CN112346000A

CN112346000A - Intelligent electric energy meter operation error data statistical processing system and method

Info

Publication number: CN112346000A
Application number: CN202011196003.9A
Authority: CN
Inventors: 荆臻; 李哲; 杨剑; 马俊; 王者龙; 曹彤; 李先志
Original assignee: Marketing Service Center of State Grid Shandong Electric Power Co Ltd; Beijing Zhixiang Technology Co Ltd
Current assignee: Marketing Service Center of State Grid Shandong Electric Power Co Ltd; Beijing Zhixiang Technology Co Ltd
Priority date: 2020-10-30
Filing date: 2020-10-30
Publication date: 2021-02-09
Anticipated expiration: 2040-10-30
Also published as: CN112346000B

Abstract

The invention provides a statistical processing system and a statistical processing method for operation error data of an intelligent electric energy meter, which are characterized in that an error statistical end closely associated with other parts of the system is designed based on error statistical requirements, a sampling error statistical result is provided by connecting a feedback receiving end and a system decision monitoring end, an online monitoring and data acquisition system and a method based on a distributed acquisition system, station area configuration information and homotype reference information are designed, meanwhile, the error statistics of UI display and system management is realized by considering that the error of the intelligent electric energy meter can come from aspects such as measurement error, underloaded error influenced by shunt running performance, sampling circuit sampling error and home location error and the like and by carrying out normalized configuration on ternary check information, and convenient system monitoring is realized based on multidimensional bottom layer monitoring parameters.

Description

Intelligent electric energy meter operation error data statistical processing system and method

Technical Field

The invention belongs to the technical field of new-generation intelligent Internet of things information, and particularly relates to a system and a method for statistical processing of operation error data of an intelligent electric energy meter.

Background

The error is the measured magnitude minus the reference magnitude. The measured quantity is referred to as measured value, and represents the quantity of the measurement result. The error that an electrical energy metering device has under specified operating conditions. The allowable basic error limit is simply called basic error limit. The working conditions refer to the verification working conditions specified in the national verification regulations. The basic error of the electric energy meter is specified by relative percentage error, and is determined according to the following steps: relative error ((measured result (indicated value) -measured true value)/measured true value) 100% since true value cannot be determined, it is actually true value agreed.

However, when the electric energy meter is used for electric energy statistics, certain calculation errors always exist due to factors of all aspects, and the errors possibly influence the cooperation relationship between a power supply enterprise and an electricity user, so that an electric power worker pays attention to the existence of the errors. The intelligent electric meter has the functions of metering basic electricity consumption of the traditional electric meter, and also has intelligent functions of bidirectional multi-rate metering, user side control, bidirectional data communication of various data transmission modes, electricity larceny prevention and the like in order to adapt to the use of an intelligent power grid and new energy.

The smart meter concept is not new. In 1993, when a static electric meter just appears, the price of the static electric meter is 10-20 times of that of an electromechanical electric meter, so the static electric meter is mainly applied to large-scale users. With the increase in the number of electric meters with telecommunication capabilities, it is imperative to develop new systems for meter reading and data management. In such systems, measurement data is beginning to be opened to systems such as distribution network automation, but these systems have not been able to effectively utilize the relevant data. Similarly, real-time energy consumption data of prepaid electricity meters is rarely used in applications such as energy management or energy conservation measures.

However, due to technical iterations and developments, mass-produced static electricity meters can achieve powerful data processing and storage capabilities at a very low cost, thereby promoting a great increase in the level of intelligence of small consumer electricity meters, and the static electricity meters gradually replace conventional electromechanical electricity meters.

The intelligent electric meter is an important component of a modern electric power system, and is not only a household electric meter but also more and more intelligent electric meters for industrial, commercial, mechanical and electric power consumption electric meters appear and are used. The metering device plays an indispensable role in the intelligent electric energy meter, and in actual work, the metering device of the intelligent electric energy meter often has operation errors, so that effective measures are required to be taken to improve the operation accuracy of the metering device. Common types of smart meters include: classifying according to phase lines: the intelligent electric meter can be divided into a single-phase intelligent electric meter, a three-phase three-wire intelligent electric meter and a three-phase four-wire intelligent electric meter according to the phase line of the intelligent electric meter. Classifying according to the working principle: the intelligent electric meter can be divided into two types, namely an induction type and an electronic type according to the working principle of the intelligent electric meter. Classifying according to the measurement accuracy grade: the accuracy grade of the intelligent electric meter can be divided into 0.2S, 0.5S, 1 grade and 2 grades. Classified according to additional functions: the intelligent electric energy meter can be divided into a multi-rate intelligent electric energy meter, a pre-payment intelligent electric energy meter, a multi-user intelligent electric energy meter, a multifunctional intelligent electric energy meter, a carrier intelligent electric energy meter and the like according to additional functions of the intelligent electric energy meter. Classifying according to load switches: the load switch according to the intelligent electric meter can be divided into an internal load switch and an external load switch. Classifying according to communication modes: the intelligent electric meter can be divided into a carrier wave, a GPRS wireless bus and an RS-485 bus according to the communication mode of the intelligent electric meter.

