CN110609249A - A metering anomaly analysis and processing system based on information collected by electric energy meters - Google Patents

Abstract

The invention relates to a measurement abnormality analysis and processing system based on information collected by electric energy meters, which is characterized in that it includes a data management module, establishment of a measurement abnormality analysis model, a generation and management module of measurement abnormality work orders, and an on-site mobile operation processing module. The present invention realizes automatic diagnosis of measurement abnormality, generation of work orders, and online full-process closed-loop management of work order processing; realizes accurate analysis of causes of measurement abnormalities, reduces invalid work orders and on-site inspection times, and saves a lot of manpower and material costs; The whole process processing method improves the level of refinement of management, innovates the working mode, and effectively standardizes the work process and quality control.

Description

A metering anomaly analysis and processing system based on information collected by electric energy meters

technical field

The invention belongs to the field of data collection and analysis of electric energy meters, and relates to a metering abnormality analysis and processing system based on information collected by electric energy meters.

Background technique

At the end of 2014, the electricity consumption information collection system covered by the national network has achieved full coverage of all power users and gateways, and realized online monitoring of metering devices and real-time collection of important information such as user load, electricity, and voltage, which can be timely, complete, and accurate. Provide basic data for advanced analysis and auxiliary decision-making research of related systems, and provide a solid information foundation for realizing intelligent two-way interaction of electric energy meters.

The current status of the large number of low-voltage station areas and the uneven construction situation makes the station area face many urgent problems in the process of improving the management level, which are mainly reflected in the following three aspects.

(1) Affected by both the energy supply side and the energy consumption side, there is a lack of scientific and comprehensive evaluation methods for the actual operation status of the smart electric energy meter in the low-voltage station area;

(2) The current online monitoring function for metering abnormalities is not accurate enough for analysis, and a large number of work orders are still generated.

(3) The operation and maintenance dispatch of smart energy meters in the low-voltage station area mainly depends on manual labor, and there is a contradiction between operation and maintenance resources and operation and maintenance requirements.

The above problems are directly related to the actual interests of users and the operating income of State Grid Corporation. Taking the State Grid Tianjin Electric Power Company as an example, the current low-voltage station area has more than 5.8 million access users. On-site troubleshooting of measurement abnormalities means a huge workload and a serious waste of power resources.

By searching the published patent documents, no published patent documents identical to the present patent application have been found.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies of the prior art, and propose a measurement abnormality analysis and processing system based on the information collected by the electric energy meter.

The present invention solves its technical problem and realizes through the following technical solutions:

A measurement abnormality analysis and processing system based on information collected by electric energy meters, characterized in that it includes a data governance module, establishment of a measurement abnormality analysis model, a generation and management module of measurement abnormality work orders, and a field mobile operation processing module;

Data governance module, including traceability governance of historical electricity data and data governance of marketing business application system;

Establish a measurement anomaly analysis model, and perform automatic diagnosis of measurement anomalies on the curated data;

The generation and management module of abnormal measurement work orders generates abnormal measurement work orders based on the diagnosis results, and performs closed-loop management on the abnormal measurement work orders;

On-site mobile operation processing module: use the mobile operation terminal APP to realize on-site order receipt, work order download, and abnormal feedback of abnormal smart meter operation errors, and support the input of error measurement data and user power load conditions of on-site general meters and user meters, and support On-site photo uploading, support for initiation of electricity consumption inspection, metering device failure process, and support for measurement result feedback of metering peripherals.

Moreover, the traceability management of the historical power data is aimed at situations such as user change of meter, file change, and station-to-house relationship change. Measurement anomaly analysis.

Moreover, the data management of the marketing business application system is as follows: one is to carry out integrity management of historical archives such as station areas, metering points, metering devices, and collection terminals; The relevance of the information supports the restoration of the power data information of the replaced meter, and realizes the traceability of the station area information before and after the change of the electric energy meter household substation file; the third is to continue to promote the governance of the substation area household substation relationship.

Moreover, the establishment of the measurement anomaly analysis model includes:

(1) Optimizing the analysis model of the unevenness of the electric energy representation value:

Increase the importance level diagnosis model of abnormality handling, eliminate the reversal of the power value, and further sort out the reasons for the abnormality of the power value.

