Streaming calculation-based intelligent electric meter error estimation method and system

Abstract

The invention relates to the technical field of electric meter error measurement, in particular to an intelligent electric meter error estimation method and system based on streaming computation, and the method comprises the steps: obtaining the incidence relation of all devices in a meter box, taking the intelligent total opening of the meter box as a total meter, and taking other electric meters in the meter box as branch meters; constructing a relation model of the total meter electric quantity, the sub-meter electric quantity and the line loss through the measurement data of the total meter and the sub-meter; solving the relation model; keeping the ammeter information in a rolling manner according to a preset mode, and performing streaming updating on the relation model through the exponential attenuation coefficient; and the error state of each electric meter in the meter box is determined through the relation model. According to the method, necessary information required in a model calculation process is reserved by a limited and unchanged memory required by the model, and certain historical information is reserved through exponential decay. According to the method, effective model calculation of the measurement switch in a limited memory is ensured, related historical information is reserved, the data noise influence of sub-tables is reduced, and the purpose of improving the calculation accuracy of the model is achieved.

Description

Technical Field

[0001] The invention relates to a smart meter error estimation method and system based on stream computing, belonging to the technical field of meter error measurement. Background Art

[0002] In the online monitoring model of smart meter inaccuracy error (hereinafter referred to as the inaccuracy model), the model establishes a model solution through the energy conservation relationship between the main meter and the household meter to obtain the error coefficient of the meter. In the area where smart measurement switches are installed, the inaccuracy model will have further room for development.

[0003] The intelligent measuring switch in the substation usually has a small memory, generally between tens and hundreds of megabytes, and is installed in the low-voltage metering box as an edge device, mainly used to store the device operating system, applications, and necessary data. The measuring switch is sufficient to perform simple monitoring and data processing tasks, but it may be limited if complex computing tasks or large-scale data processing are to be performed. Edge computing requires performing some computing tasks on edge devices to reduce dependence on central servers or the cloud to reduce latency and improve data processing and analysis efficiency. However, due to limited memory, the amount and complexity of data that edge devices can process simultaneously may be limited. Therefore, when designing an edge computing solution, the memory capacity of the intelligent measuring switch in the substation needs to be considered to ensure that the required computing tasks can be effectively performed on the edge device.

[0004] On the other hand, in the inaccuracy model, whether it is the form of establishing the energy conservation equation in the form of the total meter of the substation-household meter, or the energy conservation method of using the meter box as the unit and taking the meter box intelligent total switch as the total meter, when solving the model, only the data noise disturbance of the total meter is considered, and the disturbance of the sub-meter is not considered. Due to the large number of meter boxes or substation meters, not considering the data noise disturbance of the sub-meter will undoubtedly affect the accuracy of the calculation results of the sub-meter. Therefore, when designing edge computing solutions, it is also necessary to consider reducing the impact of data noise disturbance of the sub-meter to ensure the accuracy of the calculation results. Summary of the invention

[0005] In order to overcome the above problems, the present invention provides a smart meter error estimation method and system based on stream computing. The technical solution of the present invention is as follows: In a first aspect, a smart meter error estimation method based on stream computing comprises the following steps: Obtain the association between each device in the meter box, use the meter box smart switch as the master meter, and other meters in the meter box as sub-meters; The relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meter; Solving the relational model to obtain a solution of the relational model; The electric meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient; The error state of each electric meter in the meter box is determined by the relational model.

[0006] Furthermore, the relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meters, specifically: The master meter collects the measurement data of the master meter and each sub-meter at the same time, and repeats this step m times, and obtain several sets of measurement data; Constructing the relationship model according to the measurement data: ; in, W T,m For the m The total meter measurement data collected is a n For the n The error parameters of the electric meter are W n,m For the n Electricity meter m The measurement data collected, W loss is the fixed line loss.

