Metering and business expansion business abnormity diagnosis and analysis method and system

Abstract

The invention relates to a metering and business expansion business abnormity diagnosis analysis method and system, and the method comprises the following steps: installing an intelligent electric meter to collect the power utilization data of a user in real time, installing a sensor at a key node, and monitoring the operation state of power grid equipment; the collected data is transmitted to a data center through the Internet of Things gateway, so that the real-time performance and the integrity of the data are ensured; a distributed data warehouse is used for storing historical power consumption data and equipment state data, and a NoSQL database is used for storing and processing real-time data flow; different metering and business expansion business theme analysis modules are constructed, and metering and business expansion business abnormity diagnosis analysis is carried out; and according to a metering and business expansion business abnormity diagnosis analysis result, when abnormity is detected, an alarm is automatically sent to notify related personnel according to alarm priority setting and a response strategy. The risk management and control capability of the metering and business expansion business can be effectively improved, the safe and stable operation of the power grid is ensured, and the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid management, and in particular to a method and system for diagnosing and analyzing abnormalities in metering and business expansion services. Background Art

[0002] In modern power systems, with the rapid development of smart grid technology, power companies are facing increasingly complex power management and equipment monitoring needs. Traditional power monitoring systems usually rely on regular manual inspections and static data collection, which is not only inefficient but also difficult to detect and respond to abnormal situations in the power grid in a timely manner.

[0003] In order to improve the safety and operational efficiency of the power grid, a complete abnormal diagnosis and analysis system needs to be built. This system can not only monitor and analyze power consumption data and equipment status in real time, but also automatically generate alarms based on abnormal diagnosis results and notify relevant personnel through multiple channels. This intelligent abnormal management system can significantly improve the risk management and control capabilities of the power grid, reduce human errors, and improve overall operational efficiency. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a method and system for abnormal diagnosis and analysis of metering and business expansion services, which can effectively improve the risk management capabilities of metering and business expansion services, ensure the safe and stable operation of the power grid, and improve overall operational efficiency.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for diagnosing and analyzing abnormalities in metering and business expansion services, comprising the following steps:

[0007] Install smart meters to collect users' electricity consumption data in real time, including voltage, current, and power, and install sensors at key nodes to monitor the operating status of grid equipment;

[0008] The collected data is transmitted to the data center through the IoT gateway to ensure the real-time and integrity of the data;

[0009] Use a distributed data warehouse to store historical power consumption data and equipment status data, and use a NoSQL database to store and process real-time data streams;

[0010] Construct different metering and business expansion service theme analysis modules to conduct abnormal diagnosis and analysis of metering and business expansion services based on historical electricity consumption data, equipment status data and real-time data streams;

[0011] According to the abnormal diagnosis and analysis results of metering and business expansion services, when an abnormality is detected, an alarm is automatically sent to notify relevant personnel according to the alarm priority setting and response strategy.

[0012] Furthermore, the collected data is transmitted to the data center through the IoT gateway to ensure the real-time and integrity of the data, as follows:

[0013] Deploy an IoT gateway to collect data from smart meters and sensors. The IoT gateway should support multiple communication protocols, including MQTT and CoAP, to be compatible with different devices.

[0014] Use wireless communication technology to achieve remote data transmission, and adopt SSL / TLS to protect data privacy.

[0015] Furthermore, a distributed data warehouse is used to store historical power consumption data and equipment status data, and a NoSQL database is used to store and process real-time data streams, as follows

[0016] Data is collected through smart meters and sensors, and the IoT gateway is used for data transmission. The real-time data stream is transmitted to the data processing system through the message queue Apache Kafka.

[0017] Use the stream processing framework Apache Flink to process real-time data streams;

[0018] Write processed real-time data into a NoSQL database to support fast query and analysis;

[0019] Regularly import real-time data from NoSQL databases into distributed data warehouses for historical data storage; use ETL tools to implement batch import and conversion of data.

[0020] Furthermore, different metering and business expansion service theme analysis modules include a metering service analysis module, a business expansion service analysis module and a business expansion service abnormality management analysis module.