The diagnosis model of exception handling importance level is:

(forward/reverse total active energy indication value-∑(forward/reverse active energy each tariff electric energy indication value)|>tariff number*K

The K value in the formula is managed hierarchically according to different thresholds and can be adjusted dynamically;

(2) Optimize the analysis model of the electric energy meter flying away:

Increase the importance level of exception handling, and establish a diagnostic model for electric energy meters flying away:

Daily electricity/daily maximum theoretical electricity>K

The K value in the formula is managed hierarchically according to different thresholds and can be adjusted dynamically;

(3) Optimizing the analysis model of the electric energy meter going backwards:

According to the different calculation methods of the daily maximum theoretical power of different meter types, combined with different user types, different meter types are distinguished, and the power indication value reversal and meter replacement judgment methods are designed in the calculation model, and the power indication value is excluded in the abnormal diagnosis method In the case of turning over and changing the meter, sort out the causes of the abnormality of the electric energy meter, and provide support for the construction of the abnormal measurement knowledge base;

(4) Optimize the analysis model of the electric energy meter stop and go:

According to different users, increase the diagnosis method of the power meter stop and sort out the abnormal reasons of the power meter stop:

Specific user diagnosis method: use the electric energy meter for 3 consecutive days, and the difference between the forward and reverse active total electric energy indications is equal to 0, and the total active power monitored during this period has 3 consecutive full-point values (any three points) greater than K , as a diagnostic method;

The low-voltage user diagnosis method based on the object-oriented protocol: 1) the difference between the positive and negative active total electric energy indications of the electric energy meter within N days is equal to 0; The point value is greater than K, as a diagnostic method;

Diagnosis method for low-voltage users with four-point collection: 1) The difference between the daily forward/reverse active total electric energy indication values of the electric energy meter within N days is equal to 0; 2) During the period that meets condition 1, there are 3 values of active power greater than K , as a diagnostic method;

(5) Optimizing the analysis model of reverse power abnormality:

Set the reverse power threshold of the electric energy meter, determine the importance level of abnormal handling, and design the reverse power abnormal diagnosis method according to different task configuration conditions, specifically:

For low-voltage users who have configured reverse power collection tasks, according to the indication value of the total reverse active power of the electric energy meter is greater than 0, the reverse electric power of the electric energy meter on the day is greater than the threshold K, and the total reverse active power of low-voltage users > the total forward active power *The conditions of Q3 are judged;

For the low-voltage users who do not have the task of collecting reverse power collection, increase the power meter’s reverse active total electric energy display value twice a month to judge, and within 7 days after the meter is replaced, the forward active total electric energy display value will be reported normally within 2 days. Initiate a call-through operation for judgment;

In the calculation model, a power generation user judgment method is designed to exclude the case that the nature of electricity consumption is a power generation user; the causes of abnormal reverse power consumption are sorted out and incorporated into the algorithm model to provide support for the construction of a measurement anomaly knowledge base.

Moreover, according to the power consumption characteristics of low-voltage users, the judgment cycle of power meter stoppage is appropriately relaxed, and can be adjusted dynamically; in view of low-voltage users who are prone to stoppage after new installation or meter replacement, a new installation is added to the diagnosis method of low-voltage users. Or the judgment method of triggering the stop of the electric energy meter after the meter is changed, in order to detect the abnormality of the stop and go in time and reduce the false report.

The measurement abnormality automatic diagnosis module includes:

(1) Automated diagnosis of abnormal electric energy indication value:

Based on the abnormal algorithm model of the unevenness of the electric energy value, the master station introduces the automatic diagnosis of the master station after discovering the abnormal phenomenon of its measurement data, and freezes the positive/reverse active energy indication value through the daily recall of the electric energy meter, and the data and the master station save Comparing the daily frozen forward/reverse active energy indication data, completing a diagnosis and preliminarily eliminating data collection errors, and then performing a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter;

(2) Automatic diagnosis of the abnormality of the electric energy meter flying away:

Based on the algorithm model of the electric energy meter flying away abnormality, after the master station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the master station, and freezes the positive/reverse active total electric energy indication value and the daily value saved by the master station through the daily recall of the electric energy meter. Freeze the positive/reverse total active energy indications for comparison, complete a diagnosis and preliminarily eliminate errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter;

(3) Automatic diagnosis of the abnormality of the electric energy meter going backwards:

