CN115542237B - Uncertainty determination method and device and electronic equipment - Google Patents

Uncertainty determination method and device and electronic equipment Download PDF

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CN115542237B
CN115542237B CN202211504893.4A CN202211504893A CN115542237B CN 115542237 B CN115542237 B CN 115542237B CN 202211504893 A CN202211504893 A CN 202211504893A CN 115542237 B CN115542237 B CN 115542237B
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electric energy
quantity data
user
electric quantity
energy meter
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CN115542237A (en
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巩国栋
张喆葳
刘路畅
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Beijing Zhixiang Technology Co Ltd
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Beijing Zhixiang Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/04—Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

Abstract

The application provides a method and a device for determining uncertainty and electronic equipment, and relates to the technical field of power monitoring. The method comprises the following steps: when the uncertainty corresponding to the operation error is determined, acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and respective power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter; performing multiple groups of repeated sampling operations with returning for N times on the electric quantity data in N metering periods, and determining M groups of target repeated sampling electric quantity data according to the co-linearity indexes corresponding to the obtained repeated sampling electric quantity data; and then according to the M groups of target resample electric quantity data, uncertainty corresponding to the respective operation errors of the P user electric energy meters is determined, so that the uncertainty corresponding to the operation errors can be accurately determined, and the reliability of the detected operation errors can be described based on the uncertainty subsequently.

Description

Uncertainty determination method and device and electronic equipment
Technical Field
The present application relates to the field of power monitoring technologies, and in particular, to a method and an apparatus for determining uncertainty, and an electronic device.
Background
In an electric energy metering scene, in order to ensure the operation accuracy of the user electric energy meter, the operation error of the user electric energy meter can be detected, and the reliability of the detection result can be described through the uncertainty corresponding to the operation error.
In general, the uncertainty corresponding to the operation error refers to a degree that a detection result cannot be determined due to the existence of the operation error; conversely, the reliability of the detection result can also be described.
Therefore, how to determine the uncertainty corresponding to the operation error is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The application provides a method and a device for determining uncertainty and electronic equipment, which can accurately determine the uncertainty corresponding to an operation error, so that the reliability of the detected operation error can be described based on the uncertainty subsequently.
The application provides a method for determining uncertainty, which comprises the following steps:
acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of an assessment electric energy meter and respective power consumption data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P.
And performing multiple groups of resampling operations with returning for N times on the electric quantity data in the N metering periods, and determining M groups of target resampling electric quantity data according to the colinearity indexes corresponding to the obtained resampling electric quantity data, wherein M is a positive integer.
And according to the M groups of target resampling electric quantity data, determining uncertainty corresponding to the respective operation errors of the P user electric energy meters.
According to the uncertainty determining method provided by the application, the uncertainty corresponding to the operation error of each of the P user electric energy meters is determined according to the M groups of target resampling electric energy data, and the uncertainty determining method includes:
inputting the M groups of target resampling electric quantity data into a pre-constructed operation error detection model to obtain M operation errors corresponding to the P user electric energy meters; wherein the operation error detection model is constructed based on the electric quantity data in the N metering periods.
And aiming at each user electric energy meter, according to M running errors corresponding to the user electric energy meter, determining uncertainty corresponding to the running errors of the user electric energy meter.
According to the uncertainty determining method provided by the application, the uncertainty corresponding to the operation error of the user electric energy meter is determined according to the M operation errors corresponding to the user electric energy meter, and the uncertainty determining method comprises the following steps:
according to
Figure 865462DEST_PATH_IMAGE001
And determining uncertainty corresponding to the operation error of the user electric energy meter.
Wherein the content of the first and second substances,
Figure 445479DEST_PATH_IMAGE002
represents the uncertainty corresponding to the operation error of the ith user electric energy meter,
Figure 128265DEST_PATH_IMAGE003
，
Figure 266860DEST_PATH_IMAGE004
，
Figure 300675DEST_PATH_IMAGE005
，
Figure 418803DEST_PATH_IMAGE006
represents the mth operation error in the M operation errors corresponding to the ith user electric energy meter,
Figure 487254DEST_PATH_IMAGE007
and the vector is formed by M running errors corresponding to the ith user electric energy meter.
According to the method for determining the uncertainty, the determining of the M groups of target resampling electric quantity data according to the collinearity indexes corresponding to the obtained resampling electric quantity data comprises the following steps:
determining whether a co-linearity index corresponding to the resampled electric quantity data is in a preset range; and the preset range is determined based on initial collinearity indexes corresponding to the respective power consumption data of the P user electric energy meters.
If the target resampling electric quantity data is within the preset range, determining the resampling electric quantity data as the target resampling electric quantity data; and if the new re-sampling electric quantity data is not in the preset range, performing re-sampling operation with playback on the electric quantity data in the N metering periods for N times again, and determining whether the co-linearity index corresponding to the new re-sampling electric quantity data is in the preset range or not until the M groups of target re-sampling electric quantity data are determined.