[0007] Furthermore, the relationship model is solved to obtain a solution of the relationship model, which is specifically: Convert the relational model into a matrix form: ; Let the matrix A = ,matrix x= ,matrix b = ; Get the relational model: ; Introducing a perturbation matrix into the relationship model D =[ E , e ]: ; in, E For the matrix A The noise present in e For the matrix b Noise present in Transform the relationship model into: ( B+D )z =0; in, B =[ A , b ], z =[ x ,-1] T ; Convert solving the relational model to solving x , so that x satisfy: ; The constraints are b+e ∈ range ( A+E ); Pair Matrix B Perform singular value decomposition: ; Among them, the matrix U for m × m dimensional orthogonal matrix, matrix V for( n+ 2)×( n+ 2) dimensional orthogonal matrix, matrix S for m ×( n+ 2) dimensional matrix, u m and Respectively represent matrices U No. m Columns and matrices V No. n OK, For the matrix B The singular values ​​of , and satisfy: ; Solving the relational model ( B+D ) z =0 total least squares solution : ; in, I is the identity matrix.

[0008] Furthermore, the electric meter information is retained in a rolling manner according to a preset method, and the relationship model is stream-updated through an exponential decay coefficient, specifically: Get the total available memory size of the meter and the upper limit of the number of meters that can be accommodated in the meter box N , meter out-of-tolerance threshold and error update time; Based on the available memory size of the total meter and the upper limit of the number of meters that can be accommodated in the meter box N set upm The value of Update the matrix according to the preset error update time A T A and matrix A T b , specifically: Update Matrix A and matrix b ; The updated matrix A and matrix b Calculating the Matrix A T A and matrix A T b ; Update the matrix as follows A T A and matrix A T b : Updated matrix = l * The matrix before update + the increment at the latest time point; in, l is the attenuation coefficient, and the increment at the latest time point is the difference between the matrix after update and the matrix before update; Use the updated matrix in the master table memory A T A and matrix A T b Replace the original matrix A T A and matrix A T b .

[0009] Furthermore, the error state of each electric meter in the meter box is determined by the relational model, specifically: Obtaining a Total Least Squares Solution , the total least squares solution As the estimated value of the meter error ; According to the estimated value of the meter error , determine the error state of the meter, the error state of the meter includes: The meter error calculation has not yet begun; The meter error is normal, and the estimated value of the meter error is within the preset range; The meter is out of tolerance and the estimated error value of the meter exceeds the preset range.

[0010] In a second aspect, a smart meter error estimation system based on stream computing includes a meter box, the meter box includes a smart main switch and a plurality of electric meters, the smart main switch is used as a main meter, the electric meters are used as sub-meters, and the main meter can collect measurement data of the main meter and the sub-meters; It also includes a data processing unit, which constructs a relationship model of total meter electricity, sub-meter electricity and line loss through the measurement data of the total meter and the sub-meters, solves the relationship model to obtain a solution of the relationship model, retains the meter information in a rolling manner according to a preset method, and performs streaming update on the relationship model through an exponential decay coefficient, and determines the error state of each meter in the meter box through the relationship model; The output unit outputs the error status of each electric meter.

[0011] Furthermore, the relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meters, specifically: The master meter collects the measurement data of the master meter and each sub-meter at the same time, and repeats this step m times, and obtain several sets of measurement data; Constructing the relationship model according to the measurement data: ; in, W T,m For the m The total meter measurement data collected is a n For the n The error parameters of the electric meter are W n,m For the n Electricity meter m The measurement data collected, W loss is the fixed line loss.

[0012] Furthermore, the relationship model is solved to obtain a solution of the relationship model, which is specifically: Convert the relational model into a matrix form: ; Let the matrix A = ,matrix x= ,matrix b = ; Get the relational model: ; Introducing a perturbation matrix into the relationship model D =[ E , e ]: ; in, E For the matrix A The noise present in e For the matrix b Noise present in Transform the relationship model into: ( B+D ) z =0; in, B =[ A , b ], z =[ x ,-1] T ; Convert solving the relational model to solving x , so that x satisfy: ; The constraints are b+e ∈ range ( A+E ); Pair Matrix B Perform singular value decomposition: ; Among them, the matrix U for m × m dimensional orthogonal matrix, matrix V for( n+ 2)×( n+ 2) dimensional orthogonal matrix, matrix S for m ×( n+ 2) dimensional matrix, u m and Respectively represent matrices U No. m Columns and matrices V No. n OK, For the matrix B The singular values ​​of , and satisfy: ; Solving the relational model ( B+D ) z =0 total least squares solution : ; in, I is the identity matrix.

