Precise Analysis and Verification Method for Electric Energy Meter Measurement Data

Abstract

The present invention provides a method for accurately analyzing and verifying the metering data of an electric energy meter. The invention relates to the technical field of electric energy meter metering. The method includes: obtaining the electricity consumption measured by each electric energy meter in a preset area on the same day to obtain a metered electricity consumption list A; obtaining the first total electricity consumption QZ measured by the total electric energy meter corresponding to the preset area on the same day now ; if |HZ now -QZ noow |>ΔQ, then obtain the feature vectors of the electricity consumption situation corresponding to each user on the same day to obtain a list B of feature vectors of the electricity consumption situation on the same day; use a preset clustering algorithm to cluster all the feature vectors of the electricity consumption situation on the same day in B; determine the electric energy meters corresponding to the remaining discrete feature vectors of the electricity consumption situation on the same day after clustering as the electric energy meters to be detected; the present invention can reduce the detection time and improve the detection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity meter measurement, and particularly to a method for accurately analyzing and verifying electricity meter measurement data. Background Art

[0002] With the progress of technology, the intelligence level of communities is getting higher and higher, and the electricity meters used are basically electronic intelligent electricity meters; the electricity meters of each household in the community are usually installed in relatively fixed and hidden positions, and the environment of the installation position may be relatively harsh. Moreover, after the electricity meters are installed, they will not be replaced or maintained for several years or even more than a decade; during the use of the electricity meters, due to the long service time, the components of the electricity meters will age. At the same time, the temperature change of the environment where the electricity meters are located will also affect the accuracy of the electricity meter measurement, resulting in a large measurement error for the electricity meters. If each electricity meter is detected, it will take a long time and the detection efficiency is low. Summary of the Invention

[0003] For the above technical problems, the technical solution adopted by the present invention is as follows:

[0004] The present application provides a method for accurately analyzing and verifying electricity meter measurement data, and the method includes the following steps:

[0005] S100, obtain the electricity consumption measured by each electricity meter in a preset area on the same day to obtain a measured electricity consumption list A = (A 1 , A 2 , …, A i , …, A n ), i = 1, 2, …, n; where A i is the measured electricity consumption corresponding to the i-th electricity meter, and n is the number of electricity meters in the preset area.

[0006] S200, obtain the first total electricity consumption QZ now measured by the total electricity meters corresponding to the preset area on the same day.

[0007] S300, if |HZ now -QZ noow |>ΔQ, then obtain the feature vector of the electricity consumption situation of each user on the same day to obtain a list B of the feature vectors of the electricity consumption situation on the same day = (B 1 , B 2 , …, B i , …, B n ); where B i is the feature vector of the electricity consumption situation on the same day corresponding to the i-th user; HZ now is the second total electricity consumption corresponding to each electricity meter on the same day; ΔQ is a preset total electricity consumption difference threshold; B i =(B i，1, B i，2 , …, B i，j , …, B i，m ), j = 1, 2, …, m; B i，j is the j-th element in the daily power consumption situation feature vector corresponding to the i-th user, and m is the number of elements in the daily power consumption situation feature vector corresponding to each user.

[0008] S400. Use a preset clustering algorithm to cluster all the daily power consumption situation feature vectors in B to obtain several clusters; where each cluster includes at least two daily power consumption situation feature vectors.

[0009] S500. Determine the electric energy meters corresponding to the remaining discrete daily power consumption situation feature vectors after clustering as the electric energy meters to be detected.

[0010] The present invention has at least the following beneficial effects:

[0011] The accurate analysis and verification method for electric energy meter measurement data of the present invention obtains the power consumption measured by each electric energy meter in a preset area on the same day and the first total power consumption measured by the total electric energy meters corresponding to the preset area on the same day. If the difference between the second total power consumption corresponding to each electric energy meter on the same day and the first total power consumption is greater than the preset total power consumption difference threshold, then obtain the feature vectors of the power consumption situation corresponding to each user on the same day to obtain the list B of daily power consumption situation feature vectors; use a preset clustering algorithm to cluster all elements in all the daily power consumption situation feature vectors in B to obtain the list C of clusters; determine the electric energy meters corresponding to the clusters with the number of elements being 1 in the clusters as the electric energy meters to be detected; thus accurately determining the electric energy meters with abnormalities or large measurement errors, enabling subsequent targeted calibration of the abnormal electric energy meters, thereby reducing the detection time and improving the detection efficiency. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 is the flowchart of the accurate analysis and verification method for electric energy meter measurement data provided by the embodiment of the present invention. Detailed Embodiments

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present invention.

