Main network electric energy meter data processing method and device, equipment and medium

Abstract

The invention provides a main network electric energy meter data processing method and device, equipment and a medium, and relates to the technical field of electric power. The method comprises the following steps: acquiring electric energy data containing voltage, current, serial number and monitoring timestamp of a main network electric energy meter, and performing preliminary screening processing based on the electric energy data to determine a first abnormal main network electric energy meter; and then performing false abnormity elimination on the first abnormal main network electric energy meter, and determining a second abnormal main network electric energy meter. And finally, determining alarm information based on the second abnormal main network electric energy meter, and pushing the alarm information to terminal equipment of operation and maintenance personnel. Through the method, the whole process from data acquisition to alarm pushing is free of human intervention, and the real-time performance of main network electric energy meter data monitoring response is remarkably improved. Meanwhile, the accuracy of main network electric energy meter abnormity identification is effectively improved, the reliable operation and the operation and maintenance efficiency of a main network metering system are improved, and the operation stability of a power grid and the power supply service quality are further guaranteed.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method, apparatus, equipment and medium for processing main grid electricity meter data. Background Technology

[0002] As the core equipment for electricity metering, the accuracy of the data from the main grid electricity meter is directly related to the quality of power supply services, the efficiency of grid operation, and the protection of users' rights.

[0003] Currently, monitoring of main grid electricity meter data mainly relies on regular manual inspections and meter readings. Maintenance personnel log into the metering automation system to manually query real-time data such as voltage and current from the main grid electricity meters, export it as a spreadsheet file, and then manually determine whether there are any anomalies in the main grid electricity meters. However, manual monitoring is difficult to process massive amounts of electricity meter data, resulting in data collection delays and susceptibility to misjudgments due to human error.

[0004] In summary, improving the real-time performance of main grid electricity meter data monitoring and the reliability of anomaly detection results are urgent technical issues that need to be addressed. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for processing main grid electricity meter data, which aims to improve the real-time performance of main grid electricity meter data monitoring response and the reliability of anomaly determination results.

[0006] In a first aspect, embodiments of this application provide a method for processing main grid electricity meter data, including:

[0007] Obtain the electrical energy data of the main grid electricity meter, the electrical energy data including the voltage, current, serial number and monitoring timestamp of the main grid electricity meter;

[0008] The electrical energy data is initially screened to identify the first abnormal main grid energy meter;

[0009] The first abnormal main grid energy meter was subjected to false anomaly elimination to identify the second abnormal main grid energy meter;

[0010] Based on the second abnormal main grid power meter, alarm information is determined and the alarm information is pushed to the terminal device.

[0011] In one possible implementation, the initial screening of the electrical energy data to identify the first abnormal main grid energy meter includes:

[0012] Based on the electrical energy data, the first abnormal main grid power meter is determined by the preset loss of voltage / current judgment rules;

[0013] The rules for determining voltage / current loss include: the voltage is less than a preset voltage threshold and the duration exceeds a preset voltage loss duration threshold, or the current is less than a preset current threshold and the duration exceeds a preset current loss duration threshold.

[0014] In one possible implementation, the voltage threshold is the product of the rated voltage and a first preset ratio;

[0015] And / or,

[0016] The current threshold is the product of the rated current and the second preset ratio.

[0017] In one possible implementation, the step of performing false anomaly elimination on the first abnormal main grid energy meter and identifying the second abnormal main grid energy meter includes:

[0018] Based on the wiring configuration information of the first abnormal main grid energy meter, the first energy data of the first abnormal main grid energy meter is corrected by using the pre-acquired energy metering conversion formula to obtain the corrected energy data.

[0019] Based on the corrected electrical energy data, a trend comparison analysis is performed using historical electrical energy data from the same period to identify the second abnormal main grid electricity meter.

[0020] In one possible implementation, pushing the alarm information to the terminal device includes:

[0021] The alarm information is pushed to the terminal device through the application programming interface. The alarm information includes the second energy data of the second abnormal main grid energy meter and the abnormality type.

