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

Abstract

The invention belongs to the field of electric energy meter verification and dispatching, and provides an electric energy meter verification and dispatching supervision method, system, equipment, medium and program product, which are used for obtaining the use state demand of each dispatching target in combination with the historical use data of each dispatching target, calculating the comprehensive dispatching deviation of each batch dispatching scheme based on the dispatching target, and calculating the comprehensive dispatching deviation of each batch dispatching scheme based on the dispatching target. And obtaining an optimal scheduling planning scheme of the verification result of the disassembled electric energy meter. According to the invention, when the verification scheduling of the disassembled electric energy meter is managed, the appearance and performance of the disassembled electric energy meter are verified, and whether the disassembled electric energy meter can be recycled or not is judged; the influence on the reuse state of the disassembled electric energy meter caused by the difference between the historical use data of the user to which the disassembled electric energy meter belongs and the historical use data corresponding to the scheduling target before and after scheduling is also considered, so that the accurate screening of the scheduling scheme of the disassembled electric energy meter is realized; and the verification scheduling of the disassembled electric energy meter can be effectively supervised.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy meter verification scheduling, and particularly relates to a method, system, device, medium and program product for electric energy meter verification scheduling supervision. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] The retrieved electric energy meters usually refer to the electric energy meters that are removed from the user site due to various reasons (such as faults, upgrades, user application for calibration, etc.) and sent back to the power supply company for further processing. Before these electric energy meters are put back into use, they need to go through a series of verification and scheduling processes to ensure their accuracy and reliability.

[0004] In the prior art, for the verification and scheduling process of the retrieved electric energy meters, it often only verifies the appearance and performance of the retrieved electric energy meters, and determines whether they can be recycled based on the performance verification results, and schedules and allocates the retrieved electric energy meters that pass the verification according to the scheduling objectives; in this method, the historical usage data of the users to whom the retrieved electric energy meters belong and the historical usage data corresponding to the scheduling objectives are not considered, and the impact on the reuse status of the retrieved electric energy meters caused by the difference in the usage data of the electric energy meters between the two users before and after scheduling is not considered, thus affecting the screening of the scheduling plan for the retrieved electric energy meters. Therefore, there are significant defects in the prior art. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a method, system, device, medium and program product for electric energy meter verification scheduling supervision. The present invention takes into account the historical usage data of the users to whom the retrieved electric energy meters belong and the historical usage data corresponding to the scheduling objectives, and considers the impact on the reuse status of the retrieved electric energy meters caused by the difference in the usage data of the electric energy meters between the two users before and after scheduling, thereby improving the scheduling accuracy.

[0006] According to some embodiments, the first aspect of the present invention provides a method for electric energy meter verification scheduling supervision, adopting the following technical solution:

[0007] A method for electric energy meter verification scheduling supervision includes:

[0008] Obtain the historical usage data of the users corresponding to each retrieved electric energy meter in each verification batch, as well as the performance verification results;

[0009] Obtain the user usage status characteristics and corresponding failure risk data of the electric energy meters in the historical data, construct a relationship curve between the usage status and life of the electric energy meters, and a failure risk proportion curve of the electric energy meters in different usage states;

[0010] According to the batch verification results of the retrieved electricity meters, as well as constructing the relationship curve between the usage status and lifespan of the electricity meters and the curve of the proportion of failure risks of the electricity meters in different usage states, predict the remaining lifespan and failure risk values of each retrieved electricity meter in each verification batch, obtain the performance fluctuation range of the retrieved electricity meters in each verification batch, and generate the scheduling evaluation value for each verification batch;

[0011] Combined with the historical usage data of each scheduling target, 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;

[0012] Construct a batch scheduling plan, and based on the obtained scheduling evaluation value of the verification batch and the scheduling intervention interference coefficient of each verification batch based on the corresponding usage status requirements of each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling target, and obtain the optimal scheduling plan for the verification results of the retrieved electricity meters.

