Data preprocessing method and system for electric energy meter operation error monitoring model

文档序号：734175 发布日期：2021-04-20 浏览：6次中文

阅读说明：本技术 电能表运行误差监测模型的数据预处理方法及系统 (Data preprocessing method and system for electric energy meter operation error monitoring model ) 是由周玉张德进陈霄蔡奇新邵雪松王黎明崔高颖李悦季欣荣徐鸣飞于 2020-11-20 设计创作，主要内容包括：本申请实施例提供一种电能表运行误差监测模型的数据预处理方法及系统,通过对每个业务处理终端的台区参数的误差影响指标进行分析,并根据目标误差影响指标的历史台区误差影响参数,确定出目标误差影响指标对应的计量误差数据列表,由此可以向电能表运行装置请求更新计量误差数据列表对应的更新计量节点,提高各种误差更新对象的应用范围,并且还可以进一步根据电能表运行装置从计量误差数据列表对应的更新计量节点中选择的目标更新计量节点,对下一次向电能表运行装置下发的电能表运行程序进行更新,这样通过闭环反馈的方式可以不断提高分析的台区参数与实际电能表信息的匹配度,进而不断优化后续产生的更新计量节点。(The embodiment of the application provides a data preprocessing method and a data preprocessing system for an electric energy meter operation error monitoring model, which analyze error influence indexes of station area parameters of each service processing terminal, determine a metering error data list corresponding to a target error influence index according to historical station area error influence parameters of the target error influence index, request an electric energy meter operation device to update an updated metering node corresponding to the metering error data list, improve the application range of various error updating objects, further update a metering node according to a target selected by the electric energy meter operation device from the updated metering node corresponding to the metering error data list, update an electric energy meter operation program issued to the electric energy meter operation device next time, and continuously improve the matching degree of the analyzed station area parameters and actual electric energy meter information in a closed-loop feedback mode, and then continuously optimizing the updated metering nodes generated subsequently.)

1. A data preprocessing method of an electric energy meter operation error monitoring model is applied to a server, the server is in communication connection with a plurality of electric energy meter operation devices, and the method comprises the following steps:

receiving a platform area parameter aiming at an electric energy meter running program sent by the electric energy meter running device, and extracting a target error influence parameter of the platform area parameter according to an electric energy meter running error monitoring model; wherein the target error influence parameter comprises a target error influence index;

determining a metering error data list corresponding to the target error influence index according to a historical station area error influence parameter of the target error influence index, wherein the historical station area error influence parameter is obtained by performing deep analysis on a periodic station area parameter list generated by an electric energy meter operation device under a historical metering error node and the target error influence index corresponding to the periodic station area parameter list by the server;

requesting an updating metering node corresponding to the metering error data list from the electric energy meter running device according to the metering error data list corresponding to the target error influence index;

and updating the electric energy meter running program issued to the electric energy meter running device next time according to the target updated metering node selected by the electric energy meter running device from the updated metering nodes corresponding to the metering error data list.

2. The data preprocessing method for the electric energy meter operation error monitoring model according to claim 1, wherein the step of determining the metering error data list corresponding to the target error influence index according to the historical station area error influence parameters of the target error influence index comprises:

acquiring parameter degradation action quantity and distribution parameter values of a degradation track model of the parameter degradation action quantity from historical station area error influence parameters of the target error influence index, wherein the distribution parameter values of the degradation track model represent a distribution data state corresponding to each parameter degradation action node combination in the parameter degradation action quantity;

processing the parameter degradation action quantity according to the distribution parameter value of the degradation track model to generate degradation relation attribute information of the parameter degradation action quantity;

extracting error characteristic parameters of the parameter degradation action quantity and the degradation relation attribute information, and determining a second error gradient coding set corresponding to a first error gradient coding set corresponding to the degradation relation attribute information from the extracted current error characteristic parameter information;

performing feature fusion on the first error gradient coding set and the second error gradient coding set to obtain a third error gradient coding set;

and outputting a target metering error data list corresponding to the parameter degradation action amount according to the third error gradient coding set.

3. The data preprocessing method for the electric energy meter operation error monitoring model according to claim 1, wherein the step of processing the parameter degradation acting quantity according to the distribution parameter value of the degradation trajectory model to generate the degradation relationship attribute information of the parameter degradation acting quantity comprises:

extracting error characteristic parameters of the parameter degradation action quantity, identifying error track characteristics of a first error characteristic parameter corresponding to the obtained parameter degradation action quantity, and obtaining a first fluctuation loss relation set corresponding to the parameter degradation action quantity according to the identified error track characteristics;

extracting error characteristic parameters of the distribution parameter values of the degraded track model, identifying error track characteristics of second error characteristic parameters corresponding to the obtained distribution parameter values of the degraded track model, and obtaining a second fluctuation loss relation set corresponding to the distribution parameter values of the degraded track model according to the identified error track characteristics;

acquiring first fluctuation loss development distribution information stored in the first fluctuation loss relation set, and converting the first fluctuation loss development distribution information into corresponding first fluctuation loss development characterization characteristics;