Based on the abnormal algorithm model of the electric energy meter, after the master station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the master station, and freezes the forward/reverse total active energy indication value and the daily value saved by the master station through the daily call of the electric energy meter. Freeze the positive/reverse total active energy indications for comparison, complete a diagnosis and preliminarily eliminate errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter;

(4) Automatic diagnosis of abnormal stop of electric energy meter:

Based on the abnormal algorithm model of the electric energy meter stop and go, after the main station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the main station, and freezes the positive/reverse active total electric energy indication value and the daily value saved by the main station through daily calls to the electric energy meter. Freeze the positive/reverse total active energy indications for comparison, complete a diagnosis and preliminarily eliminate errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter;

(5) Automatic diagnosis of reverse power abnormality:

Based on the reverse power abnormality algorithm model, after the main station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the main station, and through the daily freeze of the forward/reverse active total electric energy indication value and the daily freeze stored by the main station The forward/reverse active total electric energy indications are compared, a diagnosis is completed and data collection errors are preliminarily ruled out, and a second diagnosis is carried out through the abnormal algorithm model based on the data of the through-call electric energy meter.

Moreover, the said reverse active energy indication value freezes forward/reverse active energy total, peak, peak, flat, and valley per day.

Moreover, the closed-loop management module of the abnormal work order includes:

(1) Error warning:

View the error analysis results calculated by the smart energy meter operation error diagnosis and analysis model, support statistics according to power supply unit, statistical cycle, error range and other conditions, display the number of smart meter operation errors of each unit in the form of graphs, and support power supply units to drill down, Quantities can be linked to details for viewing;

(2) Work order generation:

According to the statistical analysis results of the smart meter operation error diagnosis model, design the work order generation algorithm for the abnormal operation error of the electric energy meter, define the threshold and generation frequency of the abnormal work order generation, realize the automatic generation of the abnormal work order, and support the manual through the early warning details Generate a work order;

(3) Closed-loop work order processing:

Incorporate the abnormal operation error of the smart meter into the remote analysis wizard to analyze and locate the cause of the abnormality; when the work order is dispatched to the station manager, it supports the automatic triggering of the on-site inspection process; it supports the input feedback and white list of on-site measurement data and user power load Submit; the work order is to be archived, realize the on-site feedback information review and manual filing of the abnormal operation error of the smart meter; the completed function, based on different job roles, realize the query and recall of the abnormal operation error of the smart meter in the unfiled and archived links.

Moreover, the types of work orders include: work orders of uneven electric energy indication value, work orders of electric energy meter flying away, work orders of electric energy meter reverse movement, work orders of electric energy meter stop and reverse electric quantity work order.

Advantage of the present invention and beneficial effect are:

The present invention optimizes the original measurement abnormal analysis model, adds functions such as abnormal level analysis, abnormal cause sorting, and interference factor elimination, and increases the accuracy of abnormal analysis. Based on the metering online monitoring model, develop metering anomaly analysis and processing functions. Realized the measurement abnormality analysis model to achieve optimization in terms of abnormal level, abnormal cause sorting, and interference factor elimination; realized automatic diagnosis of measurement abnormalities, generated work orders, and online full-process closed-loop management of work order processing; realized mobile operations Terminal application transformation, on-site order acceptance, feedback, etc.

Description of drawings

FIG. 1 is an overall system architecture diagram of the present invention.

Detailed ways

The present invention will be further described in detail below through the specific examples, the following examples are only descriptive, not restrictive, and cannot limit the protection scope of the present invention with this.

A metering anomaly analysis and processing system based on information collected by electric energy meters. Its innovation lies in: including a data governance module, establishment of a metering anomaly analysis model, a generation and management module of a metering anomaly work order, and a field mobile operation processing module;

Data governance module, including traceability governance of historical electricity data and data governance of marketing business application system;

Establish a measurement anomaly analysis model, and perform automatic diagnosis of measurement anomalies on the curated data;

The generation and management module of abnormal measurement work orders generates abnormal measurement work orders based on the diagnosis results, and performs closed-loop management on the abnormal measurement work orders;

On-site mobile operation processing module: use the mobile operation terminal APP to realize on-site order receipt, work order download, and abnormal feedback of abnormal smart meter operation errors, and support the input of error measurement data and user power load conditions of on-site general meters and user meters, and support On-site photo uploading, support for initiation of electricity consumption inspection, metering device failure process, and support for measurement result feedback of metering peripherals.