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

[0014] The actual load value and time point curve refer to the electricity load amount used by the corresponding user connected to the electricity meter at different time points;

[0015] Take the position point where the absolute value of the difference between the electricity load amounts corresponding to two adjacent time points in the actual load value and time point curve is greater than or equal to the preset load difference as a load mutation node in the corresponding curve.

[0016] Furthermore, the performance verification results include the appearance status verification result and the data fluctuation situation;

[0017] The appearance status verification result in the performance verification results includes the appearance damage status and the appearance integrity status. Scrape the retrieved electricity meters in the appearance damage status and eliminate the corresponding verification batches;

[0018] The data fluctuation situation in the performance verification results is the data acquisition deviation value corresponding to the retrieved electricity meters after calibration at different load amounts.

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

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

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

[0022] Furthermore, the construction of the failure risk proportion curve of the electricity meter in different usage states includes:

[0023] Obtain the failure risk data of the electricity meters corresponding to each user in the historical data, construct a failure state-life analysis pair, denoted as (state1, fault), where state1 represents the comprehensive evaluation value of the abnormal usage state in the failure risk data of the electricity meter corresponding to the user, and fault represents the proportion of the number of users whose comprehensive evaluation value of the abnormal usage state is less than state1 among the total number of users with failure risk data.

[0024] Based on the function fitting software, perform function fitting on each failure state-life analysis pair to obtain the failure risk proportion curve of the electricity meter in different usage states, denoted as FG(x1).

[0025] Furthermore, according to the batch verification results of the retrieved electricity meters, as well as the relationship curve between the usage status and life of the constructed electricity meter and the failure risk proportion curve of the electricity meter in different usage states, predict the remaining life and failure risk values of each retrieved electricity meter in each verification batch, specifically:

[0026] Predict the corresponding remaining life prediction value by the difference between the service life corresponding to the usage status evaluation value of a retrieved electricity meter with a complete appearance state in a certain verification batch and the usage duration, and so on, to obtain the remaining life prediction values of each retrieved electricity meter with a complete appearance state in each verification batch;

[0027] Based on the failure risk proportion curve of the electricity meter in different usage states, determine the failure risk values corresponding to each retrieved electricity meter with a complete appearance state in each verification batch.

[0028] Furthermore, determine the scheduling evaluation value of each verification batch, specifically:

[0029] Obtain the product of the average value of the performance evaluation values corresponding to each retrieved electricity meter with a complete appearance state in a certain verification batch and the total number of all retrieved electricity meters with a complete appearance state in this verification batch;

[0030] Determine the scheduling evaluation value for each verification batch by using the ratio of the product to the interval length corresponding to the performance fluctuation space of the recalled electricity meters in the same verification batch.

[0031] Further, when constructing the batch scheduling plan, divide each verification batch according to the number of scheduling objectives to generate different batch scheduling plans;

[0032] In each batch scheduling plan, the minimum number of verification batches for each scheduling objective is one, and the scheduling objectives corresponding to the same verification batch are the same;

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

[0034] According to some embodiments, the second solution of the present invention provides an electricity meter verification scheduling supervision system, which adopts the following technical solutions:

[0035] The electricity meter verification scheduling supervision system includes:

[0036] A verification information acquisition module, configured to acquire the historical usage data of the users corresponding to each recalled electricity meter in each verification batch, as well as the performance verification results;

[0037] A historical feature relationship construction module, configured to acquire the user usage status features and corresponding failure risk data of the electricity meters in the historical data, construct the relationship curve between the usage status and the lifespan of the electricity meters, and the failure risk proportion curve of the electricity meters in different usage states;

[0038] A verification scheduling evaluation module, configured to predict the remaining lifespan and failure risk values of each recalled electricity meter in each verification batch according to the batch verification results of the recalled electricity meters, as well as the relationship curve between the usage status and the lifespan of the constructed electricity meters and the failure risk proportion curve of the electricity meters in different usage states, obtain the performance fluctuation interval of the recalled electricity meters in each verification batch, and generate the scheduling evaluation value of each verification batch;