acquiring second fluctuation loss development distribution information respectively stored by a plurality of fluctuation loss relation objects in the second fluctuation loss relation set, and converting each second fluctuation loss development distribution information into a corresponding second fluctuation loss development characterization feature;

calculating a fusion characteristic feature of each second fluctuation loss development characteristic feature and the first fluctuation loss development characteristic feature;

sorting the fusion characterization features corresponding to each second fluctuation loss development characterization feature, and selecting a plurality of similar fluctuation loss development characterization features from the second fluctuation loss development characterization features according to a sorting result;

performing LM algorithm processing on the similar fluctuation loss development characterization features to obtain LM algorithm feature vectors;

performing regression model feature calculation on the loss feature vectors of the first fluctuation loss relation set and the second fluctuation loss relation set, and obtaining a loss characterization parameter vector according to the regression model feature obtained through calculation; the loss characterization parameter vector comprises influence parameters corresponding to all the fluctuation loss relation objects in the second fluctuation loss relation set;

calculating a fusion feature vector of the LM algorithm feature vector and the loss characterization parameter vector, and taking a calculated result as a fluctuation loss development line increment of the first fluctuation loss development distribution information;

mapping the increment of the fluctuation loss development line to fitting loss influence data set in the parameter degradation action amount to obtain an initial loss mapping platform area error influence parameter;

carrying out error track characteristic identification on the initial loss mapping zone error influence parameters to obtain reference error track characteristics;

and obtaining the degradation relation attribute information corresponding to the parameter degradation action according to the first fluctuation loss relation set, the second fluctuation loss relation set and the reference error track characteristic.

4. The data preprocessing method for the electric energy meter operation error monitoring model according to claim 3, wherein the step of obtaining the degradation relation attribute information corresponding to the parameter degradation action amount according to the first fluctuation loss relation set, the second fluctuation loss relation set and the reference error trajectory characteristics comprises:

mapping the first fluctuation loss relation set and the second fluctuation loss relation set to each dynamic track node in the reference error track characteristic respectively to obtain mapping attribute information of each dynamic track node corresponding to the first fluctuation loss relation set and the second fluctuation loss relation set respectively;

and summarizing the mapping attribute information of each dynamic track node corresponding to the first fluctuation loss relation set and the second fluctuation loss relation set respectively to obtain the degradation relation attribute information corresponding to the parameter degradation action.

5. The data preprocessing method for the electric energy meter operation error monitoring model according to claim 2, wherein the step of performing error feature parameter extraction on the parameter degradation action amount and the degradation relation attribute information, and determining a second error gradient coding set corresponding to a first error gradient coding set corresponding to the degradation relation attribute information from the extracted current error feature parameter information comprises:

extracting error characteristic parameters from the parameter degradation action quantity and the degradation relation attribute information to obtain current error characteristic parameter information mapped in the error characteristic parameters of the parameter degradation action quantity and the degradation relation attribute information; the current error characteristic parameter information comprises line increment information of a plurality of error characteristic elements;

and determining similar line increment information of the first error gradient coding set from line increment information of a plurality of error characteristic elements contained in the current error characteristic parameter information, and using the similar line increment information as the second error gradient coding set.

6. The method for preprocessing the data of the electric energy meter operation error monitoring model according to claim 2, wherein the step of performing feature fusion on the first error gradient code set and the second error gradient code set to obtain a third error gradient code set comprises:

inputting the first error gradient coding set and the second error gradient coding set into a preset deep learning network respectively, so that the deep learning network outputs respective prediction error gradient coding sets of the first error gradient coding set and the second error gradient coding set respectively to obtain a first target error gradient coding set and a second target error gradient coding set;

performing LM algorithm calculation on the first target error gradient coding set to obtain first subject LM algorithm calculation information; extracting error characteristic parameters of the first target error gradient coding set, performing LM algorithm calculation on the extracted error gradient coding set to obtain second subject LM algorithm calculation information, and calculating fusion calculation information of the first subject LM algorithm calculation information and the second subject LM algorithm calculation information to obtain a first prediction coding characteristic set corresponding to the first target error gradient coding set;

performing LM algorithm calculation on the second target error gradient coding set to obtain LM algorithm calculation information of a third subject; extracting error characteristic parameters of the second target error gradient coding set, performing LM algorithm calculation on the extracted error gradient coding set to obtain fourth subject LM algorithm calculation information, and calculating fusion calculation information of the third subject LM algorithm calculation information and the fourth subject LM algorithm calculation information to obtain a second prediction coding characteristic set corresponding to the second target error gradient coding set;

and calculating a fusion feature set of the first prediction coding feature set and the second prediction coding feature set, and taking the obtained fusion feature set as the third error gradient coding set.