[0015] It should be noted that based on this disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, this device can be implemented and this method can be practiced using other structures and / or functions in addition to one or more of the aspects described herein.

[0016] The following will refer to Figure 1 the flowchart of the method for accurately analyzing and verifying the metering data of the electric energy meter shown, and introduce a method for accurately analyzing and verifying the metering data of the electric energy meter.

[0017] The method for accurately analyzing and verifying the metering data of the electric energy meter may include the following steps:

[0018] S100, obtain the electricity consumption measured by each electric energy meter in the preset area on the same day to obtain the metering electricity consumption list A = (A 1 , A 2 , …, A i , …, A n ), i = 1, 2, …, n; where A i is the electricity consumption measured by the i-th electric energy meter, and n is the number of electric energy meters in the preset area.

[0019] In this embodiment, the preset area can be a residential community. Taking the residential community as an example, each household in the residential community is equipped with an electric energy meter. Therefore, the electricity consumption of each user corresponding to the electric energy meter can be obtained at any time to obtain A; it should be noted that in this embodiment, the detection is carried out in units of days, and the electricity consumption measured by each electric energy meter in the preset area on the same day can be obtained at a preset time point every day; the preset time point can be 24:00 every day.

[0020] S200, obtain the first total electricity consumption QZ now measured by the total electric energy meters corresponding to the preset area on the same day.

[0021] In this embodiment, a total electricity meter is also installed in the preset area. The total electricity meter is responsible for measuring the total electricity consumption of all users in the entire preset area. It should be noted that the total electricity meter is regularly detected to ensure the accuracy of the measurement. While obtaining the electricity consumption measured by each electricity meter in the preset area on the same day, the first total electricity consumption QZ measured by the total electricity meter corresponding to the preset area on the same day is obtained. now That is, step S100 and step S200 are executed at the same moment to ensure the accuracy of subsequent calculations.

[0022] S300, if |HZ now -QZ noow |>ΔQ, then obtain the feature vector of the electricity consumption situation corresponding to each user on the same day to obtain the list B=(B 1 , B 2 , …, B i , …, B n ) of the electricity consumption situation feature vectors on the same day; where B i is the feature vector of the electricity consumption situation corresponding to the i-th user on the same day; HZ now is the second total electricity consumption corresponding to each electricity meter on the same day; ΔQ is the preset total electricity consumption difference threshold; B i =(B i，1 , B i，2 , …, B i，j , …, B i，m ), j = 1, 2, …, m; B i，j is the j-th element in the feature vector of the electricity consumption situation corresponding to the i-th user on the same day, and m is the number of elements in the feature vector of the electricity consumption situation corresponding to each user on the same day.

[0023] In this embodiment, it can be understood that if the measurement of each electricity meter corresponding to each user in the preset area is accurate, then |HZ now -QZ noow | = 0; and the measurement of the total electricity meter corresponding to the preset area is accurate. If |HZ now -QZ noow |>ΔQ, it means that there is one or more abnormal electricity meters among the electricity meters corresponding to the users. At this time, the feature vector of the electricity consumption situation corresponding to each user on the same day is obtained to obtain the list B of the electricity consumption situation feature vectors on the same day.

[0024] Furthermore, B i can be determined through the following steps:

[0025] S310, divide each day into m consecutive time periods on average to obtain the time period list T=(T 1 , T 2 , …, T j , …, Tm ); where T j is the j-th time period obtained by evenly dividing each day into m consecutive time periods.

[0026] S320. Determine the sub-electricity consumption measured within T corresponding to the i-th electricity meter on the current day as B j to obtain B i，j . i .

[0027] In this embodiment, under normal circumstances, the electricity consumption of each user within the same time period each day has a high degree of similarity; and since each day is 24 hours, each day can be evenly divided into m consecutive time periods; the duration of the time period can be 1 min - 10 min. For example, if the duration of the time period is 5 min, each day can be divided into 288 consecutive time periods; each electricity meter measures sub-electricity consumption within each time period, and the sub-electricity consumption measured within T corresponding to the i-th electricity meter on the current day can be determined as B j to obtain B i，j . i .