According to the uncertainty determination method provided by the application, the method further comprises the following steps:
and determining a correlation matrix between every two user electric energy meters in the P user electric energy meters according to the resampling electricity consumption data corresponding to the P user electric energy meters in the resampling electricity consumption data, and determining an electricity quantity correlation matrix corresponding to every two user electric energy meters according to the correlation matrix and the resampling electricity consumption data corresponding to the every two user electric energy meters.
And determining the co-linearity index corresponding to the re-sampling electric quantity data according to the electric quantity correlation matrix corresponding to the two user electric energy meters.
According to the uncertainty determining method provided by the application, determining a correlation matrix between every two user electric energy meters in the P user electric energy meters according to resampling electricity consumption data corresponding to the P user electric energy meters in the resampling electricity consumption data comprises:
according to
Figure 811398DEST_PATH_IMAGE008
And determining a correlation matrix between every two user electric energy meters in the P user electric energy meters.
Wherein the content of the first and second substances,
Figure 332510DEST_PATH_IMAGE009
a correlation matrix between the ith user power meter and the jth user power meter is represented,
Figure 254329DEST_PATH_IMAGE010
representing the covariance of the resampled power consumption data corresponding to the ith user electric energy meter and the jth user electric energy meter respectively,
Figure 410242DEST_PATH_IMAGE011
indicating the standard deviation of the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 657683DEST_PATH_IMAGE012
the standard deviation of the resampling electricity consumption data corresponding to the jth user electric energy meter is represented,
Figure 400512DEST_PATH_IMAGE013
representing the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 391601DEST_PATH_IMAGE014
represents the weight corresponding to the jth user electric energy meterAnd sampling power consumption data.
According to the uncertainty determining method provided by the application, the step of determining the electric quantity correlation matrix corresponding to the two user electric energy meters according to the correlation matrix and the resampling power consumption data corresponding to the two user electric energy meters comprises the following steps:
for each two-by-two user electric energy meter, according to
Figure 404950DEST_PATH_IMAGE015
And determining the electric quantity correlation matrix corresponding to the two user electric energy meters.
Wherein the content of the first and second substances,
Figure 823293DEST_PATH_IMAGE016
representing the electric quantity correlation matrix corresponding to the ith user electric energy meter and the jth user electric energy meter,
Figure 787838DEST_PATH_IMAGE017
average power consumption data representing the resampled power consumption data in the N metering periods corresponding to the ith user electric energy meter,
Figure 81154DEST_PATH_IMAGE018
and the average power consumption data of the resampled power consumption data in the N metering periods corresponding to the jth user electric energy meter are represented.
The present application also provides an uncertainty determination apparatus, which may include:
the acquisition unit is used for acquiring electric quantity data in N metering cycles; the electric quantity data comprises power supply quantity data of an assessment electric energy meter and respective power consumption data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P.
And the first processing unit is used for executing a plurality of groups of N times of re-sampling operations with returning on the electric quantity data in the N metering periods, and determining M groups of target re-sampling electric quantity data according to the obtained co-linearity indexes corresponding to the re-sampling electric quantity data, wherein M is a positive integer.
And the second processing unit is used for determining the uncertainty corresponding to the respective operation error of the P user electric energy meters according to the M groups of target resampling electric quantity data.
According to the uncertainty determining device provided by the application, the second processing unit is specifically configured to input the M groups of target resampling electric quantity data into a pre-constructed operation error detection model, so as to obtain M operation errors corresponding to the P user electric energy meters; wherein the operation error detection model is constructed based on the electric quantity data in the N metering periods; and aiming at each user electric energy meter, according to M running errors corresponding to the user electric energy meter, determining uncertainty corresponding to the running errors of the user electric energy meter.
According to the uncertainty determination apparatus provided by the application, the second processing unit is specifically configured to determine the uncertainty according to
Figure 447544DEST_PATH_IMAGE019
And determining uncertainty corresponding to the operation error of the user electric energy meter.
Wherein the content of the first and second substances,
Figure 36789DEST_PATH_IMAGE002
represents the uncertainty corresponding to the operation error of the ith user electric energy meter,
Figure 754209DEST_PATH_IMAGE020
，
Figure 322987DEST_PATH_IMAGE021
，
Figure 809463DEST_PATH_IMAGE022
，
Figure 569609DEST_PATH_IMAGE023
represents the mth operation error in the M operation errors corresponding to the ith user electric energy meter,
Figure 7281DEST_PATH_IMAGE024
and the vector is formed by M running errors corresponding to the ith user electric energy meter.
According to the uncertainty determining device provided by the application, the first processing unit is specifically configured to determine whether a co-linearity index corresponding to the resampled electric quantity data is within a preset range; the preset range is determined based on initial collinearity indexes corresponding to the respective power consumption data of the P user electric energy meters; if the target resampling electric quantity data is within the preset range, determining the resampling electric quantity data as the target resampling electric quantity data; and if the new re-sampling electric quantity data is not in the preset range, performing re-sampling operation with playback on the electric quantity data in the N metering periods for N times again, and determining whether the co-linearity index corresponding to the new re-sampling electric quantity data is in the preset range or not until the M groups of target re-sampling electric quantity data are determined.
