Clock asynchronization-oriented ammeter metering error estimation accuracy improving method and system

Abstract

The invention discloses a clock asynchronization-oriented ammeter metering error estimation accuracy improving method and system. The method comprises the following steps of screening out target electric energy metering data of which the load is a normal load from electric energy metering data; constructing a clock asynchronous deviation suppression strategy based on metering interval expansion; constructing an expanded electric energy conservation equation according to the clock asynchronous deviation suppression strategy of the metering interval expansion and the target electric energy metering data, constructing numerical value definition parameters of the expanded metering interval length according to the electric energy conservation equation, and collecting a plurality of numerical value definition parameters to form parameter distribution; determining the length of the minimum effective expansion metering interval according to the parameter distribution; and according to the length of the minimum effective expansion metering interval, constructing an energy conservation equation set, and solving the energy conservation equation set by using a total least square method to obtain a metering error of the user meter. Therefore, the method can reduce the solution deviation introduced to the error estimation of the user table by data asynchronization, and achieves the accurate estimation of the metering error of the user table.

Description

Technical Field

[0001] This invention relates to the field of electricity meter technology, and in particular to a method and system for improving the accuracy of metering error estimation for clock-asynchronous electricity meters. Background Technology

[0002] As a core component of the Advanced Metering Infrastructure (AMI) system, smart meters have been widely deployed in power grids worldwide. The accuracy of smart meter measurements directly impacts the interests of a wide range of users and public service companies. To ensure fair and equitable electricity trading, it is necessary to promptly obtain the metering errors of smart meters and conduct maintenance on meters with abnormal readings.

[0003] Utility companies typically conduct periodic sampling tests on smart meters nearing their expiration date to assess metering accuracy. This involves on-site testing and laboratory testing, which suffers from poor timeliness and high costs. Furthermore, the results of periodic sampling tests cannot reflect the metering accuracy of smart meters in real-time and comprehensively. Existing online estimation methods for smart meter metering errors construct an energy conservation equation for the transformer area, considering the total meter reading, user smart meter readings, and line losses within the same metering interval, by integrating electrical topology constraints and energy metering data. The metering error is then solved based on this equation. However, limitations imposed by multi-level communication time synchronization delays within the transformer area and differences in meter timekeeping capabilities prevent complete synchronization of user meter clocks. This leads to inconsistent metering intervals, violating the energy conservation condition and making it difficult to accurately obtain user metering errors, thus reducing the targeted nature of maintenance. Therefore, there is an urgent need for a method to improve the accuracy of online metering error estimation for asynchronous data, reducing the solution bias introduced by asynchronous data in user meter error estimation and achieving accurate estimation of user meter metering errors. Summary of the Invention

[0004] This invention provides a method and system for improving the accuracy of metering error estimation for asynchronous clocks, which solves the problem of reduced accuracy of error estimation based on energy conservation caused by asynchronous measurement data of user meters in a distribution area.

[0005] Firstly, a method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters is provided, including: Obtain the power metering data of the main meter of the distribution area and all user meters under the distribution area under all metering intervals, and filter out the target power metering data with normal load from the power metering data; Construct a clock asynchronous deviation suppression strategy based on metering interval extension; Based on the clock asynchronous deviation suppression strategy of the metering interval extension and the target power metering data, the extended power conservation equation is constructed. Based on the power conservation equation, the numerical definition parameter of the extended metering interval length is constructed, and multiple numerical definition parameters are aggregated to form a parameter distribution. The length of the minimum effective extended metering interval is determined based on the parameter distribution. Based on the length of the minimum effective extended metering interval, an energy conservation equation system considering the metering deviation and line loss of the user meter is constructed. The energy conservation equation system is solved using the overall least squares method to obtain the metering error of the user meter.

