converting temperature variable data into temperature offset T-T Label (C) I, T represents error influencing factor variable data in the original data set, T Label (C) Standard value data representing temperature parameters.
Then, the offset of the error influencing factors is considered to influence the metering error of the electric energy meter positively, namely, the larger the index value is, the larger the metering error is. Therefore, in order to eliminate the dimensional influence, the data needs to be subjected to forward processing. The forward processing of the offset data specifically includes:
wherein ,
raw offset data, min (x j ) The minimum value of the original offset data representing the jth error influencing factor, max (x j ) Maximum value of original offset data representing jth error influencing factor, x ij And the data after forward processing of the offset of the jth error influence factor in the ith test is represented.
Then, for each error influencing factor, the variation range is cut for N-1 times, and N slices can be obtained. Wherein each slice is a range of values. When the number of slices is large enough, the error influencing factor change in a single slice can be considered to be approximately in a linear relation with the metering error value change of the electric energy meter.
Then, for n error influencing factors, n groups of experimental sample data corresponding to each slice are selected from the original data set, and the expression of the linear relation function group constructed based on the n groups of experimental sample data corresponding to each slice is as follows:
wherein ,Yi Represents the electric energy meter measurement error value, x in the ith experimental sample i，n Offset value, a, representing the nth error influencing factor in the ith experimental sample n And a weight value representing an nth error influencing factor. It can be understood that the linear relation function set has n unknowns, and the number of equations is also n, so that the n unknowns can be solved.
And repeating the step S14 for each slice, so as to obtain the weight value corresponding to different slices of each error influence factor. And for all error influencing factors, repeating the step S13 and the step S14, so as to obtain the weight values corresponding to different slices of all error influencing factors, thereby generating a complete error compensation table. When the electric energy meter is electrified on site to perform metering work, the corresponding slice is found out in the error compensation meter according to the actual value of each error influence factor, the corresponding weight value can be obtained, and after the weight value of the error influence factor is obtained, the real-time metering error of the electric energy meter can be calculated.
It can be understood that the variable data of each error influence factor are converted into the offset data, the slice subdivision processing is carried out on each error influence factor, and the weight value corresponding to each slice can be obtained by constructing the linear relation function set based on the corresponding experimental sample data aiming at each slice, so that the calculation accuracy of the weight value of each error influence factor is greatly improved.
It can be understood that in the process of calculating the weight value corresponding to each slice, only n groups of experimental sample data are adopted for one calculation, but the rest groups of experimental sample data corresponding to each slice are not effectively utilized, and the calculated weight value has errors, so that the invention also optimizes and improves in order to further improve the accuracy of weight value calculation. Optionally, as shown in fig. 3, the step S1 further includes the following after the step S14:
step S14a: and (3) carrying out weight learning on the weight value corresponding to each slice by adopting a machine learning algorithm to obtain the weight value after optimizing each slice.
The process of obtaining the optimized weight value of each slice specifically comprises the following steps of:
firstly, presetting a learning step epsilon, and solving a weight value vector [ a ] in the step S14 1 ,a 2 ,...,a n ]As an initial value, stepwise learning is performed with n sets of experimental sample data corresponding to each slice as a base point such that:
Y n+1 ＝(a 1 +a j1 ε)x n+1,1 +(a 2 +a j2 ε)x n+1,2 +...+(a n +a jn ε)x n+1,n
wherein ,Yn+1 Represents the electric energy meter measurement error value, a, in the n+1th experimental sample jn The jth learning step number of the weight coefficient of the nth error influencing factor in the (n+1) th experimental sample is represented.
Then, the weight value of n error influence factors in the n+1th experimental sample is brought into the n+2th experimental sample, and learning is continued so that:
Y n+2 ＝[a 1 +(a k1 +a j1 )ε]x n+2,1 +[a 2 +(a k2 +a j2 )ε]x n+2,2 +...
+[(a n +(a kn +a jn )ε]x n+2,n
wherein ,akn The kth learning step number of the weight coefficient of the nth error influence factor in the (n+2) th experimental sample is represented.
Then, the above process is repeatedly performed until all experimental sample data Y corresponding to each slice are completed n+m Wherein n+m represents the number of experimental samples corresponding to the slice;
and finally, introducing a pheromone to find a global optimal solution, so as to obtain an optimal weight value corresponding to each slice. Specifically, the introduction of pheromone beta j ＝∑a ij For the learning step length sum of the weight coefficient of the jth error influence factor in each iteration process, after all experimental samples corresponding to each slice are iterated, the total learning step length value beta= Σbetais used j And taking the weight value corresponding to the minimum as the optimal weight value corresponding to the slice. For example, for a slice of the nth error influencing factor, introducing a pheromone to find a global optimal solution is as follows:
Y n+m ＝(a 1 +β 1 ε)x n+m,1 +(a 2 +β 2 ε)x n+m,2 +...+(a n +β n ε)x n+m,n
the optimal weight value corresponding to a slice of the nth error affecting factor is (a) n +β n ε)。
It can be understood that the invention takes the weight value solved by the linear equation set constructed before as an initial value, adopts a machine learning algorithm to carry out iterative learning on all experimental sample data of each slice, thereby continuously correcting the weight value, introducing information factors to find a global optimal solution, effectively utilizing all experimental sample data, further improving the calculation accuracy of the weight value, and further improving the metering accuracy of the electric energy meter.
