Electric energy meter calibration scheduling supervision method, system, device, medium and program product

Abstract

This invention belongs to the field of electricity meter verification and scheduling, and provides a method, system, equipment, medium, and program product for supervising electricity meter verification and scheduling. By combining historical usage data of various scheduling targets, the usage status requirements of each scheduling target are obtained. The comprehensive scheduling deviation of each batch of scheduling schemes based on the scheduling targets is calculated, resulting in the optimal scheduling plan for the verification results of the removed electricity meters. When managing the verification and scheduling of removed electricity meters, this invention not only verifies the appearance and performance of the removed electricity meters to determine their reusability, but also considers the impact of differences in historical usage data between the users of the removed electricity meters and the historical usage data corresponding to the scheduling targets before and after scheduling on the reuse status of the removed electricity meters. This allows for accurate selection of scheduling schemes for removed electricity meters, ensuring effective supervision of the verification and scheduling of removed electricity meters.

Description

Technical Field

[0001] This invention belongs to the field of electricity meter verification and scheduling technology, specifically relating to an electricity meter verification, scheduling and supervision method, system, equipment, medium and program product. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Dismantled electricity meters typically refer to electricity meters that have been removed from the user's site and returned to the power company for further processing due to various reasons (such as malfunction, upgrades, user recalibration, etc.). Before being put back into use, these electricity meters need to undergo a series of verification and scheduling procedures to ensure their accuracy and reliability.

[0004] In existing technologies, the verification and scheduling process for dismantled energy meters often only involves verifying the appearance and performance of the meters, determining whether they can be recycled based on the performance verification results, and then allocating qualified dismantled energy meters according to scheduling objectives. This approach does not consider the historical usage data of the users to whom the dismantled energy meters belong, nor the historical usage data corresponding to the scheduling objectives. It also fails to consider the impact of differences in the usage data of the two users before and after scheduling on the reuse status of the dismantled energy meters, which in turn affects the selection of scheduling schemes for dismantled energy meters. Therefore, existing technologies have significant shortcomings. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method, system, equipment, medium, and program product for the verification, scheduling, and supervision of electricity meters. This invention takes into account the historical usage data of the users to whom the electricity meters are removed and the historical usage data corresponding to the scheduling target, as well as the impact of the differences in electricity meter usage data between the two users before and after scheduling on the reuse status of the removed electricity meters, thereby improving scheduling accuracy.

[0006] According to some embodiments, the first solution of the present invention provides a method for verifying, scheduling, and monitoring electricity meters, which adopts the following technical solution:

[0007] A method for verifying, dispatching, and supervising electricity meters, comprising:

[0008] Obtain historical usage data and performance verification results for each user corresponding to each dismantled energy meter in each verification batch;

[0009] Obtain user usage status characteristics and corresponding fault risk data of electricity meters from historical data, construct the relationship curve between the usage status and lifespan of electricity meters, and the fault risk ratio curve of electricity meters under different usage statuses.

[0010] Based on the batch verification results of the removed electricity meters, and by constructing the relationship curve between the usage status and lifespan of the electricity meters, as well as the failure risk ratio curve of the electricity meters under different usage conditions, the remaining lifespan and failure risk value of each removed electricity meter in each verification batch are predicted, the performance fluctuation range of the removed electricity meters in each verification batch is obtained, and the scheduling evaluation value of each verification batch is generated.

[0011] By combining the historical usage data of each scheduling target, the usage status requirements of each scheduling target are obtained, and the scheduling intervention interference coefficient of each verification batch is analyzed based on the corresponding usage status requirements of each scheduling target.

[0012] A batch scheduling scheme is constructed, and based on the obtained batch scheduling evaluation value and the scheduling intervention interference coefficient of each batch based on the usage status requirements of each scheduling target, the comprehensive scheduling deviation of each batch scheduling scheme based on the scheduling target is calculated, so as to obtain the optimal scheduling planning scheme of the dismantled electricity meter verification results.

[0013] Furthermore, the historical usage data of each user corresponding to the disconnected energy meter includes usage duration, rated load value, actual load value and time point curve, and the number of load change nodes in the corresponding curve;

[0014] The actual load value versus time point curve refers to the amount of electrical load used by the user and the electricity meter at different time points.

[0015] The point where the absolute value of the difference between the actual load value and the electrical load corresponding to two adjacent time points in the time point curve is greater than or equal to the preset load difference is taken as a load change node in the corresponding curve.

[0016] Furthermore, the performance verification results include the appearance condition verification results and data fluctuations;

[0017] The performance test results include the appearance condition test results, which include the appearance damage condition and the appearance integrity condition. The disassembled energy meters with the appearance damage condition are scrapped and the corresponding test batches are removed.