7. The method for preprocessing the data of the electric energy meter operation error monitoring model as claimed in claim 2, wherein the step of outputting the target metering error data list corresponding to the parameter degradation action amount according to the third error gradient coding set comprises:

acquiring degradation nodes of a plurality of error characteristic parameters in the third error gradient coding set and a depth analysis strategy corresponding to the degradation node combination of each error characteristic parameter, wherein the degradation nodes of the error characteristic parameters comprise the degradation nodes of a first error characteristic parameter and the degradation nodes of a second error characteristic parameter, and the degradation nodes of the first error characteristic parameter and the degradation nodes of the second error characteristic parameter are the degradation node combination of the error characteristic parameters with mapping correlation between the degradation nodes of the first error characteristic parameter and the degradation nodes of the second error characteristic parameter;

performing data mining on the third error gradient code set to output a first reference data mining target corresponding to the degradation action node combination of each first error characteristic parameter and a target data mining target corresponding to the third error gradient code set;

calculating the association degree between the target data mining target and each first reference data mining target to obtain data mining target similar parameters between the degradation action node of each corresponding first error characteristic parameter and the third error gradient coding set;

identifying all degradation data sections in the degradation node of each first error characteristic parameter and calculating regression model characteristics corresponding to the degradation node of each first error characteristic parameter;

generating degradation action distribution information of degradation action nodes of corresponding error characteristic parameters according to all the degradation action data segments and the regression model characteristics;

generating an operation error label corresponding to the degradation action node combination of each error characteristic parameter according to the depth analysis strategy;

obtaining respective first action clusters of the degradation action nodes of each error characteristic parameter by using each piece of degradation action distribution information corresponding to the operation error label;

clustering according to the degradation action node combination of the second error characteristic parameter and the first reference data mining target corresponding to the degradation action node combination of each error characteristic parameter to obtain a second action cluster of the degradation action node of each error characteristic parameter;

determining a degradation action node of the target error characteristic parameter from the degradation action nodes of the plurality of error characteristic parameters according to the first action cluster and the second action cluster;

and combining the degradation action nodes of the target error characteristic parameters with corresponding first reference data mining targets as second data mining targets, and mapping the second data mining targets to the third error gradient coding set to output a target metering error data list corresponding to the parameter degradation action.

8. The data preprocessing method for the electric energy meter operation error monitoring model according to claim 7, wherein the deep analysis strategy comprises a degradation analysis coding clustering strategy for degradation action nodes of the error characteristic parameters and a coding parameter value clustering strategy corresponding to the degradation action node combination of the error characteristic parameters;

the step of generating the operation error label corresponding to the degradation action node combination of each error characteristic parameter according to the depth analysis strategy comprises the following steps:

carrying out coding parameter value clustering on the degradation action nodes of the error characteristic parameters according to the coding parameter value clustering strategy, and generating metering error data list coding parameter values corresponding to the degradation action node combinations of the error characteristic parameters;

carrying out degradation analysis coding clustering on the degradation action nodes of the error characteristic parameters according to the degradation analysis coding clustering strategy to generate degradation analysis coding clustering results corresponding to the degradation action node combinations of the error characteristic parameters;

and generating an operation error label of a degradation action node corresponding to the error characteristic parameter according to the metering error data list coding parameter value and the degradation analysis coding clustering result.

9. The method for preprocessing the data of the electric energy meter operation error monitoring model according to any one of claims 1-8, characterized in that the method further comprises:

acquiring historical station area error influence parameters of the target error influence indexes;

the step of obtaining the historical station area error influence parameters of the target error influence indexes comprises the following steps:

acquiring a periodic transformer area parameter list generated by the electric energy meter running device under a historical metering error node and a target error influence index corresponding to the periodic transformer area parameter list from the electric energy meter running device;

acquiring error correction parameters of the error correction table items corresponding to the electric energy meter running program under the target error influence indexes, classifying the error correction parameters under the target error influence indexes according to preset target error correction categories, and respectively generating an error correction parameter set of each target error correction category;

acquiring associated parameter data of each error correction parameter in an error correction parameter set of each target error correction category, which is matched with the periodic table area parameter list, aiming at each target error correction category, and performing deep analysis on the associated parameter data list of each target error correction category based on the request update content characteristics corresponding to the target error correction category to obtain historical table area error influence parameters of each target error correction category;

and obtaining historical district error influence parameters which mark the target error correction categories and are included by the target error influence indexes from the historical district error influence parameters of the target error correction categories.

10. The data preprocessing system of the electric energy meter operation error monitoring model is characterized by comprising a server and a plurality of electric energy meter operation devices in communication connection with the server;

the server is used for receiving the platform area parameters aiming at the electric energy meter running program sent by the electric energy meter running device and extracting target error influence parameters of the platform area parameters according to an electric energy meter running error monitoring model; wherein the target error influence parameter comprises a target error influence index;

the server is used for determining a metering error data list corresponding to the target error influence index according to a historical station area error influence parameter of the target error influence index, wherein the historical station area error influence parameter is obtained by performing deep analysis on a periodic station area parameter list generated by an electric energy meter operation device under a historical metering error node and the target error influence index corresponding to the periodic station area parameter list by the server;

the server is used for requesting the electric energy meter running device to update the updated metering node corresponding to the metering error data list;

and the server is used for updating the electric energy meter running program issued to the electric energy meter running device next time according to the target updated metering node selected by the electric energy meter running device from the updated metering nodes corresponding to the metering error data list.