The first generation quartz clock-controlled time-sharing electric energy meter which is produced mainly in early days. The electric energy meter is connected with a quartz clock through a lead to drive peak and valley electromagnetic counters respectively at different time intervals, peak and valley electric quantities and total electric quantity are displayed respectively, and the peak and valley electric quantities are deducted according to the total electric quantity to be the electric quantity at a normal time interval. The time-sharing charging electric energy meter has poor reliability. The timing segmentation precision is too low (the minimum segmentation is 5min), the interference is easy to happen, the time interval adjustment is troublesome, the use function is single, the special requirements in time-sharing charging cannot be met, and the timing segmentation is basically eliminated and stopped at present.

The second generation electromechanical integrated structure time-sharing electric energy meter. The electric energy meter is based on a 1.0-level induction system electric energy meter core, adopts an infrared photoelectric converter, a pulse output and Central Processing Unit (CPU) and a singlechip circuit, uses an attached keyboard program or an infrared wireless keyboard to set various demands, clocks, time periods and double holidays, and can protect the maximum demands of the month, the maximum demands of the previous month and the maximum demands of the peak, the average and the valley of the month for display and storage. The device is provided with a pulse output and an RS-232 serial communication port, and is convenient for remote data transmission and monitoring. The instrument has precise and reliable performance, the function can meet the time-sharing charging requirement at the present stage of China, the production process is mature, the price has competitiveness, and the instrument is the most widely applied product in China at present. But the defects in the United states are that all manufacturers develop special single-chip microcomputers by themselves, and the defects of poor product compatibility and difficult maintenance exist. Common products of this series are DF68, DF93, DTF33, DF86, DSF20, DIF2, DF32, DTF864, MRZ, DSD66, etc.

The domestic defined intelligent instrument is an instrument which takes a microprocessor as a core, can store measurement information and can analyze and synthesize measurement results in real time and make various judgment abilities. The intelligent automatic zero setting system generally has an automatic measurement function, strong data processing capacity, automatic zero setting and unit conversion functions, simple fault prompting, a man-machine interaction function, an operation panel and a display, and certain artificial intelligence. An electronic multifunctional electric energy meter using a microprocessor is generally defined as a smart meter, and features such as a communication function (carrier, GPRS, ZigBee, and the like), multi-user metering, and metering for a specific user (e.g., an electric locomotive) are introduced into the concept of the smart meter.

Combining the various definitions, one can consider: the intelligent electric meter is an intelligent instrument taking microprocessor application and network communication technology as cores, has the capabilities of automatic metering/measuring, data processing, two-way communication, function expansion and the like, and can realize the functions of two-way metering, remote/local communication, real-time data interaction, charging of various electricity prices, remote power off and supply, electric energy quality monitoring, reading of a water-gas heat meter, interaction with a user and the like. The intelligent metering system constructed on the basis of the intelligent electric meter can support the requirements of the intelligent power grid on load management, distributed power supply access, energy efficiency, power grid dispatching, electric power market transaction, emission reduction and the like. The full load state of the equipment in the electricity peak period can increase the possibility of error occurrence, so the installation and management of the intelligent electric energy meter metering device are required to be combined with the electricity utilization condition. The method comprises the steps of starting analysis from factors such as the electricity utilization scale of a user, the electricity consumption amount and the house type, predicting the load condition and the change condition of the electric energy, and adjusting internal factors of the mutual inductor according to a scientific prediction result. The method has the advantages that the fact that the current transformer transformation ratio is guaranteed is very important, the fact that the current transformer transformation ratio can effectively avoid the metering problem under the abnormal condition is confirmed, and the method is very important for improving the economic benefit of an electric power enterprise and meeting the daily life requirements of people. When the electric energy meter and the metering device start to operate, relevant personnel are required to go to the site for inspection, the error wiring of the electric energy meter is adjusted according to the actual electricity utilization condition of a user, and then the metering performance is evaluated. The work completion effect of the link has great influence on the accuracy of the intelligent electric energy meter metering device, and workers must be ensured to complete work according to relevant regulations. And then recording the work flow, uniformly uploading the collected data information and storing the data information in a database, and paying attention to the safety management of the information in the database to provide a basis for related work in the future. The intelligent electric energy meter has a high technical level and is relatively less prone to damage, but in practical application, the problem that the intelligent electric energy meter still has errors is found, application accuracy is affected, loss is brought, performance, states, operation parameters and dynamic errors of the intelligent electric energy meter are counted and monitored, the method becomes an important part in intelligent power grid management, and higher requirements are provided for operation error data counting and monitoring of the intelligent electric energy meter.