Among them, the traceability management of historical power data is aimed at situations such as user change of meter, file change, and station household change relationship. analyze.

The data governance of the marketing business application system includes first, the integrity management of historical archives such as the station area, metering point, metering device, and collection terminal. The second is to ensure the relevance of historical archive information such as meter replacement and account cancellation and station area information, support the restoration of electricity data information of replaced meters, and realize the traceability of the station area information before and after the change of the electric energy meter household change file. The third is to continue to promote the governance of the relationship between households in the Taiwan area.

Among them, the construction of measurement anomaly analysis model includes the following contents:

(1) Optimizing the analysis model of the unevenness of the electric energy representation value

In order to deepen the governance efficiency and management level of the uneven power value data, improve the accuracy of abnormal cause analysis, increase the importance level of abnormal handling, eliminate the reversal of the electric energy value, and further sort out the reasons for the abnormal power value, add measurement anomaly knowledge Library to support abnormal analysis applications.

The diagnostic model of the importance level of exception handling is: |(forward/reverse total active power indication value-∑(forward/reverse active power each tariff electric energy indication value)|>the K value in the tariff number*K is carried out according to different thresholds Hierarchical management can be adjusted dynamically.

(2) Optimizing the analysis model of electric energy meter flying away

Increase the importance level of exception handling, according to the diagnosis model of the electric energy meter flying away: daily electricity/daily maximum theoretical electricity>K, the K value in K is classified and managed according to different thresholds, which can be dynamically adjusted. According to the different calculation methods of the daily maximum theoretical power of different meter types, combined with different user types, a model is created to distinguish different meter types. Adjust the calculation method of the maximum theoretical power, and design multiple threshold intervals for the ratio of the positive/reverse daily power of the electric energy meter (without multiplying the transformation ratio) to the maximum theoretical electric power of the electric energy meter. The threshold can be adjusted dynamically. Add the reversal judgment method, design the electric energy representation value reversal judgment method in the calculation model, and the abnormal diagnosis and recovery algorithm need to exclude the electric energy representation value reversal. Sort out the causes of the abnormality of the energy meter flying away, such as the failure of the collection equipment, the failure of the energy meter, and the daily electricity consumption of the user exceeding the power receiving capacity, etc., to provide support for the construction of the knowledge base of the measurement anomaly.

(3) Optimizing the analysis model of the electric energy meter going backwards

According to different meter types, the daily maximum theoretical power calculation method is different, and combined with different user types, different meter types are distinguished. In the calculation model, the judgment method of power indication value reversal and table change is designed, and the abnormal diagnosis method excludes the situation of electric energy representation value reversal and table change. Sort out the causes of the abnormality of the electric energy meter going backwards, such as the wrong meter reading parameters of the collection equipment, the failure of the collection equipment, the failure of the electric energy meter, etc., to provide support for the construction of the abnormal knowledge base of measurement.

(4) Optimizing the stop-and-go analysis model of the electric energy meter

According to different users, add the diagnosis method of the electric energy meter stoppage and sort out the abnormal causes of the electric energy meter stoppage.

Specific user diagnosis method: use the electric energy meter for 3 consecutive days, and the difference between the forward and reverse active total electric energy indications is equal to 0, and the total active power monitored during this period has 3 consecutive full-point values (any three points) greater than K , as a diagnostic method.

The low-voltage user diagnosis method based on the object-oriented protocol: 1) the difference between the positive and negative active total electric energy indications of the electric energy meter within N days is equal to 0; Point values greater than K, as a diagnostic method.

Diagnosis method for low-voltage users with four-point collection: 1) The difference between the daily forward/reverse active total electric energy indication values of the electric energy meter within N days is equal to 0; 2) During the period that meets condition 1, there are 3 values of active power greater than K , as a diagnostic method.

According to the power consumption characteristics of low-voltage users, the judging cycle of the power meter stopping is appropriately relaxed, and it can be adjusted dynamically. In view of the phenomenon that low-voltage users are prone to stop and go after new installation or meter replacement, the judgment method of triggering the stop of the electric energy meter after new installation or meter replacement is added to the diagnosis method of low-voltage users, in order to detect stop and go abnormalities in time and reduce missed reports.