[0018] The data fluctuations in the performance verification results refer to the data acquisition deviations of the calibrated and dismantled energy meters under different load conditions.

[0019] Furthermore, the relationship curve between the usage status and lifespan of the constructed electricity meter includes:

[0020] Obtain the user usage status characteristics of each user's corresponding electricity meter from historical data, construct a state-lifetime analysis pair, denoted as (state, life), where life represents the lifespan of the corresponding electricity meter when it is scrapped in the user usage status characteristics of the user's corresponding electricity meter; and state represents the usage status evaluation value of the corresponding electricity meter.

[0021] Based on the binary linear equation function model, function fitting is performed on each state lifetime analysis pair to obtain the relationship curve between the usage state and lifetime of the electricity meter.

[0022] Furthermore, the construction of the fault risk percentage curve for the electricity meter under different usage conditions includes:

[0023] Obtain the fault risk data of each user's corresponding electricity meter from the historical data, construct the fault state life analysis pair, denoted as (state1, fault). state1 represents the comprehensive evaluation value of abnormal use status in the fault risk data of the user's corresponding electricity meter, and fault represents the proportion of the number of users whose comprehensive evaluation value of abnormal use status is less than state1 in the total number of users with fault risk data.

[0024] Based on function fitting software, function fitting is performed on each fault state lifetime analysis pair to obtain the fault risk ratio curve of the electricity meter under different usage states, denoted as FG(x1).

[0025] Furthermore, based on the batch verification results of the removed energy meters, and by constructing the relationship curve between the usage status and lifespan of the energy meters, and the failure risk ratio curve of the energy meters under different usage conditions, the remaining lifespan and failure risk value of each removed energy meter in each verification batch are predicted, specifically as follows:

[0026] The remaining life prediction value is predicted by the difference between the service life and the service duration corresponding to the service status assessment value of a certain energy meter in a certain inspection batch that is in good condition. Similarly, the remaining life prediction value of each energy meter in a certain inspection batch that is in good condition is obtained.

[0027] Based on the failure risk ratio curve of electricity meters under different usage conditions, the failure risk value corresponding to each electricity meter in the intact appearance in each verification batch is determined.

[0028] Further, the scheduling evaluation value for each inspection batch is determined as follows:

[0029] Obtain the average performance evaluation value of each energy meter in a certain batch of inspection that is in good condition and the product of the total number of energy meters in that batch that are in good condition.

[0030] The scheduling evaluation value for each verification batch is determined by using the ratio of the product to the interval length corresponding to the performance fluctuation space of the removed energy meters in the same verification batch.

[0031] Furthermore, when constructing the batch scheduling scheme, each inspection batch is divided according to the number of scheduling targets to generate different batch scheduling schemes;

[0032] In each batch scheduling scheme, there must be at least one verification batch for each scheduling target, and the scheduling target corresponding to the same verification batch must be the same.

[0033] The number of the inspection batches is greater than or equal to the number of scheduling targets.

[0034] According to some embodiments, the second aspect of the present invention provides an energy meter verification, dispatching, and monitoring system, which adopts the following technical solution:

[0035] The electricity meter verification, dispatch, and monitoring system includes:

[0036] The verification information acquisition module is used to acquire historical usage data of each user corresponding to each dismantled energy meter in each verification batch, as well as the performance verification results;

[0037] The historical feature relationship construction module is used to obtain the user usage status characteristics of electricity meters and the corresponding fault risk data in historical data, construct the relationship curve between the usage status and lifespan of electricity meters, and the fault risk ratio curve of electricity meters under different usage statuses.

[0038] The verification scheduling and evaluation module is used to predict the remaining life and fault risk value of each removed energy meter in each verification batch based on the batch verification results of the removed energy meters, as well as the relationship curve between the usage status and lifespan of the energy meters and the fault risk ratio curve of the energy meters under different usage states. It also obtains the performance fluctuation range of the removed energy meters in each verification batch and generates the scheduling evaluation value for each verification batch.

[0039] The interference coefficient analysis module is used to combine the historical usage data of each scheduling target to obtain the usage status requirements of each scheduling target, and to analyze the scheduling intervention interference coefficient of each verification batch based on the corresponding usage status requirements of each scheduling target.

[0040] The verification scheduling planning and management module is used to construct batch scheduling schemes and, based on the obtained batch scheduling evaluation values ​​and the scheduling intervention interference coefficients of each batch based on the usage status requirements of each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling scheme based on the scheduling target, and obtain the optimal scheduling planning scheme for the verification results of the dismantled energy meters.