[0021] Furthermore, the measurement business analysis module includes:

[0022] Meter operation analysis: Use status check technology to monitor whether the energy metering device has wiring errors, inaccurate meters, or abnormal status errors;

[0023] Analysis of abnormal bottom meter readings: It is divided into two parts, namely, analysis of abnormal bottom meter readings before meter replacement and analysis of abnormal bottom meter readings after meter replacement. For analysis of abnormal bottom meter readings before meter replacement, first divide the user range to be analyzed, analyze and compare the readings of any metering point of the transformer with the readings of the next billing cycle, and determine whether there is a logical abnormality. For analysis of abnormal bottom meter readings after meter replacement, in addition to the determination of whether the meter has been replaced, it is also necessary to collect meter replacement work order data, and determine whether there is an abnormality based on the work order data combined with the last collected readings of the month.

[0024] Analysis of abnormal reverse active power: Based on the electrical parameters obtained by the metering automation system, analyze and sort out the types of reverse active power, establish corresponding identification rules, and monitor the abnormal reverse active power situation;

[0025] Analysis of the discrepancy between the calibrated current value of the running electric energy meter and the configuration of the transformer: Collect the electric energy parameters, measurement records, and transformer information of the electric energy metering, convert and process the data, collect statistical indicators, create application themes, and assist staff in analyzing the equipment configuration and operation status;

[0026] Analysis of abnormal metering processing for high-voltage users: Carry out analysis from two perspectives: current failure of high-voltage users’ meters and voltage phase failure of high-voltage users, and set thresholds for duration of abnormal current, voltage and meters;

[0027] Meter delivery and installation analysis: Access the meter delivery and delivery records, count the meter delivery and delivery status by time, status, and delivery and delivery reasons, formulate abnormal warning rules for meter delivery and delivery, and analyze whether there are abnormal conditions such as untimely meter delivery and delivery installation, and untimely pre-delivery and delivery installation;

[0028] Analysis of large amount of electricity produced by agricultural drainage:

[0029] Determine the list of users with large agricultural power consumption based on the basic customer files, extract the corresponding historical power consumption data and electricity price information, and verify whether the customer's actual electricity price is consistent with the nature of electricity consumption, whether there is high-price low-connection, and whether there is illegal power theft;

[0030] Analysis of the periodic rotation of electric energy metering equipment: Monitor the periodic rotation of electric energy metering equipment, and count the periodic rotation of equipment according to the operation data, service life, manufacturer, installation location, and maintenance record data of the electric energy meter, analyze and determine the electric energy meters that can be used again after expiration, and locate the electric energy meters with hidden faults;

[0031] Abnormal billing rate analysis: Identify whether the electricity meter is installed with a current transformer, a voltage transformer, or both a current transformer and a voltage transformer, analyze whether the calculated electricity rate or the nameplate rate is consistent with the actual situation, monitor the accuracy of the billing rate, and if there is an abnormal billing rate, generate a list of difference data between the correct and incorrect rates based on the actual rate.

[0032] Furthermore, the business expansion business analysis module specifically includes:

[0033] Abnormal analysis of account cancellation process: Extract customer account cancellation process data, calculate the processing time of the account cancellation process, determine the information archiving link and data archiving status in the process, formulate abnormal rules based on the user's arrears and prepaid balance, and analyze whether there are abnormal processes that should be completed but not completed, should be terminated but not terminated, etc.

[0034] Analysis of abnormal modification of transformer capacity information: Extract key customer information process data and analyze changes in billing parameters of transformers or high-voltage motors in the process, including modification of transformer key billing parameters, addition of transformer file information, and deletion of transformer file information. By monitoring key fields including operating status, primary and standby nature, nameplate capacity, planned recovery date, deactivation date, and first operation time, obtain information on transformer capacity that has not been modified in the normal process for user files;

[0035] Abnormal power outage time analysis: Collect power outage information from the power distribution GPMS system, combine it with the power outage and power supply data from the marketing system, and analyze whether the power supply time of new users is inconsistent with the GPMS time, whether the difference between the shutdown time and the actual shutdown time of the shutdown equipment exceeds the threshold, etc., to ensure timely power supply and timely power outage;

