CN117131353A - Method and device for determining out-of-tolerance electric energy meter, electronic equipment and storage medium - Google Patents
Method and device for determining out-of-tolerance electric energy meter, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for determining an out-of-tolerance electric energy meter, electronic equipment and a storage medium. The method comprises the steps of periodically obtaining a combined intelligent electric energy meter corresponding to a target electric energy box; inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected; and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter. The problem of the detection difficulty that detects a plurality of unusual out-of-tolerance electric energy meters and cause is solved, through constructing out-of-tolerance electric energy meter identification model, out-of-tolerance electric energy meter identification can be carried out more fast effectively, the speed of out-of-tolerance electric energy meter identification has been improved, the calculated amount of out-of-tolerance electric energy meter identification has been reduced.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and apparatus for determining an out-of-tolerance electric energy meter, an electronic device, and a storage medium.
Background
With the perfection of the automatic acquisition function of the electricity consumption information acquisition system, the intelligent electric energy meter is widely applied. However, the accuracy of electric quantity measurement is directly affected by the performance of the intelligent electric energy meter, and the out-of-tolerance electric energy meter monitoring system detects out that the performance of the electric energy meter in operation is out of compliance, namely out-of-tolerance, and then the electric energy meter is replaced by a power grid company. The current monitoring system of the out-of-tolerance meter mainly comprises the steps of constructing a line loss model by collecting data such as long-term historical electric quantity and voltage of the electric energy meter, solving the model to obtain an error coefficient of each electric energy meter, and judging that the error coefficient is larger than the out-of-tolerance value as the out-of-tolerance electric energy meter.
The inventors have found that the following drawbacks exist in the prior art in the process of implementing the present invention: at present, single abnormal out-of-tolerance electric energy meter detection is generally carried out through a fit out-of-tolerance electric energy meter detection model, but a scene of a plurality of out-of-tolerance electric energy meters needs circulation searching calculation with extremely large calculation amount, the calculation amount is relatively large, and the determination of the out-of-tolerance electric energy meters is relatively slow.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for determining an out-of-tolerance electric energy meter, so that the recognition rate of the out-of-tolerance electric energy meter is improved, and the calculation amount of the out-of-tolerance electric energy meter is reduced.
According to an aspect of the present invention, there is provided a method for determining an out-of-tolerance electric energy meter, including:
periodically acquiring a combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected;
inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected;
and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter.
According to another aspect of the present invention, there is provided an out-of-tolerance electric energy meter determining apparatus, including:
the combined intelligent electric energy meter acquisition module is used for periodically acquiring the combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected;
the electric energy meter identification result determining module is used for inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected;
And the electric energy meter identification result judging module is used for judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter or not, and if so, determining that the intelligent electric energy sub-meter to be detected is the out-of-tolerance electric energy meter.
According to another aspect of the present invention, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for determining an out-of-tolerance electric energy meter according to any one of the embodiments of the present invention when executing the computer program.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for determining an out-of-tolerance electric energy meter according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the combined intelligent electric energy meter corresponding to the target electric energy box is obtained periodically; inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected; and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter. The problem of the detection difficulty that detects a plurality of unusual out-of-tolerance electric energy meters and cause is solved, through constructing out-of-tolerance electric energy meter identification model, out-of-tolerance electric energy meter identification can be carried out more fast effectively, the speed of out-of-tolerance electric energy meter identification has been improved, the calculated amount of out-of-tolerance electric energy meter identification has been reduced.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for determining an out-of-tolerance electric energy meter according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for determining an out-of-tolerance electric energy meter according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "target," "current," and the like in the description and claims of the present invention and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for determining an out-of-tolerance electric energy meter according to an embodiment of the present invention, where the method may be performed by an out-of-tolerance electric energy meter determining device, and the out-of-tolerance electric energy meter determining device may be implemented in hardware and/or software.
Accordingly, as shown in fig. 1, the method includes:
s110, periodically acquiring a joint intelligent electric energy meter corresponding to the target electric energy box.
The combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected.
The combined intelligent electric energy meter can be an electric energy meter which acquires a plurality of intelligent electric energy sub-meters corresponding to the intelligent electric energy summary meter.