According to the uncertainty determination device provided by the application, the device further comprises a third processing unit.
The third processing unit is configured to determine a correlation matrix between every two user electric energy meters in the P user electric energy meters according to the resampling power consumption data corresponding to the P user electric energy meters in the resampling power consumption data, and determine a power correlation matrix corresponding to the every two user electric energy meters according to the correlation matrix and the resampling power consumption data corresponding to the every two user electric energy meters; and determining the co-linearity index corresponding to the re-sampling electric quantity data according to the electric quantity correlation matrix corresponding to the two user electric energy meters.
According to the uncertainty determination device provided by the present application, the third processing unit is specifically configured to perform the following operations
Figure 878285DEST_PATH_IMAGE025
And determining a correlation matrix between every two user electric energy meters in the P user electric energy meters.
Wherein, the first and the second end of the pipe are connected with each other,
Figure 219268DEST_PATH_IMAGE026
is shown asA correlation matrix between the i-th user power meter and the j-th user power meter,
Figure 415894DEST_PATH_IMAGE027
representing the covariance of the resampled power consumption data corresponding to the ith user electric energy meter and the jth user electric energy meter respectively,
Figure 78213DEST_PATH_IMAGE028
indicating the standard deviation of the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 18487DEST_PATH_IMAGE029
indicating the standard deviation of the resampling power consumption data corresponding to the jth user electric energy meter,
Figure 31618DEST_PATH_IMAGE030
representing the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 867987DEST_PATH_IMAGE031
and representing the resampling power consumption data corresponding to the jth user electric energy meter.
According to the uncertainty determining device provided by the application, the third processing unit is specifically configured to determine, for each pair of electric energy meters of the users, the uncertainty of the electric energy meter according to
Figure 280252DEST_PATH_IMAGE032
And determining the electric quantity correlation matrix corresponding to the two user electric energy meters.
Wherein the content of the first and second substances,
Figure 493058DEST_PATH_IMAGE033
representing the electric quantity correlation matrix corresponding to the ith user electric energy meter and the jth user electric energy meter,
Figure 808633DEST_PATH_IMAGE034
average power consumption data representing the resampled power consumption data in the N metering periods corresponding to the ith user electric energy meter,
Figure 347062DEST_PATH_IMAGE035
and the average power consumption data of the resampled power consumption data in the N metering periods corresponding to the jth user electric energy meter are represented.
The present application further provides 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 the method for determining uncertainty as described in any of the above when executing the program.
The present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of uncertainty determination as any of the above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements a method of uncertainty determination as described in any of the above.
According to the method, the device and the electronic equipment for determining the uncertainty, when the uncertainty corresponding to the operation error is determined, the electric quantity data in N metering periods are obtained; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and respective power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter; performing multiple groups of N-time re-sampling operations on the electric quantity data in N metering periods, and determining M groups of target re-sampling electric quantity data according to the co-linearity indexes corresponding to the obtained re-sampling electric quantity data; and then according to the M groups of target resample electric quantity data, uncertainty corresponding to the respective operation errors of the P user electric energy meters is determined, so that the uncertainty corresponding to the operation errors can be accurately determined, and the reliability of the detected operation errors can be described based on the uncertainty subsequently.
Drawings
In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for determining uncertainty according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an uncertainty determining apparatus according to an embodiment of the present application;
fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
To make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the embodiments of the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In the description of the text of the present application, the character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The technical scheme provided by the embodiment of the application can be applied to an electric energy metering scene. In an electric energy metering scene, in order to ensure the operation accuracy of the user electric energy meter, the operation error of the user electric energy meter can be detected, and the reliability of the detection result can be described through the uncertainty corresponding to the operation error. Therefore, how to determine the uncertainty corresponding to the operation error is an urgent problem to be solved by those skilled in the art.
In order to accurately determine the uncertainty corresponding to the operation error, the embodiment of the application provides a method for determining the uncertainty, and electric quantity data in N metering periods are obtained; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and respective power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter; performing multiple groups of N-time re-sampling operations on the electric quantity data in N metering periods, and determining M groups of target re-sampling electric quantity data according to the co-linearity indexes corresponding to the obtained re-sampling electric quantity data; and according to the M groups of target resample electric quantity data, uncertainty corresponding to the respective operation error of the P user electric energy meters is determined.
Wherein N and P are both positive integers, N is greater than or equal to P, and M is a positive integer.
The operation of resampling the electric quantity data in the N metering periods is performed, and the uncertainty corresponding to the operation error of each of the P user electric energy meters is determined according to the M groups of target resampling electric quantity data obtained by the resampling operation, so that the uncertainty corresponding to the operation error can be accurately determined, and the reliability of the detected operation error can be described subsequently based on the uncertainty.