(5) Optimizing the analysis model of reverse electricity abnormality

Set the reverse power threshold of the electric energy meter, determine the importance level of abnormal handling, and design the reverse power abnormality diagnosis method according to different task configuration conditions.

For low-voltage users who have configured reverse power collection tasks, according to the indication value of the total reverse active power of the electric energy meter is greater than 0, the reverse electric power of the electric energy meter on the day is greater than the threshold K, and the total reverse active power of low-voltage users > the total forward active power *Q3 conditions are judged. For the low-voltage users who do not have the task of collecting reverse power collection, increase the power meter’s reverse active total electric energy display value twice a month to judge, and within 7 days after the meter is replaced, the forward active total electric energy display value will be reported normally within 2 days. Initiate a call-through operation for judgment. In the calculation model, the determination method of power generation users is designed to exclude the case that the nature of electricity consumption is power generation users. The reasons for the abnormality of the reverse electric quantity are sorted out and incorporated into the algorithm model to provide support for the construction of the measurement anomaly knowledge base.

Among them, the automatic diagnosis of abnormal measurement includes the following contents:

(1) Automated diagnosis of uneven and abnormal electric energy values

Based on the abnormal algorithm model of uneven electric energy value, after the main station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the main station, and through the daily call of the electric energy meter, the positive/reverse active energy indication value is frozen daily (the positive/reverse active energy value is frozen daily Active power total, peak, peak, flat, valley) data are compared with the daily frozen forward/reverse active energy indication value (daily frozen forward/reverse active total, peak, peak, flat, valley) data saved by the master station, Complete a diagnosis and preliminarily eliminate the errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter.

(2) Automatic diagnosis of electric energy meter flying away abnormality

Based on the algorithm model of the electric energy meter flying away abnormality, after the master station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the master station, and freezes the positive/reverse active total electric energy indication value and the daily value saved by the master station through the daily recall of the electric energy meter. Freeze the positive/reverse total active energy indications for comparison, complete a diagnosis and preliminarily eliminate the errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the transparent call electric energy meter

(3) Automatic diagnosis of the abnormality of the electric energy meter going backwards

Based on the abnormal algorithm model of the electric energy meter, after the master station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the master station, and freezes the forward/reverse total active energy indication value and the daily value saved by the master station through the daily call of the electric energy meter. Freeze the positive/reverse total active energy indications for comparison, complete a diagnosis and preliminarily eliminate errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter.

(4) Automatic diagnosis of abnormal stop and go of electric energy meter

Based on the abnormal algorithm model of the electric energy meter stop and go, after the main station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the main station, and freezes the positive/reverse active total electric energy indication value and the daily value saved by the main station through daily calls to the electric energy meter. Freeze the positive/reverse total active energy indications for comparison, complete a diagnosis and preliminarily eliminate errors in the collected data, and then conduct a second diagnosis through the abnormal algorithm model based on the data of the through-call electric energy meter.

(5) Automatic diagnosis of reverse power abnormality

Based on the reverse power abnormality algorithm model, after the main station discovers the abnormal phenomenon of its metering data, it introduces the automatic diagnosis of the main station, and through the daily freeze of the forward/reverse active total electric energy indication value and the daily freeze stored by the main station The forward/reverse active total electric energy indications are compared, a diagnosis is completed and data collection errors are preliminarily ruled out, and a second diagnosis is carried out through the abnormal algorithm model based on the data of the through-call electric energy meter.

Among them, the closed-loop management of abnormal warning work orders includes the following contents:

(1) Error warning

View the error analysis results calculated by the smart energy meter operation error diagnosis and analysis model, support statistics according to power supply unit, statistical cycle, error range and other conditions, display the number of smart meter operation errors of each unit in the form of graphs, and support power supply units to drill down, Quantities can be linked to details for viewing

(2) Work order generation

According to the statistical analysis results of the smart meter operation error diagnosis model, design the work order generation algorithm for the abnormal operation error of the electric energy meter, define the threshold and generation frequency of the abnormal work order generation, realize the automatic generation of the abnormal work order, and support the manual through the early warning details Generate a ticket. The types of work orders include: work orders of uneven electric energy indication value, work orders of electric energy meter flying away, work orders of electric energy meter reverse movement, work orders of electric energy meter stop and work orders, and work orders of reverse electric quantity.