[0006] Secondly, a system for improving the accuracy of metering error estimation for clock-asynchronous electricity meters is provided, including: The data acquisition module is used to acquire the power metering data of the main meter of the distribution area and all user meters under the distribution area under all metering intervals, and to filter out the target power metering data with normal load from the power metering data; The metering interval extension module is used to construct a clock asynchronous deviation suppression strategy based on the metering interval extension. The parameter definition module is communicatively connected to the data acquisition module and the metering interval extension module. It is used to construct the extended energy conservation equation based on the clock asynchronous deviation suppression strategy of the metering interval extension and the target energy metering data, construct the numerical definition parameter of the extended metering interval length based on the energy conservation equation, and collect multiple numerical definition parameters to form a parameter distribution. An extension length determination module, communicatively connected to the parameter definition module, is used to determine the length of the minimum effective extension measurement interval based on the parameter distribution; and... The metering error estimation module is communicatively connected to the extension length determination module. It is used to construct a set of energy conservation equations that take into account the metering deviation and line loss of the user meter based on the length of the minimum effective extended metering interval, and solve the set of energy conservation equations using the overall least squares method to obtain the metering error of the user meter.

[0007] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described above.

[0008] Compared with existing technologies, the advantages of this invention are as follows: It acquires the electricity metering data of the main meter and all user meters within the distribution area under all metering intervals, and filters out target electricity metering data with normal load from the electricity metering data; then, it constructs a clock asynchronous deviation suppression strategy based on metering interval expansion; and based on the clock asynchronous deviation suppression strategy and the target electricity metering data, it constructs an expanded electricity conservation equation, and based on the electricity conservation equation, constructs a numerical definition parameter for the length of the expanded metering interval, and gathers multiple numerical definition parameters to form a parameter distribution; then, it determines the length of the minimum effective expanded metering interval based on the parameter distribution; finally, based on the length of the minimum effective expanded metering interval, it constructs a set of energy conservation equations considering user meter metering deviation and line loss, and solves the energy conservation equations using the overall least squares method to obtain the user meter metering error. Therefore, it can reduce the solution deviation introduced by data asynchronousity to user meter error estimation, and achieve accurate estimation of user meter metering error. Attached Figure Description

[0009] Figure 1 This is an electrical connection topology diagram of the main power meter and user sub-meters within a transformer substation, provided in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters, provided in one embodiment of the present invention. Figure 3 This is a schematic diagram of the system structure of a method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters according to the present invention. Detailed Implementation

[0010] Referring now to specific embodiments of the invention, examples of which are illustrated in the accompanying drawings. Although the invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. Rather, it is intended to cover variations, modifications, and equivalents included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0011] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] Note: The examples described below are merely specific examples and are not intended to limit the embodiments of the present invention to the specific steps, values, conditions, data, order, etc. Those skilled in the art can utilize the concept of the present invention to construct more embodiments not mentioned herein by reading this specification.

[0013] Please see Figure 1 The user meter is the smart meter for the user in the distribution area, which records data such as the user's energy consumption. The master meter is the total energy meter under the transformer in the distribution area, which records data such as the total energy consumption of the distribution area. The line loss is the loss of the power supply line connecting the master meter and the user smart meters.

[0014] Please see Figure 2 The present invention provides a flowchart illustrating a method for improving the accuracy of metering error estimation in clock-asynchronous electricity meters, comprising: Step S100: Obtain the power metering data of the main meter of the distribution area and all user meters under the distribution area under all metering intervals, and filter out the target power metering data with normal load from the power metering data.

[0015] Specifically, the aforementioned electricity metering data should be the electricity metering data of the main meter of the distribution area and all user meters under the same metering interval, wherein in the first... i indivual Metering interval Below, the total electricity metering data is recorded as follows: , No. j The electricity metering data for each user's meter is recorded as follows: .