The invention provides a statistical processing system and a statistical processing method for running error data of an intelligent electric energy meter, which are characterized in that an error statistical end closely associated with other parts of the system is designed based on error statistical requirements, a sampling error statistical result is provided by connecting a feedback receiving end with a system decision monitoring end, an online monitoring and data acquisition system and a method based on a distributed acquisition system, platform area configuration information and homotype reference information are designed, meanwhile, the error of the intelligent electric energy meter can come from aspects such as measurement error, undercurrent performance influence underload error, sampling circuit sampling error and home location error, etc. and the configuration of normalization is carried out on ternary check information by considering the error of the intelligent electric energy meter, three-phase error check information is introduced by carrying out global consideration on factors influencing the error of the intelligent electric energy meter, namely: firstly, associating first error information of the intelligent electric energy meter with anchoring information of the intelligent electric energy meter, after introducing first station zone configuration information balance, second station zone configuration information balance and third station zone configuration information balance, and performing resetting and normalization processing on first check information after a fourth parameter first electric energy meter configuration acquisition process; secondly, cluster control is carried out in a second check information cluster mode, check information of the electric energy meter is collected in a distributed angle, and distributed second check information collection and balance are achieved through multi-to-one collection of single-point information by a multi-point check meter and multi-to-multi mapping of multi-point information by the multi-point check meter; thirdly, a homotype verification matrix is originally introduced to carry out three-level correction on the online error verification of the intelligent electric energy meter, and the accurate control of the error verification is assisted through the dynamic updating of the system and the homotype verification mode. Considering that in the error measurement, verification and correction process of the intelligent electric energy meter, the error of the intelligent electric energy meter can come from various aspects such as measurement error, light load error influenced by shunt running performance, sampling error of a sampling circuit, attribution error and the like, the system and the method for counting and processing the operation error data of the comprehensive intelligent electric energy meter at multiple angles are realized by carrying out normalized configuration on the ternary verification information, the technical problems of the prior art such as the lack of feedback parameters and imperfect statistics on the error verification of the intelligent electric energy meter are avoided, the better error detection effect and the higher precision of online error verification of the intelligent electric energy meter compared with the prior art are realized, the system and the method for counting and processing the operation error data of the comprehensive intelligent electric energy meter at multiple angles are provided by carrying out normalized configuration on the ternary verification information, and the error statistics which is convenient for UI display and system management is realized, and the data conversion logic of the error statistic end is provided, and convenient system monitoring is realized based on multidimensional bottom monitoring parameters.

Disclosure of Invention

The invention aims to provide a statistical processing system and a statistical processing method for operation error data of an intelligent electric energy meter, which are superior to those of the prior art.

In order to achieve the purpose, the technical scheme of the invention is as follows:

the system for statistically processing the operation error data of the intelligent electric energy meter comprises the following modules:

the system monitoring end is used for receiving system monitoring information of the online monitoring system and is connected with the error counting end;

the error counting end is used for receiving the feedback error information of the feedback receiving end, and sending the feedback error information to the system monitoring end in a list mode after data counting for the system monitoring end to perform data monitoring and system decision;

the feedback receiving end is used for being connected with the check information rectification module and receiving the integration error check parameters fed back by the check information rectification module;

the system comprises a first distribution area configuration balancing module, a second distribution area configuration balancing module and a control module, wherein the first distribution area configuration balancing module is used for collecting first distribution area configuration information, and the first distribution area configuration information is used for representing check feedback weights of all distribution areas in the intelligent electric energy meter network;

a second block configuration balancing module, configured to collect and balance second block configuration information, where:

the second zone configuration information is a type of error factor pre-configured in the zone,

equalizing the second zone configuration information at least comprises: establishing a distribution area configuration table for recording each distribution area and the type of the corresponding pre-configured error factor;

the third station area configuration balancing module is used for collecting and balancing third station area configuration information and recording standard pre-configured error factor types and corresponding influence factors;

the system acquisition terminal is used for acquiring basic configuration of the object intelligent electric energy meter needing online error check, and the basic configuration at least comprises:

the identification of the object intelligent electric energy meter is used for identifying the type and the ID of the intelligent electric energy meter;

the platform zone information of the object intelligent electric energy meter;

the homotype matrix anchoring value of the object intelligent electric energy meter is used for anchoring the intelligent electric energy meter cluster to which the object intelligent electric energy meter belongs in a homotype verification matrix;

the platform region anchoring module is used for determining a first verification information base number corresponding to the object intelligent electric energy meter based on first platform region configuration information, second platform region configuration information, third platform region configuration information and object intelligent electric energy meter basic configuration, and transmitting the first verification information base number to the first verification information homing module;

the first check information resetting module is used for resetting the first check information base number into frames;

the first check information normalization module is used for analyzing the first check information base number to perform frame structure normalization, acquiring the first check information base number and obtaining first check information based on the first check information base number;

the second check information cluster acquisition module performs multi-point check by using the electric energy meter check meter distributed cluster and sends a check result to the second check information cluster balancing module;