[0039] An interference coefficient analysis module, configured to combine the historical usage data of each scheduling objective to obtain the usage status requirements of each scheduling objective, and analyze the scheduling intervention interference coefficients of each verification batch based on the usage status requirements corresponding to each scheduling objective;

[0040] A verification scheduling planning and management module, configured to construct a batch scheduling plan, and based on the obtained scheduling evaluation value of the verification batch and the scheduling intervention interference coefficients of each verification batch based on the usage status requirements corresponding to each scheduling objective, calculate the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling objective, and obtain the optimal scheduling plan for the recalled electricity meter verification results.

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

[0042] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps in the electric energy meter verification scheduling supervision 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 on the memory and executable on the processor. When the processor executes the program, it implements the steps in the electric energy meter verification scheduling supervision 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, and the program in the software code executes the steps in the electric energy meter verification scheduling 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 the present invention manages the verification scheduling of the retrieved electric energy meters, it not only verifies the appearance and performance of the retrieved electric energy meters to determine whether they can be recycled, but also takes into account the influence of the historical usage data of the users to whom the retrieved electric energy meters belong and the difference in the historical usage data corresponding to the scheduling target before and after scheduling on the reuse status of the retrieved electric energy meters. Furthermore, it realizes the accurate screening of the scheduling scheme for the retrieved electric energy meters, ensuring the effective supervision of the verification scheduling of the retrieved electric energy meters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0050] Figure 1 It is a flowchart of a method for supervising the verification scheduling of an electric energy meter in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0052] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

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

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

[0055] Embodiment 1

[0056] As Figure 1 shown, this embodiment provides a method for scheduling and supervising the verification of electric energy meters. This embodiment takes the application of this method to a server as an example. It can be understood that this method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication methods, and this application does not limit this here. In this embodiment, the method includes the following steps:

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

[0058] The historical usage data of each user corresponding to the retrieved electric energy meters includes the usage duration, the rated load value, the actual load value and the time point curve, and the number of load mutation nodes in the corresponding curve;

[0059] The actual load value and time point curve refer to the electric energy load used by the user connected to the electric energy meter at different time points;

[0060] The position point where the absolute value of the difference between the electric energy load amounts corresponding to two adjacent time points in the actual load value and time point curve is greater than or equal to the preset load difference is used as a load mutation node in the corresponding curve;

[0061] The performance verification results include the appearance state verification result and the data fluctuation condition;

[0062] In the appearance status inspection result of the performance inspection result, it includes the appearance damage status and the appearance integrity status. The retrieved electricity meters in the appearance damage status are scrapped, and the corresponding inspection batches are excluded;

[0063] The data fluctuation situation in the performance inspection result is the data acquisition deviation value corresponding to the retrieved electricity meters after calibration at different load levels.

[0064] In this embodiment, when inspecting the retrieved electricity meters, it is considered from two aspects: the appearance situation and the performance data fluctuation situation. Regarding the appearance aspect, mainly check whether the appearance of the electricity meter is damaged, such as damaged wiring terminals, damaged buttons, display failures, etc. The electricity meters with unqualified appearance will be directly scrapped; while regarding the performance data fluctuation aspect, it is mainly reflected in the data acquisition deviation situation corresponding to the retrieved electricity meters after calibration at different load levels for quantitative analysis, providing data support for calculating the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling target and obtaining the best scheduling plan for the inspection result of the retrieved electricity meters in the subsequent steps.