Hereinafter, the uncertainty determination method provided by the present application will be described in detail by the following specific examples. It is to be understood that the following detailed description may be combined with other embodiments, and that the same or similar concepts or processes may not be repeated in some embodiments.
Example one
Fig. 1 is a flowchart illustrating an uncertainty determination method according to an embodiment of the present application, where the uncertainty determination method may be implemented by software and/or hardware. For example, referring to fig. 1, the method for determining the uncertainty may include:
s101, acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and respective power consumption data of P user electric energy meters corresponding to the assessment electric energy meter.
Wherein N and P are both positive integers, and N is greater than or equal to P.
For example, in the embodiment of the present application, N may be greater than or equal to P +10, N may also be equal to 2P, and may be specifically set according to actual needs.
For example, when acquiring the electric quantity data in N metering cycles, the electric quantity data in N metering cycles sent by other electronic devices may be received, the electric quantity data in N metering cycles may also be acquired from a local storage, and the electric quantity data in N metering cycles may also be acquired from other third party databases, for example, the electric quantity data in N metering cycles, and the like.
After acquiring the electric quantity data in N metering cycles, the following S102 may be executed:
s102, performing multiple groups of repeated sampling operations with returning for N times on the electric quantity data in N metering periods, and determining M groups of target repeated sampling electric quantity data according to the co-linearity indexes corresponding to the obtained repeated sampling electric quantity data.
Wherein M is a positive integer.
It can be understood that the resampled electric quantity data obtained by each resampling operation includes N periods of electric quantity data, and the electric quantity data includes power supply quantity data of the assessment electric energy meter and power consumption quantity data of the P user electric energy meters.
In the embodiment of the present application, when the resampling operation is performed to determine M groups of target resampling electric quantity data, based on the collinearity index corresponding to the resampling electric quantity data, that is, a collinearity requirement is introduced, so that the collinearity corresponding to the target resampling electric quantity data obtained by the resampling operation is kept as consistent as possible with the collinearity corresponding to the electric quantity data in the N metering periods obtained in the above step S101, which can improve the accuracy of the subsequently determined operation error of the user electric energy meter, and thus improve the accuracy of the determined uncertainty.
For example, in the embodiment of the present application, when determining M groups of target resampled electric quantity data according to the obtained collinearity index corresponding to the resampled electric quantity data, it may be determined whether the collinearity index corresponding to the resampled electric quantity data is within a preset range; the preset range is determined based on initial collinearity indexes corresponding to respective power consumption data of the P user electric energy meters; if the collinearity index corresponding to the resampled electric quantity data is in the preset range, it is indicated that the collinearity corresponding to the resampled electric quantity data is approximately consistent with the collinearity corresponding to the electric quantity data in the N metering periods in the S101, and therefore, the resampled electric quantity data can be determined as the target resampled electric quantity data. On the contrary, if the collinearity index corresponding to the resampled electric quantity data is not in the preset range, it indicates that the collinearity difference between the collinearity corresponding to the resampled electric quantity data and the collinearity corresponding to the electric quantity data in the N measurement periods in S101 is large, so that the N times of resampling operations with the feedback are performed on the electric quantity data in the N measurement periods again, and whether the collinearity index corresponding to the new resampled electric quantity data is in the preset range is determined until M groups of target resampled electric quantity data are determined.
Illustratively, the preset range may be
Figure 983973DEST_PATH_IMAGE036
The method can be specifically set according to actual needs, and the embodiment of the application is only set according to the actual needs
Figure 470DEST_PATH_IMAGE037
The description is illustrative, but not intended to limit the scope of the embodiments of the present application. Wherein, the first and the second end of the pipe are connected with each other,
Figure 436131DEST_PATH_IMAGE038
and the initial collinearity indexes are corresponding to the respective power consumption data of the P user electric energy meters.
Therefore, when M groups of target resample electric quantity data are determined according to the co-linear indexes corresponding to the obtained resample electric quantity data, the resampling operation needs to be repeatedly executed for multiple times, and the co-linear indexes of the resample electric quantity data obtained by the resampling operation for each time need to be judged until the M groups of target resample electric quantity data are determined.
For example, in the embodiment of the present application, when determining the co-linear index corresponding to the resampled electric quantity data, a correlation matrix between every two user electric energy meters in the P user electric energy meters may be determined according to the resampled electric quantity data corresponding to the P user electric energy meters in the resampled electric quantity data, and an electric quantity correlation matrix corresponding to every two user electric energy meters may be determined according to the correlation matrix and the resampled electric quantity data corresponding to the every two user electric energy meters; and determining the colinearity index corresponding to the resampled electric quantity data according to the electric quantity correlation matrixes corresponding to the two user electric energy meters.
For example, when determining the correlation matrix between every two user electric energy meters in the P user electric energy meters according to the resampling electricity consumption data corresponding to the P user electric energy meters in the resampling electricity consumption data, the correlation matrix may be determined according to the correlation matrix
Figure 378417DEST_PATH_IMAGE039
And determining a correlation matrix between every two user electric energy meters in the P user electric energy meters.