[0036] Analysis of power supply before signing a contract: For high-voltage users, collect the process nodes of high-voltage new installation and high-voltage capacity increase of the business expansion application type completed during the statistical period, as well as the temporary power consumption process information of the completed meter installation and the customer's signed contract information, compare the contract signing time, the power user's signing date, and the GPMS power supply time, and monitor the situation of power supply before signing a contract;

[0037] Analysis of business expansion process filing: Extract business expansion business process data, and statistically analyze the business filing status based on process status, business acceptance category, time and other dimensions. Compare the month of installation and removal date of any metering point in the electric energy meter solution with the completion month of business expansion process information filing. If the values ​​of the two months are greater than the specified threshold, extract the information of the process separately, monitor and supervise it, trace the progress of the process, and prevent the risk of customer electricity consumption and electricity fee loss caused by irregular business.

[0038] Statistical analysis of multiple households at one address: Based on the marketing customer profile, analyze and determine whether there is a situation of multiple households at one address, support the export function of abnormal data list according to a certain template, and assist staff to confirm and clean up multiple households at one address;

[0039] Analysis of contract signing status: Collect basic data on contract signing, combine it with marketing customer files, and divide the user types into high-voltage users, low-voltage non-residential users, residential users, temporary users, and entrusted power supply users. Formulate verification rules for the completion of contract signing, and count various contracts including new signing, supplementary signing, change, renewal, and termination. Compare the contract signing status of each user category with the corresponding benchmark value to calculate the contract signing completion rate.

[0040] Furthermore, the business expansion service abnormality management and analysis module includes four monitoring points: the number of meter replacements at the same metering point, overdue electricity usage by temporary electricity users, work order return rate, and work order termination rate; the monitoring point of the number of meter replacements at the same metering point stipulates that abnormal data will be generated if the number of times the same metering point exceeds a certain threshold within a certain period; the monitoring point of overdue electricity usage by temporary electricity users stipulates that customers should be reminded to go through the extension procedures several days before the expiration of the temporary electricity user contract, and monitor whether the electricity users go through the extension procedures. If the expired users do not go through the extension procedures, corresponding abnormal data will be generated; the two monitoring points of work order return rate and work order termination rate refer to the ratio of the number of work order return to the cumulative number of work orders within the period, and the ratio of the cumulative number of work order terminations to the cumulative number of archived work orders. Monitoring records are generated when they exceed certain thresholds.

[0041] A metering and business expansion service abnormality diagnosis and analysis system includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the above-mentioned metering and business expansion service abnormality diagnosis and analysis method.

[0042] A computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method steps described above.

[0043] The present invention has the following beneficial effects:

[0044] 1. The present invention can effectively improve the risk management and control capabilities of metering and business expansion services, ensure the safe and stable operation of the power grid, and improve the overall operational efficiency;

[0045] 2. The present invention constructs analysis modules for different business themes, conducts in-depth abnormal diagnosis analysis for metering and business expansion business, and comprehensively detects abnormal power consumption and equipment status through meter operation analysis, meter bottom degree abnormal analysis, reverse active abnormality analysis, etc.; optimizes business processes and reduces human errors through abnormal account cancellation process analysis, abnormal transformer capacity modification information analysis, etc.; ensures the standardization of contract signing and the accuracy of user files through contract signing analysis, one-address multi-household statistical analysis, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0048] refer to Figure 1 In this embodiment, a method for diagnosing and analyzing abnormalities in metering and business expansion services is provided, comprising the following steps:

[0049] Install smart meters to collect users' electricity consumption data in real time, including voltage, current, and power, and install sensors at key nodes to monitor the operating status of grid equipment;

[0050] The collected data is transmitted to the data center through the Internet of Things (IoT) gateway to ensure the real-time and integrity of the data;

[0051] Use distributed data warehouses (such as Apache Hive) to store historical power consumption data and device status data, and NoSQL databases (such as Apache Cassandra) to store and process real-time data streams;

[0052] Construct different metering and business expansion service theme analysis modules to conduct abnormal diagnosis and analysis of metering and business expansion services based on historical electricity consumption data, equipment status data and real-time data streams;

[0053] According to the abnormal diagnosis and analysis results of metering and business expansion services, when an abnormality is detected, an alarm is automatically sent to notify relevant personnel according to the alarm priority setting and response strategy.