[0016] The definition of a normal load should be limited according to the actual specifications of the user's electricity meter. Optionally, when the user meter's accuracy class is preset level 2 and the connection method is direct connection, if the current in the current metering interval is greater than 10% of the reference current, then the load in the current metering interval is considered a normal load, and the electricity metering data of the corresponding main electricity meter and all user meters are retained. If the user meter's accuracy class is preset level 2 and the connection method is via a current transformer, if the current in the current metering interval is greater than 5% of the reference current, then the load in the current metering interval is considered a normal load, and the electricity metering data of the corresponding main electricity meter and all user meters are retained. Therefore, the target electricity metering dataset that can be used for user meter metering error estimation is... This is the electricity metering data under normal load.

[0017] Step S200: Construct a clock asynchronous deviation suppression strategy based on metering interval extension.

[0018] Specifically, in the i Each electricity metering interval Next, the When the clock of the user's electricity meter is asynchronous, it causes The electricity metering results have a time asynchronous deviation. The magnitude of its time asynchronous deviation depends on The proportion of single-time electricity metering intervals and the current load fluctuation situation. Among them, time asynchronous... The size depends on the delay caused by the master table hierarchical broadcast time synchronization and the first The timekeeping capability of the meter, during the period between two time calibrations... This can be considered a value with an upper limit; the load fluctuation curve has a predictable extreme value, which is related to the electricity consumption behavior of users in that area, therefore... There is an upper limit to the numerical variation. This can be achieved by extending the interval between single energy metering operations and reducing time-asynchronous... The proportion of single electricity metering intervals This reduces the time asynchrony deviation. The proportion of single-use electricity metering data This improves the accuracy of solving the following error estimation equations.

[0019] Step S300: Based on the clock asynchronous deviation suppression strategy of the metering interval extension and the target power metering data, construct the extended power conservation equation, construct the numerical definition parameter of the extended metering interval length based on the power conservation equation, and gather multiple numerical definition parameters to form a parameter distribution.

[0020] Specifically, based on the aforementioned metering interval expansion strategy, the original... Individual metering intervals are combined to form an extended single metering interval. That is, connected in the time dimension. The summation of individual electricity metering data forms the expanded electricity metering data.

[0021] The larger the value, the smaller the proportion of time-asynchronous error in the electricity metering data of a single interval. Considering the data accumulation time and the real-time nature of the error estimation results, The value cannot be infinitely large, therefore it needs to be properly controlled.

[0022] The energy conservation equation, which includes the energy from user metering errors, energy losses, and time-asynchronous deviations, can be expressed as follows:

[0023] Where, For the extended equation of conservation of electrical energy, To reduce bus loss in the back-end area, To account for the electricity consumption caused by measurement deviations in the user's meter, To account for the power consumption caused by asynchronous time errors, N is the number of user tables.

[0024] because The value is related to the square of the electrical energy consumed. The value is related to the electrical energy consumed, and can be constructed. The numerical parameters are defined as follows:

[0025]

[0026]

[0027] Where, To define a parameter for the extended measurement interval length, For the extended energy conservation equation A specific functional relationship.

[0028] Therefore, The numerical distributions are consistent with the numerical distributions of the electrical energy consumed by the transformer substation. It is only related to the asynchronous timing of the electricity meter and load fluctuations, and can be regarded as a random disturbance with an upper limit. The value and There is a negative correlation.

[0029] Collect numerical definition parameters for different measurement intervals To form a parameter value distribution As shown in the following formula:

[0030] Where, For parametric distribution, M represents the number of metering intervals after expansion, M represents the number of metering intervals before expansion, and m represents the number of metering intervals required to merge into a single expanded metering interval.

[0031] Step S400: Determine the length of the minimum effective extended measurement interval based on the parameter distribution.