[0041] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0042] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the electricity meter verification, scheduling, and monitoring method described in the first aspect above.

[0043] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0044] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the electricity meter verification, scheduling, and monitoring method described in the first aspect above.

[0045] According to some embodiments, a fifth aspect of the present invention provides a computer device.

[0046] A computer program product includes software code, wherein the program in the software code performs the steps in the electricity meter verification, scheduling and supervision method described in the first aspect above.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] When managing the verification and scheduling of dismantled energy meters, this invention not only verifies the appearance and performance of the dismantled energy meters to determine whether they can be recycled, but also considers the impact of the differences between the historical usage data of the users to whom the dismantled energy meters belong and the historical usage data corresponding to the scheduling target before and after the scheduling on the reuse status of the dismantled energy meters. This enables accurate screening of the dismantled energy meter scheduling scheme and ensures effective supervision of the verification and scheduling of dismantled energy meters. Attached Figure Description

[0049] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0050] Figure 1 This is a flowchart of an energy meter verification, scheduling, and supervision method according to an embodiment of the present invention. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0053] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0054] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0055] Example 1

[0056] like Figure 1 As shown, this embodiment provides a method for the verification, scheduling, and supervision of electricity meters. This embodiment uses the application of this method to a server as an example for illustration. It is understood that this method can also be applied to terminals, and can also be applied to systems including terminals, servers, and other components, and is implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps:

[0057] Step S100: Obtain the historical usage data of each user corresponding to the dismantled energy meter in each verification batch, as well as the performance verification results;

[0058] The historical usage data for each user whose electricity meter has been removed includes usage duration, rated load value, actual load value and time point curve, and the number of load change nodes in the corresponding curve.

[0059] The actual load value versus time point curve refers to the amount of electrical load used by the user and the electricity meter at different time points.

[0060] The point where the absolute value of the difference between the actual load value and the electrical load corresponding to two adjacent time points in the time point curve is greater than or equal to the preset load difference is taken as a load change node in the corresponding curve.

[0061] The performance verification results include the appearance condition verification results and data fluctuations.

[0062] The performance test results include the appearance condition test results, which include the appearance damage condition and the appearance integrity condition. The disassembled energy meters with the appearance damage condition are scrapped and the corresponding test batches are removed.

[0063] The data fluctuations in the performance verification results refer to the data acquisition deviations of the calibrated and dismantled energy meters under different load conditions.

[0064] In this embodiment, the verification of the dismantled energy meters is considered from two aspects: appearance and performance data fluctuation. Regarding appearance, the main focus is on checking for damage to the meter's exterior, such as damaged wiring terminals, damaged buttons, or display malfunctions. Meters that fail to meet appearance standards will be scrapped. As for performance data fluctuation, the main focus is on quantitatively analyzing the data acquisition deviations of the calibrated dismantled energy meters under different loads. This provides data support for subsequent steps to calculate the comprehensive scheduling deviation of each batch of scheduling schemes based on the scheduling objectives and to obtain the optimal scheduling plan based on the verification results of the dismantled energy meters.

[0065] Step S200: Obtain the user usage status characteristics and corresponding fault risk data of the electricity meter in historical data, construct the relationship curve between the usage status and lifespan of the electricity meter, and the fault risk ratio curve of the electricity meter under different usage statuses.

[0066] A. Construct a curve showing the relationship between the usage status and lifespan of the electricity meter, including:

[0067] Obtain the user usage status characteristics of each user's corresponding electricity meter from historical data, construct a state-lifetime analysis pair, denoted as (state, life), where life represents the lifespan of the corresponding electricity meter when it is scrapped in the user usage status characteristics of the user's corresponding electricity meter; and state represents the usage status evaluation value of the corresponding electricity meter.

[0068] The formula for calculating state is as follows:

[0069] state=[μ·(Load-rated·t)+node] / life (1);

[0070] Where Lode represents the integral value of the function on the curve of actual load value versus time point in the historical usage data of the user's corresponding electricity meter during the usage period, where the function value is greater than the rated load value; rated represents the rated load value in the historical usage data of the user's corresponding electricity meter during the usage period; t represents the usage duration corresponding to the point in the curve of actual load value versus time point in the Lode that the function value is greater than the rated load value; node represents the number of load change nodes in the historical usage data of the user's corresponding electricity meter during the usage period; and μ represents the preset state evaluation factor.

[0071] Based on the binary linear equation function model, the function fitting is performed on each state lifetime analysis pair to obtain the relationship curve between the usage state and lifetime of the energy meter.

[0072] In the bivariate linear equation function model y=a / (x+b)+c, x is the independent variable -state, y is the dependent variable -life, and a, b and c are all constants.