[0022] In one possible implementation, pushing the alarm information to the terminal device via an application programming interface includes:

[0023] If the application programming interface fails to push the alarm information, the alarm information is stored in a message queue and pushed again after a preset interval.

[0024] When the number of retries for push notifications reaches a preset number, the log information of push failures will be stored in the local database.

[0025] In one possible implementation, the method further includes:

[0026] The electrical energy data is preprocessed to obtain preprocessed electrical energy data; the preprocessing includes at least one of missing value processing, outlier processing, and standardization processing.

[0027] Secondly, embodiments of this application provide a data processing apparatus for main grid electricity meters, comprising:

[0028] The first processing module is used to acquire the electrical energy data of the main grid electricity meter, the electrical energy data including the voltage, current, number and monitoring timestamp of the main grid electricity meter;

[0029] The second processing module is used to perform preliminary screening of the electrical energy data to identify the first abnormal main grid electricity meter.

[0030] The third processing module is used to eliminate false anomalies in the first abnormal main grid energy meter and identify the second abnormal main grid energy meter.

[0031] The fourth processing module is used to determine alarm information based on the second abnormal main grid power meter and push the alarm information to the terminal device.

[0032] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0033] The memory stores computer-executed instructions;

[0034] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0036] This application provides a method, apparatus, device, and medium for processing main grid energy meter data. The method involves acquiring energy data from main grid energy meters, including voltage, current, serial number, and timestamp; then performing rule-based initial screening on this data to identify a first-order abnormal main grid energy meter; next, performing false anomaly elimination on the first-order abnormal main grid energy meter to accurately identify a second-order abnormal main grid energy meter reflecting actual metering or line faults by eliminating false anomalies caused by data fluctuations or configuration differences; finally, generating alarm information based on the confirmed second-order abnormal main grid energy meters and pushing it to relevant terminal devices. This method effectively improves the accuracy of main grid energy meter anomaly identification, reduces false alarms, and, through an automated alarm push mechanism, ensures that grid maintenance personnel can promptly learn about and handle genuine anomalies, thereby guaranteeing the reliable operation and maintenance efficiency of the main grid metering system, ensuring grid operation stability, and guaranteeing the quality of power supply services. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] Figure 1 A flowchart illustrating a method for processing main grid electricity meter data provided in this application. Figure 1 ;

[0039] Figure 2 A flowchart illustrating a method for processing main grid electricity meter data provided in this application. Figure 2 ;

[0040] Figure 3 A flowchart illustrating a method for processing main grid electricity meter data provided in this application. Figure 3 ;

[0041] Figure 4 A schematic diagram of a main grid electricity meter data processing device provided in this application;

[0042] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application.

[0043] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0045] The application background of this application is explained as follows:

[0046] Against the backdrop of the power industry being deeply empowered by information technology, communication technology, and automation technology, digital transformation and smart grid construction are continuously advancing. Smart meters and metering automation systems have become core facilities for electricity metering. As the core equipment for electricity metering, the main grid electricity meter's current, voltage, and other electrical load data are crucial to ensuring metering accuracy. This accuracy is a core requirement for ensuring the quality of power supply services, grid operation efficiency, user rights, and metering maintenance.

[0047] Main grid electricity meters are distributed along high-voltage transmission lines, substations, and key nodes in the distribution network, undertaking the responsibility of large-scale electricity data collection and transmission. However, with the expansion of the power grid and the intensification of electricity load fluctuations, main grid electricity meters need to monitor electrical energy parameters such as voltage and current in real time and quickly identify abnormal states such as voltage loss and current loss to ensure the reliability of metering data.

[0048] Currently, monitoring of main grid electricity meter data primarily relies on regular manual inspections and meter readings. Maintenance personnel log into the automated metering system to manually query real-time energy data such as voltage and current from the main grid electricity meters, exporting it as a spreadsheet file. They then manually verify the meter parameters (rated voltage, rated current, etc.) against the meter reading data, using experience to determine if there are any anomalies such as voltage or current loss. Once an anomaly is identified in the main grid electricity meter data, the meter number and anomaly type are manually recorded, and relevant personnel are notified by phone or email to resolve the issue.