Wherein, the first and the second end of the pipe are connected with each other,
Figure 266738DEST_PATH_IMAGE040
representing a correlation matrix between the ith user electric energy meter and the jth user electric energy meter in the P user electric energy meters,
Figure 86927DEST_PATH_IMAGE041
representing the covariance of the corresponding resampled power consumption data of the ith user electric energy meter and the jth user electric energy meter,
Figure 111515DEST_PATH_IMAGE042
indicating the standard deviation of the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 227632DEST_PATH_IMAGE043
indicating the standard deviation of the resampling electricity consumption data corresponding to the jth user electric energy meter,
Figure 337670DEST_PATH_IMAGE044
representing the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 695970DEST_PATH_IMAGE014
and representing the resampling power consumption data corresponding to the jth user electric energy meter.
Wherein the content of the first and second substances,
Figure 339179DEST_PATH_IMAGE045
，
Figure 124732DEST_PATH_IMAGE046
，
Figure 987646DEST_PATH_IMAGE047
，
Figure 149637DEST_PATH_IMAGE048
，
Figure 650282DEST_PATH_IMAGE049
，
Figure 872316DEST_PATH_IMAGE050
，
Figure 691367DEST_PATH_IMAGE051
，
Figure 890005DEST_PATH_IMAGE052
，
Figure 743692DEST_PATH_IMAGE053
，
Figure 136627DEST_PATH_IMAGE054
，
Figure 974133DEST_PATH_IMAGE055
representing the resampling power consumption data of the t metering period corresponding to the ith user electric energy meter,
Figure 725531DEST_PATH_IMAGE056
and indicating the resampling power consumption data of the t metering period corresponding to the j user electric energy meter.
After the correlation matrix between every two user electric energy meters in the P user electric energy meters is determined, the electric quantity correlation matrix corresponding to every two user electric energy meters can be determined according to the correlation matrix and the resampling power consumption data corresponding to every two user electric energy meters.
For example, when the electric quantity correlation matrix corresponding to each two user electric energy meters is determined according to the correlation matrix and the resampling power consumption data corresponding to each two user electric energy meters, for each two user electric energy meters, the correlation matrix may be determined according to the resampling power consumption data corresponding to each two user electric energy meters
Figure 699303DEST_PATH_IMAGE057
And determining the electric quantity correlation matrix corresponding to every two user electric energy meters.
Wherein the content of the first and second substances,
Figure 997561DEST_PATH_IMAGE058
representing the electric quantity correlation matrix corresponding to the ith user electric energy meter and the jth user electric energy meter,
Figure 555319DEST_PATH_IMAGE059
average power consumption data representing the resampled power consumption data in N metering periods corresponding to the ith user electric energy meter,
Figure 597224DEST_PATH_IMAGE018
and average power consumption data of the resampled power consumption data in N metering periods corresponding to the jth user electric energy meter are represented.
When the co-linear index corresponding to the resampled electric quantity data is determined according to the electric quantity correlation matrixes corresponding to the two user electric energy meters, the off-diagonal elements of the obtained electric quantity correlation matrixes can be summed, and the following formula 1 can be referred to obtain an electric quantity comprehensive correlation coefficient, namely the co-linear index corresponding to the resampled electric quantity data.
Figure 691082DEST_PATH_IMAGE060
Wherein the content of the first and second substances,
Figure 160241DEST_PATH_IMAGE061
and expressing the colinearity index corresponding to the resampled electric quantity data.
With reference to the above description, after determining M groups of target resampled electric quantity data according to the collinearity index corresponding to the obtained resampled electric quantity data, the following S103 may be performed:
s103, according to the M groups of target resampling electric quantity data, uncertainty corresponding to the respective operation errors of the P user electric energy meters is determined.
For example, when uncertainty corresponding to the respective operation errors of the P user electric energy meters is determined according to the M groups of target resampling electric energy data, the M groups of target resampling electric energy data may be input into a pre-constructed operation error detection model to obtain M operation errors corresponding to the P user electric energy meters; for each user electric energy meter, the uncertainty corresponding to the operation error of the user electric energy meter can be determined according to M operation errors corresponding to the user electric energy meter; the operation error detection model is constructed based on electric quantity data in N metering periods.
For example, the mth operation error corresponding to the ith user electric energy meter can be recorded as
Figure 208224DEST_PATH_IMAGE062
，
Figure 53821DEST_PATH_IMAGE063
，
Figure 736606DEST_PATH_IMAGE064
。
It should be noted that, a method for constructing an operation error detection model based on electric quantity data in N metering cycles is similar to an existing method for constructing an operation error detection model based on electric quantity data, and reference may be made to related description of the existing method for constructing an operation error detection model based on electric quantity data, and here, the embodiment of the present application is not described again.
After M operation errors corresponding to P user electric energy meters are obtained in the operation error detection model, the uncertainty corresponding to the operation error of the user electric energy meter can be determined according to the M operation errors corresponding to the user electric energy meter aiming at each user electric energy meter.