[0028] S400. Use a preset clustering algorithm to cluster all the feature vectors of the daily electricity consumption situations in B to obtain several clusters; where each cluster includes at least two feature vectors of the daily electricity consumption situations.

[0029] In this embodiment, the preset clustering algorithm can be the DBSCAN clustering algorithm. When clustering all the feature vectors of the daily electricity consumption situations in B, the clustering conditions include: each cluster includes at least two feature vectors of the daily electricity consumption situations; thus, it is possible to cluster the relatively similar feature vectors of the daily electricity consumption situations into one cluster; it can be understood that C p contains at least two feature vectors of the daily electricity consumption situations.

[0030] S500. Determine the electricity meters corresponding to the discrete feature vectors of the daily electricity consumption situations remaining after clustering as the electricity meters to be detected.

[0031] In this embodiment, when clustering B, the clustering conditions include that each cluster includes at least two feature vectors of the daily electricity consumption situations; therefore, it is possible to cluster the similar feature vectors of the daily electricity consumption situations into one cluster as much as possible; and if there are still discrete feature vectors of the daily electricity consumption situations under this condition, it means that the discrete feature vectors of the daily electricity consumption situations cannot be clustered into one cluster with any other feature vector of the daily electricity consumption situation, thus it is possible to determine that the electricity meters corresponding to the feature vectors of the daily electricity consumption situations are abnormal or have inaccurate measurement, and determine them as the electricity meters to be detected for further detection.

[0032] Further, after step S500, the method may further include the following steps:

[0033] S600, if the number of electricity meters to be detected is greater than 1, obtain the priority of each electricity meter to be detected to obtain a priority list D = (D 1 , D 2 , …, D r , …, D s ), r = 1, 2, …, s; where D r is the priority corresponding to the r-th electricity meter to be detected, and s is the number of electricity meters to be detected.

[0034] In this embodiment, if the number of electricity meters to be detected is 1, the electricity meter to be detected can be directly detected; however, if the number of electricity meters to be detected is multiple, for example: the number of electricity meters to be detected is 50, not all of the 50 electricity meters to be detected may have problems, and maybe only 10 have problems; if each electricity meter to be detected is detected one by one, it will take a long time; therefore, it is necessary to obtain the priority of each electricity meter to be detected to obtain a priority list D.

[0035] Further, D r can be determined through the following steps:

[0036] S610, determine the current electricity consumption weight of each user according to A and HZ now , to obtain a current electricity consumption weight list λ = (λ 1 , λ 2 , …, λ i , …, λ n ); where λ i is the current electricity consumption weight corresponding to the i-th user; λ i = A i / HZ now .

[0037] In this embodiment, after obtaining the electricity consumption measured by each electricity meter in the preset area on the same day, the current electricity consumption weight of each user can be determined, and then λ can be obtained; it can be understood that the current electricity consumption weight of each user is the ratio of the electricity consumption measured by the electricity meter of each user to the second total electricity consumption corresponding to each electricity meter on the same day.

[0038] S620, obtain each target day within a preset historical time period to obtain a target day list E = (E 1 , E 2 , …, E c , …, E d ), c = 1, 2, …, d; where E cThe c-th target day within a preset historical time period, where d is the number of target days within the preset historical time period; |E c，1 -E c，2 | ≤ ΔQ; E c，1 Is the second total power consumption corresponding to each user's electricity meter on the c-th target day within the historical time period, E c，2 Is the first total power consumption measured by the total electricity meters corresponding to the preset area on the c-th target day within the historical time period.

[0039] In this embodiment, the preset historical time period can be several days before the current day. For example, it can be 100 days before the current day; it is possible to obtain the second total power consumption corresponding to each user's electricity meter and the first total power consumption measured by the total electricity meters for each day. If the absolute value of the difference between the two is less than or equal to ΔQ, it indicates that the measurement data for that day is normal, and that day is determined as the target day, thereby obtaining E.

[0040] Furthermore, ΔQ is determined according to the number of users in the preset area, and ΔQ is positively correlated with the number of users in the set area; it can be understood that the more users there are in the preset area, the relatively larger the cumulative error corresponding to each user's electricity meter, and ΔQ is set relatively larger accordingly; the setting of ΔQ can be determined based on historical data.