[0049] However, the monitoring method relying on manual inspection and meter reading of main grid electricity meters has the following drawbacks: 1) Low efficiency: Maintenance personnel need to manually log into the metering automation system to read meters and then manually combine the meter parameters to determine whether there are any abnormalities in the main grid electricity meters. This makes it difficult to process massive amounts of data, resulting in data acquisition delays, long analysis cycles, and susceptibility to misjudgments due to human error. 2) Delayed response: Manual monitoring cannot achieve 24 / 7 coverage. The untimely detection of abnormal data from main grid electricity meters leads to delays in troubleshooting, affecting the timeliness and accuracy of metering system operation and maintenance, which in turn can cause power supply service disputes and affect the stability of the power grid.

[0050] In summary, existing methods for monitoring grid electricity meter data are no longer adequate to keep pace with the development of smart grids. Overcoming the limitations of the current monitoring model that relies on manual inspection and meter reading of main grid electricity meter data, improving the efficiency of processing massive amounts of main grid electricity meter data, reducing human error, improving the reliability of main grid electricity meter anomaly detection results, and ensuring grid operation stability and power supply service quality are urgent technical issues that need to be addressed in the context of current smart grid construction.

[0051] Based on the aforementioned technical problems, the inventor, as a member of the power grid operation and maintenance team of a power supply bureau, discovered during the research on monitoring main grid energy meter data that, by introducing a multi-dimensional anomaly determination method based on the main grid energy meter's energy data, involving "dynamic threshold - historical comparison - parameter correction," the problem of high false alarm rates was solved. Then, alarm information from main grid energy meters exhibiting anomalies was pushed to terminal devices. Through a complete technical route of "data acquisition - analysis and processing - anomaly determination - alarm push," a fully automated monitoring solution was formed, achieving accurate identification and immediate alarm of main grid energy data. Based on this, this application provides a method, apparatus, device, and medium for processing main grid energy meter data.

[0052] This application provides a method, apparatus, equipment, and medium for processing main grid electricity meter data, which can be deployed on the power supply bureau's metering and maintenance platform and applied to main grid electricity meter monitoring scenarios at key nodes of high-voltage transmission lines, substations, and distribution networks.

[0053] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0054] Figure 1 A flowchart illustrating a method for processing main grid electricity meter data provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0055] S101: Obtain the electrical energy data of the main grid electricity meter. The electrical energy data includes the voltage, current, serial number, and monitoring timestamp of the main grid electricity meter.

[0056] In this step, electrical energy data refers to the core information set that reflects the operating status of the main grid electricity meter, the electricity metering results, and the data acquisition background. It includes voltage, current, the main grid electricity meter number, and the monitoring timestamp. It is the key data foundation for realizing the verification of the accuracy of electricity metering, the identification of equipment anomalies, and operation and maintenance management.

[0057] Specifically, by interfacing with the metering automation system through an interface adapter component, the system automatically captures electricity meter data at preset time intervals, and simultaneously collects related information such as meter number and monitoring time. For example, through an intermediate plugin compatible with a certain version of the metering automation system of a substation, the system automatically captures the voltage values ​​(e.g., 220kV) and voltage values ​​(e.g., 500A) of the main grid electricity meters of a substation every 10 minutes, recording the meter number (e.g., "SN-2023-001") and the capture timestamp (e.g., "2023-10-01 12:00:00"). The collected electricity meter data is then stored in a local database as a structured data set in a unified format (e.g., JSON or CSV). This automated collection of main grid electricity meter data eliminates the need for manual intervention, solving the problems of data lag and omissions inherent in traditional manual meter reading. It ensures the real-time nature and completeness of electricity data, providing a reliable data foundation for subsequent anomaly analysis and metering maintenance of the main grid electricity meters.

[0058] S102: Perform preliminary screening of electrical energy data to identify the first abnormal main grid electricity meter.

[0059] In one possible implementation, the electrical energy data is first preprocessed to obtain preprocessed electrical energy data; the preprocessing includes at least one of missing value processing, outlier processing, and standardization processing.