the second check information cluster balancing module is configured to perform cluster balancing on the multipoint check result sent by the second check information cluster collecting module, where the cluster balancing at least includes:

associating the error check result of each point with a check weight value of a corresponding electric energy meter check meter preset by a system;

setting a dynamic weight threshold value based on the electric energy meter calibrator check weight corresponding to each point in the cluster, wherein the dynamic weight threshold value is used for filtering a check result of the electric energy meter calibrator check weight below the dynamic weight threshold value, and the dynamic weight threshold value can be a system preset value or needs a user to input the check result at a second check information cluster balancing module interface;

selecting the remaining multi-point error check results to obtain an error check result mean value as second check information;

the homomorphic verification matrix is used for storing the dynamic error mean values of various intelligent electric energy meters, configuring and outputting the dynamic error mean values based on the object intelligent electric energy meter base, inquiring the intelligent electric energy meter cluster to which the object intelligent electric energy meter belongs, and outputting the dynamic error mean values of all types of intelligent electric energy meters in the intelligent electric energy meter cluster;

the dynamic error mean value of each type of intelligent electric energy meter is the mean value of the historical error calculation result of each type of intelligent electric energy meter by the system and is stored in the corresponding matrix element of each type of intelligent electric energy meter in the same type verification matrix;

the third verification information generation module combines third verification information based on the dynamic error mean values of all types of intelligent electric energy meters in the output intelligent electric energy meter cluster and sends the third verification information to the verification information rectification module;

the check information rectification module generates an integrated error check parameter based on the first check information, the second check information and the third check information, wherein the integrated error check parameter at least comprises:

the intelligent electric energy meter identification is used for identifying the type and the ID of the intelligent electric energy meter,

the intelligent electric energy meter error checking parameters are used for carrying out intelligent electric energy meter error checking and executing feedback correction;

sending the integrated error check parameters to a feedback receiving end;

and the feedback receiving end sends the integrated error check parameters to the system monitoring end for decision reference, error check and correction, and sends the integrated error check parameters to the same type of verification matrix through the system acquisition end for updating the dynamic error mean value of the intelligent electric energy meter of the corresponding type.

Preferably, the influence factor is dynamically adjusted by the system according to the satisfaction degree of the online error check result of the intelligent electric energy meter or is preset by the system,

and the value range of the influence factor is more than or equal to 0 and less than or equal to 1.

Preferably, the homotype matrix anchoring value is used for anchoring the intelligent electric energy meter cluster to which the target intelligent electric energy meter belongs in the homotype verification matrix, and specifically includes:

and performing determinant detection in the same-type verification matrix by using the same-type anchoring value, obtaining matrix elements and positions thereof which are the same as the same-type anchoring value, separating out all matrix elements corresponding to the intelligent electric energy meter cluster to which the positions belong, and pushing the matrix elements to a third verification information generation module.

Preferably, the homotypic verification matrix is composed of a plurality of matrix elements, each matrix element corresponds to an intelligent electric energy meter type and is a quaternion value { a, X, C, V }, where a identifies the intelligent electric energy meter type, X identifies the homotypic anchor value, C identifies the cluster identifier thereof, and V identifies the dynamic error mean value thereof, and is used to determine the matrix elements belonging to the same cluster.

Preferably, the setting of the cluster identifier may use any one of the following setting manners:

clustering according to the same delivery model;

clustering according to the distribution of the no-load threshold interval;

and clustering according to the phase lines.

In addition, the invention further provides a control method of the intelligent electric energy meter operation error data statistical processing system, and the method comprises the following steps:

an initialization step:

debugging and using a system monitoring end to receive system monitoring information of an online monitoring system, wherein the system monitoring end is connected with an error counting end;

debugging and using an error statistic terminal to receive feedback error information of a feedback receiving terminal, and sending the feedback error information to a system monitoring terminal in a list mode after data statistics is carried out so that the system monitoring terminal can carry out data monitoring and system decision;

debugging and using a feedback receiving terminal, wherein the feedback receiving terminal is connected with the check information rectifying module and receives the integrated error check parameters fed back by the check information rectifying module;

the method comprises the following steps: acquiring first district configuration information based on a first district configuration balancing module, wherein the first district configuration information is used for representing verification feedback weights of all districts in an intelligent electric energy meter network;

step two: acquiring and balancing second station zone configuration information based on a second station zone configuration balancing module, wherein:

step three: acquiring and balancing third zone configuration information based on a third zone configuration balancing module, and recording standard pre-configured error factor types and corresponding influence factors;

step four: the method comprises the following steps of using a system acquisition end to acquire basic configuration of an object intelligent electric energy meter needing online error check, wherein the basic configuration at least comprises the following steps:

the identity of the subject smart energy meter,

the platform zone information of the object intelligent electric energy meter;