For example, assume that a joint intelligent electric energy meter of a building in a certain district is obtained, wherein the joint intelligent electric energy meter comprises 1 intelligent electric energy summary meter and 50 intelligent electric energy sub-meters. Generally, the sum of the electric energy of the 50 intelligent electric energy sub-meters is equal to the electric energy value of the total intelligent electric energy table.
S120, inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected.
Wherein, the out-of-tolerance electric energy meter identification model may be a model capable of out-of-tolerance electric energy meter identification.
In this embodiment, a plurality of intelligent electric energy sub-meters to be detected and an intelligent electric energy summary meter are required to be input into the out-of-tolerance electric energy meter identification model together. For example, the 1 intelligent electric energy summary table and the 50 intelligent electric energy sub-tables are input into an out-of-tolerance electric energy meter identification model together to identify electric energy meter identification results corresponding to the 50 intelligent electric energy sub-tables respectively, that is, the 50 intelligent electric energy sub-tables respectively belong to the out-of-tolerance electric energy meter or the non-out-of-tolerance electric energy meter.
In this embodiment, through the pre-trained recognition model of the out-of-tolerance electric energy meter, the abnormal conditions of a plurality of intelligent electric energy sub-meters can be recognized at the same time, so as to obtain the recognition result of the electric energy meter.
And S130, judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy sub-meter to be detected is the out-of-tolerance electric energy meter.
The electric energy meter identification result comprises an out-of-tolerance electric energy meter and a non-out-of-tolerance electric energy meter.
In the previous example, the electric energy meter identification results corresponding to the 50 intelligent electric energy sub-meters are obtained, 5 out-of-tolerance electric energy meters are assumed, and the remaining 45 electric energy meters are non-out-of-tolerance electric energy meters. That is, the 45 non-out-of-tolerance electric energy meters are normal electric energy meters; the 5 out-of-tolerance electric energy meters are abnormal electric energy meters.
Optionally, after the determining that the intelligent electric energy sub-meter to be detected is an out-of-tolerance electric energy meter, the method further includes: acquiring at least one out-of-tolerance electric energy item data corresponding to the intelligent electric energy sub-meter to be detected; and determining the electric meter error value according to the data of each out-of-tolerance electric energy item through a preset least square regression algorithm fitting model.
The out-of-tolerance power item data may be data having a deviation from the standard power item data.
It can be understood that different standard electric energy item data have a certain error range value, and if the electric energy item data are within the error range, the electric energy item data are non-out-of-tolerance electric energy item data; otherwise, the electric energy item data is out-of-tolerance electric energy item data. Exemplary, assume that the standard power term data is 45 degrees, and the error range value isDegree. Assuming that the power term data is 45.2 degrees, the power term data is non-out-of-tolerance power term data. Assuming that the power term data is 45.6 degrees, the power term data is out-of-tolerance power term data.
Further, after determining that 5 out-of-tolerance electric energy meters exist, a plurality of out-of-tolerance electric energy item data corresponding to each out-of-tolerance electric energy meter are required to be obtained respectively. Assume that 5 out-of-tolerance electric energy meters are respectively: out-of-tolerance electric energy meter 1, out-of-tolerance electric energy meter 2, out-of-tolerance electric energy meter 3, out-of-tolerance electric energy meter 4 and out-of-tolerance electric energy meter 5. Suppose that the out-of-tolerance electric energy meter 1 includes 6 out-of-tolerance electric energy item data. The 6 out-of-tolerance electric energy item data and the ammeter residual data obtained through the out-of-tolerance electric energy meter identification model are required to be fitted to the model through a preset least square regression algorithm, so that the ammeter out-of-tolerance value is determined. The abnormal condition of the combined intelligent electric energy meter can be fed back by using the ammeter ultra-difference value.
It can be understood that if the electric meter ultra-difference value is larger, the abnormal condition of the combined intelligent electric energy meter is more serious; otherwise, if the ammeter ultra-difference value is smaller, the abnormal condition of the combined intelligent ammeter is indicated to be less serious.