[0060] Preprocessing of electrical energy data refers to the preliminary data cleaning process performed on the raw electrical energy data before the initial screening. Its purpose is to remove data noise, standardize the data format, and ensure the accuracy and reliability of subsequent initial screening analysis. This preprocessing includes at least one of the following: missing value handling, outlier handling, and standardization.

[0061] Among them, missing value handling is for cases of missing values ​​or data due to communication fluctuations or momentary equipment failures during the data collection process. It uses interpolation methods (such as linear interpolation, filling with the average of data from adjacent time points) or filling with feature values ​​based on similar time periods of historical electrical energy data to complete missing data entries and avoid interference from incomplete data on the initial screening results.

[0062] Outlier handling refers to replacing invalid data, such as extreme anomalies that exceed the normal metering range caused by data acquisition interference or equipment false alarms, with the average of data from adjacent time points or removing invalid data points to ensure that the data participating in the initial screening conforms to the normal fluctuation pattern of the main grid's electrical energy data.

[0063] Standardization processing refers to addressing the issue of inconsistent data formats caused by differences in acquisition accuracy, measurement range, and data units among different models of main grid energy meters. This is achieved through normalization (e.g., converting voltage and current data into proportional coefficients relative to rated values) or standardized conversion (e.g., unifying data acquisition frequency and converting data units) to standardize the data from various energy meters into a unified benchmark. This eliminates the impact of equipment configuration differences on the application of initial screening rules, enabling batch calculations and analyses to be performed efficiently based on consistent data standards. Ultimately, the above preprocessing operations yield optimized energy data, providing a reliable data foundation for subsequent initial screening analysis based on preset judgment rules, and accurately identifying the first abnormal main grid energy meter that meets the abnormal conditions.

[0064] Specifically, based on the metering standards and operation and maintenance experience of the power industry, the pre-processed electrical energy data is analyzed in batches according to the established conditions for voltage and current loss judgment. The first abnormal main grid energy meter is initially identified. In other words, the first abnormal main grid energy meter refers to the main grid energy meter that meets the preset abnormal judgment rules after initial screening but has not yet undergone further precise verification. This type of energy meter is only in a suspected abnormal state, providing a clear analysis object for subsequent false anomaly elimination, and ultimately achieving accurate identification of the real abnormal electricity meter.

[0065] S103: Perform false anomaly elimination on the first abnormal main grid energy meter and identify the second abnormal main grid energy meter.

[0066] In this step, false anomaly elimination refers to the technical process of identifying the first abnormal grid energy meter from the initial screening process and using multi-dimensional reference data for verification and analysis. This process eliminates false anomalies caused by non-metering fault factors such as fluctuations in normal grid operation, data acquisition deviations, or equipment configuration differences, while retaining genuine metering anomalies. The second abnormal grid energy meter refers to the genuine abnormal grid energy meter whose abnormal state, after verification through the false anomaly elimination process, is confirmed to be caused by metering faults or line anomalies, rather than by interference from normal operating conditions. This represents a precise screening and identification of the first abnormal grid energy meter.

[0067] Specifically, the energy data of the first abnormal main grid energy meter is compared and analyzed with the historical data of the main grid energy meter during the same period to eliminate false anomalies caused by normal load fluctuations of the power grid. At the same time, the wiring parameters of the associated meter are corrected and calculated to identify the truly abnormal main grid energy meter, namely the second abnormal main grid energy meter.

[0068] S104: Based on the second abnormal main grid power meter, determine the alarm information and push the alarm information to the terminal device.

[0069] In this step, alarm information refers to standardized notification data formed by integrating core information related to the actual abnormal status of the second abnormal main grid energy meter. It includes at least the unique identifier of the second abnormal main grid energy meter (e.g., meter number), the type of abnormality (e.g., current loss or voltage loss), the timestamp of the abnormality, and core abnormal data (e.g., abnormal voltage / current values). Its core function is to provide maintenance personnel with accurate and directly usable information for handling abnormalities. Terminal equipment refers to electronic devices used daily by maintenance personnel that can receive alarm notifications, including smartphones, tablets, and dedicated maintenance terminals with network communication capabilities, ensuring that alarm information reaches maintenance personnel in real time.