Technical Field

The application relates to the technical field of electric energy meter operation error monitoring, in particular to a data preprocessing method and system of an electric energy meter operation error monitoring model.

Background

The accuracy of electric energy meter measurement is directly related to economic benefits of electric power enterprises and users, and research on electric energy meter measurement errors by electric power operation units for a long time mainly focuses on aspects such as subsequent maintenance and analysis of influence factors of the electric energy meter measurement errors. However, due to the limitations of data and analysis technologies, the work for predicting the metering error of the electric energy meter is still less, and in the related technologies, the error estimation work in the subsequent application process can be facilitated by predicting the error update object, so how to improve the application range of various error update objects, and how to continuously optimize the continuously generated updated metering nodes to the electric energy meter running program is an urgent technical problem to be solved.

Disclosure of Invention

In order to overcome the above-mentioned deficiencies in the prior art at least, the present application aims to provide a data preprocessing method and system for an electric energy meter operation error monitoring model, wherein an error influence index of a platform area parameter of each service processing terminal is analyzed, and a metering error data list corresponding to a target error influence index is determined according to a historical platform area error influence parameter of the target error influence index, so that an updated metering node corresponding to the metering error data list can be requested to be updated by an electric energy meter operation device, the application range of various error updating objects is increased, and an electric energy meter operation program issued to the electric energy meter operation device next time can be updated according to a target updated metering node selected by the electric energy meter operation device from the updated metering node corresponding to the metering error data list, so that the matching degree of the analyzed platform area parameter and the actual electric energy meter information can be continuously increased by a closed-loop feedback manner, and then continuously optimizing the updated metering nodes generated subsequently.

In a first aspect, the present application provides a data preprocessing method for an electric energy meter operation error monitoring model, which is applied to a server, where the server is in communication connection with a plurality of electric energy meter operation devices, and the method includes:

requesting the electric energy meter running device to update an updated metering node corresponding to the metering error data list;

In a possible implementation manner of the first aspect, the determining, according to the historical station area error influence parameter of the target error influence indicator, a metering error data list corresponding to the target error influence indicator includes:

performing feature fusion on the first error gradient coding set and the second error gradient coding set to obtain a third error gradient coding set;

and outputting a target metering error data list corresponding to the parameter degradation action amount according to the third error gradient coding set.

In a possible implementation manner of the first aspect, the step of processing the parametric degradation operation quantity according to the distribution parameter value of the degradation trajectory model to generate degradation relationship attribute information of the parametric degradation operation quantity includes:

calculating a fusion characteristic feature of each second fluctuation loss development characteristic feature and the first fluctuation loss development characteristic feature;

performing LM algorithm processing on the similar fluctuation loss development characterization features to obtain LM algorithm feature vectors;

carrying out error track characteristic identification on the initial loss mapping zone error influence parameters to obtain reference error track characteristics;

In a possible implementation manner of the first aspect, the step of obtaining the degradation relation attribute information corresponding to the parameter degradation acting quantity according to the first fluctuation loss relation set, the second fluctuation loss relation set, and the reference error trajectory feature includes:

In a possible implementation manner of the first aspect, the step of extracting error feature parameters from the parametric degradation amount and the degradation relation attribute information, and determining a second error gradient code set corresponding to a first error gradient code set corresponding to the degradation relation attribute information from the extracted current error feature parameter information includes:

In a possible implementation manner of the first aspect, the step of performing feature fusion on the first error gradient coding set and the second error gradient coding set to obtain a third error gradient coding set includes:

In a possible implementation manner of the first aspect, the step of outputting the target metering error data list corresponding to the parameter degradation operation amount according to the third error gradient coding set includes:

generating an operation error label corresponding to the degradation action node combination of each error characteristic parameter according to the depth analysis strategy;

In a possible implementation manner of the first aspect, the depth analysis policy includes a degradation analysis coding clustering policy on degradation nodes of the error characteristic parameters, and a coding parameter value clustering policy corresponding to a degradation node combination of the error characteristic parameters;

In a possible implementation manner of the first aspect, the method further includes:

acquiring historical station area error influence parameters of the target error influence indexes;

the step of obtaining the historical station area error influence parameters of the target error influence indexes comprises the following steps:

In a second aspect, an embodiment of the present application further provides a data preprocessing device for an electric energy meter operation error monitoring model, which is applied to a server, where the server is communicatively connected to a plurality of electric energy meter operation devices, and the device includes:

the receiving module is used for receiving the platform area parameters aiming at the electric energy meter running program sent by the electric energy meter running device and extracting target error influence parameters of the platform area parameters according to an electric energy meter running error monitoring model; wherein the target error influence parameter comprises a target error influence index;

the determining module is used for determining a metering error data list corresponding to the target error influence index according to a historical station area error influence parameter of the target error influence index, wherein the historical station area error influence parameter is obtained by performing deep analysis on a periodic station area parameter list generated by an electric energy meter operating device under a historical metering error node and the target error influence index corresponding to the periodic station area parameter list by the server;

the request updating module is used for requesting the electric energy meter running device to update the updated metering node corresponding to the metering error data list;

and the updating module is used for updating the electric energy meter running program issued to the electric energy meter running device next time according to the target updated metering node selected by the electric energy meter running device from the updated metering nodes corresponding to the metering error data list.