step five: determining a first verification information base number corresponding to the object intelligent electric energy meter based on first platform area configuration information, second platform area configuration information, third platform area configuration information and object intelligent electric energy meter basic configuration by using a platform area anchoring module, and transmitting the first verification information base number to a first verification information homing module;

step six: using a first check information homing module to home the first check information base number into a frame;

step seven: analyzing the first check information base number by using a first check information normalization module to perform frame structure of a frame, acquiring the first check information base number, and obtaining first check information based on the first check information base number;

step eight: performing multi-point verification by using a second verification information cluster acquisition module and an electric energy meter calibrator distributed cluster, and sending a verification result to a second verification information cluster balancing module;

step nine: using a second checking information cluster balancing module to perform cluster balancing on the multipoint checking result sent by the second checking information cluster acquisition module, wherein the cluster balancing at least comprises:

step ten: storing the dynamic error mean values of various intelligent electric energy meters by using a homotype verification matrix, configuring and outputting based on the basis of the target intelligent electric energy meter, inquiring the intelligent electric energy meter cluster to which the target intelligent electric energy meter belongs, and outputting all types of intelligent electric energy meters in the intelligent electric energy meter cluster;

step eleven: combining third check information by using a third check information generation module based on the output dynamic error mean values of all types of intelligent electric energy meters in the intelligent electric energy meter cluster, and sending the third check information to a check information rectification module;

step twelve: generating an integration error check parameter based on the first check information, the second check information and the third check information by using a check information rectification module, wherein the integration error check parameter at least comprises:

step thirteen: sending the integration error check parameter to a feedback receiving end;

clustering according to the same delivery model;

clustering according to the distribution of the no-load threshold interval;

and clustering according to the phase line distribution.

Drawings

FIG. 1 is a basic system level structure diagram illustrating the system and method for statistical processing of operating error data of an intelligent electric energy meter according to the present invention;

fig. 2 and 3 are basic block diagrams illustrating an embodiment of a statistical processing method for operation error data of an intelligent electric energy meter according to the present invention;

FIG. 4 is a schematic diagram illustrating another embodiment of the homomorphic calibration matrix in the system and method for statistical processing of operational error data of an intelligent electric energy meter according to the present invention.

Fig. 5 is a schematic diagram illustrating a preferred embodiment of the statistical processing system for operation error data of an intelligent electric energy meter when matrix elements in a homotype verification matrix are matched in the method.

Fig. 6 is a schematic diagram illustrating a preferred embodiment of an error statistics end in the system and method for statistical processing of operation error data of an intelligent electric energy meter according to the present invention.

Detailed Description

The following describes several embodiments and advantageous effects of the system and method for statistical processing of operation error data of intelligent electric energy meter claimed in the present invention in order to facilitate more detailed examination and decomposition of the present invention.

For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.

It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

It should be understood that although the terms first, second, etc. may be used in embodiments of the invention to describe methods and corresponding apparatus, these keywords should not be limited to these terms. These terms are only used to distinguish keywords from each other. For example, the first verification information, the first zone configuration information, and the first power meter may also be referred to as the second verification information, the second zone configuration information, and the second power meter, and similarly, the second verification information, the second zone configuration information, and the second power meter may also be referred to as the first verification information, the first zone configuration information, and the first power meter, without departing from the scope of the embodiments of the present invention.

The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.

As shown in fig. 1, the system of the intelligent electric energy meter operation error data statistical processing method as claimed in the present invention comprises:

as a superimposable preferred embodiment, the first block configuration information represents check feedback weights of each block in the intelligent electric energy meter network, and the feedback weights are positive numbers not greater than 1, and correspond to block information of each block one to one, and the block information may be a block identifier.

The second transformer area configuration information may include a plurality of preconfigured error factor types, such as cable length, transformer area terrain complexity, transformer transformation ratio, transformer area total resistance stability parameters, and the like; correspondingly, the station area configuration table records the types of the plurality of pre-configured error factors;

the third block configuration balancing module records the standard pre-configured error factor type and the corresponding influence factor, and can be a weight value of the pre-configured error factor type in error calculation to represent the importance degree of factors such as cable length, block terrain complexity, transformer transformation ratio, block total resistance stability parameters and the like in each block, wherein the influence factor is a system preset value and can also be a dynamic adjustment value of the system.

the platform zone information of the object intelligent electric energy meter;

as a preferred embodiment that can be superimposed, the first verification information homing module regards the first zone configuration information as a first load field, regards the second zone configuration information as a second load field, regards the third zone configuration information as a third load field, regards the basic configuration of the object intelligent electric energy meter as a fourth load field, places the basic configuration of the object intelligent electric energy meter on a load part of a transmission data frame according to a sequence from small to large or other specific sequences, and uses a specific isolation field for isolation and transmission;

as a preferred embodiment that can be superimposed, the specific isolation field is used for field gap filling from the first load field to the fourth load field, and is used for isolating each load field;

as a preferred embodiment that can be superimposed, the specific isolated field is binary code 11100011;

as a preferred embodiment that can be superimposed, the first parity information normalization module parses the received data frame and obtains the load field, anchors a specific isolation field, for example, anchors the specific isolation field binary 11100011; analyzing the rest load fields except the specific isolation field into a first load field to a fourth load field in sequence;

as a preferred embodiment that can be superimposed, after the first load field to the fourth load field are obtained, the first verification information normalization module calculates the first verification information by using the following formula:

P＝S*(M1*K1+M2*K2+…+MN*KN)/N；

the system comprises a system acquisition end, a platform area and a platform area, wherein P is a first check information value and is used for representing an influence parameter of the platform area configuration on error check, S is a check feedback weight of a target platform area in the first platform area configuration information, and the target platform area is obtained by matching and searching based on electric energy meter basic configuration acquired by the system acquisition end; m1 is a preconfigured error factor type 1, K1 is an influence factor of the preconfigured error factor type 1, M2 is a preconfigured error factor type 2, K2 is an influence factor of the preconfigured error factor type 2, and so on, N is a total number of preconfigured error factor types in the corresponding station zone.

It should be noted that the third block configuration equalizing module records the types of the standard preconfigured error factors and the corresponding impact factors, and at this time, the number of the types of the standard preconfigured error factors may be greater than N, because some types of preconfigured error factors may be missing in a single block, that is, there is no corresponding related factor causing an error or an error improvement in the block.

as a preferred embodiment that can be superimposed, for example, for the intelligent electric energy meter P, the L-to-1 calibration is performed by using a distributed electric energy meter calibration instrument cluster. Namely, L electric energy meter check meters with different quality parameters and setting precision in a distributed electric energy meter check meter cluster are adopted to respectively check the intelligent electric energy meters P, and then carrying out normalization calculation to obtain error check values, checking the weight values of the check meters of the electric energy meters in the system preset cluster, the weight values can be stored in the storage units of the check meters of the electric energy meters in a block chain mode in a distributed mode, and can also be stored in the system management node of the distributed system, for example, the weight value setting range is [0, 100], Y electric energy meter check results with the weight value below 85 are removed from the cluster results, the rest L-Y results are averaged, for example, an arithmetic average value, or obtaining the averaged second check information by a specific mean model to obtain a cluster mean, such as a poisson distribution mean calculation model.

sending the integration error check parameter to a feedback receiving end;

As a superimposable preferred embodiment, the influence factor is dynamically adjusted by the system according to the satisfaction degree of the online error check result of the intelligent electric energy meter, or is preset by the system,

As a superimposable preferred embodiment, the homotypic matrix anchoring value is used for anchoring the intelligent electric energy meter cluster to which the target intelligent electric energy meter belongs in the homotypic verification matrix, specifically:

As a preferred embodiment that can be superimposed, the homotypic verification matrix is composed of a plurality of matrix elements, each matrix element corresponds to an intelligent electric energy meter type and is a quaternion value { a, X, C, V }, where a identifies the intelligent electric energy meter type, X identifies the homotypic anchor value, C identifies the cluster identifier thereof, and V identifies the dynamic error mean value thereof, and is used to determine the matrix elements belonging to the same cluster.

As a superimposable preferred embodiment, the setting of the cluster identifier may use any one of the following setting modes:

clustering according to the same delivery model;

clustering according to the distribution of the no-load threshold interval;

and clustering according to the phase lines.

Referring to the attached fig. 2-3 of the specification, the attached fig. 2-3 of the specification illustrate basic block diagrams of an embodiment of the statistical processing method for the operation error data of the intelligent electric energy meter. The method comprises the following steps:

s101: an initialization step:

s102: acquiring first district configuration information based on a first district configuration balancing module, wherein the first district configuration information is used for representing verification feedback weights of all districts in an intelligent electric energy meter network;

s104: acquiring and balancing second station zone configuration information based on a second station zone configuration balancing module, wherein:

s106: acquiring and balancing third zone configuration information based on a third zone configuration balancing module, and recording standard pre-configured error factor types and corresponding influence factors;

S108: the method comprises the following steps of using a system acquisition end to acquire basic configuration of an object intelligent electric energy meter needing online error check, wherein the basic configuration at least comprises the following steps:

the identity of the subject smart energy meter,

the platform zone information of the object intelligent electric energy meter;

s110: determining a first verification information base number corresponding to the object intelligent electric energy meter based on first platform area configuration information, second platform area configuration information, third platform area configuration information and object intelligent electric energy meter basic configuration by using a platform area anchoring module, and transmitting the first verification information base number to a first verification information homing module;

s112: using a first check information homing module to home the first check information base number into a frame;

s114: analyzing the first check information base number by using a check information normalization module to perform frame structure of a normalized frame, acquiring the first check information base number, and obtaining first check information based on the first check information base number;