Alternatively, in another embodiment of the present invention, multiple weight calculation may be performed in step S14, where each calculation selects a different combination of experimental sample data, i.e., n groups of experimental sample data selected at each calculation are not completely identical, and then an average value is calculated based on the multiple weight calculation results.
It can be understood that in the step S2, after the electric energy meter is powered on to perform metering work on the mounting site of the electric energy meter, actual values of various error influencing factors of the electric energy meter in an actual working environment, such as an actual voltage harmonic distortion rate, an actual current harmonic distortion rate, a current temperature, a current humidity and the like, are obtained. The intelligent internet of things electric energy meter is provided with a 5G communication module, a 5G communication link from an upper computer to the 5G communication module to the internet of things metering module/management module can be established, so that communication time delay is greatly reduced, the millisecond level is reached, and the ultra-low time delay characteristic of the 5G communication millisecond level is utilized, so that real-time and synchronous acquisition of various error influence factors can be completed.
It can be understood that in the step S3, the corresponding slice is found in the error compensation table according to the actual value of each error influencing factor, so as to obtain the corresponding weight value thereof, so that the current measurement error value of the electric energy meter can be calculated, and then the current measurement result of the electric energy meter is compensated and corrected in real time based on the current measurement error value.
In addition, as shown in fig. 4, another embodiment of the present invention further provides a system for calibrating errors of an electric energy meter based on a 5G module, preferably adopting the error real-time calibration method as described above, the system includes:
the weight value calculation module is used for applying different error influence factor variables to the electric energy meter and collecting error value data of the electric energy meter during the process of powering on the electric energy meter mounting table body to perform meter calibration operation, so as to obtain weight values of errors caused by the error influence factors and generate an error compensation table;
the data acquisition module is used for acquiring actual values of various error influence factors of the electric energy meter in an actual working environment after the electric energy meter is electrified on the mounting site for metering work;
and the real-time calibration module is used for searching the weight value of each error influence factor in the error compensation table according to the actual value, calculating the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value.
It can be appreciated that in the electric energy meter error calibration system based on the 5G module of the present embodiment, during the meter calibration operation performed by powering up the mounting table body of the electric energy meter to be calibrated, different error influencing factor variables are applied to the electric energy meter to be calibrated, and error value data of the electric energy meter are collected, so that a weight value of each error influencing variable for the error value of the electric energy meter can be calculated, thereby generating an error compensation meter in advance. And then, after the electric energy meter is electrified on the mounting site for metering work, the actual values of various error influence factors of the electric energy meter in the actual working environment are obtained in real time. And finally, searching a weight value of each error influence factor in a pre-constructed error compensation table according to the actual value, so as to calculate and obtain a current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value. According to the electric energy meter error calibration system based on the 5G module, the weight value of each error influence variable on the electric energy meter error value is obtained in advance, and the error compensation meter is constructed, so that the real-time metering error value of the electric energy meter can be calculated only by looking up a table according to the actual value of each error influence factor when calibration is carried out, the real-time calibration of the electric energy meter can be realized, and the high-precision metering requirement of the intelligent internet of things electric energy meter can be met.
In addition, another embodiment of the present invention also provides an electronic device, including a processor and a memory, where the memory stores a computer program, and the processor is configured to execute the steps of the method described above by calling the computer program stored in the memory.
In addition, another embodiment of the present invention also provides a computer readable storage medium storing a computer program for performing a 5G module based power meter error calibration, the computer program executing the steps of the method as described above when run on a computer.
Forms of general computer-readable storage media include: a floppy disk (floppy disk), a flexible disk (flexible disk), hard disk, magnetic tape, any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a Random Access Memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a FLASH erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium from which a computer can read. The instructions may further be transmitted or received over a transmission medium. The term transmission medium may include any tangible or intangible medium that may be used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communications signals or their communications with intangible medium that facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise a bus for transmitting a computer data signal.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. The electric energy meter error calibration method based on the 5G module is characterized by comprising the following steps of:
applying different error influence factor variables to the electric energy meter and collecting error value data of the electric energy meter during the meter calibration operation when the electric energy meter mounting table body is electrified, and obtaining weight values of errors caused by the error influence factors so as to generate an error compensation table;
after the electric energy meter is electrified on the mounting site for metering work, acquiring actual values of various error influence factors of the electric energy meter in an actual working environment;
and searching the weight value of each error influence factor in the error compensation table according to the actual value, calculating to obtain the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value.