[0032] Specifically, with As the value of increases, The numerical distribution is affected The smaller the interference, the better the distribution. The smaller the variance, the better the fit. The numerical distribution. Given the number of data points combined over the measurement period. To address the limitations of time-clock timing and to balance the accuracy and timeliness of the assessment, the principle of diminishing marginal effects is introduced to suppress asynchronous time clock deviations in order to determine the minimum effective extended measurement interval length. This principle reveals that: when The value increases with a fixed step size, and the measurement interval expansion affects the distribution. The variance reduction effect shows a weakening trend. If, after a certain step change, the adjacent distributions... and The variance difference is very small, indicating that it will continue to increase. When the marginal benefit of variance suppression approaches zero, at this point... This is the length of the minimum effective extended metering interval.

[0033] The specific calculation formula is as follows:

[0034] Where, Distributions variance This represents an acceptable level of variance variation.

[0035] The specific method for determining the minimum effective extended measurement interval is as follows: based on Different values ​​of can be used to obtain the distribution. And calculate the variance Then draw the normalized version about change curve Then, the change point detection method is used to determine... The specific steps for obtaining the value are as follows:

[0036] If the curve exists There are several changing points, and the entire curve is divided into sections based on these changing points. The data in each segment is approximated by the mean of that segment, with the goal of minimizing the sum of squared errors within each segment. The sum of squared errors over a certain interval is shown below:

[0037] In the formula, the interval It represents a segment of the curve.

[0038] Change curve There is one variable point that divides the curve into two segments. The overall optimization objective is:

[0039] Based on the above optimization objectives, the change points of the variation curve can be obtained, and the minimum effective extended metering interval length can be determined. .

[0040] Step S500: Based on the length of the minimum effective extended metering interval, construct a set of energy conservation equations that take into account the metering deviation and line loss of the user meter, and solve the set of energy conservation equations using the overall least squares method to obtain the metering error of the user meter.

[0041] Specifically, the method for constructing the energy conservation equations that take into account the metering deviation and line loss of user meters is shown in the following equation:

[0042] In the formula, For the first Measurement error of user table number; is the loss coefficient; m is the length of the determined minimum effective extended metering interval; To extend the metering cycle to the Mth period, the total electricity meter reading for the distribution area; To extend the Mth measurement period, the first Electricity metering data for user number [number].

[0043] Furthermore, the energy conservation equations are refined as follows:

[0044] In the formula, This is the coefficient matrix of the system of equations; For the first The electricity metering data vector is formed by aggregating the electricity metering data of user table No. 1 at different periods. , ; This is a vector of line loss data collected from line loss data at different periods. ; The vector to be solved contains the measurement error and loss coefficient of the user table; , is the observation vector of the total meter's electricity consumption value; The above energy conservation equations are solved using the overall least squares method to obtain an accurate estimate of the metering error of the user's meter.

[0045] First, construct the augmented matrix. ,according to right Perform singular value decomposition.

[0046] In the formula, It is a singular value matrix. , It is a unitary matrix.

[0047] Then, the solution result is obtained according to the following formula.

[0048]

[0049] In the formula, yes The last column.

[0050] Center front The numerical results represent The metering error of each smart meter is estimated, and then the metering performance evaluation result of the smart meter is formed by comparing the specific value with its threshold.

[0051] In summary, this invention first selects a preliminary dataset suitable for estimating user meter measurement errors; it then analyzes the deviation characteristics introduced by user meter clock asynchrony in error estimation and proposes a clock asynchrony deviation suppression strategy based on meter interval extension; it establishes an energy conservation equation including user meter measurement error energy, loss energy, and data asynchrony deviation energy, and proposes a numerical definition parameter for the extended meter interval length, aggregating parameter values ​​from multiple meter intervals to form a parameter distribution; it proposes a principle of diminishing marginal effect in suppressing time clock asynchrony deviation, reducing marginal benefits by calculating the variance of the parameter distribution before and after extension, and determining the minimum effective extended meter interval; using the minimum effective extended meter interval, it establishes a set of energy conservation equations considering user meter measurement deviation and line loss, and solves them using the overall least squares method to achieve accurate estimation of user meter measurement errors. This invention effectively improves the accuracy of meter error estimation under clock asynchrony in user electricity meters in the distribution area, enhances the intelligent operation and maintenance level of metered electricity meters with measurement errors, and ensures the accuracy and reliability of charging pile electricity metering.