P＝S*(M1*K1+M2*K2+…+MN*KN)/N；

S116: performing multi-point verification by using a second verification information cluster acquisition module and an electric energy meter calibrator distributed cluster, and sending a verification result to a second verification information cluster balancing module;

s118: using a second checking information cluster balancing module to perform cluster balancing on the multipoint checking result sent by the second checking information cluster acquisition module, wherein the cluster balancing at least comprises:

S120: storing the dynamic error mean values of various intelligent electric energy meters by using a homotype verification matrix, configuring and outputting based on the basis of the target intelligent electric energy meter, inquiring the intelligent electric energy meter cluster to which the target intelligent electric energy meter belongs, and outputting all types of intelligent electric energy meters in the intelligent electric energy meter cluster;

s122: combining third check information by using a third check information generation module based on the output dynamic error mean values of all types of intelligent electric energy meters in the intelligent electric energy meter cluster, and sending the third check information to a check information rectification module;

s124: generating an integration error check parameter based on the first check information, the second check information and the third check information by using a check information rectification module, wherein the integration error check parameter at least comprises:

as a preferred embodiment that can be superimposed, a dedicated field exists in the identifier of the intelligent electric energy meter for indicating the type of the intelligent electric energy meter, and the system can know the type anchor value of the intelligent electric energy meter according to the type of the intelligent electric energy meter;

as a superimposable preferred embodiment, the error-checking parameter G of the intelligent electric energy meter for representing the error in the integrated error-checking parameter can be calculated in the following preferred manner:

G＝(T2+T3)*(P*100％)*50％；

g is an intelligent electric energy meter error check parameter in the integrated error check parameters and is used for measuring the error deviation size needing to be corrected, T2 is second check information, T3 is third check information, and P is first check information.

S126: sending the integration error check parameter to a feedback receiving end; and the feedback receiving end sends the integrated error check parameters to the system monitoring end for decision reference, error check and correction, and sends the integrated error check parameters to the same type of verification matrix through the system acquisition end for updating the dynamic error mean value of the intelligent electric energy meter of the corresponding type.

Referring to fig. 4-5 of the specification, fig. 4 is a schematic diagram illustrating another preferred embodiment of the same type calibration matrix in the system and method for statistical processing of operation error data of an intelligent electric energy meter according to the present invention. Fig. 5 is a schematic diagram illustrating a preferred embodiment of the statistical processing system for operation error data of an intelligent electric energy meter when matrix elements in a homotype verification matrix are matched in the method.

In fig. 4, a preferred embodiment of a homomorphic verification matrix is labeled, wherein the homomorphic verification matrix stores a plurality of matrix elements, each matrix element representing a type of the smart meter classified according to a specific classification basis and a historical dynamic error mean thereof.

With reference to figure 5 of the drawings,

the same type anchor value is preset in the system and is informed to a same type verification matrix, and each type of electric energy meter can preset the same type anchor value corresponding to each model, and when basic information of the electric energy meter is collected, the same type anchor value can be determined according to the model and is used as one item in the basic information of the electric energy meter;

as another stackable preferred embodiment, the initial homotype anchor value and the dynamic error value of each electric energy meter type in the homotype verification matrix can be initially set by a system administrator according to one or more of experience, initialization data, configuration data and the like of the system administrator, and when the system does not have historical data, the initial homotype anchor value and/or the dynamic error value of the intelligent electric energy meter of the type are/is used.

Referring to fig. 5 again, it is assumed that the type-identical anchor value of the target intelligent electric energy meter is a character-type parameter FLY after being collected and judged, searching corresponding matrix elements of the type of the target intelligent electric energy meter and the intelligent electric energy meter CLUSTER to which the same type verification matrix belongs through the same type anchoring value, wherein the intelligent electric energy meter CLUSTER to which the same type verification matrix belongs is 'CLUSTER 1', and another matrix element "{ 33, NEF, CLUSTER1, 0.03% }" belonging to the CLUSTER "CLUSTER 1" is precipitated, which, of course, there may be other matrix elements which are not represented in the drawing but belong to "CLUSTER 1", that is, similar types of intelligent electric energy meters, and the average value of the dynamic errors in the quaternary parameters of the type of intelligent electric energy meter corresponding to these matrix elements (may be the average value of the historical dynamic errors counted by the same-type calibration matrix, that is, after each error calculation is updated, the historical dynamic errors of the corresponding type of intelligent electric energy meter are updated and a new average value is obtained) is sent to the third verification information generation module.

As a superimposable preferred embodiment, the third verification information generating module obtains the third verification information by averaging the dynamic error average results in the quaternary parameters of the intelligent electric energy meter of the type corresponding to the matrix elements, such as an arithmetic average, or obtaining a cluster average through a specific average model, such as a poisson distribution average calculation model, and sends the third verification information to the verification information rectifying module.

clustering according to the same delivery model;

clustering according to the distribution of the no-load threshold interval;

and clustering according to the phase line distribution.