[0070] Specifically, alarm information is pushed to terminal devices through a preset communication interface. After receiving the alarm information, the terminal devices remind maintenance personnel through pop-up windows, voice prompts or message notifications, providing accurate data support for maintenance personnel to quickly carry out on-site verification and fault handling, and realizing closed-loop management from data collection to anomaly judgment and maintenance response.

[0071] The method for processing main grid energy meter data provided in this application automatically collects the energy data of main grid energy meters and sequentially performs data preprocessing, initial screening and identification, and false anomaly elimination to identify the truly abnormal main grid energy meters. Alarm information generated based on the energy data of the truly abnormal main grid energy meters is then pushed to the terminal devices of maintenance personnel. This method effectively solves the data lag and omission problems inherent in traditional manual meter reading, achieving efficient and accurate identification of abnormal main grid energy meter states. It improves the automation level and response speed of metering and maintenance, providing technical support for the stable operation and reliable management of the power system.

[0072] Figure 2 A flowchart illustrating a method for processing main grid electricity meter data provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method for processing the main grid electricity meter data is described in detail. The method includes:

[0073] S201: Obtain the current and voltage of the main grid energy meter.

[0074] For example, as mentioned in S101, the interface adapter component that interfaces with the metering automation system automatically captures the current and voltage of the main grid energy meter at preset time intervals (such as every 10 minutes), providing a data basis for subsequent judgment based on current and voltage to determine whether the main grid energy meter has current loss or voltage loss abnormalities.

[0075] S202: Based on the electrical energy data, the first abnormal main grid energy meter is determined through the preset loss of voltage / current judgment rules.

[0076] In this step, the voltage / current loss judgment rule is a standardized judgment logic that is pre-set to identify metering anomalies of the main grid energy meters, based on power industry metering standards such as "DL / T 614-2017 Multifunctional Energy Meter" and "DL / T825-2019 Operation and Management Regulations for Energy Metering Devices" and combined with the main grid operation and maintenance practice experience. Specifically, it includes voltage loss judgment rule and current loss judgment rule.

[0077] In one possible implementation, the undervoltage / current loss determination rule includes: the voltage is less than a preset voltage threshold and the duration exceeds a preset undervoltage duration threshold, or the current is less than a preset current threshold and the duration exceeds a preset current loss duration threshold.

[0078] Among them, the voltage threshold is the product of the rated voltage and the first preset ratio. The first preset ratio refers to the voltage anomaly threshold ratio preset according to actual operation and maintenance needs. In the main grid scenario, it is usually set to 80%~90%. The preset voltage loss duration threshold refers to the shortest continuous duration for which the voltage must be less than the voltage threshold. Its core function is to eliminate false voltage anomalies caused by non-continuous factors such as instantaneous fluctuations in the power grid and communication interference, avoid misjudgments triggered by instantaneous data fluctuations, and ensure the accuracy of voltage loss anomaly judgment. In the main grid scenario, this threshold is usually set to 30 seconds to 5 minutes.

[0079] The current threshold is the product of the rated current and the second preset ratio. The second preset ratio refers to the current abnormality threshold ratio preset according to actual operation and maintenance needs. In the main grid scenario, it is usually set to 5%~10%. The preset current loss duration threshold refers to the shortest continuous duration for which the current must be less than the current threshold. It is consistent with the value logic of the preset voltage loss duration threshold (mostly 30 seconds to 5 minutes). It is used to eliminate false current abnormalities caused by non-continuous factors such as instantaneous load fluctuations of the power grid and data acquisition interference, avoid misjudgment triggered by instantaneous data fluctuations, and ensure the accuracy of current loss abnormality judgment.