[0054] In this embodiment, the collected data is transmitted to the data center through the Internet of Things gateway to ensure the real-time and integrity of the data, as follows:

[0055] Deploy an IoT gateway to collect data from smart meters and sensors. The IoT gateway should support multiple communication protocols, including MQTT and CoAP, to be compatible with different devices.

[0056] Use wireless communication technologies (such as Wi-Fi, 4G / 5G, LoRaWAN) to achieve remote data transmission, and use SSL / TLS to protect data privacy.

[0057] In this embodiment, a distributed data warehouse is used to store historical power consumption data and device status data, and a NoSQL database is used to store and process real-time data streams, as follows:

[0058] Data is collected through smart meters and sensors, and the IoT gateway is used for data transmission. The real-time data stream is transmitted to the data processing system through the message queue Apache Kafka.

[0059] Use the stream processing framework Apache Flink to process real-time data streams;

[0060] Write processed real-time data into a NoSQL database to support fast query and analysis;

[0061] Regularly import real-time data from NoSQL databases into distributed data warehouses for historical data storage; use ETL tools (such as Apache NiFi or Apache Sqoop) to implement batch import and transformation of data.

[0062] In this embodiment, different metering and business expansion service subject analysis modules include a metering service analysis module, a business expansion service analysis module and a business expansion service abnormality management analysis module.

[0063] In this embodiment, the metering service analysis module includes:

[0064] Meter operation analysis: Use status check technology to monitor whether the energy metering device has wiring errors, inaccurate meters, or abnormal status errors;

[0065] Analysis of abnormal bottom meter readings: It is divided into two parts, namely, analysis of abnormal bottom meter readings before meter replacement and analysis of abnormal bottom meter readings after meter replacement. For analysis of abnormal bottom meter readings before meter replacement, first divide the user range to be analyzed, analyze and compare the readings of any metering point of the transformer with the readings of the next billing cycle, and determine whether there is a logical abnormality. For analysis of abnormal bottom meter readings after meter replacement, in addition to the determination of whether the meter has been replaced, it is also necessary to collect meter replacement work order data, and determine whether there is an abnormality based on the work order data combined with the last collected readings of the month.

[0066] Analysis of abnormal reverse active power: Based on the electrical parameters obtained by the metering automation system, analyze and sort out the types of reverse active power, establish corresponding identification rules, and monitor the abnormal reverse active power situation;

[0067] Analysis of the discrepancy between the calibrated current value of the running electric energy meter and the configuration of the transformer: Collect the electric energy parameters, measurement records, and transformer information of the electric energy metering, convert and process the data, collect statistical indicators, create application themes, and assist staff in analyzing the equipment configuration and operation status;

[0068] Analysis of abnormal metering processing for high-voltage users: Carry out analysis from two perspectives: current failure of high-voltage users’ meters and voltage phase failure of high-voltage users, and set thresholds for duration of abnormal current, voltage and meters;

[0069] Meter delivery and installation analysis: Access the meter delivery and delivery records, count the meter delivery and delivery status by time, status, and delivery and delivery reasons, formulate abnormal warning rules for meter delivery and delivery, and analyze whether there are abnormal conditions such as untimely meter delivery and delivery installation, and untimely pre-delivery and delivery installation;

[0070] Analysis of large amount of electricity produced by agricultural drainage:

[0071] Determine the list of users with large agricultural power consumption based on the basic customer files, extract the corresponding historical power consumption data and electricity price information, and verify whether the customer's actual electricity price is consistent with the nature of electricity consumption, whether there is high-price low-connection, and whether there is illegal power theft;

[0072] Analysis of the periodic rotation of electric energy metering equipment: Monitor the periodic rotation of electric energy metering equipment, and count the periodic rotation of equipment according to the operation data, service life, manufacturer, installation location, and maintenance record data of the electric energy meter, analyze and determine the electric energy meters that can be used again after expiration, and locate the electric energy meters with hidden faults;

[0073] Abnormal billing rate analysis: Identify whether the electricity meter is installed with a current transformer, a voltage transformer, or both a current transformer and a voltage transformer, analyze whether the calculated electricity rate or the nameplate rate is consistent with the actual situation, monitor the accuracy of the billing rate, and if there is an abnormal billing rate, generate a list of difference data between the correct and incorrect rates based on the actual rate.