Optionally, before the periodically acquiring the joint intelligent electric energy meter corresponding to the target electric energy box, the method further includes: acquiring a history combined intelligent electric energy meter; inputting the historical combined intelligent electric energy meter into a pre-constructed electric energy meter misalignment model, and calculating the quantity value of each target power supply; acquiring freezing electricity consumption of each target day, and respectively calculating the statistical line loss of each ammeter and the line loss of each ammeter misalignment model according to the target electricity supply quantity value; calculating line loss and statistical line loss of each ammeter according to each ammeter misalignment model to respectively calculate residual data of each ammeter; according to the target daily freezing electricity consumption and the residual data of the electric meters, performing optimization search processing through a target optimization function which is built in advance and is based on a lasso algorithm, and obtaining each alpha coefficient queue; obtaining each alpha coefficient array according to each alpha coefficient queue, and carrying out average value calculation on each alpha coefficient array to obtain an alpha average coefficient array; and determining a lasso model coefficient array and a historical out-of-tolerance electric energy meter index according to the alpha average coefficient array, and training according to the lasso model coefficient array and the historical out-of-tolerance electric energy meter index to obtain an out-of-tolerance electric energy meter identification model.
The history combined intelligent electric energy meter can be a sub-meter of a history within a period time, specifically, can be a history combined intelligent electric energy meter within one month, and can also be a history combined intelligent electric energy meter within half year.
The ammeter misalignment model may be a model that can calculate a target power supply metering value for each intelligent power meter. The target power supply amount value may be a statistical value of the power supply amount of each intelligent electric energy meter over a period of time. The target daily freezing electricity consumption can be the statistical electricity consumption of each intelligent electricity energy sub-meter daily freezing electricity consumption in a period of time. The electricity meter statistical line loss can be the electric quantity loss of the electric quantity in the electric wire transmission process according to the total intelligent electric energy table and the electric quantity counted by each intelligent electric energy sub-meter. The line loss can be calculated by the ammeter misalignment model, and statistics of the electric quantity loss of the electric quantity in the electric wire transmission process can be carried out on the intelligent electric energy summary meter and each intelligent electric energy sub-meter through the ammeter misalignment model.
The electricity meter residual error data can be used for counting the residual errors among the line losses of the historical joint intelligent electric energy meter in different modes. The alpha coefficient queue may be a queue composed of an output coefficient matrix, a characteristic coefficient matrix, and an alpha coefficient matrix. In particular, the output coefficient matrix may be Is a matrix of (a); the characteristic coefficient matrix may be->Is a matrix of (a); the alpha coefficient matrix may be +.>P represents the number of the history combined intelligent electric energy meters; m represents the number of outputs of the alpha coefficient.
Wherein the alpha coefficient array may be an array of coefficients determined by outputting a coefficient matrix and a characteristic coefficient matrix. The alpha average coefficient array may be an average array calculated from a plurality of alpha coefficient arrays. The historical out-of-tolerance electric energy meter index can be an index of a historical joint intelligent electric energy meter corresponding to each non-zero coefficient in the alpha average coefficient array.
In this embodiment, the training of the identification model of the out-of-tolerance electric energy meter can be performed through the history combined intelligent electric energy meter, so that the identification processing operation of the out-of-tolerance electric energy meter can be performed according to the trained identification model of the out-of-tolerance electric energy meter.
Optionally, inputting the historical combined intelligent electric energy meter into a pre-constructed ammeter misalignment model, and calculating the target power supply quantity value includes: inputting the historical combined intelligent electric energy meter into a pre-constructed electric energy meter misalignment model, and calculating the quantity value of each target power supply; the formula of the ammeter misalignment model is as follows: Wherein->Express historical intelligent electric energy summary->A target power supply amount value for the day; />Representing metering Point->In->The target day of the day freezes the electricity consumption, metering point +.>The number of the intelligent electric energy meters is the same as that of the historical intelligent electric energy meters; />Representing metering Point->Is a relative error of (2); />Representing historical smart electricityRelative error of the summary energy table;for measuring point->Is the total number of (3); />Representing an initial relative error; />Indicate->Line loss on day.