[0013] Furthermore, the electric meter information is retained in a rolling manner according to a preset method, and the relationship model is stream-updated through an exponential decay coefficient, specifically: Get the total available memory size of the meter and the upper limit of the number of meters that can be accommodated in the meter box N , meter out-of-tolerance threshold and error update time; Based on the available memory size of the total meter and the upper limit of the number of meters that can be accommodated in the meter box N set up m The value of Update Matrix A and matrix b ; The updated matrix A and matrix b Calculating the Matrix A T A and matrix A T b ; Update the matrix as follows A T A and matrix A T b : Updated matrix = l * The matrix before updating + the increment at the latest time point; in, l is the attenuation coefficient, and the increment at the latest time point is the difference between the matrix after update and the matrix before update; Use the updated matrix in the master table memory A T A and matrix A T b Replace the original matrix A T A and matrix A T b .

[0014] Furthermore, the error state of each electric meter in the meter box is determined by the relational model, specifically: Obtaining a Total Least Squares Solution , the total least squares solution As the estimated value of the meter error ; According to the estimated value of the meter error , determine the error state of the meter, the error state of the meter includes: The meter error calculation has not yet begun; The meter error is normal, and the estimated value of the meter error is within the preset range; The meter is out of tolerance and the estimated error value of the meter exceeds the preset range.

[0015] The present invention has the following beneficial effects: The present invention utilizes the total-score relationship in the measurement switch to construct an inaccuracy model. By combining total least squares with streaming calculations, not only the necessary information required in the model calculation process is retained with the limited and invariant model required memory, but also certain historical information is retained through exponential decay. At the same time, the total least squares method is used to take into account the data noise disturbance of the total-score table. This ensures that the measurement switch can perform effective model calculations under limited memory, retains relevant historical information, and reduces the impact of data noise in the score table, thereby achieving the purpose of improving the calculation accuracy of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flow chart of the method of the present invention.

[0017] Figure 2 Schematic diagram of the meter box connection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. "First", "second" and similar words used in the present invention do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In order to keep the following description of the embodiments of the present invention clear and concise, the present invention omits the detailed description of some known functions and known components.

[0020] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] First, refer to Figure 1-2 , a smart meter error estimation method based on stream computing, comprising the following steps: Obtain the association between the devices in the meter box, use the meter box smart switch as the master meter, and other meters in the meter box as sub-meters; The relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meter; Solving the relational model to obtain a solution of the relational model; The electric meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient; The error state of each electric meter in the meter box is determined by the relational model.

[0022] In one embodiment of the present invention, a relationship model of the total meter power, the sub-meter power and the line loss is constructed by using the measurement data of the total meter and the sub-meter, specifically: The master meter collects the measurement data of the master meter and each sub-meter at the same time, and repeats this step m times, and obtain several sets of measurement data; Constructing the relationship model according to the measurement data: ; in, W T,m For the m The total meter measurement data collected is a n For the n The error parameters of the electric meter are Wn,m For the n Electricity meter m The measurement data collected, W loss is the fixed line loss.

[0023] In one embodiment of the present invention, solving the relationship model to obtain a solution to the relationship model is specifically: Convert the relational model into a matrix form: ; Let the matrix A = ,matrix x= ,matrix b = ; Get the relational model: ; Introducing a perturbation matrix into the relationship model D =[ E , e ]: ; in, E For the matrix A The noise present in e For the matrix b Noise present in Transform the relationship model into: ( B+D ) z =0; in, B =[ A , b ], z =[ x ,-1] T ; Convert solving the relational model to solving x , so that x satisfy: ; The constraints are b+e ∈ range ( A+E ); Pair Matrix B Perform singular value decomposition: ; Among them, the matrix U for m × mdimensional orthogonal matrix, matrix V for( n+ 2)×( n+ 2) dimensional orthogonal matrix, matrix S for m ×( n+ 2) dimensional matrix, u m and Respectively represent matrices U No. m Columns and matrices V No. n OK, For the matrix B The singular values ​​of , and satisfy: ; Solving the relational model ( B+D ) z =0 total least squares solution : ; in, I is the identity matrix.