[0074] In this embodiment, the business expansion service analysis module specifically includes:

[0075] Abnormal analysis of account cancellation process: Extract customer account cancellation process data, calculate the processing time of the account cancellation process, determine the information archiving link and data archiving status in the process, formulate abnormal rules based on the user's arrears and prepaid balance, and analyze whether there are abnormal processes that should be completed but not completed, should be terminated but not terminated, etc.

[0076] Analysis of abnormal modification of transformer capacity information: Extract key customer information process data and analyze changes in billing parameters of transformers or high-voltage motors in the process, including modification of transformer key billing parameters, addition of transformer file information, and deletion of transformer file information. By monitoring key fields including operating status, primary and standby nature, nameplate capacity, planned recovery date, deactivation date, and first operation time, obtain information on transformer capacity that has not been modified in the normal process for user files;

[0077] Abnormal power outage time analysis: Collect power outage information from the power distribution GPMS system, combine it with the power outage and power supply data from the marketing system, and analyze whether the power supply time of new users is inconsistent with the GPMS time, whether the difference between the shutdown time and the actual shutdown time of the shutdown equipment exceeds the threshold, etc., to ensure timely power supply and timely power outage;

[0078] Analysis of power supply before signing a contract: For high-voltage users, collect the process nodes of high-voltage new installation and high-voltage capacity increase of the business expansion application type completed during the statistical period, as well as the temporary power consumption process information of the completed meter installation and the customer's signed contract information, compare the contract signing time, the power user's signing date, and the GPMS power supply time, and monitor the situation of power supply before signing a contract;

[0079] Analysis of business expansion process filing: Extract business expansion business process data, and statistically analyze the business filing status based on process status, business acceptance category, time and other dimensions. Compare the month of installation and removal date of any metering point in the electric energy meter solution with the completion month of business expansion process information filing. If the values ​​of the two months are greater than the specified threshold, extract the information of the process separately, monitor and supervise it, trace the progress of the process, and prevent the risk of customer electricity consumption and electricity fee loss caused by irregular business.

[0080] Statistical analysis of multiple households at one address: Based on the marketing customer profile, analyze and determine whether there is a situation of multiple households at one address, support the export function of abnormal data list according to a certain template, and assist staff to confirm and clean up multiple households at one address;

[0081] Analysis of contract signing status: Collect basic data on contract signing, combine it with marketing customer files, and divide the user types into high-voltage users, low-voltage non-residential users, residential users, temporary users, and entrusted power supply users. Formulate verification rules for the completion of contract signing, and count various contracts including new signing, supplementary signing, change, renewal, and termination. Compare the contract signing status of each user category with the corresponding benchmark value to calculate the contract signing completion rate.

[0082] In this embodiment, the business expansion service abnormality management and analysis module includes four monitoring points: the number of meter replacements at the same metering point, overdue electricity usage by temporary electricity users, work order return rate, and work order termination rate; the monitoring point of the number of meter replacements at the same metering point stipulates that abnormal data is generated when the number of times the same metering point exceeds a certain threshold within a certain period; the monitoring point of overdue electricity usage by temporary electricity users stipulates that customers should be reminded to go through the extension procedures several days before the expiration of the temporary electricity user contract, and whether the electricity user goes through the extension procedures is monitored. If the user fails to go through the extension procedures when the contract expires, corresponding abnormal data is generated; the two monitoring points of work order return rate and work order termination rate refer to the ratio of the number of work order return to the cumulative number of work orders within the period, and the ratio of the cumulative number of work order terminations to the cumulative number of archived work orders, respectively. Monitoring records are generated when they exceed certain thresholds.

[0083] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0087] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for abnormal diagnosis and analysis of metering and business expansion services, characterized in that: The following steps are involved: Install smart meters to collect users' electricity consumption data in real time, including voltage, current, and power, and install sensors at key nodes to monitor the operating status of grid equipment; The collected data is transmitted to the data center through the IoT gateway to ensure the real-time and integrity of the data; Use a distributed data warehouse to store historical power consumption data and equipment status data, and use a NoSQL database to store and process real-time data streams; Construct different metering and business expansion service theme analysis modules to conduct abnormal diagnosis and analysis of metering and business expansion services based on historical electricity consumption data, equipment status data and real-time data streams; According to the abnormal diagnosis and analysis results of metering and business expansion services, when an abnormality is detected, an alarm is automatically sent to notify relevant personnel according to the alarm priority setting and response strategy.