[0041] S630, obtain the historical electricity consumption weights corresponding to each target day in E to obtain the historical electricity consumption weight list set η = (η 1 , η 2 , …, η c , …, η d ); where η c Is the historical electricity consumption weight corresponding to the c-th target day; η c = (η c，1 , η c，2 , …, η c，i , …, η c，n ); η c，i Is the historical electricity consumption weight corresponding to the i-th user on the c-th target day; η c，i = LA c，i / HZ c ; LA c，i Is the measured electricity consumption corresponding to the i-th user on the c-th target day; HZ c Is the second total power consumption corresponding to each electricity meter within the preset area on the c-th target day.

[0042] In this embodiment, the electricity consumption measured by each user's electricity meter on each target day, so the historical electricity consumption weight list corresponding to each user can be obtained, thereby obtaining η.

[0043] S640, according to λ and η, determine the weight similarity of each historical power consumption weight list in λ and η, so as to obtain a weight similarity list ω=(ω 1 ,ω 2 ,…,ω c ,…,ω d ), where ω c For λ and η c The similarity between .

[0044] In the present embodiment, it can be understood that the target day is a day with normal electricity consumption metering, and the current day is a day with abnormal electricity consumption metering data. The similarity between the current electricity consumption weight list corresponding to the current day and the historical electricity consumption weight list corresponding to each target day can be obtained to obtain ω; it should be noted that in the present embodiment, the current electricity consumption weight list and the historical electricity consumption weight list can be used as vectors to calculate the similarity between the two. Those skilled in the art can use the existing vector similarity determination method to determine the similarity between the current electricity consumption weight list and the historical electricity consumption weight list, which will not be elaborated here.

[0045] S650, traverse ω, if ω c >ω', the cth target day is determined as the designated day to obtain the designated day list F = (F 1 , F 2 , …, F e , …, F g ), e = 1, 2, ..., g; where F e is the e-th designated day determined, g is the number of designated days determined; ω' is the preset weighted similarity threshold.

[0046] In this embodiment, if ω c >ω', indicating λ and η c With a high similarity, the cth target day is determined as the designated day, so as to determine the detection priority through the feature vector of the historical power consumption corresponding to the designated day.

[0047] S660, according to the rth electric energy meter YU to be detected r The corresponding daily electricity consumption feature vector and YU in each specified day in F r The corresponding historical electricity consumption feature vector determines D r .

[0048] Further, step S660 may include the following steps:

[0049] S661, Get YU r The corresponding daily electricity consumption feature vector WT r =(WT r，1 , WT r，2 , …, WTr，j , …, WT r，m ), where WT r，j It is the sub-power consumption measured by the rth electric energy meter to be tested in the jth time period of the day.

[0050] S662, obtain YU in each specified day in F r The corresponding historical electricity consumption feature vector is used to obtain YU r The corresponding historical electricity consumption feature vector list θ r =(θ r，1 ,θ r，2 ,…,θ r，e ,…,θ r，g ), where θ r，e for YU r The feature vector of historical electricity consumption corresponding to the e-th specified day.

[0051] S663, Get WT r With θ r The vector similarity of each historical electricity consumption feature vector in , to obtain the vector similarity list μ r =(μ r，1 , μ r，2 ,…,μ r，e ,…,μ r，g ), where μ r，e WT r With θ r，e The vector similarity between ;

[0052] S664, according to μ r , determine D r =1-MIN(μ r ), where MIN() is the preset minimum value function.

[0053] S700, sort all priorities in D to obtain a sorted priority list D'=(D' 1 , D' 2 , …, D' r , …, D' s ), where D' r is the rth priority obtained after sorting all priorities in D; D' a >D' a+1 ; a=1,2,…,s-1.

[0054] S800, calibrating and detecting the electric energy meter to be detected corresponding to each priority level in D' in turn.

[0055] In this embodiment, it can be understood that YU r The measurement error of μ is gradually increasing.r，e The smaller it is, the more it represents YU r The corresponding measurement error is larger; D r = 1 - MIN(μ r ), the smaller MIN(μ r ), the larger D r , that is, the higher the detection priority of YU r , so that it is possible to detect the electricity meters with larger recording errors first, and thus it is possible to calibrate the electricity meters with larger errors more quickly. During the detection process, if |HZ now - QZ noow | ≤ ΔQ, there is no need to detect the remaining large number of electricity meters to be detected, thereby improving the detection efficiency.