In the embodiment, the ammeter misalignment model is based on the target electricity supply quantity value, the target daily freezing electricity consumption and the metering pointRelative error of (2), relative error of total table of historical intelligent electric energy, metering point +.>Total number, initial relative error and +.>The line loss of the day.
Optionally, obtaining the daily freezing electricity consumption of each target, and respectively calculating the statistical line loss of each ammeter and the calculated line loss of each ammeter misalignment model according to the target electricity supply quantity value, including: according to the target power supply quantity value and the target daily freezing power consumption, the method passes through the formulaTo calculate and obtain the statistical line loss of each ammeterThe method comprises the steps of carrying out a first treatment on the surface of the According to the target power supply quantity value and the target daily freezing power consumption, the method passes through the formula To calculate the line loss of each ammeter misalignment model>。
In this embodiment, the electricity meter counts the line loss according to the target electricity supply amount value and the target daily freezing electricity consumption. Further, according to the relative error of the history intelligent electric energy total table, the target electric energy supply quantity value, the target daily freezing electric energy consumption and the metering pointRelative error, initial relative error and +.>And (3) calculating the line loss of the ammeter misalignment model by using the line loss of the day.
The historical combined intelligent electric energy meters can calculate corresponding electric meter statistical line loss and electric meter misalignment model calculation line loss.
Optionally, the calculating the line loss according to each ammeter misalignment model and calculating the line loss according to each ammeter statistics respectively to calculate each ammeter residual data includes: by the formulaTo calculate the residual data of each ammeter>。
Exemplary, assuming that the statistical line loss of the ammeter is 1.2 degrees, the line loss calculated by the ammeter misalignment model is 0.9 degrees, the ammeter residual data can be further calculatedThe value of (2) is 0.3 degrees.
Optionally, the alpha coefficient queue includes: outputting a coefficient matrix, a characteristic coefficient matrix and an alpha coefficient matrix; obtaining each alpha coefficient array according to each alpha coefficient queue, And performing average value calculation on each alpha coefficient array to obtain an alpha average coefficient array, including: determining an alpha non-zero coefficient sum according to the characteristic coefficient matrix, and determining an alpha non-zero coefficient index corresponding to each alpha non-zero coefficient; determining each alpha coefficient array according to each alpha non-zero coefficient index and the output coefficient matrix; according to the formulaPerforming average value calculation on each alpha coefficient array to obtain an alpha average coefficient array +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Represents an alpha coefficient array, n represents the number of alpha coefficient arrays, and k is +.>A positive integer therebetween.
In this embodiment, the number of coefficients of the feature coefficient matrix that are not zero needs to be summed according to columns to obtain an alpha non-zero coefficient sum, and the alpha non-zero coefficient sum corresponding to the index value, that is, the alpha non-zero coefficient index, is taken out.
Further, according to the alpha non-zero coefficient index, index searching is carried out on the output coefficient matrix, and the corresponding numerical value is searched and used as an alpha coefficient array.
Specifically, for the characteristic coefficient matrix (the characteristic coefficient matrix is named asThe output coefficient matrix is named +.>) The non-zero labeling processing of alpha in (a) can be specifically: To calculate the number alpha is not 0, i.eTo calculate the alpha non-zero coefficient sum (i.e.)>）。
Further, determining an alpha non-zero coefficient index corresponding to each alpha non-zero coefficient, specifically, taking out the index with the alpha non-zero coefficient and the median value as the number N of the out-of-tolerance electric energy meters (preset out-of-tolerance electric energy meters), namely,。
correspondingly, the alpha value corresponding to the index is used as an alpha coefficient array, namely。
Next, according to the formulaPerforming average value calculation on each alpha coefficient array to obtain an alpha average coefficient array +.>. Will->And as a lasso model alpha coefficient, performing lasso modeling on ammeter residual data and historical intelligent electric energy sub-meter data to obtain a lasso model coefficient array and a historical out-of-tolerance electric energy meter index, and training according to the lasso model coefficient array and the historical out-of-tolerance electric energy meter index to obtain an out-of-tolerance electric energy meter identification model.