[0065] Step S200: Obtain the user usage status characteristics and corresponding failure risk data of the electricity meters in the historical data, construct the relationship curve between the usage status and life of the electricity meters, and the failure risk proportion curve of the electricity meters in different usage states;

[0066] A. Construct the relationship curve between the usage status and life of the electricity meters, including:

[0067] Obtain the user usage status characteristics of the electricity meters corresponding to each user in the historical data, construct the status-life analysis pair, denoted as (state, life), where life represents the service life of the corresponding electricity meter when it is scrapped in the user usage status characteristics of the corresponding electricity meter of the user; state represents the usage status evaluation value of the corresponding electricity meter of the user;

[0068] The calculation formula of state is specifically:

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

[0070] Among them, Lode represents the integral value of the function of the points where the actual load value in the historical usage data of the corresponding electricity meter of the user during use is greater than the rated load value in the curve of the actual load value and the time point; rated represents the rated load value in the historical usage data of the corresponding electricity meter of the user during use; t represents the usage duration corresponding to the points where the function value of the curve of the actual load value and the time point is greater than the rated load value corresponding to Lode; node represents the number of load mutation nodes in the historical usage data of the corresponding electricity meter of the user during use; μ represents the preset state evaluation factor;

[0071] Based on the binary linear equation function model, function fitting is performed on the analysis pairs of the lifetimes in each state to obtain the relationship curve between the usage state and the lifetime of the electricity meter;

[0072] In the binary 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 the curve of the proportion of failure risks of the electricity meter in different usage states, including:

[0074] Obtain the failure risk data of the electricity meters corresponding to each user in the historical data, construct the analysis pairs of the failure state lifetimes, denoted as (state1, fault), where state1 represents the comprehensive evaluation value of the abnormal usage state in the failure risk data of the electricity meter corresponding to the user, and the value of state1 is equal to the product of the corresponding state and life of the user;

[0075] The said fault represents the proportion of the number of users whose comprehensive evaluation value of the abnormal usage state corresponding to each user is less than state1 in the total number of users with failure risk data;

[0076] Based on the function fitting software, using the Sigmoid function as the function model, function fitting is performed on each analysis pair of the failure state lifetimes to obtain the curve of the proportion of failure risks of the electricity meter in different usage states, denoted as FG(x1), with the variable being the usage state and the dependent variable being the proportion of failure risks.

[0077] Step S300: According to the batch verification results of the retrieved electricity meters, as well as the constructed relationship curve between the usage state and the lifetime of the electricity meter and the curve of the proportion of failure risks of the electricity meter in different usage states, predict the remaining lifetimes and failure risk values of each retrieved electricity meter in each verification batch, obtain the performance fluctuation range of the retrieved electricity meters in each verification batch, and generate the scheduling evaluation value for each verification batch;

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

[0079] During the process of predicting the remaining lifetimes and failure risk values of each retrieved electricity meter in each verification batch, the predicted remaining lifetime value of the i-th retrieved electricity meter with the appearance inspection result of the complete appearance state in the j-th verification batch is denoted as SY (i,j) and the failure risk value of the i-th retrieved electricity meter with the appearance inspection result of the complete appearance state in the j-th verification batch is denoted as GF (i,j) .

[0080] Predict the corresponding remaining life prediction value based on the difference between the service life and the service duration corresponding to the service status evaluation value of a returned electricity meter with a complete appearance in a certain verification batch. The remaining life prediction value SY (i,j) of the returned electricity meter with the i-th appearance inspection result of complete appearance in the j-th verification batch is calculated as follows:

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

[0082] where state (i,j) represents the service status evaluation value corresponding to the returned electricity meter with the i-th appearance inspection result of complete appearance in the j-th verification batch; FM(state (i,j) ) represents the function value corresponding to the user's service status evaluation value of the electricity meter being state (i,j) in the relationship curve between the service status and life of the electricity meter; T (i,j) represents the service duration in the historical usage data of the user corresponding to the returned electricity meter with the i-th appearance inspection result of complete appearance in the j-th verification batch;

[0083] And so on, obtain the remaining life prediction values of the returned electricity meters with complete appearance in each verification batch.