(3) Closed-loop work order processing

Incorporate the abnormal operation error of the smart meter into the remote analysis wizard to analyze and locate the cause of the abnormality; when the work order is dispatched to the station manager, it supports the automatic triggering of the on-site inspection process; it supports the input feedback and white list of on-site measurement data and user power load Submit; the work order is to be archived, realize the on-site feedback information review and manual filing of the abnormal operation error of the smart meter; the completed function, based on different job roles, realize the query and recall of the abnormal operation error of the smart meter in the unfiled and archived links.

The present invention proposes a metering anomaly analysis and processing system based on the information collected by electric energy meters, adding content such as traceability management of historical data, and continuously optimizing the metering online monitoring model from the aspects of abnormal level, abnormal cause sorting, and eliminating interference factors, etc., increasing Accuracy of exception analysis. Based on the metering online monitoring model, it realizes the online full-process closed-loop management of automatic diagnosis of metering anomalies, generation of work orders, and work order processing. Accurate analysis of the cause of abnormal measurement can be realized, the number of invalid work orders and on-site inspections can be reduced, and a lot of manpower and material costs can be saved. The online whole-process processing method improves the level of refinement of management, innovates the working mode, and effectively standardizes the work process and quality control.

Although the embodiments and drawings of the present invention are disclosed for the purpose of illustration, those skilled in the art can understand that various replacements, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims Therefore, the scope of the present invention is not limited to what is disclosed in the embodiments and drawings.

Claims (9)

1. The utility model provides a measurement anomaly analysis processing system based on electric energy meter information collection which characterized in that: the system comprises a data management module, a generation management module for establishing a metering abnormity analysis model and a metering abnormity work order, and a field mobile operation processing module;

the data management module comprises traceability management of historical electric quantity data and data management of a marketing business application system;

establishing a measurement anomaly analysis model, and automatically diagnosing measurement anomaly of the treated data;

the abnormal metering work order generation management module generates an abnormal metering work order according to the diagnosis result and carries out closed-loop management on the abnormal metering work order;

the field mobile operation processing module uses a mobile operation terminal APP to realize field order receiving, work order downloading and abnormal feedback of the intelligent meter operation error abnormity, supports the input of error measurement data of a field general meter and a user meter and the condition of the user power consumption load, supports field shooting and uploading, supports the initiation of power consumption inspection and the fault flow of a metering device, and supports the feedback of the measurement result of a metering peripheral.

2. The metering anomaly analysis and processing system based on the electric energy meter collected information according to claim 1, characterized in that: the traceability of the historical electric quantity data is controlled by aiming at the conditions of user form change, file change, station-to-station relation change and the like, the system can record the data before and after change at the same time, thereby realizing the traceability of the historical electric quantity data before and after change and supporting the abnormal metering analysis.

3. The metering anomaly analysis and processing system based on the electric energy meter collected information according to claim 1, characterized in that: the data governance of the marketing business application system is as follows: firstly, historical archive integrity management of a transformer area, a metering point, a metering device, an acquisition terminal and the like is carried out; secondly, the relevance of historical archive information such as a disassembled and replaced meter, a sales counter and the like and the station area information is ensured, the reduction of the electric quantity data information of the disassembled and replaced meter is supported, and the traceability of the station area information of the electric energy meter before and after the change of the gear of the electric energy meter user is realized; and thirdly, continuously promoting the treatment work of the indoor variable relationship of the transformer area.

4. The metering anomaly analysis and processing system based on the electric energy meter collected information according to claim 1, characterized in that: the establishment of the abnormal metering analysis model comprises the following steps:

(1) optimizing an electric energy representation value unevenness analysis model:

adding an abnormal processing importance grade diagnosis model, eliminating the turning condition of the electric energy representation value, further combing the abnormal reasons of the uneven electric energy representation value, adding into a metering abnormal knowledge base, supporting abnormal analysis application,

the anomaly handling importance level diagnosis model is as follows:

(forward/reverse active total electric energy indicating value-sigma (forward/reverse active each rate electric energy indicating value) | > rate number K

The K value in the formula is subjected to hierarchical management according to different threshold values and can be dynamically adjusted;