2. The method for calibrating the error of the electric energy meter based on the 5G module according to claim 1, wherein the process of applying different error influencing factor variables to the electric energy meter and collecting error value data of the electric energy meter to obtain the weight value of the error caused by each error influencing factor to generate the error compensation meter comprises the following steps:
applying internal and external typical error influence factor variables with different degrees to the electric energy meter, and recording the metering error value of the electric energy meter under the influence of the variables to form an original data set;
converting the variable data of the error influencing factors in the original data set into offset data, and carrying out forward processing on the offset data;
for each error influencing factor, carrying out N-1 times of segmentation on the variation range of the error influencing factor to obtain N slices;
screening experimental sample data corresponding to each slice from an original data set, constructing a linear relation function set based on the experimental sample data corresponding to each slice, and solving to obtain a weight value corresponding to each slice;
and repeating the slicing and solving processes to obtain the weight value corresponding to each slice of all error influence factors, thereby generating a complete error compensation table.
3. The method for calibrating the error of the electric energy meter based on the 5G module according to claim 2, wherein the forward processing of the offset data is specifically:
wherein ,
raw offset data, min (x j ) The minimum value of the original offset data representing the jth error influencing factor, max (x j ) Maximum value of original offset data representing jth error influencing factor, x ij And the data after forward processing of the offset of the jth error influence factor in the ith test is represented.
4. The method for calibrating the error of the electric energy meter based on the 5G module according to claim 2, wherein for n error influencing factors, n groups of experimental sample data corresponding to each slice are screened out from the original data set, and the expression of the linear relation function group constructed based on the n groups of experimental sample data corresponding to each slice is as follows:
wherein ,Yi Represents the electric energy meter measurement error value, x in the ith experimental sample i，n Offset value, a, representing the nth error influencing factor in the ith experimental sample n And a weight value representing an nth error influencing factor.
5. The method for calibrating the error of the electric energy meter based on the 5G module as set forth in claim 4, wherein after solving for the weight value corresponding to each slice, the method further comprises the following steps:
and (3) carrying out weight learning on the weight value corresponding to each slice by adopting a machine learning algorithm to obtain the weight value after optimizing each slice.
6. The method for calibrating the error of the electric energy meter based on the 5G module according to claim 5, wherein the process of obtaining the optimized weight value of each slice by performing weight learning on the weight value corresponding to each slice by adopting a machine learning algorithm is specifically as follows:
the learning step length epsilon is preset, and n groups of experimental sample data corresponding to each slice are used as base points for gradual learning, so that:
Y n+1 ＝(a 1 +a j1 ε)x n+1,1 +(a 2 +a j2 ε)x n+1,2 +...+(a n +a jn ε)x n+1,n
wherein ,Yn+1 Represents the electric energy meter measurement error value, a, in the n+1th experimental sample jn The jth learning step number of the weight coefficient of the nth error influence factor in the (n+1) th experimental sample is represented;
bringing the weight value of n error influence factors in the n+1th experimental sample into the n+2th experimental sample, and continuing learning to enable the weight value to be obtained:
Y n+2 ＝[a 1 +(a k1 +a j1 )ε]x n+2,1 +[a 2 +(a k2 +a j2 )ε]x n+2,2 +...+[(a n +(a kn +a jn )ε]x n+2,n
wherein ,akn A kth learning step number of a weight coefficient representing an nth error influence factor in an nth+2th experiment sample;
repeatedly executing the process until the weight learning of all experimental sample data corresponding to each slice is completed;
and introducing a pheromone to search a global optimal solution, so as to obtain an optimal weight value corresponding to each slice.
7. The method for calibrating the error of the electric energy meter based on the 5G module according to claim 6, wherein the process of introducing the pheromone to find the global optimal solution and obtaining the optimal weight value corresponding to each slice is specifically as follows:
introduction of pheromone beta j ＝∑a ij For the learning step length sum of the weight coefficient of the jth error influence factor in each iteration process, after all experimental samples corresponding to each slice are iterated, the total learning step length value beta= Σbetais used j And taking the weight value corresponding to the minimum as the optimal weight value corresponding to the slice.
8. Electric energy meter error calibration system based on 5G module, characterized by comprising:
the weight value calculation module is used for applying different error influence factor variables to the electric energy meter and collecting error value data of the electric energy meter during the process of powering on the electric energy meter mounting table body to perform meter calibration operation, so as to obtain weight values of errors caused by the error influence factors and generate an error compensation table;
the data acquisition module is used for acquiring actual values of various error influence factors of the electric energy meter in an actual working environment after the electric energy meter is electrified on the mounting site for metering work;
and the real-time calibration module is used for searching the weight value of each error influence factor in the error compensation table according to the actual value, calculating the current metering error value of the electric energy meter, and carrying out real-time compensation and correction on the current metering result of the electric energy meter based on the current metering error value.
9. An electronic device comprising a processor and a memory, the memory having stored therein a computer program for executing the steps of the method according to any of claims 1-7 by invoking the computer program stored in the memory.
10. A computer readable storage medium storing a computer program for performing a 5G module based power meter error calibration, wherein the computer program when run on a computer performs the steps of the method of any of claims 1 to 7.
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