[0084] Based on the failure risk proportion curve of the electricity meter under different service states, determine the failure risk value corresponding to each returned electricity meter with a complete appearance in each verification batch. The failure risk value GF (i,j) of the returned electricity meter with the i-th appearance inspection result of complete appearance in the j-th verification batch is calculated as follows:

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

[0086] where state1 (i,j) represents the comprehensive evaluation value of the abnormal service state corresponding to the returned electricity meter with the i-th appearance inspection result of complete appearance in the j-th verification batch; FG(state1 (i,j) ) represents the function value corresponding to x1 being state1 (i,j) in FG(x1).

[0087] The performance fluctuation range of the retrieved electricity meters in each inspection batch is the range formed by the maximum and minimum values among the performance evaluation values corresponding to each of the retrieved electricity meters in the corresponding inspection batch; the performance evaluation value corresponding to the i-th retrieved electricity meter with a complete appearance inspection result in the j-th inspection batch is denoted as XN (i,j) , specifically:

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

[0089] The scheduling evaluation value of the j-th inspection batch is denoted as DP j ,

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

[0091] XNj represents the average value of the performance evaluation values corresponding to the retrieved electricity meters with a complete appearance inspection result in the j-th inspection batch; count j represents the total number of retrieved electricity meters with a complete appearance inspection result in the j-th inspection batch; Length j represents the interval length corresponding to the performance fluctuation range of the retrieved electricity meters in the j-th inspection batch;

[0092] When count j = 0, it is determined that H{XNj·count j / Length j}= 0;

[0093] When count j = 1, it is determined that H{XNj·count j / Length j}= XNj;

[0094] When count j > 1, it is determined that H{XNj·count j / Length j}= XNj·count j / Length j .

[0095] Step S400: Combine the historical usage data of each scheduling objective to obtain the usage status requirements of each scheduling objective, and analyze the scheduling intervention interference coefficients of each inspection batch based on the usage status requirements corresponding to each scheduling objective;

[0096] The usage status requirement of the scheduling target is a set composed of the 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 requirement 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) represents the average value of the usage status evaluation values corresponding to the recalled electricity meters in each appearance complete state in the j-th verification batch; state{k} represents the average value of the usage status evaluation values corresponding to the historical usage data of each user in the k-th scheduling target; FM(state (,j) ) represents the function value corresponding to the usage status evaluation value of the electricity meter corresponding to the user being state (,j) in the relationship curve between the usage status and life of the electricity meter; FM(state{k}) represents the function value corresponding to the usage status evaluation value of the electricity meter corresponding to the user being state{k} in the relationship curve between the usage status and life of the electricity meter.

[0099] Step S500: Construct a batch scheduling plan, and based on the obtained scheduling evaluation value of the verification batch, and the scheduling intervention interference coefficient of each verification batch based on the usage status requirement corresponding to each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling target, and obtain the optimal scheduling plan for the verification result of the recalled electricity meter;

[0100] When constructing the batch scheduling plan, divide each verification batch according to the number of scheduling targets to generate different batch scheduling plans. In each batch scheduling plan, the minimum number of verification batches for each scheduling target is one, and the scheduling targets corresponding to the same verification batch are the same. The number of verification batches is greater than or equal to the number of scheduling targets;

[0101] In this embodiment, if there are 4 verification batches, which are respectively denoted as A, B, C, and D,

[0102] If the scheduling targets are 2, which are respectively denoted as Yin and Mao;

[0103] Since the minimum number of verification batches for each scheduling target in each batch scheduling plan is one, and the scheduling targets corresponding to the same verification batch are the same;

[0104] Furthermore, the combination method of the verification batch and the scheduling target is as follows:

[0105] Batch scheduling plan one: {A, B, C} → {Yin}; {D} → {Mao};

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0123] Batch scheduling plan 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] Among them, GX {g,r} represents the scheduling intervention interference coefficient of the r-th verification batch in the g-th batch scheduling plan corresponding to the usage status requirement based on the corresponding scheduling objective; 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 in the appearance integrity state 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] Embodiment 2