Referring to fig. 6, fig. 6 is a schematic diagram illustrating a preferred embodiment of an error statistics end in the system and method for statistical processing of operation error data of an intelligent electric energy meter according to the present invention. And the error counting end receives the input of the feedback input end and preset information transmitted by the system, such as a preset intelligent electric energy meter type ID, an intelligent electric energy meter ID range and the like, counts the dynamic error data obtained by the system from multi-parameter normalization calculation according to the input of the feedback receiving end and stores the dynamic error data into a counting list.

Referring to fig. 6 again, in the preferred embodiment shown in fig. 6, the error transmission data obtained after the multiple or single operation of the intelligent electric energy meter error data statistical processing system for dynamic error monitoring and calculation can be transmitted, specifically, in the form of, for example, integrating error checking parameters.

The transmitted information comprises an intelligent electric energy meter with the ID DIS1-emeter1, the type of the intelligent electric energy meter is 33 th type (33 can be any preset type of system calibration, and a system preset corresponding relation exists between the type of the intelligent electric energy meter and the type of the conventional electric energy meter, such as a three-phase three-wire intelligent electric meter), and the mean value of the calculated dynamic error is 0.73%; for another example, the transmitted information includes an intelligent electric energy meter with an ID DIS2-emeter2, the type of the intelligent electric energy meter is 34 th type (34 may be any preset type of system calibration, and there is a system preset corresponding relationship with the existing electric energy meter type), and the average calculated dynamic error is 1.65%.

In all the above embodiments, in order to meet the requirements of some special data transmission and read/write functions, the above method and its corresponding devices may add devices, modules, devices, hardware, pin connections or memory and processor differences to expand the functions during the operation process.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described method, apparatus and unit may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the method steps into only one logical or functional division may be implemented in practice in another manner, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as individual steps of the method, apparatus separation parts may or may not be logically or physically separate, or may not be physical units, and may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, the method steps, the implementation thereof, and the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

The above-described method and apparatus may be implemented as an integrated unit in the form of a software functional unit, which may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), an NVRAM, a magnetic disk, or an optical disk, and various media capable of storing program codes.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

It should be noted that: the above embodiments are only used to explain and illustrate the technical solution of the present invention more clearly, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An intelligent electric energy meter operation error data statistical processing system comprises the following modules:

the platform zone information of the object intelligent electric energy meter;

sending the integrated error check parameters to a feedback receiving end;

2. The statistical processing system for the operation error data of the intelligent electric energy meter according to claim 1, wherein the influence factor is dynamically adjusted by the system according to the satisfaction degree of the online error check result of the intelligent electric energy meter, or is preset by the system,

3. The statistical processing system for the operation error data of the intelligent electric energy meters as claimed in claim 1, wherein the anchoring values of the same type matrix are used for anchoring the clusters of the intelligent electric energy meters to which the target intelligent electric energy meters belong in the same type verification matrix, specifically:

4. The statistical processing system for the operation error data of the intelligent electric energy meter according to claim 3, characterized in that:

the homotype verification matrix is composed of a plurality of matrix elements, each matrix element corresponds to an intelligent electric energy meter type and is a quaternion value { A, X, C, V }, wherein A identifies the intelligent electric energy meter type, X identifies a homotype anchor value, C identifies a cluster identifier of the homotype anchor value, and V identifies a dynamic error mean value of the homotype anchor value and is used for determining the matrix elements belonging to the same cluster.

5. The statistical processing system for the operation error data of the intelligent electric energy meter according to claim 1, characterized in that:

the cluster identifier may be set in any one of the following manners:

clustering according to the same delivery model;

clustering according to the distribution of the no-load threshold interval;

and clustering according to the phase lines.

6. A statistical processing method for operation error data of an intelligent electric energy meter comprises the following steps:

an initialization step:

the identity of the subject smart energy meter,

the platform zone information of the object intelligent electric energy meter;

the same type matrix anchoring value of the object intelligent electric energy meter is used for anchoring the intelligent electric energy meter cluster to which the object intelligent electric energy meter belongs in the same type verification matrix.

7. The statistical processing method for the operation error data of the intelligent electric energy meter as claimed in claim 6, wherein the influence factor is dynamically adjusted by the system according to the satisfaction degree of the online error check result of the intelligent electric energy meter, or is preset by the system,

8. The statistical processing method for the operation error data of the intelligent electric energy meter according to claim 6, wherein the anchoring value of the same type matrix is used for anchoring the intelligent electric energy meter cluster to which the target intelligent electric energy meter belongs in the same type verification matrix, and specifically comprises the following steps:

9. The statistical processing method for the operation error data of the intelligent electric energy meter as claimed in claim 8, characterized in that:

10. The statistical processing method for the operation error data of the intelligent electric energy meter as claimed in claim 6, characterized in that:

the cluster identifier may be set in any one of the following manners:

clustering according to the same delivery model;

clustering according to the distribution of the no-load threshold interval;

and clustering according to the phase line distribution.