[0073] B. Construct fault risk percentage curves for electricity meters under different usage conditions, including:

[0074] Obtain the fault risk data of each user's corresponding electricity meter from the historical data, construct the fault state life analysis pair, denoted as (state1, fault), where state1 represents the comprehensive evaluation value of abnormal usage status in the fault risk data of the user's corresponding electricity meter, and the value of state1 is equal to the product of the corresponding user's state and life.

[0075] The fault refers to the percentage of users whose comprehensive evaluation value of abnormal usage status is less than state1 in the total number of users with fault risk data.

[0076] Based on function fitting software, the Sigmoid function is used as the function model to fit the lifetime analysis pairs of various fault states, and the fault risk ratio curve of the electricity meter under different usage states is obtained, denoted as FG(x1), where the variable is the usage state and the dependent variable is the fault risk ratio.

[0077] Step S300: Based on the batch verification results of the removed energy meters, and constructing the relationship curve between the usage status and lifespan of the energy meters, and the failure risk ratio curve of the energy meters under different usage states, predict the remaining lifespan and failure risk value of each removed energy meter in each verification batch, obtain the performance fluctuation range of the removed energy meters in each verification batch, and generate the scheduling evaluation value of each verification batch.

[0078] The batch verification results of the removed electricity meters include the historical usage data of each user corresponding to the removed electricity meter in the corresponding verification batch, as well as the performance verification results;

[0079] In the process of predicting the remaining lifespan and fault risk value of each dismantled energy meter in each inspection batch, the predicted remaining lifespan value of the dismantled energy meter whose appearance condition inspection result is intact in the j-th inspection batch is denoted as SY. (i,j) Let GF be the fault risk value of the i-th energy meter in the j-th batch of inspection where the appearance condition inspection result is intact. (i,j) .

[0080] The remaining life prediction value is predicted based on the difference between the service life and the usage time of a disassembled energy meter in a certain inspection batch that is in a state of complete appearance. The remaining life prediction value SY for the i-th energy meter in the j-th inspection batch that is in a state of complete appearance after inspection is also given. (i,j) The calculation formula is as follows:

[0081] SY (i,j) =FM(state) (i,j) -T (i,j) (2);

[0082] Where, state (i,j) FM represents the usage status assessment value corresponding to the i-th removed energy meter in the j-th batch of the inspection, whose appearance condition inspection result is intact; (i,j) The curve representing the relationship between the usage status and lifespan of an electricity meter shows the user's assessment value for the meter's usage status as "state". (i,j) The corresponding function value at time T; (i,j) This represents the usage duration within the historical usage data of the user corresponding to the i-th energy meter in the j-th batch of inspection where the appearance condition inspection result is intact.

[0083] By analogy, the remaining life prediction value of each disassembled energy meter in its intact condition in each batch of inspections is obtained.

[0084] Based on the failure risk percentage curve of electricity meters under different usage conditions, the failure risk value corresponding to each electricity meter returned in an intact appearance in each verification batch is determined. The failure risk value GF of the electricity meter returned in the j-th verification batch with the ith appearance condition verification result being in an intact appearance is then calculated. (i,j) The calculation formula is as follows:

[0085] GF (i,j) =FG(state1) (i,j) (3);

[0086] Among them, state1 (i,j) FG(state1) represents the comprehensive evaluation value of abnormal usage status corresponding to the i-th energy meter in the j-th batch of inspection, whose appearance condition inspection result is intact; (i,j) ) indicates that x1 in FG(x1) is state1 (i,j) The corresponding function value at that time.

[0087] The performance fluctuation range of the removed energy meters in each verification batch is defined as the range formed by the maximum and minimum values ​​of the performance evaluation values ​​corresponding to each removed energy meter in the corresponding verification batch; the performance evaluation value corresponding to the i-th removed energy meter in the j-th verification batch whose appearance condition verification result is intact is denoted as XN. (i,j) Specifically:

[0088] XN (i,j) =SY (i,j) ·[1-GF (i,j) (4);

[0089] Let DP be the scheduling evaluation value of the j-th verification batch. j ,

[0090] DP j =H{XNj·count j / Length j} (5);

[0091] XNj represents the average performance evaluation value of each removed energy meter whose appearance condition is intact in the j-th batch of inspections; count j This represents the total number of removed energy meters in the j-th batch whose appearance condition inspection results are in an intact state; Length j This represents the length of the performance fluctuation range of the energy meters removed from the j-th batch of verification.

[0092] When count j When = 0, determine H{XNj·count} j / Length j} = 0;

[0093] When count j When = 1, determine H{XNj·count} j / Length j}=XNj;

[0094] When count j When H{XNj·count} > 1, determine H{XNj·count}. j / Length j}=XNj·count j / Length j .