[0056] For the method for accurately analyzing and verifying the measurement data of the electricity meter in this embodiment, obtain the electricity consumption measured by each electricity meter in the preset area on the same day and the first total electricity consumption measured by the total electricity meters corresponding to the preset area on the same day. If the difference between the second total electricity consumption corresponding to each electricity meter on the same day and the first total electricity consumption is greater than the preset total electricity consumption difference threshold, then obtain the feature vector of the electricity consumption situation corresponding to each user on the same day to obtain the list B of the feature vectors of the electricity consumption situation on the same day; use the preset clustering algorithm to cluster all elements in all the feature vectors of the electricity consumption situation on the same day in B to obtain the cluster list C; determine the electricity meters corresponding to the clusters with the number of elements in the cluster being 1 as the electricity meters to be detected; thus accurately determine the electricity meters with anomalies or large measurement errors, so that subsequent targeted calibration of the abnormal electricity meters can be carried out, thereby reducing the detection time and improving the detection efficiency.

[0057] Furthermore, when there are multiple electricity meters to be detected, determine the detection priority of each electricity meter to be detected, and start detecting from the electricity meters to be detected with higher detection priority, thereby improving the detection efficiency.

[0058] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, however, this does not require or imply that these steps must be executed in this specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0059] The embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one segment of program related to a method for implementing a method in the method embodiment. The at least one instruction or the at least one segment of program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0060] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the readable storage medium (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0061] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0062] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0063] The program code for performing the operations of this application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0064] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0065] The electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0066] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor described above, the at least one memory described above, and a bus connecting different system components (including the memory and the processor).

[0067] Wherein, the memory stores program code, and the program code can be executed by the processor, so that the processor executes the steps in various embodiments described in this specification.

[0068] The memory may include a readable medium in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0069] The memory may also include a program / utilities having a set (at least one) of program modules. Such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0070] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus structures.

[0071] The electronic device may also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface. And, the electronic device may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through the bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0072] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0073] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product runs on an electronic device, the program code is used to cause the electronic device to execute the steps in the method according to various exemplary embodiments of the present invention described above in this specification.