In a third aspect, an embodiment of the present application further provides a data preprocessing system for an electric energy meter operation error monitoring model, where the data preprocessing system for the electric energy meter operation error monitoring model includes a server and a plurality of electric energy meter operation devices in communication connection with the server;

the server is used for requesting the electric energy meter running device to update the updated metering node corresponding to the metering error data list;

In a fourth aspect, an embodiment of the present application further provides a server, where the server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one electric energy meter operating device, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform a data preprocessing method for an electric energy meter operating error monitoring model in the first aspect or any one of possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer is caused to execute a data preprocessing method for an electric energy meter operation error monitoring model in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the above aspects, by analyzing the error influence index of the platform area parameter of each service processing terminal, and determining a metering error data list corresponding to the target error influence index according to the historical station area error influence parameters of the target error influence index, therefore, the method can request the electric energy meter running device to update the updated metering nodes corresponding to the metering error data list, improve the application range of various error updating objects, and can further update the metering node according to a target selected by the electric energy meter running device from the updated metering nodes corresponding to the metering error data list, and updating the electric energy meter running program issued to the electric energy meter running device next time, so that the matching degree of the analyzed distribution room parameters and the actual electric energy meter information can be continuously improved in a closed-loop feedback mode, and further, the subsequently generated updated metering nodes are continuously optimized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is an application scenario diagram of a data preprocessing system of an electric energy meter operation error monitoring model according to an embodiment of the present application;

fig. 2 is a schematic flow chart of a data preprocessing method of an electric energy meter operation error monitoring model according to an embodiment of the present application;

fig. 3 is a functional module schematic diagram of a data preprocessing device of an electric energy meter operation error monitoring model according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a server for implementing the data preprocessing method for the electric energy meter operation error monitoring model according to the embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interactive schematic diagram of a data preprocessing system 10 of an electric energy meter operation error monitoring model according to an embodiment of the present application. The data preprocessing system 10 for the power meter operation error monitoring model can include a server 100 and a power meter operation device 200 communicatively connected to the server 100. The data preprocessing system 10 of the power meter operation error monitoring model shown in fig. 1 is only one possible example, and in other possible embodiments, the data preprocessing system 10 of the power meter operation error monitoring model may also include only a part of the components shown in fig. 1 or may also include other components.

In this embodiment, the server 100 and the electric energy meter operating device 200 in the data preprocessing system 10 for the electric energy meter operation error monitoring model may cooperatively execute the data preprocessing method for the electric energy meter operation error monitoring model described in the following method embodiment, and for the specific steps executed by the server 100 and the electric energy meter operating device 200, reference may be made to the detailed description of the following method embodiment.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of a data preprocessing method of an electric energy meter operation error monitoring model provided in this embodiment, where the data preprocessing method of the electric energy meter operation error monitoring model provided in this embodiment may be executed by the server 100 shown in fig. 1, and the data preprocessing method of the electric energy meter operation error monitoring model is described in detail below.

Step S110, receiving the platform area parameters aiming at the electric energy meter running program sent by the electric energy meter running device 200, and extracting target error influence parameters of the platform area parameters according to the electric energy meter running error monitoring model.

And step S120, determining a metering error data list corresponding to the target error influence index according to the historical station area error influence parameters of the target error influence index.

In step S130, an update metering node corresponding to the update metering error data list is requested from the electric energy meter operating device 200.

In this embodiment, the target error influence parameter may include, for example, a target error influence indicator, and the target error influence indicator may be used to indicate an indicator data segment having an error influence on the target power meter statistical process.

In this embodiment, the historical block error influence parameter may be a historical block error influence parameter obtained by performing deep analysis on a periodic block parameter list generated by the electric energy meter operating device 200 under the historical metering error node and a target error influence index corresponding to the periodic block parameter list by the server. The periodic cell parameter may refer to a statistical process for each periodic cell.

Based on the above steps, the present embodiment analyzes the error impact index of the station area parameter of each service processing terminal, and determining a metering error data list corresponding to the target error influence index according to the historical station area error influence parameters of the target error influence index, therefore, the method can request the electric energy meter running device to update the updated metering nodes corresponding to the metering error data list, improve the application range of various error updating objects, and can further update the metering node according to a target selected by the electric energy meter running device from the updated metering nodes corresponding to the metering error data list, and updating the electric energy meter running program issued to the electric energy meter running device next time, so that the matching degree of the analyzed distribution room parameters and the actual electric energy meter information can be continuously improved in a closed-loop feedback mode, and further, the subsequently generated updated metering nodes are continuously optimized.

In one possible implementation, step S120 may be implemented by the following exemplary substeps, which are described in detail below.