Specifically, take outSub-table index corresponding to non-zero coefficient in (a)>Namely, the index of the out-of-tolerance electric energy meter is +.>。
Furthermore, data fitting processing of a least square regression algorithm fitting model can be performed according to ammeter residual data and historical intelligent ammeter data, and the obtained fitting coefficient can be the historical superdifference value corresponding to the superdifference meter. When the least square regression algorithm fitting model meets the accuracy requirement, the least square regression algorithm fitting model is added into the out-of-tolerance electric energy meter identification model, and the out-of-tolerance electric energy meter value can be calculated in real time.
According to the technical scheme, the combined intelligent electric energy meter corresponding to the target electric energy box is obtained periodically; inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected; and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter. The problem of the detection difficulty that detects a plurality of unusual out-of-tolerance electric energy meters and cause is solved, through constructing out-of-tolerance electric energy meter identification model, out-of-tolerance electric energy meter identification can be carried out more fast effectively, the speed of out-of-tolerance electric energy meter identification has been improved, the calculated amount of out-of-tolerance electric energy meter identification has been reduced.
Example two
Fig. 2 is a schematic structural diagram of a device for determining an out-of-tolerance electric energy meter according to a second embodiment of the present invention. The device for determining the out-of-tolerance electric energy meter provided by the embodiment of the invention can be realized through software and/or hardware, and can be configured in terminal equipment or a server to realize the method for determining the out-of-tolerance electric energy meter. As shown in fig. 2, the apparatus includes: the intelligent electric energy meter acquiring module 210, the electric energy meter identification result determining module 220 and the electric energy meter identification result judging module 230 are combined.
The combined intelligent electric energy meter acquisition module 210 is configured to periodically acquire a combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected;
the electric energy meter identification result determining module 220 is configured to input the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model, so as to obtain electric energy meter identification results corresponding to each intelligent electric energy sub-meter to be detected respectively;
the electric energy meter identification result judging module 230 is configured to judge whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if yes, determine that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter.
According to the technical scheme, the combined intelligent electric energy meter corresponding to the target electric energy box is obtained periodically; inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected; and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter. The problem of the detection difficulty that detects a plurality of unusual out-of-tolerance electric energy meters and cause is solved, through constructing out-of-tolerance electric energy meter identification model, out-of-tolerance electric energy meter identification can be carried out more fast effectively, the speed of out-of-tolerance electric energy meter identification has been improved, the calculated amount of out-of-tolerance electric energy meter identification has been reduced.
Optionally, the ammeter ultra-difference value determining module may be specifically configured to: after the intelligent electric energy sub-meter to be detected is determined to be the out-of-tolerance electric energy meter, acquiring at least one out-of-tolerance electric energy item data corresponding to the intelligent electric energy sub-meter to be detected; and determining the electric meter error value according to the data of each out-of-tolerance electric energy item through a preset least square regression algorithm fitting model.
Optionally, the method further includes a training module for the recognition model of the out-of-tolerance electric energy meter, which can be specifically used for: acquiring a historical joint intelligent electric energy meter before periodically acquiring the joint intelligent electric energy meter corresponding to the target electric energy box; inputting the historical combined intelligent electric energy meter into a pre-constructed electric energy meter misalignment model, and calculating the quantity value of each target power supply; acquiring freezing electricity consumption of each target day, and respectively calculating the statistical line loss of each ammeter and the line loss of each ammeter misalignment model according to the target electricity supply quantity value; calculating line loss and statistical line loss of each ammeter according to each ammeter misalignment model to respectively calculate residual data of each ammeter; according to the target daily freezing electricity consumption and the residual data of the electric meters, performing optimization search processing through a target optimization function which is built in advance and is based on a lasso algorithm, and obtaining each alpha coefficient queue; obtaining each alpha coefficient array according to each alpha coefficient queue, and carrying out average value calculation on each alpha coefficient array to obtain an alpha average coefficient array; and determining a lasso model coefficient array and a historical out-of-tolerance electric energy meter index according to the alpha average coefficient array, and training according to the lasso model coefficient array and the historical out-of-tolerance electric energy meter index to obtain an out-of-tolerance electric energy meter identification model.