[0095] Step S400: Combine the historical usage data of each scheduling target to obtain the usage status requirements of each scheduling target, and analyze the scheduling intervention interference coefficient of each verification batch based on the corresponding usage status requirements of each scheduling target.

[0096] The usage status requirements of the scheduling target are a set of historical usage data corresponding to each user within the corresponding scheduling target; the scheduling intervention interference coefficient of the j-th verification batch based on the usage status requirements corresponding to the k-th scheduling target is denoted as GX. (j,k) ,

[0097] GX (j,k) =FM(state{k}) / FM(state) (,j) (6);

[0098] Where, state (,j) FM(state{k}) represents the average usage status assessment value corresponding to each removed energy meter in its complete appearance state in the j-th inspection batch; state{k} represents the average usage status assessment value corresponding to the historical usage data of each user in the k-th scheduling target; FM(state{k}) represents the average usage status assessment value corresponding to each user in its complete appearance state in the j-th inspection batch. (,j) The curve representing the relationship between the usage status and lifespan of an electricity meter shows the user's assessment value for the meter's usage status as "state". (,j) The function value corresponding to the time; FM(state{k}) represents the function value corresponding to the user's energy meter usage status assessment value of state{k} in the relationship curve between the usage status and lifespan of the energy meter.

[0099] Step S500: Construct a batch scheduling scheme, and based on the obtained batch scheduling evaluation value and the scheduling intervention interference coefficient of each batch based on the usage status requirements of each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling scheme based on the scheduling target, and obtain the best scheduling planning scheme for the dismantled electricity meter verification results.

[0100] When constructing the batch scheduling scheme, each verification batch is divided according to the number of scheduling targets to generate different batch scheduling schemes. Each batch scheduling scheme has at least one verification batch for each scheduling target, and the same verification batch corresponds to the same scheduling target. The number of verification batches is greater than or equal to the number of scheduling targets.

[0101] In this embodiment, if there are four testing batches, they will be labeled as A, B, C, and D, respectively.

[0102] If there are two scheduling targets, they are denoted as Yin and Mao respectively;

[0103] Since each batch scheduling scheme has at least one verification batch for each scheduling target, and the scheduling target corresponding to the same verification batch is the same;

[0104] The combination of inspection batches and scheduling targets is as follows:

[0105] Batch scheduling scheme 1: {A, B, C} → {Yin}; {Ding} → {Mao};

[0106] Batch scheduling scheme two: {A, B, C} → {Mao}; {D} → {Yin};

[0107] Batch scheduling scheme three: {A, B, D} → {Yin}; {C} → {Mao};

[0108] Batch scheduling scheme four: {A, B, D} → {Mao}; {C} → {Yin};

[0109] Batch scheduling scheme five: {A, C, D} → {Yin}; {B} → {Mao};

[0110] Batch scheduling scheme six: {A, C, D} → {Mao}; {B} → {Yin};

[0111] Batch scheduling scheme seven: {B, C, D} → {Yin}; {A} → {Mao};

[0112] Batch scheduling scheme eight: {B, C, D} → {Mao}; {A} → {Yin};

[0113] Batch scheduling scheme nine: {A, B} → {Yin}; {C, D} → {Mao};

[0114] Batch scheduling scheme ten: {A, B} → {Mao}; {C, D} → {Yin};

[0115] Batch scheduling scheme eleven: {A, C} → {Yin}; {B, D} → {Mao};

[0116] Batch scheduling scheme 12: {A, C} → {Mao}; {B, D} → {Yin};

[0117] Batch scheduling scheme thirteen: {A, D} → {Yin}; {B, C} → {Mao};

[0118] Batch scheduling scheme fourteen: {A, D} → {Mao}; {B, C} → {Yin};

[0119] Batch scheduling scheme 15: {B, C} → {Yin}; {A, D} → {Mao};

[0120] Batch scheduling scheme sixteen: {B, C} → {Mao}; {A, D} → {Yin};

[0121] Batch scheduling scheme seventeen: {B, D} → {Yin}; {A, C} → {Mao};

[0122] Batch scheduling scheme eighteen: {B, D} → {Mao}; {A, C} → {Yin};

[0123] Batch scheduling scheme nineteen: {C, D} → {Yin}; {A, B} → {Mao};

[0124] Batch scheduling plan twenty: {C, D} → {Mao}; {A, B} → {Yin}.