(2) optimizing a flying analysis model of the electric energy meter:

increasing the importance level of exception handling, and establishing a diagnosis model of electric energy meter flying:

daily electric quantity/daily maximum theoretical electric quantity > K

The K value in the formula is subjected to hierarchical management according to different threshold values and can be dynamically adjusted;

(3) optimizing a backward walking analysis model of the electric energy meter:

according to different daily maximum theoretical electric quantity calculation methods of different meter types and different user types, different meter types are distinguished, an electric energy expression value overturning and meter changing judgment method is designed in a calculation model, the conditions of electric energy expression value overturning and meter changing are eliminated in an abnormal diagnosis method, the generation reason of the electric energy meter falling abnormality is combed, and support is provided for the construction of a metering abnormality knowledge base;

(4) optimizing an electric energy meter stop analysis model:

increasing an electric energy meter stop-go diagnosis method according to different users and combing the reasons of the electric energy meter stop-go abnormality:

the diagnosis method for the special transformer user comprises the following steps: adopting a diagnostic method that the difference value of forward/reverse active total electric energy indication values of the electric energy meter for 3 continuous days is equal to 0, and 3 continuous integral point values of total active power greater than K are monitored in the time period;

the low-voltage user diagnosis method of the object-oriented protocol comprises the following steps: adopting 1) the difference values of the daily positive/reverse active total electric energy indication values of the electric energy meter within N days to be equal to 0; 2) when the active power is monitored to have 3 continuous integer values greater than K in the time period meeting the condition 1, the active power is used as a diagnosis method;

the low-voltage user diagnosis method with four-point acquisition comprises the following steps: 1) the difference values of the daily positive/negative active total electric energy indication values of the electric energy meter within N days are all equal to 0; 2) when the condition 1 is met, 3 values of the active power are monitored to be larger than K, and the active power is used as a diagnosis method;

(5) optimizing a reverse electric quantity abnormity analysis model:

setting a reverse electric quantity threshold value of the electric energy meter, judging the importance level of the abnormal handling, and designing a reverse electric quantity abnormal diagnosis method according to different task configuration conditions, wherein the method specifically comprises the following steps:

aiming at a low-voltage user with a configured reverse electric quantity acquisition task, judging according to the condition that the reverse active total electric quantity indication value of the electric energy meter is greater than 0, the reverse electric quantity of the electric energy meter is greater than a threshold value K on the same day, and the reverse active total electric quantity of the low-voltage user is greater than the forward active total electric quantity Q3;

for low-voltage users without configured reverse electric quantity acquisition tasks, reverse active total electric energy indication values of the electric energy meter are recalled twice per month for judgment, and recall operation is initiated for judgment on the 2 nd day after the normal reporting of the forward active total electric energy indication values within 7 days after the meter is replaced;

designing a power generation user judgment method in a calculation model, and excluding the condition that the power utilization property is a power generation user; and (4) combing the generation reason of the abnormal reverse electric quantity, bringing the generation reason into an algorithm model, and providing support for the construction of a metering abnormal knowledge base.

5. The metering anomaly analysis and processing system based on the electric energy meter acquisition information is characterized in that: according to the electricity utilization characteristics of low-voltage users, the stop-off judgment period of the electric energy meter is properly widened, and the electric energy meter can be dynamically adjusted; aiming at the phenomenon that a low-voltage user is easy to stop running after a new meter is installed or replaced, a judgment method for stopping running of an electric energy meter is triggered after the new meter is installed or replaced in a diagnosis method of the low-voltage user, so that abnormal running can be found in time, and the condition of missing report can be reduced.

6. The metering anomaly analysis and processing system based on the electric energy meter collected information according to claim 1, characterized in that: the metering anomaly automatic diagnosis module comprises:

(1) automatically diagnosing the unevenness abnormality of the electric energy representation value:

on the basis of an abnormal algorithm model with uneven electric energy representation values, after the master station finds that the measured data is abnormal, the master station introduces the automatic diagnosis of the master station, through recalling the daily frozen positive/reverse active electric energy representation values of the electric energy meter every day, the data is compared with the daily frozen positive/reverse active electric energy representation value data stored by the master station, primary diagnosis is completed, errors in collected data are preliminarily eliminated, and secondary diagnosis is carried out through the abnormal algorithm model again according to the recalling the electric energy meter data;