For example, when the uncertainty corresponding to the operation error of the user electric energy meter is determined according to the M operation errors corresponding to the user electric energy meter, the uncertainty corresponding to the operation error of the user electric energy meter may be determined according to
Figure 642245DEST_PATH_IMAGE065
And determining the uncertainty corresponding to the operation error of the user electric energy meter so as to determine the uncertainty corresponding to the operation error.
Wherein, the first and the second end of the pipe are connected with each other,
Figure 909016DEST_PATH_IMAGE066
represents the uncertainty corresponding to the operation error of the ith user electric energy meter,
Figure 292724DEST_PATH_IMAGE067
，
Figure 361174DEST_PATH_IMAGE068
，
Figure 673600DEST_PATH_IMAGE069
，
Figure 929132DEST_PATH_IMAGE070
represents the mth operation error in the M operation errors corresponding to the ith user electric energy meter,
Figure 116531DEST_PATH_IMAGE071
and the vector is formed by M running errors corresponding to the ith user electric energy meter.
It can be seen that in the embodiment of the present application, when the uncertainty corresponding to the operation error is determined, the electric quantity data in N metering cycles are obtained; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and respective power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter; performing multiple groups of N-time re-sampling operations on the electric quantity data in N metering periods, and determining M groups of target re-sampling electric quantity data according to the co-linearity indexes corresponding to the obtained re-sampling electric quantity data; and then according to the M groups of target resample electric quantity data, uncertainty corresponding to the respective operation errors of the P user electric energy meters is determined, so that the uncertainty corresponding to the operation errors can be accurately determined, and the reliability of the detected operation errors can be described based on the uncertainty subsequently.
The uncertainty determination device provided in the present application is described below, and the uncertainty determination device described below and the uncertainty determination method described above may be referred to in correspondence with each other.
Fig. 2 is a schematic structural diagram of an uncertainty determination apparatus according to an embodiment of the present application, and for example, please refer to fig. 2, the uncertainty determination apparatus 20 may include:
an obtaining unit 201, configured to obtain electric quantity data in N metering cycles; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and respective power consumption data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P.
The first processing unit 202 is configured to perform multiple sets of resampling operations with playback on the electric quantity data in N metering cycles, and determine M sets of target resampling electric quantity data according to a collinearity index corresponding to the obtained resampling electric quantity data, where M is a positive integer.
And the second processing unit 203 is configured to determine uncertainty corresponding to the operation error of each of the P user electric energy meters according to the M groups of target resample electric quantity data.
Optionally, the second processing unit 203 is specifically configured to input M groups of target resampling electric quantity data into a pre-constructed operation error detection model, so as to obtain M operation errors corresponding to the P user electric energy meters; the operation error detection model is constructed based on electric quantity data in N metering periods; and aiming at each user electric energy meter, according to M operation errors corresponding to the user electric energy meter, determining uncertainty corresponding to the operation errors of the user electric energy meter.
Optionally, a second processing unit 203, in particular for processing according to
Figure 39488DEST_PATH_IMAGE072
And determining uncertainty corresponding to the operation error of the user electric energy meter.
Wherein the content of the first and second substances,
Figure 519885DEST_PATH_IMAGE073
represents the uncertainty corresponding to the operation error of the ith user electric energy meter,
Figure 997134DEST_PATH_IMAGE074
，
Figure 988224DEST_PATH_IMAGE075
，
Figure 1573DEST_PATH_IMAGE076
，
Figure 419916DEST_PATH_IMAGE077
represents the mth operation error in the M operation errors corresponding to the ith user electric energy meter,
Figure 915619DEST_PATH_IMAGE078
and the vector is formed by M running errors corresponding to the ith user electric energy meter.
Optionally, the first processing unit 202 is specifically configured to determine whether a collinearity index corresponding to the resampled electric quantity data is within a preset range; the preset range is determined based on initial collinearity indexes corresponding to power consumption data of the P user electric energy meters; if the current power is within the preset range, determining the resampled power data as target resampled power data; and if the new re-sampling electric quantity data is not in the preset range, performing N times of re-sampling operation with playback on the electric quantity data in the N metering periods again, and determining whether the co-linearity index corresponding to the new re-sampling electric quantity data is in the preset range or not until M groups of target re-sampling electric quantity data are determined.
Optionally, the uncertainty determination apparatus 20 further comprises a third processing unit.
The third processing unit is used for determining a correlation matrix between every two user electric energy meters in the P user electric energy meters according to the resampling power consumption data corresponding to the P user electric energy meters in the resampling power consumption data, and determining a power correlation matrix corresponding to every two user electric energy meters according to the correlation matrix and the resampling power consumption data corresponding to the every two user electric energy meters; and determining the co-linearity index corresponding to the re-sampled electric quantity data according to the electric quantity correlation matrix corresponding to every two user electric energy meters.