[0080] For example, suppose a main grid energy meter configured on a 10kV main grid line has a rated voltage of 100V (secondary rated voltage) and a rated current of 5A (secondary rated current). Based on main grid maintenance requirements and industry standards, a first preset ratio of 85% and a second preset ratio of 8% are set. Both the preset voltage loss duration threshold and the preset current loss duration threshold are set to 60 seconds. When performing anomaly screening on the energy data of this energy meter based on the above parameters, if the system collects that the real-time voltage of the energy meter remains stable at 82V for 70 seconds (less than the voltage threshold 100V × 85% = 85V), it meets the voltage loss judgment rule, and the main grid energy meter is determined to be the first abnormal main grid energy meter. The main grid energy meter is identified as the first abnormal main grid energy meter if: 1) the real-time current of the energy meter remains at 0.35A for 80 seconds (less than the current threshold of 5A × 8% = 0.4A) and the corresponding real-time voltage is 98V (within the normal range, excluding power outage scenarios); 2) the current loss judgment rule is met, and the main grid energy meter is identified as the first abnormal main grid energy meter. However, if the voltage is 83V but only lasts for 20 seconds before recovering to 90V, or if the current is 0.38A but only lasts for 40 seconds, these conditions do not meet the voltage / current loss judgment rule because they do not reach the preset duration threshold, and therefore will not be identified as the first abnormal main grid energy meter, effectively eliminating false anomalies caused by instantaneous fluctuations.

[0081] S203: Based on the wiring configuration information of the first abnormal main grid energy meter, the first energy data of the first abnormal main grid energy meter is corrected by using the pre-acquired energy metering conversion formula to obtain the corrected energy data.

[0082] In this step, the wiring configuration information refers to the core technical information related to the electrical connection method, metering circuit parameters, and wiring type of the main grid energy meter's connection to the power grid. This information can be obtained in advance from the metering automation system and is a prerequisite for the application of the energy metering conversion formula, directly affecting the accuracy of the energy data correction. Specifically, it includes: the wiring method of the main grid energy meter and current transformers, voltage transformers (such as the three-phase three-wire "V / V" wiring, three-phase four-wire "Y / Y" wiring commonly used in main grid high-voltage metering), the transformer ratio parameters (ratio of rated primary current to secondary current of the current transformer, ratio of rated primary voltage to secondary voltage of the voltage transformer), wiring polarity, the wiring group of the metering circuit (such as Y / Y-12, V / V-0, etc.), and the basic parameters such as rated voltage and rated current marked on the meter nameplate. This provides accurate configuration parameter support for the energy metering conversion formula, making the corrected energy data more consistent with actual operating conditions and providing a reliable basis for subsequent false anomaly elimination and real anomaly determination.

[0083] The electricity metering conversion formula refers to a standardized formula that converts the collected raw data such as voltage or current into actual electrical energy or is used to correct metering deviations based on the hardware configuration such as the meter wiring method and transformer ratio parameters. Its core purpose is to eliminate metering deviations caused by different wiring methods and transformer ratio configurations, and to ensure the accuracy of anomaly detection.

[0084] For example, if the wiring method of the first abnormal main grid energy meter is three-phase three-wire, then the energy metering conversion formula can be: Where E represents the actual electrical energy value (unit: kWh), used to compare with the electrical energy data of the first abnormal main grid energy meter in a suspected abnormal state; U2 represents the effective value of the secondary side line voltage collected by the first abnormal main grid energy meter (unit: V); I2 represents the effective value of the secondary side line current collected by the first abnormal main grid energy meter (unit: A). K represents the power factor. VT Indicates the voltage mutual inductance ratio (commonly found in main grids, K). VT =10000 / 100=100, meaning the primary side 10kV is converted to the secondary side 100V); K CT Indicates the current inductance ratio (commonly found in main grids, K). CT =600 / 5=120, that is, the primary side 600A is converted to the secondary side 5A). Indicates the measurement time interval (unit: h, e.g., if the data acquisition cycle is 15 minutes, then...). ).

[0085] If the wiring method of the first abnormal main grid energy meter is three-phase four-wire, then the energy metering conversion formula can be: , among which, U A U B UC These represent the effective values ​​of the secondary side voltages of phases A, B, and C collected by the first abnormal main grid energy meter; I A I B I C These represent the effective values ​​of the secondary currents of phases A, B, and C collected by the first abnormal main grid energy meter; , , These represent the power factors of phases A, B, and C, respectively.