[0125] Denote the comprehensive scheduling deviation of the g-th batch scheduling plan based on the scheduling objective as ZDPg,

[0126]

[0127] where, GX {g,r} represents the scheduling intervention interference coefficient corresponding to the usage status requirement based on the corresponding scheduling objective for the r-th verification batch in the g-th batch scheduling plan; DP {g,r} represents the scheduling evaluation value of the r-th verification batch in the g-th batch scheduling plan; PW {g,r} represents the data fluctuation situation corresponding to each recovered electricity meter with a complete appearance status in the performance verification results of the r-th verification batch in the g-th batch scheduling plan. When the load is W {g,r} the average value of the corresponding data acquisition deviation value; W {g,r} represents the average value of the relative actual load value of the actual load value and the time point curve in the historical usage data of each user within the corresponding scheduling objective of the r-th verification batch in the g-th batch scheduling plan; the relative actual load value is the quotient obtained by dividing the integral value of the actual load value and the time point curve within the usage duration by the usage duration; rg represents the total number of verification batches in the g-th batch scheduling plan;

[0128] The best scheduling planning scheme for the verification results of the recovered electricity meters is the batch scheduling plan with the smallest comprehensive scheduling deviation corresponding to the scheduling objective.

[0129] Example Two

[0130] This example provides an electricity meter verification scheduling supervision system, including:

[0131] A verification information acquisition module, used to acquire the historical usage data and performance verification results of each user corresponding to each recovered electricity meter in each verification batch;

[0132] A historical feature relationship construction module, used to acquire the user usage status characteristics and corresponding failure risk data of the electricity meter in the historical data, construct the relationship curve between the usage status and life of the electricity meter, and the failure risk proportion curve of the electricity meter under different usage statuses;

[0133] The verification scheduling and evaluation module is used to predict the remaining life and fault risk value of each removed energy meter in each verification batch based on the batch verification results of the removed energy meters, as well as the relationship curve between the usage status and lifespan of the energy meters and the fault risk ratio curve of the energy meters under different usage states. It also obtains the performance fluctuation range of the removed energy meters in each verification batch and generates the scheduling evaluation value for each verification batch.

[0134] The interference coefficient analysis module is used to combine the historical usage data of each scheduling target to obtain the usage status requirements of each scheduling target, and to analyze the scheduling intervention interference coefficient of each verification batch based on the corresponding usage status requirements of each scheduling target.

[0135] The verification scheduling planning and management module is used to construct batch scheduling schemes and, based on the obtained batch scheduling evaluation values ​​and the scheduling intervention interference coefficients of each batch based on the usage status requirements of each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling scheme based on the scheduling target, and obtain the optimal scheduling planning scheme for the verification results of the dismantled energy meters.

[0136] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0137] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0138] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0139] Example 3

[0140] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the electricity meter verification, scheduling, and monitoring method described in Embodiment 1 above.

[0141] Example 4

[0142] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the electricity meter verification, scheduling, and supervision method described in Embodiment 1 above.

[0143] Example 5

[0144] This embodiment provides a computer program product, including software code, wherein the program in the software code executes the steps of the electricity meter verification, scheduling and supervision method as described in Embodiment 1 above.

[0145] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments 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.