[0052] See also Figure 3 As shown in the figure, an embodiment of the present invention provides a system for improving the accuracy of metering error estimation for clock-asynchronous electricity meters, comprising: The data acquisition module is used to acquire the power metering data of the main meter of the distribution area and all user meters under the distribution area under all metering intervals, and to filter out the target power metering data with normal load from the power metering data; The metering interval extension module is used to construct a clock asynchronous deviation suppression strategy based on the metering interval extension. The parameter definition module is communicatively connected to the data acquisition module and the metering interval extension module. It is used to construct the extended energy conservation equation based on the clock asynchronous deviation suppression strategy of the metering interval extension and the target energy metering data, construct the numerical definition parameter of the extended metering interval length based on the energy conservation equation, and collect multiple numerical definition parameters to form a parameter distribution. An extension length determination module, communicatively connected to the parameter definition module, is used to determine the length of the minimum effective extension measurement interval based on the parameter distribution; and... The metering error estimation module is communicatively connected to the extension length determination module. It is used to construct a set of energy conservation equations that take into account the metering deviation and line loss of the user meter based on the length of the minimum effective extended metering interval, and solve the set of energy conservation equations using the overall least squares method to obtain the metering error of the user meter.

[0053] Therefore, the online estimation accuracy improvement scheme for smart meter metering error based on clock asynchrony provided in this embodiment of the invention is used to solve the problem of reduced accuracy of error estimation based on energy conservation caused by asynchronous measurement data of user meters in the distribution area, and can realize accurate estimation of user meter metering error.

[0054] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0055] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements all or part of the method steps of the above method.

[0056] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0057] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor. When the processor executes the computer program, it implements all or part of the method steps described above.

[0058] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0059] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for improving the accuracy of metering error estimation in clock-asynchronous electricity meters, characterized in that, include: Obtain the power metering data of the main meter of the distribution area and all user meters under the distribution area under all metering intervals, and filter out the target power metering data with normal load from the power metering data; Construct a clock asynchronous deviation suppression strategy based on metering interval extension; Based on the clock asynchronous deviation suppression strategy of the metering interval extension and the target power metering data, the extended power conservation equation is constructed. Based on the power conservation equation, the numerical definition parameter of the extended metering interval length is constructed, and multiple numerical definition parameters are aggregated to form a parameter distribution. The length of the minimum effective extended metering interval is determined based on the parameter distribution. Based on the length of the minimum effective extended metering interval, an energy conservation equation system considering the metering deviation and line loss of the user meter is constructed. The energy conservation equation system is solved using the overall least squares method to obtain the metering error of the user meter.

2. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 1, characterized in that, The step of filtering out target electricity metering data with normal load from the electricity metering data includes: When the accuracy level of the user meter is detected to be at the preset level and the access method is direct access, and the current in the current metering interval is greater than the first preset percentage of the reference current, the load of the current metering interval is determined to be a normal load. When the accuracy level of the user meter is detected to be at the preset level and the access method is via current transformer, and the current in the current metering interval is greater than the second preset percentage of the reference current, the load of the current metering interval is determined to be a normal load.

3. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 1, characterized in that, The proposed clock asynchronous skew suppression strategy based on metering interval extension includes: When asynchronous operation of the user meter clock is detected during a single metering interval, the asynchronous time value and the asynchronous time deviation of the energy metering data during the single metering interval are obtained. Multiple single metering intervals are then merged into a single extended metering interval to reduce the proportion of the time asynchronous value in the user table in a single metering interval, and the proportion of the time asynchronous deviation in the energy metering data in a single metering interval.

4. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 3, characterized in that, The step of constructing the extended energy conservation equation based on the clock asynchronous deviation suppression strategy extended according to the metering interval and the target energy metering data includes: Based on m single measurement intervals Merging to form a single extended metering interval Based on the target energy metering data, the extended energy conservation equation is constructed as follows: Where, This is the extended equation for the conservation of electrical energy. To reduce bus loss in the back-end area; To cover the electricity consumption caused by measurement deviations in the user's meter after expansion; To account for the power consumption caused by asynchronous time errors after expansion; Metering interval The corresponding number A logo, ; For user table number, N is the number of user tables.

5. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 4, characterized in that, The process of constructing an extended metering interval length based on the energy conservation equation, and aggregating multiple numerically defined parameters to form a parameter distribution, includes: The numerical definition of the extended metering interval length, based on the aforementioned energy conservation equation, is shown in the following formula: Where, Define parameters for the extended measurement interval length; For the extended energy conservation equation A specific functional relationship; The numerically defined parameters are then aggregated to form a parameter distribution as shown in the following equation: In the formula, For parametric distribution; M represents the number of metering intervals after expansion; M represents the number of metering intervals before expansion; and m represents the number of metering intervals required to merge into a single expanded metering interval.

6. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 4, characterized in that, Determining the length of the minimum effective extended measurement interval based on the parameter distribution includes: Calculate the variance of the parameter distribution corresponding to the combination of m single measurement intervals into a single extended measurement interval, plot the normalized variance variation curve with respect to the number of measurement intervals m, and use the change point detection method to determine the value of the variation curve with respect to the number of measurement intervals m, so as to obtain the length m of the minimum effective extended measurement interval.

7. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 1, characterized in that, The method for constructing the energy conservation equations that take into account the metering deviation and line loss of user meters is shown in the following equation: Where, For the Measurement error of user table number; is the loss coefficient; m is the length of the determined minimum effective extended metering interval; To extend the metering cycle to the Mth period, the total electricity meter reading for the distribution area; To extend the Mth measurement period, the first Electricity metering data for user number [number].

8. The method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in claim 7, characterized in that, The method of solving the energy conservation equations using the overall least squares method yields the user meter measurement error, including: The energy conservation equations are transformed into the following objective formula: Where, , is the coefficient matrix of the system of equations; For the first The electricity metering data vector is formed by aggregating the electricity metering data of user table No. 1 at different periods. ， ； This is a vector of line loss data collected from line loss data at different periods. ； Let be the vector to be solved; , is the observation vector of the total meter's electricity consumption value; T stands for transpose; The target formula is then solved using the overall least squares method to obtain the measurement error of the user table.

9. A system for improving the accuracy of metering error estimation in clock-asynchronous electricity meters, characterized in that, include: The data acquisition module is used to acquire the power metering data of the main meter of the distribution area and all user meters under the distribution area under all metering intervals, and to filter out the target power metering data with normal load from the power metering data; The metering interval extension module is used to construct a clock asynchronous deviation suppression strategy based on the metering interval extension. The parameter definition module is communicatively connected to the data acquisition module and the metering interval extension module. It is used to construct the extended energy conservation equation based on the clock asynchronous deviation suppression strategy of the metering interval extension and the target energy metering data, construct the numerical definition parameter of the extended metering interval length based on the energy conservation equation, and collect multiple numerical definition parameters to form a parameter distribution. The extension length determination module is communicatively connected to the parameter definition module and is used to determine the length of the minimum effective extension measurement interval based on the parameter distribution. as well as, The metering error estimation module is communicatively connected to the extension length determination module. It is used to construct a set of energy conservation equations that take into account the metering deviation and line loss of the user meter based on the length of the minimum effective extended metering interval, and solve the set of energy conservation equations using the overall least squares method to obtain the metering error of the user meter.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for improving the accuracy of metering error estimation for clock-asynchronous electricity meters as described in any one of claims 1 to 8.

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