[0130] This embodiment 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 features and corresponding failure risk data of the electricity meters in the historical data, construct the relationship curve between the usage status and life of the electricity meters, and the failure risk proportion curve of the electricity meters in different usage states;

[0133] The verification scheduling evaluation module is used to predict the remaining life and failure risk values of each recalled electricity meter in each verification batch based on the batch verification results of the recalled electricity meters, as well as construct the relationship curve between the usage status and life of the electricity meters and the failure risk proportion curve of the electricity meters in different usage states, obtain the performance fluctuation range of the recalled electricity meters in each verification batch, and generate the scheduling evaluation value of 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 analyze the scheduling intervention interference coefficients of each verification batch based on the usage status requirements corresponding to each scheduling target;

[0135] The verification scheduling planning and management module is used to construct a batch scheduling plan, and based on the obtained verification batch scheduling evaluation value and the scheduling intervention interference coefficients of each verification batch based on the usage status requirements corresponding to each scheduling target, calculate the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling target, and obtain the optimal scheduling plan for the verification results of the recalled electricity 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 the above Embodiment 1. It should be noted that the above modules can be executed in a computer system such as a set of computer executable instructions as part of the system.

[0137] In the above embodiments, the descriptions of each embodiment have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

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

[0139] Embodiment 3

[0140] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps in the electricity meter verification scheduling supervision method described in the above Embodiment 1.

[0141] Embodiment 4

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

[0143] Example 5

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

[0145] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0147] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above various methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0150] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. The method for monitoring and dispatching electric energy meter verification is characterized in that: include: Obtain the historical usage data and performance verification results of each user corresponding to each removed electric energy meter in each verification batch; Obtain the user usage status characteristics and corresponding fault risk data of the electric energy meter in the historical data, and construct the relationship curve between the usage status and life of the electric energy meter, as well as the fault risk ratio curve of the electric energy meter under different usage status; According to the batch calibration results of the returned energy meters, as well as the relationship curve between the usage status and life of the energy meters and the fault risk ratio curve of the energy meters under different usage statuses, the remaining life and fault risk value of each returned energy meter in each calibration batch are predicted, the performance fluctuation range of the returned energy meters in each calibration batch is obtained, and the scheduling evaluation value of each calibration batch is generated; 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 inspection batch based on the usage status requirements of each scheduling target; Construct a batch scheduling plan, and based on the obtained calibration batch scheduling evaluation value and the scheduling intervention interference coefficient of each calibration batch based on each scheduling target corresponding to the usage status demand, calculate the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling target, and obtain the optimal scheduling planning plan for the calibration results of the dismantled electric energy meters.

2. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: The historical usage data of each user corresponding to each removed electric energy meter includes usage time, rated load value, actual load value and time point curve and the number of load mutation nodes in the corresponding curve; The actual load value and time point curve refers to the electric energy load used by the user connected to the electric energy meter at different time points; A position point where the absolute value of the difference between the actual load value and the electric energy 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 mutation node in the corresponding curve.

3. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: The performance test results include appearance test results and data fluctuations; The appearance status test results in the performance test results include appearance damaged status and appearance intact status. The dismantled electric energy meters in appearance damaged status are scrapped and the corresponding test batches are eliminated; The data fluctuation in the performance verification result is the data collection deviation value corresponding to different load amounts of the calibrated disassembled electric energy meter.

4. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: The constructing of the relationship curve between the usage status and life of the electric energy meter includes: Obtain the user usage status characteristics of the electric energy meter corresponding to each user in the historical data, and construct a state-life analysis pair, recorded as (state, life), where life represents the service life of the corresponding electric energy meter in the user usage status characteristics when the corresponding electric energy meter is scrapped; state represents the usage status evaluation value of the electric energy meter corresponding to the user; Based on the binary linear equation function model, function fitting is performed on each state life analysis pair to obtain the relationship curve between the use state and life of the electric energy meter.

5. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: The constructing of the fault risk ratio curve of the electric energy meter under different usage conditions includes: Obtain the fault risk data of the electric energy meter corresponding to each user in the historical data, and construct a fault state life analysis pair, recorded as (state1, fault), where state1 represents the comprehensive evaluation value of the abnormal usage state in the fault risk data of the electric energy meter corresponding to the user, and fault represents the proportion of the number of users whose comprehensive evaluation value of the abnormal usage state corresponding to each user is less than state1 in the total number of users with fault risk data; Based on the function fitting software, function fitting is performed on the life analysis pairs of each fault state to obtain the fault risk ratio curve of the electric energy meter under different usage conditions, which is recorded as FG(x1).

6. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: According to the batch calibration results of the returned electric energy meters, the relationship curve between the use status and life of the electric energy meters and the fault risk ratio curve of the electric energy meters under different use status are constructed to predict the remaining life and fault risk value of each returned electric energy meter in each calibration batch, specifically: The corresponding remaining life prediction value is predicted by the difference between the service life and the usage time corresponding to the usage status evaluation value of a certain disassembled electric energy meter in an intact appearance in a certain calibration batch, and so on, to obtain the remaining life prediction value of each disassembled electric energy meter in an intact appearance in each calibration batch; Based on the failure risk ratio curves of the electric energy meter under different usage conditions, the failure risk value corresponding to each dismantled electric energy meter in each calibration batch with intact appearance is determined.

7. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: Determine the scheduling evaluation value of each verification batch, specifically: Obtain the product of the average value of the performance evaluation values ​​corresponding to each returned electric energy meter in a certain calibration batch and the total number of all returned electric energy meters in a certain calibration batch in a complete appearance; The dispatch evaluation value of each calibration batch is determined by using the ratio of the product to the interval length corresponding to the performance fluctuation space of the electric energy meters removed from the same calibration batch.

8. The electric energy meter verification dispatching and supervision method according to claim 1, characterized in that: When constructing the batch scheduling plan, each inspection batch is divided according to the number of scheduling targets to generate different batch scheduling plans; There is at least one calibration batch for each scheduling target in each batch scheduling plan, and the scheduling targets corresponding to the same calibration batch are the same; The number of the inspection batches is greater than or equal to the number of scheduling targets.

9. The electric energy meter verification dispatching supervision system is characterized by: include: The calibration information acquisition module is used to obtain the historical usage data of the corresponding users of each disassembled electric energy meter in each calibration batch, as well as the performance calibration results; A historical feature relationship building module is used to obtain the user usage status characteristics and corresponding fault risk data of the electric energy meter in the historical data, and to build a relationship curve between the usage status and life of the electric energy meter, as well as a fault risk ratio curve of the electric energy meter under different usage statuses; The calibration scheduling evaluation module is used to predict the remaining life and failure risk value of each returned electric energy meter in each calibration batch according to the calibration results of the batches of returned electric energy meters, as well as to construct the relationship curve between the use status and life of the electric energy meters and the failure risk ratio curve of the electric energy meters under different use statuses, obtain the performance fluctuation range of the returned electric energy meters in each calibration batch, and generate the scheduling evaluation value of each calibration 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 analyze the scheduling intervention interference coefficient of each inspection batch based on the usage status requirements corresponding to each scheduling target; The calibration scheduling planning management module is used to construct batch scheduling plans, and based on the obtained calibration batch scheduling evaluation values ​​and the scheduling intervention interference coefficient of each calibration batch based on each scheduling target corresponding to the usage status requirements, calculate the comprehensive scheduling deviation of each batch scheduling plan based on the scheduling target, and obtain the optimal scheduling planning plan for the calibration results of the dismantled electric energy meters.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the electric energy meter calibration, dispatching and supervision method as described in any one of claims 1 to 8 are implemented.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the electric energy meter calibration, dispatching and supervision method as described in any one of claims 1 to 8 are implemented.

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

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