[0024] In one embodiment of the present invention, the electric meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient, specifically: Get the total available memory size of the meter and the upper limit of the number of meters that can be accommodated in the meter box N , meter out-of-tolerance threshold and error update time; Based on the available memory size of the total meter and the upper limit of the number of meters that can be accommodated in the meter box N set up m The value of Update the matrix according to the preset error update time A T A and matrix A T b , specifically: Update Matrix A and matrix b ; The updated matrix A and matrix b Calculating the Matrix A T A and matrix A T b ; Update the matrix as follows A T A and matrixA T b : Updated matrix = l * The matrix before update + the increment at the latest time point; in, l is the attenuation coefficient, and the increment at the latest time point is the difference between the matrix after update and the matrix before update; Use the updated matrix in the master table memory A T A and matrix A T b Replace the original matrix A T A and matrix A T b .

[0025] In the present invention, the memory requirements for model data storage are similar to those for matrix A and matrix b The size of these two matrices is related to the number of meters, m After the parameters are preset, the size of the stored data does not change before and after each update. Therefore, when initially setting up the system, you can maximize the use of the total table's available memory without worrying about subsequent data exceeding the memory limit. Ultimately, reasonable and effective calculations can be achieved under limited memory conditions.

[0026] In one embodiment of the present invention, the error state of each electric meter in the meter box is determined by the relational model, specifically: Obtaining a Total Least Squares Solution , the total least squares solution As the estimated value of the meter error ; According to the estimated value of the meter error , determine the error state of the meter, the error state of the meter includes: The meter error calculation has not yet begun; The meter error is normal, and the estimated value of the meter error is within the preset range; The meter is out of tolerance and the estimated error value of the meter exceeds the preset range.

[0027] In a second aspect, a smart meter error estimation system based on stream computing includes a meter box, the meter box includes a smart main switch and a plurality of electric meters, the smart main switch is used as a main meter, the electric meters are used as sub-meters, and the main meter can collect measurement data of the main meter and the sub-meters; It also includes a data processing unit, which constructs a relationship model of total meter electricity, sub-meter electricity and line loss through the measurement data of the total meter and the sub-meters, solves the relationship model to obtain a solution of the relationship model, retains the meter information in a rolling manner according to a preset method, and performs streaming update on the relationship model through an exponential decay coefficient, and determines the error state of each meter in the meter box through the relationship model; The output unit outputs the error status of each electric meter.

[0028] In one embodiment of the present invention, a relationship model of the total meter power, the sub-meter power and the line loss is constructed by using the measurement data of the total meter and the sub-meter, specifically: The master meter collects the measurement data of the master meter and each sub-meter at the same time, and repeats this step m times, and obtain several sets of measurement data; Constructing the relationship model according to the measurement data: ; in, W T,m For the m The total meter measurement data collected is a n For the n The error parameters of the electric meter are W n,m For the n Electricity meter m The measurement data collected, W loss is the fixed line loss.

[0029] In one embodiment of the present invention, solving the relationship model to obtain a solution to the relationship model is specifically: Convert the relational model into a matrix form: ; Let the matrix A = ,matrix x= ,matrix b = ; Get the relational model: ; Introducing a perturbation matrix into the relationship model D =[ E , e ]: ; in, E For the matrix AThe noise present in e For the matrix b Noise present in Transform the relationship model into: ( B+D ) z =0; in, B =[ A , b ], z =[ x ,-1] T ; Convert solving the relational model to solving x , so that x satisfy: ; The constraints are b+e ∈ range ( A+E ); Pair Matrix B Perform singular value decomposition: ; Among them, the matrix U for m × m dimensional orthogonal matrix, matrix V for( n+ 2)×( n+ 2) dimensional orthogonal matrix, matrix S for m ×( n+ 2) dimensional matrix, u m and Respectively represent matrices U No. m Columns and matrices V No. n OK, For the matrix B The singular values ​​of , and satisfy: ; Solving the relational model ( B+D ) z =0 total least squares solution : ; in, I is the identity matrix.