And a substep S121, obtaining parameter degradation action amount and distribution parameter values of a degradation track model of the parameter degradation action amount from historical station zone error influence parameters of the target error influence index, wherein the distribution parameter values of the degradation track model can represent a distribution data state corresponding to each parameter degradation action node combination in the parameter degradation action amount.

And a substep S122, processing the parameter degradation action quantity according to the distribution parameter value of the degradation track model, and generating degradation relation attribute information of the parameter degradation action quantity.

And a substep S123 of extracting error characteristic parameters from the parameter degradation action quantity and the degradation relation attribute information, and determining a second error gradient code set corresponding to the first error gradient code set corresponding to the degradation relation attribute information from the extracted current error characteristic parameter information.

And a substep S124, performing feature fusion on the first error gradient coding set and the second error gradient coding set to obtain a third error gradient coding set.

And a substep S125, outputting a target metering error data list corresponding to the parameter degradation action amount according to the third error gradient coding set.

Exemplarily, in the sub-step S122, it can be realized by the following specific embodiments.

(1) And extracting error characteristic parameters of the parameter degradation action quantity, identifying error track characteristics of a first error characteristic parameter corresponding to the obtained parameter degradation action quantity, and obtaining a first fluctuation loss relation set corresponding to the parameter degradation action quantity according to the identified error track characteristics.

(2) And extracting error characteristic parameters of the distribution parameter values of the degraded track model, identifying error track characteristics of second error characteristic parameters corresponding to the obtained distribution parameter values of the degraded track model, and obtaining a second fluctuation loss relation set corresponding to the distribution parameter values of the degraded track model according to the identified error track characteristics.

(3) And obtaining first fluctuation loss development distribution information stored in the first fluctuation loss relation set, and converting the first fluctuation loss development distribution information into corresponding first fluctuation loss development characterization characteristics.

(4) And obtaining second fluctuation loss development distribution information which is respectively stored by a plurality of fluctuation loss relation objects in a second fluctuation loss relation set, and converting each second fluctuation loss development distribution information into a corresponding second fluctuation loss development characterization feature.

(5) And calculating a fusion characteristic of each second fluctuation loss development characteristic and the first fluctuation loss development characteristic.

(6) And sequencing the fusion characterization features corresponding to each second fluctuation loss development characterization feature, and selecting a plurality of similar fluctuation loss development characterization features from the plurality of second fluctuation loss development characterization features according to the sequencing result.

(7) And performing LM algorithm processing on the plurality of similar fluctuation loss development characterization features to obtain LM algorithm feature vectors.

(8) And performing regression model feature calculation on the loss feature vectors of the first fluctuation loss relation set and the second fluctuation loss relation set, and obtaining a loss characterization parameter vector according to the regression model features obtained through calculation. The loss characterization parameter vector includes an influence parameter corresponding to each fluctuation loss relation object in the second fluctuation loss relation set.

(9) And calculating a fusion feature vector of the LM algorithm feature vector and the loss characterization parameter vector, and taking a calculated result as the fluctuation loss development line increment of the first fluctuation loss development distribution information.

(10) And mapping the increment of the fluctuation loss development line to fitting loss influence data set in the parameter degradation action amount to obtain an initial loss mapping platform area error influence parameter.

(11) And carrying out error track characteristic identification on the initial loss mapping platform area error influence parameters to obtain reference error track characteristics.

(12) And obtaining degradation relation attribute information corresponding to the parameter degradation action amount according to the first fluctuation loss relation set, the second fluctuation loss relation set and the reference error track characteristics.

Exemplarily, in the sub-step S123, it can be realized by the following specific embodiments.

(1) And extracting error characteristic parameters of the parameter degradation action quantity and the degradation relation attribute information to obtain current error characteristic parameter information mapped in the error characteristic parameters of the parameter degradation action quantity and the degradation relation attribute information.

In this embodiment, the current error characteristic parameter information includes line increment information of a plurality of error characteristic elements.

(2) And determining similar line increment information of the first error gradient coding set from the line increment information of a plurality of error characteristic elements contained in the current error characteristic parameter information, and taking the similar line increment information as a second error gradient coding set.

Exemplarily, in the sub-step S124, it can be realized by the following specific embodiments.

(1) And respectively inputting the first error gradient coding set and the second error gradient coding set into a preset deep learning network, so that the deep learning network respectively outputs the respective prediction error gradient coding sets of the first error gradient coding set and the second error gradient coding set to obtain a first target error gradient coding set and a second target error gradient coding set.

(2) And performing LM algorithm calculation on the first target error gradient coding set to obtain LM algorithm calculation information of a first subject. And extracting error characteristic parameters of the first target error gradient coding set, performing LM algorithm calculation on the extracted error gradient coding set to obtain second subject LM algorithm calculation information, calculating fusion calculation information of the first subject LM algorithm calculation information and the second subject LM algorithm calculation information, and obtaining a first prediction coding characteristic set corresponding to the first target error gradient coding set.