(2) automatically diagnosing the flying abnormality of the electric energy meter:

based on the electric energy meter flying abnormal algorithm model, the main station introduces the main station for automatic diagnosis after finding the abnormal phenomenon of the metering data, completes primary diagnosis and preliminarily eliminates data acquisition errors by comparing daily frozen forward/reverse active total electric energy indication values of the electric energy meter with daily frozen forward/reverse active total electric energy indication values stored by the main station, and performs secondary diagnosis through the abnormal algorithm model again according to the data of the electric energy meter recalled;

(3) automatically diagnosing the backward walking abnormality of the electric energy meter:

based on the electric energy meter backward walking abnormal algorithm model, the main station introduces the main station for automatic diagnosis after finding the abnormal phenomenon of the metering data, completes primary diagnosis and preliminarily eliminates data acquisition errors by comparing daily frozen forward/reverse active total electric energy indication values of the thoroughly-called electric energy meter with daily frozen forward/reverse active total electric energy indication values stored by the main station, and performs secondary diagnosis through the abnormal algorithm model again according to the thoroughly-called electric energy meter data;

(4) automatically diagnosing the stop and go abnormity of the electric energy meter:

based on the electric energy meter stop-and-go abnormal algorithm model, the main station introduces the automatic diagnosis of the main station after finding the abnormal phenomenon of the metering data, completes the primary diagnosis and preliminarily eliminates the data acquisition error by comparing the daily frozen forward/reverse active total electric energy indication value of the electric energy meter with the daily frozen forward/reverse active total electric energy indication value stored by the main station through the recall of the electric energy meter every day, and performs the secondary diagnosis through the abnormal algorithm model again according to the data of the electric energy meter through the recall;

(5) automatic diagnosis of reverse electric quantity abnormality:

based on the reverse electric quantity abnormity algorithm model, the main station introduces the automatic diagnosis of the main station after finding the abnormity phenomenon of the metering data, the daily frozen forward/reverse active total electric energy indication value of the electric energy meter is recalled thoroughly every day and is compared with the daily frozen forward/reverse active total electric energy indication value stored by the main station, the primary diagnosis is completed, the error of the collected data is eliminated preliminarily, and the secondary diagnosis is carried out through the abnormity algorithm model again according to the data recalled thoroughly.

7. The metering anomaly analysis and processing system based on the electric energy meter acquisition information is characterized in that: the indication value of the reverse active electric energy is daily freezing positive/reverse active total, peak, flat and valley.

8. The metering anomaly analysis and processing system based on the electric energy meter collected information according to claim 1, characterized in that: the closed-loop management module of the abnormal work order comprises:

(1) error early warning:

checking an error analysis result calculated by the intelligent electric energy meter operation error diagnosis analysis model, supporting statistics according to conditions such as a power supply unit, a statistics period, an error range and the like, displaying the operation error quantity of each unit intelligent meter in a chart mode, supporting a power supply unit to drill down, and enabling the quantity to be linked to details for checking;

(2) and (3) generating a work order:

according to the statistical analysis result of the intelligent meter operation error diagnosis model, a work order generation algorithm for abnormal operation errors of the electric energy meter is designed, a threshold value and a generation frequency of abnormal work orders are defined, automatic generation of the abnormal work orders is achieved, and meanwhile manual work order generation through early warning detail is supported;

(3) closed-loop work order processing:

bringing the running error abnormity of the intelligent meter into a remote analysis guide, and analyzing and positioning the reason of the abnormity; when the work order is dispatched to the platform area manager, the automatic triggering of the on-site inspection process is supported; the method supports the input feedback of field measurement data and the user power load condition and the submission of a white list; the work order is to be filed, and auditing and manual filing of the field feedback information of the abnormal operation error of the intelligent meter are realized; and the handling of the abnormal operation errors of the intelligent meter in the non-filed and filed links, such as inquiry, recall and the like is realized based on different post roles.

9. The metering anomaly analysis and processing system based on the electric energy meter acquisition information is characterized in that: the work order types include: the electric energy indicating value is not a smooth work order, the electric energy meter flying work order, the electric energy meter reversing work order, the electric energy meter stopping work order and the reverse electric quantity work order.