[0074] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for the purpose of illustration and not for the purpose of limiting the scope of the present invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for accurately analyzing and verifying electric energy meter measurement data, characterized in that: The method comprises the following steps: S100, obtaining the power consumption measured by each electric energy meter in the preset area on the day to obtain a measured power consumption list A=(A1, A2, ..., A i , …, A n ), i=1, 2,...,n; among them, A i is the power consumption corresponding to the i-th electric energy meter, and n is the number of electric energy meters in the preset area; S200, obtaining the first total power consumption QZ measured by the total power meter corresponding to the preset area on the day now ; S300, if |HZ now -QZ noow |>ΔQ, then obtain the characteristic vector of the electricity consumption of each user on that day to obtain the characteristic vector list of the electricity consumption on that day B=(B1,B2,…,B i , …, B n ); where B i is the characteristic vector of the electricity consumption of the i-th user on that day; HZ now is the second total power consumption corresponding to each electric energy meter on that day; ΔQ is the preset total power consumption difference threshold; B i =(B i，1 , B i，2 , …, B i，j , …, B i，m ), j = 1, 2, …, m; B i，j is the jth element in the daily electricity consumption feature vector corresponding to the ith user, and m is the number of elements in the daily electricity consumption feature vector corresponding to each user; S400, clustering all the daily electricity usage feature vectors in B using a preset clustering algorithm to obtain a plurality of clusters; wherein each cluster includes at least two daily electricity usage feature vectors; S500, determining the electric energy meter corresponding to the discrete daily electricity consumption feature vector remaining after clustering as the electric energy meter to be detected; S600: If the number of the electric energy meters to be detected is greater than 1, obtain the priority of each electric energy meter to be detected to obtain a priority list D=(D1, D2, ..., D r , …, D s ), r = 1, 2, …, s; where D r is the priority corresponding to the rth electric energy meter to be detected, and s is the number of electric energy meters to be detected; S700, sort all priorities in D to obtain a sorted priority list D'=(D'1, D'2, ..., D' r , …, D' s ); where D' r is the rth priority obtained after sorting all priorities in D; D' a >D' a+1 ; a = 1, 2, ..., s-1; S800, calibrating and testing the electric energy meters to be tested corresponding to each priority level in D' in turn; D r Determine by following these steps: S610, according to A and HZ now , determine the current power consumption weight of each user to obtain the current power consumption weight list λ=(λ1,λ2,…,λ i , …, λ n ); where λ i is the current power consumption weight corresponding to the i-th user; i =A i / HZ now ; S620, obtaining each target day in the preset historical time period to obtain a target day list E=(E1, E2, ..., E c , …, E d ), c = 1, 2, …, d; where E c The cth target day in the preset historical time period, d is the number of target days in the preset historical time period; |E c，1 -E c，2 |≤ΔQ;E c，1 is the second total power consumption of each user’s electric energy meter on the cth target day in the historical time period, E c，2 The first total power consumption measured by the total power meter corresponding to the preset area on the cth target day in the historical time period; S630, obtaining the historical power consumption weight corresponding to each target day in E, so as to obtain a historical power consumption weight list set η=(η1, η2, ..., η c ,…,η d ); where η c is the historical electricity consumption weight corresponding to the cth target day; η c =(η c，1 , η c，2 ,…,η c，i ,…,η c，n );η c，i is the historical electricity consumption weight corresponding to the i-th user on the c-th target day; η c，i =LA c，i / HZ c LA c，i is the metered electricity consumption corresponding to the i-th user in the c-th target day; HZ c The second total power consumption corresponding to each electric energy meter in the preset area on the cth target day; S640, according to λ and η, determine the weight similarity of each historical power consumption weight list in λ and η to obtain a weight similarity list ω=(ω1,ω2,…,ω c ,…,ω d ); where ω c For λ and η c The similarity between S650, traverse ω, if ω c >ω', the cth target day is determined as the designated day to obtain the designated day list F = (F1, F2, ..., F e , …, F g ), e=1, 2, …, g; where F e is the e-th designated day determined, g is the number of designated days determined; ω' is the preset weighted similarity threshold; S660, according to the rth electric energy meter YU to be detected r The corresponding daily electricity consumption feature vector and YU in each specified day in F r The corresponding historical electricity consumption feature vector determines D r .

2. The method for accurately analyzing and verifying the electric energy meter measurement data according to claim 1 is characterized in that: B i Determine by following these steps: S310, divide each day into m consecutive time periods to obtain a time period list T = (T1, T2, ..., T j ,…,T m ); where T j is the jth time period obtained by evenly dividing each day into m consecutive time periods; S320: The T of T corresponding to the i-th electric energy meter on that day is j The sub-power consumption of internal metering is determined as B i，j , to get B i .

3. The method for accurate analysis and verification of electric energy meter measurement data according to claim 1 is characterized in that: The preset clustering algorithm includes the DBSCAN clustering algorithm.

4. The method for accurately analyzing and verifying the electric energy meter measurement data according to claim 1 is characterized in that: Step S660 includes the following steps: S661, Get YU r The corresponding daily electricity consumption feature vector WT r =(WT r，1 , WT r，2 ,…,WT r，j ,…,WT r，m ); where WT r，j for YU r The sub-power consumption measured in the jth time period of the day; S662, obtain YU in each specified day in F r The corresponding historical electricity consumption feature vector is used to obtain YU r The corresponding historical electricity consumption feature vector list θ r =(θ r，1 ,θ r，2 ,…,θ r，e ,…,θ r，g ); where θ r，e for YU r The characteristic vector of historical electricity consumption corresponding to the e-th specified day; S663, Get WT r With θ r The vector similarity of each historical electricity consumption feature vector in , to obtain the vector similarity list μ r =(μ r，1 , μ r，2 ,…,μ r，e ,…,μ r，g ); where μ r，e WT r With θ r，e The vector similarity between ; S664, according to μ r , determine D r =1-MIN(μ r ); where MIN() is the preset minimum value function.

5. The method for accurate analysis and verification of electric energy meter measurement data according to claim 1 is characterized in that: ΔQ is determined according to the number of users in a preset area, and ΔQ is positively correlated with the number of users in the preset area.

6. The method for accurate analysis and verification of electric energy meter measurement data according to claim 2 is characterized in that: The duration of the time period ranges from 1min to 10min.

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