[0146] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0147] 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 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] 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 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0149] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0150] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for supervising the scheduling of the verification of electric energy meters, characterized in that, include: Obtain historical usage data and performance verification results for each user corresponding to each dismantled energy meter in each verification batch; Obtain user usage status characteristics and corresponding fault risk data of electricity meters from historical data, construct the relationship curve between the usage status and lifespan of electricity meters, and the fault risk ratio curve of electricity meters under different usage statuses. Based on the batch verification results of the removed electricity meters, and by constructing the relationship curve between the usage status and lifespan of the electricity meters, as well as the failure risk ratio curve of the electricity meters under different usage conditions, the remaining lifespan and failure risk value of each removed electricity meter in each verification batch are predicted, the performance fluctuation range of the removed electricity meters in each verification batch is obtained, and the scheduling evaluation value of each verification batch is generated. By combining the historical usage data of each scheduling target, the usage status requirements of each scheduling target are obtained, and the scheduling intervention interference coefficient of each verification batch is analyzed based on the corresponding usage status requirements of each scheduling target. The usage state requirement of the scheduling target is a set composed of historical usage data corresponding to each user in the corresponding scheduling target; the scheduling intervention interference coefficient of the first determining batch based on the usage state requirement of the first scheduling target is denoted as , ； in, Indicates the first The average value of the usage status assessment value corresponding to each disassembled energy meter in complete appearance in each batch of inspection; Indicates the first The average of the usage status evaluation values ​​corresponding to the historical usage data of each user in each scheduling target; In the curve showing the relationship between the usage status and lifespan of an electricity meter, the user's assessment value for the usage status of the corresponding electricity meter is... The corresponding function value at that time; In the curve showing the relationship between the usage status and lifespan of an electricity meter, the user's assessment value for the usage status of the corresponding electricity meter is... The corresponding function value at that time; Construct batch scheduling schemes, and based on the obtained batch scheduling evaluation values ​​and the scheduling intervention interference coefficients of each batch based on the usage status requirements of each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling scheme based on the scheduling target, and obtain the optimal scheduling planning scheme for the dismantled electricity meter verification results. When constructing the batch scheduling scheme, each verification batch is divided according to the number of scheduling targets to generate different batch scheduling schemes. Each batch scheduling scheme has at least one verification batch for each scheduling target, and the same verification batch corresponds to the same scheduling target. The number of verification batches is greater than or equal to the number of scheduling targets. Let ZDPg denote the comprehensive scheduling deviation of the g-th batch scheduling scheme based on the scheduling objective. ； in, Indicates the first In the batch scheduling scheme, the first Each batch of verification is based on the scheduling intervention interference coefficient corresponding to the usage status requirements of the corresponding scheduling target; Indicates the first The scheduling evaluation value of the r-th verification batch in a batch scheduling scheme; Indicates the first The first batch scheduling scheme Within each batch of inspections, for each disassembled energy meter in its intact condition, the load value is within the range of data fluctuations in the performance inspection results corresponding to the various disassembled energy meters. The average value of the corresponding data acquisition deviation; Indicates the first The first batch scheduling scheme Within the corresponding scheduling target of each inspection batch, the average value of the actual load value and the relative actual load value of the time point curve in the historical usage data of each user; the relative actual load value is the quotient of the integral value of the actual load value and the time point curve over the usage time divided by the usage time. Indicates the first The total number of batches to be inspected in each batch scheduling plan.

2. The method of claim 1, wherein the method further comprises: The historical usage data for each user whose electricity meter has been removed includes usage duration, rated load value, actual load value and time point curve, and the number of load change nodes in the corresponding curve. The actual load value versus time point curve refers to the amount of electrical load used by the user and the electricity meter at different time points. The point where the absolute value of the difference between the actual load value and the electrical load corresponding to two adjacent time points in the time point curve is greater than or equal to the preset load difference is taken as a load change node in the corresponding curve.

3. The method of claim 1, wherein the method further comprises: The performance verification results include the appearance condition verification results and data fluctuations. The performance test results include the appearance condition test results, which include the appearance damage condition and the appearance integrity condition. The disassembled energy meters with the appearance damage condition are scrapped and the corresponding test batches are removed. The data fluctuations in the performance verification results refer to the data acquisition deviations of the calibrated and dismantled energy meters under different load conditions.

4. The method of claim 1, wherein, The relationship curve between the usage status and lifespan of the constructed electricity meter includes: Obtain the user usage status characteristics of each user's corresponding electricity meter from historical data, construct state-lifetime analysis pairs, denoted as... , This indicates the lifespan of the corresponding electricity meter at the time of its scrapping, as shown in the user usage status characteristics of the user's electricity meter. This indicates the usage status assessment value of the user's corresponding electricity meter; Based on the binary linear equation function model, function fitting is performed on each state lifetime analysis pair to obtain the relationship curve between the usage state and lifetime of the electricity meter.

5. The method of claim 1, wherein, The construction of the fault risk ratio curve for electricity meters under different usage conditions includes: Obtain fault risk data for each user's corresponding electricity meter from historical data, construct fault state-lifetime analysis pairs, denoted as... , This represents the comprehensive assessment value of abnormal usage status in the fault risk data of the user's corresponding electricity meter. This indicates that the overall evaluation value of abnormal usage status for each user is less than [a certain value]. The percentage of users in the total number of users with data at risk of failure; Based on the function fitting software, the function fitting is carried out on the life analysis of each fault state, and the fault risk proportion curve of the electric energy meter under different use states is obtained, denoted as .

6. The method of claim 1, wherein, Based on the batch verification results of the removed energy meters, and by constructing the relationship curve between the usage status and lifespan of the energy meters, and the failure risk ratio curve of the energy meters under different usage conditions, the remaining lifespan and failure risk value of each removed energy meter in each verification batch are predicted, specifically as follows: The remaining life prediction value is predicted by the difference between the service life and the service duration corresponding to the service status assessment value of a certain energy meter in a certain inspection batch that is in good condition. Similarly, the remaining life prediction value of each energy meter in a certain inspection batch that is in good condition is obtained. Based on the failure risk ratio curve of electricity meters under different usage conditions, the failure risk value corresponding to each electricity meter in the intact appearance in each verification batch is determined.