[0030] In one embodiment of the present invention, the electric meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient, specifically: Get the total available memory size of the meter and the upper limit of the number of meters that can be accommodated in the meter box N , meter out-of-tolerance threshold and error update time; Based on the available memory size of the total meter and the upper limit of the number of meters that can be accommodated in the meter box N set up m The value of Update the matrix according to the preset error update time A T A and matrix A T b , specifically: Update Matrix A and matrix b ; The updated matrix A and matrix b Calculating the Matrix A T A and matrix A T b ; Update the matrix as follows A T A and matrix A T b : Updated matrix = l * The matrix before updating + the increment at the latest time point; in, l is the attenuation coefficient, and the increment at the latest time point is the difference between the matrix after update and the matrix before update; Use the updated matrix in the master table memory A T A and matrix A T b Replace the original matrix A T A and matrix A T b .

[0031] In one embodiment of the present invention, the error state of each electric meter in the meter box is determined by the relational model, specifically: Obtaining a Total Least Squares Solution , the total least squares solution As the estimated value of the meter error ; According to the estimated value of the meter error , determine the error state of the meter, the error state of the meter includes: The meter error calculation has not yet begun; The meter error is normal, and the estimated value of the meter error is within the preset range; The meter is out of tolerance and the estimated error value of the meter exceeds the preset range.

[0032] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0033] The units involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a unit does not limit the unit itself in some cases.

[0034] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0035] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) to form a technical solution.

[0036] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0037] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0038] For the present invention, there are still the following points to be explained: (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0039] (2) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0040] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure made using the contents of the present invention's specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart meter error estimation method based on stream computing, characterized in that: The following steps are involved: Obtain the association between the devices in the meter box, use the meter box smart switch as the master meter, and other meters in the meter box as sub-meters; The relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meter; Solving the relational model to obtain a solution of the relational model; The electric meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient; The error state of each electric meter in the meter box is determined by the relational model.

2. According to the smart meter error estimation method based on stream computing according to claim 1, it is characterized in that: The relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meters, specifically: The master meter collects the measurement data of the master meter and each sub-meter at the same time, and repeats this step m times, and obtain several sets of measurement data; Constructing the relationship model according to the measurement data: ； in, W T,m For the m The total meter measurement data collected is a n For the n The error parameters of the electric meter are W n,m For the n Electricity meter m The measurement data collected, W loss is the fixed line loss.

3. According to the smart meter error estimation method based on stream computing according to claim 2, it is characterized in that: Solve the relational model to obtain a solution to the relational model, specifically: Convert the relational model into a matrix form: ； Let the matrix A = ,matrix x= ,matrix b = ; Get the relational model: ； Introducing a perturbation matrix into the relationship model D =[ E , e ]: ； in, E For the matrix A The noise present in e For the matrix b Noise present in Transform the relationship model into: （ B+D ） z =0； in, B =[ A , b ], z =[ x ,-1] T ; Convert solving the relational model to solving x , so that x satisfy: ； The constraints are b+e ∈ range ( A+E ); Pair Matrix B Perform singular value decomposition: ； Among them, the matrix U for m × m dimensional orthogonal matrix, matrix V for( n+ 2)×( n+ 2) dimensional orthogonal matrix, matrix Σ for m ×( n+ 2) dimensional matrix, u m and Respectively represent matrices U No. m Columns and matrices V No. n OK, For the matrix B The singular values ​​of , and satisfy: ； Solving the relational model ( B+D ) z =0 total least squares solution : ； in, I is the identity matrix.

4. The smart meter error estimation method based on stream computing according to claim 3 is characterized in that: The meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient, specifically: Get the total available memory size of the meter and the upper limit of the number of meters that can be accommodated in the meter box N , meter out-of-tolerance threshold and error update time; Based on the available memory size of the total meter and the upper limit of the number of meters that can be accommodated in the meter box N set up m The value of Update the matrix according to the preset error update time A T A and matrix A T b , specifically: Update Matrix A and matrix b ; The updated matrix A and matrix b Calculating the Matrix A T A and matrix A T b ; Update the matrix as follows A T A and matrix A T b : Updated matrix = λ * The matrix before updating + the increment at the latest time point; in, λ is the attenuation coefficient, and the increment at the latest time point is the difference between the matrix after update and the matrix before update; Use the updated matrix in the master table memory A T A and matrix A T b Replace the original matrix A T A and matrix A T b .