Optionally, the out-of-tolerance electric energy meter recognition model training module may be further specifically configured to: inputting the historical combined intelligent electric energy meter into a pre-constructed electric energy meter misalignment model, and calculating the quantity value of each target power supply; the formula of the ammeter misalignment model is as follows:wherein->Express historical intelligent electric energy summary->A target power supply amount value for the day; />Representing metering Point->In->The target day of the day freezes the electricity consumption, metering point +.>The number of the intelligent electric energy meters is the same as that of the historical intelligent electric energy meters; />Representing metering Point->Is a relative error of (2); />Representing the relative error of the history intelligent electric energy summary table; />For measuring point->Is the total number of (3); />Representing an initial relative error; />Indicate->Line loss on day.
Optionally, the out-of-tolerance electric energy meter recognition model training module may be further specifically configured to: according to the target power supply quantity value and the target daily freezing power consumption, the method passes through the formulaTo calculate the statistical line loss of each ammeter>The method comprises the steps of carrying out a first treatment on the surface of the According to the target power supply quantity value and the target daily freezing power consumption, the method passes through the formulaTo calculate the line loss of each ammeter misalignment model>。
Optionally, the out-of-tolerance electric energy meter recognition model training module may be further specifically configured to: by the formula To calculate the residual data of each ammeter>。
Optionally, the alpha coefficient queue includes: output coefficient matrix, characteristic coefficient matrix and alpha coefficient matrix.
Optionally, the out-of-tolerance electric energy meter recognition model training module may be further specifically configured to: determining an alpha non-zero coefficient sum according to the characteristic coefficient matrix, and determining an alpha non-zero coefficient index corresponding to each alpha non-zero coefficient; determining each alpha coefficient array according to each alpha non-zero coefficient index and the output coefficient matrix; according to the formulaPerforming average value calculation on each alpha coefficient array to obtain an alpha average coefficient array +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Represents an alpha coefficient array, n represents the number of alpha coefficient arrays, and k is +.>A positive integer therebetween.
The out-of-tolerance electric energy meter determining device provided by the embodiment of the invention can execute the out-of-tolerance electric energy meter determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example III
Fig. 3 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement a third embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the out-of-tolerance electric energy meter determination method.
In some embodiments, the out-of-tolerance electric energy meter determination method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above-described out-of-tolerance electric energy meter determination method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the out-of-tolerance ammeter determination method in any other suitable manner (e.g., by means of firmware).
The method comprises the following steps: periodically acquiring a combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected; inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected; and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Example IV
A fourth embodiment of the present invention also provides a computer-readable storage medium containing computer-readable instructions, which when executed by a computer processor, are configured to perform a method of determining an out-of-tolerance electric energy meter, the method comprising: periodically acquiring a combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected; inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected; and judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter.
Of course, the computer-readable storage medium provided in the embodiments of the present invention is not limited to the above-described method operations, and may also perform the related operations in the method for determining an out-of-tolerance electric energy meter provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the above out-of-tolerance electric energy meter determining device, each unit and module included are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for determining the out-of-tolerance electric energy meter is characterized by comprising the following steps of:
periodically acquiring a combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected;
inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected;
And judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter, if so, determining that the intelligent electric energy meter to be detected is the out-of-tolerance electric energy meter.
2. The method of claim 1, further comprising, after the determining that the smart power meter to be detected is an out-of-tolerance power meter:
acquiring at least one out-of-tolerance electric energy item data corresponding to the intelligent electric energy sub-meter to be detected;
and determining the electric meter error value according to the data of each out-of-tolerance electric energy item through a preset least square regression algorithm fitting model.