(3) And performing LM algorithm calculation on the second target error gradient coding set to obtain LM algorithm calculation information of a third subject. And extracting error characteristic parameters of the second target error gradient coding set, performing LM algorithm calculation on the extracted error gradient coding set to obtain fourth subject LM algorithm calculation information, calculating fusion calculation information of the third subject LM algorithm calculation information and the fourth subject LM algorithm calculation information, and obtaining a second prediction coding characteristic set corresponding to the second target error gradient coding set.

(4) And calculating a fusion feature set of the first prediction coding feature set and the second prediction coding feature set, and taking the obtained fusion feature set as a third error gradient coding set.

Exemplarily, in the sub-step S125, it can be realized by the following specific embodiments.

(1) And acquiring degradation nodes of a plurality of error characteristic parameters in the third error gradient coding set and a depth analysis strategy corresponding to the degradation node combination of each error characteristic parameter, wherein the degradation nodes of the error characteristic parameters comprise the degradation nodes of the first error characteristic parameters and the degradation nodes of the second error characteristic parameters, and the degradation nodes of the first error characteristic parameters and the degradation nodes of the second error characteristic parameters are the degradation node combination of the error characteristic parameters which are in mapping association with each other.

(2) And performing data mining on the third error gradient coding set to output a first reference data mining target corresponding to the degradation action node combination of each first error characteristic parameter and a target data mining target corresponding to the third error gradient coding set.

(3) And calculating the association degree between the target data mining target and each first reference data mining target so as to obtain the data mining target similar parameters between the degradation action node of each corresponding first error characteristic parameter and the third error gradient coding set.

(4) All degradation data segments in the degradation node of each first error characteristic parameter are identified, and regression model characteristics corresponding to the degradation node of each first error characteristic parameter are calculated.

(5) And generating degradation action distribution information of the degradation action nodes of the corresponding error characteristic parameters according to all the degradation action data segments and the regression model characteristics.

(6) And generating a running error label corresponding to the degradation action node combination of each error characteristic parameter according to a depth analysis strategy.

As an example, the depth analysis strategy may include a degradation analysis coding clustering strategy for degradation action nodes of the error characteristic parameters, and a coding parameter value clustering strategy corresponding to the degradation action node combination of the error characteristic parameters. Based on the method, the coding parameter value clustering can be carried out on the degradation action nodes of the error characteristic parameters according to the coding parameter value clustering strategy, the metering error data list coding parameter values corresponding to the degradation action node combinations of the error characteristic parameters are generated, then the degradation analysis coding clustering is carried out on the degradation action nodes of the error characteristic parameters according to the degradation analysis coding clustering strategy, the degradation analysis coding clustering results corresponding to the degradation action node combinations of the error characteristic parameters are generated, and therefore the operation error labels of the degradation action nodes corresponding to the error characteristic parameters can be generated according to the metering error data list coding parameter values and the degradation analysis coding clustering results.

(7) And obtaining respective first action clusters of the degradation action nodes of each error characteristic parameter by using each piece of degradation action distribution information corresponding to the operation error label.

(8) And clustering according to the degradation action node combination of the second error characteristic parameter and the first reference data mining target corresponding to the degradation action node combination of each error characteristic parameter to obtain a second action cluster of the degradation action node of each error characteristic parameter.

(9) And determining the degradation action node of the target error characteristic parameter from the degradation action nodes of the plurality of error characteristic parameters according to the first action cluster and the second action cluster.

(10) And combining the degradation action nodes of the target error characteristic parameters with the corresponding first reference data mining targets as second data mining targets, and mapping the second data mining targets to a third error gradient coding set to output a target metering error data list corresponding to the parameter degradation action.

Exemplarily, the degeneration nodes of the error characteristic parameters further include a degeneration node of a third error characteristic parameter, before (7), the mapping translation parameters of each first reference data mining target relative to each reference data mining target in the reference data mining target set corresponding to the degeneration node of the third error characteristic parameter may be further calculated, and then all the mapping translation parameters corresponding to each first reference data mining target are fused to obtain the fusion mapping parameters corresponding to each first reference data mining target.

In this reference, all the first reference data mining targets may be arranged in sequence according to the fusion mapping parameter corresponding to each first reference data mining target, the priority parameter of each first reference data mining target is determined according to the sequence after the arrangement of each first reference data mining target, and then the fusion mapping parameter corresponding to each first reference data mining target is processed according to the priority parameter of each first reference data mining target, so as to generate the weighted fusion mapping parameter of the degradation action node of each error characteristic parameter.

Thus, in step (7), the weighted fusion mapping parameters of the degradation function nodes of each error characteristic parameter can be clustered by using each degradation function distribution information corresponding to the operation error label, and a first function cluster corresponding to the degradation function node combination of each error characteristic parameter is obtained.

In a possible implementation manner, before step S120, the data preprocessing method for the electric energy meter operation error monitoring model provided in this embodiment may further include step S101 of obtaining a historical platform area error influence parameter of the target error influence index.

For example, step S101 may be specifically realized by the following substeps.