2. A method for abnormal diagnosis and analysis of metering and business expansion services according to claim 1, characterized in that: The collected data is transmitted to the data center through the IoT gateway to ensure the real-time and integrity of the data, as follows: Deploy an IoT gateway to collect data from smart meters and sensors. The IoT gateway should support multiple communication protocols, including MQTT and CoAP, to be compatible with different devices. Use wireless communication technology to achieve remote data transmission, and adopt SSL / TLS to protect data privacy.

3. A method for abnormal diagnosis and analysis of metering and business expansion services according to claim 1, characterized in that: The distributed data warehouse is used to store historical power consumption data and equipment status data, and the NoSQL database is used to store and process real-time data streams, as follows Data is collected through smart meters and sensors, and the IoT gateway is used for data transmission. The real-time data stream is transmitted to the data processing system through the message queue Apache Kafka. Use the stream processing framework Apache Flink to process real-time data streams; Write processed real-time data into a NoSQL database to support fast query and analysis; Regularly import real-time data from NoSQL databases into distributed data warehouses for historical data storage; use ETL tools to implement batch import and conversion of data.

4. A method for abnormal diagnosis and analysis of metering and business expansion services according to claim 1, characterized in that: The different metering and business expansion service theme analysis modules include a metering service analysis module, a business expansion service analysis module and a business expansion service abnormality management analysis module.

5. A method for abnormal diagnosis and analysis of metering and business expansion services according to claim 4, characterized in that: The metering service analysis module includes: Meter operation analysis: Use status check technology to monitor whether the energy metering device has wiring errors, inaccurate meters, or abnormal status errors; Analysis of abnormal bottom meter readings: It is divided into two parts, namely, analysis of abnormal bottom meter readings before meter replacement and analysis of abnormal bottom meter readings after meter replacement. For analysis of abnormal bottom meter readings before meter replacement, first divide the user range to be analyzed, analyze and compare the readings of any metering point of the transformer with the readings of the next billing cycle, and determine whether there is a logical abnormality. For analysis of abnormal bottom meter readings after meter replacement, in addition to the determination of whether the meter has been replaced, it is also necessary to collect meter replacement work order data, and determine whether there is an abnormality based on the work order data combined with the last collected readings of the month. Analysis of abnormal reverse active power: Based on the electrical parameters obtained by the metering automation system, analyze and sort out the types of reverse active power, establish corresponding identification rules, and monitor the abnormal reverse active power situation; Analysis of the discrepancy between the calibrated current value of the running electric energy meter and the configuration of the transformer: Collect the electric energy parameters, measurement records, and transformer information of the electric energy metering, convert and process the data, collect statistical indicators, create application themes, and assist staff in analyzing the equipment configuration and operation status; Analysis of abnormal metering processing for high-voltage users: Carry out analysis from two perspectives: current failure of high-voltage users’ meters and voltage phase failure of high-voltage users, and set thresholds for duration of abnormal current, voltage and meters; Meter delivery and installation analysis: Access the meter delivery and delivery records, count the meter delivery and delivery status by time, status, and delivery and delivery reasons, formulate abnormal warning rules for meter delivery and delivery, and analyze whether there are abnormal conditions such as untimely meter delivery and delivery installation, and untimely pre-delivery and delivery installation; Analysis of large amount of electricity produced by agricultural drainage: Determine the list of users with large agricultural power consumption based on the basic customer files, extract the corresponding historical power consumption data and electricity price information, and verify whether the customer's actual electricity price is consistent with the nature of electricity consumption, whether there is high-price low-connection, and whether there is illegal power theft; Analysis of the periodic rotation of electric energy metering equipment: Monitor the periodic rotation of electric energy metering equipment, and count the periodic rotation of equipment according to the operation data, service life, manufacturer, installation location, and maintenance record data of the electric energy meter, analyze and determine the electric energy meters that can be used again after expiration, and locate the electric energy meters with hidden faults; Abnormal billing rate analysis: Identify whether the electricity meter is installed with a current transformer, a voltage transformer, or both a current transformer and a voltage transformer, analyze whether the calculated electricity rate or the nameplate rate is consistent with the actual situation, monitor the accuracy of the billing rate, and if there is an abnormal billing rate, generate a list of difference data between the correct and incorrect rates based on the actual rate.