7. The method of claim 1, wherein the method further comprises: The scheduling evaluation value for each inspection batch is determined as follows: Obtain the average performance evaluation value of each energy meter in a certain batch of inspection that is in good condition and the product of the total number of energy meters in that batch that are in good condition. The scheduling evaluation value for each verification batch is determined by using the ratio of the product to the interval length corresponding to the performance fluctuation space of the removed energy meters in the same verification batch.

8. The method of claim 1, wherein, When constructing the batch scheduling scheme, the various inspection batches are divided according to the number of scheduling targets to generate different batch scheduling schemes; In each batch scheduling scheme, there must be at least one verification batch for each scheduling target, and the scheduling target corresponding to the same verification batch must be the same. The number of the inspection batches is greater than or equal to the number of scheduling targets.

9. The power meter verification scheduling supervision system, characterized in that, include: The verification information acquisition module is used to acquire historical usage data of each user corresponding to each dismantled energy meter in each verification batch, as well as the performance verification results; The historical feature relationship construction module is used to obtain the user usage status characteristics of electricity meters and the corresponding fault risk data in historical data, construct the relationship curve between the usage status and lifespan of electricity meters, and the fault risk ratio curve of electricity meters under different usage statuses. The verification scheduling and evaluation module is used to predict the remaining life and fault risk value of each removed energy meter in each verification batch based on the batch verification results of the removed energy meters, as well as the relationship curve between the usage status and lifespan of the energy meters and the fault risk ratio curve of the energy meters under different usage states. It also obtains the performance fluctuation range of the removed energy meters in each verification batch and generates the scheduling evaluation value for each verification batch. The interference coefficient analysis module is used to combine the historical usage data of each scheduling target to obtain the usage status requirements of each scheduling target, and to analyze the scheduling intervention interference coefficient of each verification batch based on the corresponding usage status requirements of each scheduling target. The usage status requirements of the scheduling target are a set of historical usage data corresponding to each user within the corresponding scheduling target; the first The test batch is based on the first The scheduling intervention interference coefficient corresponding to the usage state requirement of each scheduling target is denoted as: , ； in, Indicates the first The average value of the usage status assessment value corresponding to each removed energy meter in complete appearance in each batch of inspection; Indicates the first The average value of the usage status evaluation value corresponding to the historical usage data of each user in each scheduling target; In the curve showing the relationship between the usage status and lifespan of an electricity meter, the user's assessment value for the usage status of the corresponding electricity meter is... The corresponding function value at that time; In the curve showing the relationship between the usage status and lifespan of an electricity meter, the user's assessment value for the usage status of the corresponding electricity meter is... The corresponding function value at that time; The verification scheduling planning and management module is used to construct batch scheduling schemes and, based on the obtained batch scheduling evaluation values ​​and the scheduling intervention interference coefficients of each batch based on the usage status requirements of each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling scheme based on the scheduling target, and obtain the optimal scheduling planning scheme for the verification results of the dismantled energy meters. When constructing the batch scheduling scheme, each verification batch is divided according to the number of scheduling targets to generate different batch scheduling schemes. Each batch scheduling scheme has at least one verification batch for each scheduling target, and the same verification batch corresponds to the same scheduling target. The number of verification batches is greater than or equal to the number of scheduling targets. Let ZDPg denote the comprehensive scheduling deviation of the g-th batch scheduling scheme based on the scheduling objective. ； in, Indicates the first In the batch scheduling scheme, the first Each batch of verification is based on the scheduling intervention interference coefficient corresponding to the usage status requirements of the corresponding scheduling target; Indicates the first The scheduling evaluation value of the r-th verification batch in a batch scheduling scheme; Indicates the first The first batch scheduling scheme Within each batch of inspections, for each disassembled energy meter in its intact condition, the load value is within the range of data fluctuations in the performance inspection results corresponding to the various disassembled energy meters. The average value of the corresponding data acquisition deviation; Indicates the first The first batch scheduling scheme Within the corresponding scheduling target of each inspection batch, the average value of the actual load value and the relative actual load value of the time point curve in the historical usage data of each user; the relative actual load value is the quotient of the integral value of the actual load value and the time point curve over the usage time divided by the usage time. Indicates the first The total number of batches to be inspected in each batch scheduling plan.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the steps in the electricity meter verification, scheduling and supervision method as described in any one of claims 1-8.

11. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the electricity meter verification, scheduling and supervision method as described in any one of claims 1-8.

12. A computer program product comprising software code, characterized in that, The program in the software code executes the steps of the electricity meter verification, scheduling and supervision method as described in any one of claims 1-8.

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