3. The method of claim 1, further comprising, prior to periodically acquiring the associated smart power meter corresponding to the target power tank:
acquiring a history combined intelligent electric energy meter;
inputting the historical combined intelligent electric energy meter into a pre-constructed electric energy meter misalignment model, and calculating the quantity value of each target power supply;
acquiring freezing electricity consumption of each target day, and respectively calculating the statistical line loss of each ammeter and the line loss of each ammeter misalignment model according to the target electricity supply quantity value;
calculating line loss and statistical line loss of each ammeter according to each ammeter misalignment model to respectively calculate residual data of each ammeter;
According to the target daily freezing electricity consumption and the residual data of the electric meters, performing optimization search processing through a target optimization function which is built in advance and is based on a lasso algorithm, and obtaining each alpha coefficient queue;
obtaining each alpha coefficient array according to each alpha coefficient queue, and carrying out average value calculation on each alpha coefficient array to obtain an alpha average coefficient array;
and determining a lasso model coefficient array and a historical out-of-tolerance electric energy meter index according to the alpha average coefficient array, and training according to the lasso model coefficient array and the historical out-of-tolerance electric energy meter index to obtain an out-of-tolerance electric energy meter identification model.
4. The method of claim 3, wherein inputting the historical joint intelligent electric energy meter into a pre-built meter misalignment model, calculating each target power supply amount value comprises:
inputting the historical combined intelligent electric energy meter into a pre-constructed electric energy meter misalignment model, and calculating the quantity value of each target power supply;
the formula of the ammeter misalignment model is as follows:wherein->Express historical intelligent electric energy summary->A target power supply amount value for the day; />Representing metering Point->In- >The target day of the day freezes the electricity consumption, metering point +.>The number of the intelligent electric energy meters is the same as that of the historical intelligent electric energy meters; />Representing metering Point->Is a relative error of (2); />Representing the relative error of the history intelligent electric energy summary table; />For measuring point->Is the total number of (3); />Representing an initial relative error; />Indicate->Line loss on day.
5. The method of claim 4, wherein obtaining each target daily freezing power consumption and calculating each meter statistical line loss and each meter misalignment model calculation line loss from each target power supply amount value, respectively, comprises:
according to the target power supply quantity value and the target daily freezing power consumption, the method passes through the formulaTo calculate the statistical line loss of each ammeter>；
According to the target power supply quantity value and the target daily freezing power consumption, the method passes through the formulaTo calculate the line loss of each ammeter misalignment model>。
6. The method of claim 5, wherein calculating the line loss and the statistical line loss for each meter based on each meter misalignment model to calculate the respective meter residual data comprises:
by the formulaTo calculate the residual data of each ammeter >。
7. The method of claim 6, wherein the alpha coefficient queue comprises: outputting a coefficient matrix, a characteristic coefficient matrix and an alpha coefficient matrix;
obtaining each alpha coefficient array according to each alpha coefficient queue, and carrying out average value calculation on each alpha coefficient array to obtain an alpha average coefficient array, wherein the method comprises the following steps:
determining an alpha non-zero coefficient sum according to the characteristic coefficient matrix, and determining an alpha non-zero coefficient index corresponding to each alpha non-zero coefficient;
determining each alpha coefficient array according to each alpha non-zero coefficient index and the output coefficient matrix;
according to the formulaPerforming average value calculation on each alpha coefficient array to obtain an alpha average coefficient array +.>；
Wherein,represents an alpha coefficient array, n represents the number of alpha coefficient arrays, and k is +.>A positive integer therebetween.
8. An out-of-tolerance electric energy meter determining device, comprising:
the combined intelligent electric energy meter acquisition module is used for periodically acquiring the combined intelligent electric energy meter corresponding to the target electric energy box; the combined intelligent electric energy meter comprises an intelligent electric energy summary meter and at least one intelligent electric energy sub-meter to be detected;
The electric energy meter identification result determining module is used for inputting the intelligent electric energy summary list and each intelligent electric energy sub-meter to be detected into a pre-trained out-of-tolerance electric energy meter identification model to obtain electric energy meter identification results respectively corresponding to each intelligent electric energy sub-meter to be detected;
and the electric energy meter identification result judging module is used for judging whether the electric energy meter identification result is an out-of-tolerance electric energy meter or not, and if so, determining that the intelligent electric energy sub-meter to be detected is the out-of-tolerance electric energy meter.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a method of determining an out-of-tolerance electric energy meter according to any one of claims 1-7 when executing the computer program.
10. A computer readable storage medium storing computer instructions for causing a processor to perform a method of determining an out of tolerance electric energy meter according to any one of claims 1-7.
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