Optionally, a third processing unit, in particular for processing according to
Figure 179241DEST_PATH_IMAGE079
And determining a correlation matrix between every two user electric energy meters in the P user electric energy meters.
Wherein the content of the first and second substances,
Figure 575325DEST_PATH_IMAGE080
a correlation matrix between the ith user power meter and the jth user power meter is represented,
Figure 898990DEST_PATH_IMAGE081
representing the covariance of the resampling power consumption data corresponding to the ith user electric energy meter and the jth user electric energy meter respectively,
Figure 616411DEST_PATH_IMAGE082
indicating the standard deviation of the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 173470DEST_PATH_IMAGE083
indicating the standard deviation of the resampling power consumption data corresponding to the jth user electric energy meter,
Figure 925525DEST_PATH_IMAGE084
representing the resampling power consumption data corresponding to the ith user electric energy meter,
Figure 685671DEST_PATH_IMAGE085
and representing the resampling power consumption data corresponding to the jth user electric energy meter.
Optionally, a third processing unit, specifically configured to, for each pair of user electric energy meters, determine
Figure 624808DEST_PATH_IMAGE086
And determining the electric quantity correlation matrix corresponding to every two user electric energy meters.
Wherein the content of the first and second substances,
Figure 994347DEST_PATH_IMAGE087
an electric quantity correlation matrix corresponding to the ith user electric energy meter and the jth user electric energy meter is shown,
Figure 335330DEST_PATH_IMAGE088
average power consumption data representing the resampled power consumption data in N metering periods corresponding to the ith user electric energy meter,
Figure 266377DEST_PATH_IMAGE035
and average power consumption data of the resampled power consumption data in N metering periods corresponding to the jth user electric energy meter are shown.
The uncertainty determining apparatus 20 provided in the embodiment of the present application may implement the technical solution of the uncertainty determining method in any of the above embodiments, and its implementation principle and beneficial effects are similar to those of the uncertainty determining method, and reference may be made to the implementation principle and beneficial effects of the uncertainty determining method, which are not described herein again.
Fig. 3 is a schematic entity structure diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 3, the electronic device may include: a processor (processor) 310, a communication Interface (communication Interface) 320, a memory (memory) 330 and a communication bus 340, wherein the processor 310, the communication Interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call logic instructions in the memory 330 to perform a method of uncertainty determination, the method comprising: acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of an assessment electric energy meter and power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P; performing multiple groups of resampling operation with returning for N times on the electric quantity data in N metering periods, and determining M groups of target resampling electric quantity data according to a co-linearity index corresponding to the obtained resampling electric quantity data, wherein M is a positive integer; and according to the M groups of target resample electric quantity data, determining uncertainty corresponding to the respective operation error of the P user electric energy meters.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present application also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer-readable storage medium, the computer program, when executed by a processor, being capable of executing the method for determining uncertainty provided by the above methods, the method comprising: acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of the assessment electric energy meter and power consumption data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P; performing multiple groups of resampling operations with returning for N times on the electric quantity data in N metering periods, and determining M groups of target resampling electric quantity data according to a collinearity index corresponding to the obtained resampling electric quantity data, wherein M is a positive integer; and according to the M groups of target resample electric quantity data, determining uncertainty corresponding to the respective operation error of the P user electric energy meters.
In yet another aspect, the present application also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for determining uncertainty provided by the above methods, the method comprising: acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of an assessment electric energy meter and power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P; performing multiple groups of resampling operation with returning for N times on the electric quantity data in N metering periods, and determining M groups of target resampling electric quantity data according to a co-linearity index corresponding to the obtained resampling electric quantity data, wherein M is a positive integer; and according to the M groups of target resample electric quantity data, determining uncertainty corresponding to the respective operation error of the P user electric energy meters.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes 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 various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (9)

1. A method of uncertainty determination, comprising:
acquiring electric quantity data in N metering periods; the electric quantity data comprises power supply quantity data of an assessment electric energy meter and power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P;
performing a plurality of groups of resampling operations with returning for N times on the electric quantity data in the N metering periods, and determining whether a co-linearity index corresponding to the resampling electric quantity data is in a preset range; the preset range is determined based on initial collinearity indexes corresponding to the respective power consumption data of the P user electric energy meters; if the current power is within the preset range, determining the resampled power data as target resampled power data; if the new resampling electric quantity data is not in the preset range, performing N times of resampling operation with returning on the electric quantity data in the N metering periods again, and determining whether a co-linearity index corresponding to the new resampling electric quantity data is in the preset range or not until M groups of target resampling electric quantity data are determined, wherein M is a positive integer;
and according to the M groups of target resampling electric quantity data, determining uncertainty corresponding to the respective operation errors of the P user electric energy meters.
2. The method for determining the uncertainty according to claim 1, wherein the determining the uncertainty corresponding to the operation error of each of the P user electric energy meters according to the M groups of target resampled electric energy data includes:
inputting the M groups of target resampling electric quantity data into a pre-constructed operation error detection model to obtain M operation errors corresponding to the P user electric energy meters; wherein the operation error detection model is constructed based on the electric quantity data in the N metering periods;
and aiming at each user electric energy meter, according to M running errors corresponding to the user electric energy meter, determining uncertainty corresponding to the running errors of the user electric energy meter.