In the substep S1011, a periodic cell parameter list generated by each electric energy meter operating device 200 under the historical metering error node and a target error influence index corresponding to the periodic cell parameter list are obtained from each electric energy meter operating device 200.

Step S1012, obtaining error correction parameters of the error correction table entry corresponding to the electric energy meter running program under the target error influence index, classifying the error correction parameters under each target error influence index according to a predetermined target error correction category, and generating an error correction parameter set of each target error correction category respectively.

The error correction parameter may be used to indicate a content statistical label of a specific application of the error correction table entry corresponding to the electric energy meter running program under the target error influence index of each periodic table area parameter.

In this embodiment, the predetermined target error correction category may be flexibly selected according to actual design requirements.

Step S1013, for each target error correction category, obtaining associated parameter data of each error correction parameter in the error correction parameter set of the target error correction category, which is matched with the periodic table area parameter list, and performing deep analysis on the associated parameter data list of each target error correction category based on the request update content feature corresponding to the target error correction category to obtain a historical table area error influence parameter of each target error correction category.

Step S1014 obtains, from the historical station area error influence parameters of each target error correction category, the historical station area error influence parameters that mark the target error correction category and are included in the target error influence index.

Based on the above steps, in this embodiment, by considering the error correction parameters of the error correction table entry corresponding to the electric energy meter running program under the target error influence indexes corresponding to the periodic station area parameter list, and then classifying the error correction parameters under each target error influence index based on the predetermined target error correction category, the difference between different target error influence indexes and target error correction categories is considered, so that the depth analysis is performed on the associated parameter data list of each target error correction category based on the request update content characteristic corresponding to the target error correction category, and the accuracy of the depth analysis can be predicted to be improved, so that the depth analysis result can be more matched with the actual service scene.

Fig. 3 is a schematic functional module diagram of a data preprocessing device 300 of an electric energy meter operation error monitoring model according to an embodiment of the present disclosure, and in this embodiment, functional modules of the data preprocessing device 300 of the electric energy meter operation error monitoring model may be divided according to a method embodiment executed by the server 100, that is, the following functional modules corresponding to the data preprocessing device 300 of the electric energy meter operation error monitoring model may be used to execute each method embodiment executed by the server 100. The data preprocessing device 300 of the power meter operation error monitoring model may include a receiving module 310, a determining module 320, an update requesting module 330, and an updating module 340, where functions of the functional modules of the data preprocessing device 300 of the power meter operation error monitoring model are described in detail below.

A receiving module 310, configured to receive a distribution room parameter for an electric energy meter running program sent by the electric energy meter running device 200, and extract a target error influence parameter of the distribution room parameter according to an electric energy meter running error monitoring model; wherein the target error impact parameter comprises a target error impact indicator. The receiving module 310 may be configured to perform the step S110, and the detailed implementation of the receiving module 310 may refer to the detailed description of the step S110.

The determining module 320 is configured to determine, according to a historical block error influence parameter of the target error influence index, a metering error data list corresponding to the target error influence index, where the historical block error influence parameter is obtained by performing deep analysis on a periodic block parameter list generated by the electric energy meter operating device 200 under a historical metering error node and the target error influence index corresponding to the periodic block parameter list by the server. The determining module 320 may be configured to perform the step S120, and the detailed implementation of the determining module 320 may refer to the detailed description of the step S120.

A request updating module 330, configured to request the electric energy meter operating device 200 to update the updated metering node corresponding to the metering error data list. The request update module 330 may be configured to execute the step S130, and the detailed implementation manner of the request update module 330 may refer to the detailed description of the step S130.

The updating module 340 is configured to update an electric energy meter running program issued to the electric energy meter running device next time according to a target updated metering node selected by the electric energy meter running device from the updated metering nodes corresponding to the metering error data list. The updating module 340 may be configured to perform the step S140, and the detailed implementation of the updating module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the receiving module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the receiving module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 is a schematic diagram illustrating a hardware structure of a server 100 for implementing the data preprocessing method for the electric energy meter operation error monitoring model according to the embodiment of the present disclosure, and as shown in fig. 4, the server 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the receiving module 310, the determining module 320, the request updating module 330, and the updating module 340 included in the data preprocessing device 300 for the power meter operation error monitoring model shown in fig. 3), so that the processor 110 can execute the data preprocessing method for the power meter operation error monitoring model according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected via the bus 130, and the processor 110 can be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the power meter operation device 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the server 100, which implement similar principles and technical effects, and this embodiment is not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the application also provides a readable storage medium, wherein the readable storage medium stores a computer execution instruction, and when a processor executes the computer execution instruction, the data preprocessing method of the electric energy meter operation error monitoring model is realized.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, or as a stand-alone software package on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or digital financial services terminal. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and lists are processed, the use of alphanumeric characters, or other designations in this specification is not intended to limit the order in which the processes and methods of this specification are performed, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented through interactive services, they may also be implemented through software-only solutions, such as installing the described system on an existing digital financial services terminal or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

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Data preprocessing method and system for electric energy meter operation error monitoring model

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