6. A method for abnormal diagnosis and analysis of metering and business expansion services according to claim 4, characterized in that: The business expansion service analysis module specifically includes: Abnormal analysis of account cancellation process: Extract customer account cancellation process data, calculate the processing time of the account cancellation process, determine the information archiving link and data archiving status in the process, formulate abnormal rules based on the user's arrears and prepaid balance, and analyze whether there are abnormal processes that should be completed but not completed, should be terminated but not terminated, etc. Analysis of abnormal modification of transformer capacity information: Extract key customer information process data and analyze changes in billing parameters of transformers or high-voltage motors in the process, including modification of transformer key billing parameters, addition of transformer file information, and deletion of transformer file information. By monitoring key fields including operating status, primary and standby nature, nameplate capacity, planned recovery date, deactivation date, and first operation time, obtain information on transformer capacity that has not been modified in the normal process for user files; Abnormal power outage time analysis: Collect power outage information from the power distribution GPMS system, combine it with the power outage and power supply data from the marketing system, and analyze whether the power supply time of new users is inconsistent with the GPMS time, whether the difference between the shutdown time and the actual shutdown time of the shutdown equipment exceeds the threshold, etc., to ensure timely power supply and timely power outage; Analysis of power supply before signing a contract: For high-voltage users, collect the process nodes of high-voltage new installation and high-voltage capacity increase of the business expansion application type completed during the statistical period, as well as the temporary power consumption process information of the completed meter installation and the customer's signed contract information, compare the contract signing time, the power user's signing date, and the GPMS power supply time, and monitor the situation of power supply before signing a contract; Analysis of business expansion process filing: Extract business expansion business process data, and statistically analyze the business filing status based on process status, business acceptance category, time and other dimensions. Compare the month of installation and removal date of any metering point in the electric energy meter solution with the completion month of business expansion process information filing. If the values ​​of the two months are greater than the specified threshold, extract the information of the process separately, monitor and supervise it, trace the progress of the process, and prevent the risk of customer electricity consumption and electricity fee loss caused by irregular business. Statistical analysis of multiple households at one address: Based on the marketing customer profile, analyze and determine whether there is a situation of multiple households at one address, support the export function of abnormal data list according to a certain template, and assist staff to confirm and clean up multiple households at one address; Analysis of contract signing status: Collect basic data on contract signing, combine it with marketing customer files, and divide the user types into high-voltage users, low-voltage non-residential users, residential users, temporary users, and entrusted power supply users. Formulate verification rules for the completion of contract signing, and count various contracts including new signing, supplementary signing, change, renewal, and termination. Compare the contract signing status of each user category with the corresponding benchmark value to calculate the contract signing completion rate.

7. A method for abnormal diagnosis and analysis of metering and business expansion services according to claim 4, characterized in that: The business expansion service abnormality management and analysis module includes four monitoring points: the number of meter replacements at the same metering point, overdue electricity usage by temporary electricity users, work order return rate, and work order termination rate; the monitoring point for the number of meter replacements at the same metering point stipulates that abnormal data will be generated if the number of times the same metering point exceeds a certain threshold within a certain period; the monitoring point for overdue electricity usage by temporary electricity users stipulates that customers should be reminded to go through the extension procedures a few days before the expiration of the contract of temporary electricity users, and whether the electricity users go through the extension procedures will be monitored. If the expired users do not go through the extension procedures, corresponding abnormal data will be generated; the two monitoring points of work order return rate and work order termination rate refer to the ratio of the number of work order return to the cumulative number of work orders within the period, and the ratio of the cumulative number of work order terminations to the cumulative number of archived work orders, respectively. Monitoring records are generated when they exceed a certain threshold.

8. A measurement and business expansion service abnormality diagnosis and analysis system, characterized in that: It includes a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the abnormal diagnosis and analysis method for metering and business expansion services as described in any one of claims 1 to 7.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 7.

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