5. The smart meter error estimation method based on stream computing according to claim 4 is characterized in that: The error state of each electric meter in the meter box is determined by the relational model, specifically: Obtaining a Total Least Squares Solution , the total least squares solution As the estimated value of the meter error ; According to the estimated value of the meter error , determine the error state of the meter, the error state of the meter includes: The meter error calculation has not yet begun; The meter error is normal, and the estimated value of the meter error is within the preset range; The meter is out of tolerance and the estimated error value of the meter exceeds the preset range.

6. A smart meter error estimation system based on streaming computing, characterized in that: The meter box includes a smart main switch and a plurality of electric meters. The smart main switch is used as a main meter, and the electric meters are used as sub-meters. The main meter can collect measurement data of the main meter and the sub-meters. It also includes a data processing unit, which constructs a relationship model of total meter electricity, sub-meter electricity and line loss through the measurement data of the total meter and the sub-meters, solves the relationship model to obtain a solution of the relationship model, retains the meter information in a rolling manner according to a preset method, and performs streaming update on the relationship model through an exponential decay coefficient, and determines the error state of each meter in the meter box through the relationship model; The output unit outputs the error status of each electric meter.

7. The smart meter error estimation system based on stream computing according to claim 6 is characterized in that: The relationship model between the total meter power, sub-meter power and line loss is constructed through the measurement data of the total meter and sub-meters, specifically: The master meter collects the measurement data of the master meter and each sub-meter at the same time, and repeats this step m times, and obtain several sets of measurement data; Constructing the relationship model according to the measurement data: ； in, W T,m For the m The total meter measurement data collected is a n For the n The error parameters of the electric meter are W n,m For the n Electricity meter m The measurement data collected, W loss is the fixed line loss.

8. The smart meter error estimation system based on stream computing according to claim 7 is characterized in that: Solve the relational model to obtain a solution to the relational model, specifically: Convert the relational model into a matrix form: ； Let the matrix A = ,matrix x= ,matrix b = ; Get the relational model: ； Introducing a perturbation matrix into the relationship model D =[ E , e ]: ； in, E For the matrix A The noise present in e For the matrix b Noise present in Transform the relationship model into: （ B+D ） z =0； in, B =[ A , b ], z =[ x ,-1] T ; Convert solving the relational model to solving x , so that x satisfy: ； The constraints are b+e ∈ range ( A+E ); Pair Matrix B Perform singular value decomposition: ； Among them, the matrix U for m × m dimensional orthogonal matrix, matrix V for( n+ 2)×( n+ 2) dimensional orthogonal matrix, matrix Σ for m ×( n+ 2) dimensional matrix, u m and Respectively represent matrices U No. m Columns and matrices V No. n OK, For the matrix B The singular values ​​of , and satisfy: ； Solving the relational model ( B+D ) z =0 total least squares solution : ； in, I is the identity matrix.

9. The smart meter error estimation system based on stream computing according to claim 8, characterized in that: The meter information is retained in a rolling manner according to a preset method, and the relationship model is updated in a streaming manner through an exponential decay coefficient, specifically: Get the total available memory size of the meter and the upper limit of the number of meters that can be accommodated in the meter box N , meter out-of-tolerance threshold and error update time; Based on the available memory size of the total meter and the upper limit of the number of meters that can be accommodated in the meter box N set up m The value of Update the matrix according to the preset error update time A T A and matrix A T b , specifically: Update Matrix A and matrix b ; The updated matrix A and matrix b Calculating the Matrix A T A and matrix A T b ; Update the matrix as follows A T A and matrix A T b : Updated matrix = λ * The matrix before updating + the increment at the latest time point; in, λ is the attenuation coefficient, and the increment at the latest time point is the difference between the matrix after update and the matrix before update; Use the updated matrix in the master table memory A T A and matrix A T b Replace the original matrix A T A and matrix A T b .

10. The smart meter error estimation system based on stream computing according to claim 9, characterized in that: The error state of each electric meter in the meter box is determined by the relational model, specifically: Obtaining a Total Least Squares Solution , the total least squares solution As the estimated value of the meter error ; According to the estimated value of the meter error , determine the error state of the meter, the error state of the meter includes: The meter error calculation has not yet begun; The meter error is normal, and the estimated value of the meter error is within the preset range; The meter is out of tolerance and the estimated error value of the meter exceeds the preset range.

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