3. The method for determining the uncertainty according to claim 2, wherein the determining the uncertainty corresponding to the operation error of the consumer electric energy meter according to the M operation errors corresponding to the consumer electric energy meter comprises:
according to
Figure QLYQS_1
Determining uncertainty corresponding to the operation error of the user electric energy meter;
wherein the content of the first and second substances,
Figure QLYQS_2
represents the uncertainty corresponding to the operating error of the ith user power meter, and->
Figure QLYQS_3
，
Figure QLYQS_4
，/>
Figure QLYQS_5
，/>
Figure QLYQS_6
Represents the mth operation error of the M operation errors corresponding to the ith user electric energy meter, and then>
Figure QLYQS_7
And the vector is formed by M running errors corresponding to the ith user electric energy meter.
4. The method of uncertainty determination according to claim 1, further comprising:
determining a correlation matrix between every two user electric energy meters in the P user electric energy meters according to the resampling power consumption data corresponding to the P user electric energy meters in the resampling power consumption data, and determining a power correlation matrix corresponding to the every two user electric energy meters according to the correlation matrix and the resampling power consumption data corresponding to the every two user electric energy meters;
and determining the co-linearity index corresponding to the re-sampling electric quantity data according to the electric quantity correlation matrix corresponding to the two user electric energy meters.
5. The method for determining the uncertainty according to claim 4, wherein the determining the correlation matrix between every two of the P user electric energy meters according to the resampled power consumption data corresponding to the P user electric energy meters in the resampled power consumption data comprises:
according to
Figure QLYQS_8
Determining a correlation matrix between every two user electric energy meters in the P user electric energy meters;
wherein the content of the first and second substances,
Figure QLYQS_9
represents a correlation matrix between an ith subscriber energy meter and a jth subscriber energy meter, and->
Figure QLYQS_10
Representing the covariance of the resampled power consumption data corresponding to the ith user electric energy meter and the jth user electric energy meter respectively, and->
Figure QLYQS_11
Represents the standard deviation of the resampling power consumption data corresponding to the ith user electric energy meter, and then is used for judging whether the power consumption data is greater than the standard deviation or not>
Figure QLYQS_12
Represents the standard deviation of the resampled power consumption data corresponding to the jth user electric energy meter, and/or the specific value thereof>
Figure QLYQS_13
Representing the resampling power consumption data corresponding to the ith user electric energy meter>
Figure QLYQS_14
And representing the resampling power consumption data corresponding to the jth user electric energy meter.
6. The method for determining the uncertainty according to claim 5, wherein the determining the electric quantity correlation matrix corresponding to each two pairs of user electric energy meters according to the correlation matrix and the resampling power consumption data corresponding to each two pairs of user electric energy meters comprises:
for each two-by-two user electric energy meter, according to
Figure QLYQS_15
Determining an electric quantity correlation matrix corresponding to the two user electric energy meters;
wherein, the first and the second end of the pipe are connected with each other,
Figure QLYQS_16
represents the electric quantity correlation matrix corresponding to the ith user electric energy meter and the jth user electric energy meter, and is used for judging whether the electric quantity correlation matrix is greater than or equal to the preset value>
Figure QLYQS_17
Representing the ith userAverage power consumption data of the resampling power consumption data in the N metering periods corresponding to the electric energy meter, and/or the like>
Figure QLYQS_18
And the average power consumption data of the resampled power consumption data in the N metering periods corresponding to the jth user electric energy meter are represented.
7. An apparatus for uncertainty determination, comprising:
the acquisition unit is used for acquiring electric quantity data in N metering cycles; the electric quantity data comprises power supply quantity data of an assessment electric energy meter and power consumption quantity data of P user electric energy meters corresponding to the assessment electric energy meter, N and P are positive integers, and N is larger than or equal to P;
the first processing unit is used for executing a plurality of groups of resampling operations with returning for N times on the electric quantity data in the N metering periods and determining whether a co-linearity index corresponding to the resampling electric quantity data is in a preset range or not; the preset range is determined based on initial collinearity indexes corresponding to the respective power consumption data of the P user electric energy meters; if the current power is within the preset range, determining the resampled power data as target resampled power data; if the new re-sampling electric quantity data is not in the preset range, re-sampling the electric quantity data in the N metering periods for N times, and determining whether a co-linearity index corresponding to the new re-sampling electric quantity data is in the preset range or not until M groups of target re-sampling electric quantity data are determined, wherein M is a positive integer;
and the second processing unit is used for determining the uncertainty corresponding to the respective operation error of the P user electric energy meters according to the M groups of target resampling electric quantity data.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of uncertainty determination according to any of claims 1 to 6 when executing the program.
9. A non-transitory computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the method for uncertainty determination according to any one of claims 1 to 6.
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