[0086] For the first abnormal main grid energy meter in a suspected abnormal state, if there is a phase deviation in its meter wiring, such as a broken phase in a three-phase three-wire "V / V" connection or reversed polarity, the first energy data of the first abnormal main grid energy meter is corrected using a correction coefficient. The correction formula can be expressed as: ,in, This represents the corrected electrical energy data. K represents the wiring correction factor (valued according to industry standards, e.g., K=2 when one phase voltage is broken in a three-phase three-wire system, or when polarity is reversed). (K=1 during normal wiring).

[0087] S204: Based on the corrected electrical energy data, a trend comparison analysis is performed using historical electrical energy data from the same period to identify the second abnormal main grid electricity meter.

[0088] In this step, the historical energy data refers to the set of historical energy data within the same time period (e.g., the same working day and time period, or the same seasonal electricity consumption period) compared to the current monitoring period of the first abnormal main grid energy meter. This data is extracted from the historical monitoring data stored in the metering automation system and includes key indicators such as voltage, current, and energy load for the corresponding time period, serving as a benchmark for comparison with the current corrected data. The second abnormal main grid energy meter refers to a real abnormal main grid energy meter whose abnormal state, confirmed through trend comparison analysis of the corrected energy data, is not caused by differences in wiring configuration, data deviation, or normal fluctuations in the power grid, but by metering faults or line anomalies. This represents a precise screening and identification of the first abnormal main grid energy meter.

[0089] Specifically, a normal fluctuation range of historical data for the same period is established by using statistical analysis methods (such as calculating the mean, standard deviation, and fluctuation range of the data). The corrected electrical energy data is then quantitatively compared with this range to determine whether the corrected electrical energy data deviates from the historical normal operating trend.

[0090] For example, based on the meter number of the first abnormal main grid electricity meter, retrieve the historical electricity data for that meter during the same period. If the current monitoring period is Monday morning from 9:00 to 9:15, select all Monday morning electricity data from the past three months during the same period to ensure the reasonableness of the comparison benchmark. Subsequently, perform statistical analysis on the historical electricity data, calculate the statistical parameters of core indicators such as effective voltage value, current fluctuation amplitude, and electricity load value, and establish a historical normal fluctuation range. For example, use the mean of the historical data ± 3 times the standard deviation as the normal fluctuation boundary to form a standardized comparison benchmark. Next, the corrected energy data (with metering deviations caused by wiring configuration differences eliminated) is compared with the established historical normal fluctuation range. If the corrected energy data is within the historical normal fluctuation range, it indicates that the anomaly is a false anomaly caused by normal load fluctuations in the power grid, and the energy meter is removed from the first abnormal main grid energy meter. If the corrected energy data exceeds the historical normal fluctuation range, it indicates that the anomaly is not caused by interference under normal operating conditions, but is a real metering anomaly or line anomaly. The energy meter confirmed to have a real anomaly is locked as the second abnormal main grid energy meter, completing the accurate determination from suspected anomaly to real anomaly, providing a reliable basis for subsequent alarm push and operation and maintenance handling.

[0091] S205: Based on the second abnormal main grid energy meter, determine the alarm information and push the alarm information to the terminal device through the application programming interface. The alarm information includes the second energy data of the second abnormal main grid energy meter and the abnormality type.

[0092] An Application Programming Interface (API) is a pre-integrated interface used to enable data communication between an anomaly monitoring and metering automation system and terminal equipment. It has stable data transmission capabilities and can support real-time push of alarm information.

[0093] For example, the API interface could be the elink API interface, which is a core service channel in the power grid system used to realize standardized and secure data interaction between internal business platforms and external terminal applications. It fully considers the stringent requirements of power production for stability and security. It not only simplifies the integration complexity between heterogeneous systems through a unified protocol and data format, but also ensures that alarm information is not lost or omitted in abnormal situations such as network fluctuations through built-in message persistence, failure retry and other fault tolerance mechanisms, and is ultimately accurately delivered to the terminals of operation and maintenance personnel.

[0094] Specifically, the alarm information includes the unique identifier of the second abnormal main grid power meter (such as the meter number), the type of abnormality (such as current loss or voltage loss), the timestamp of the abnormality, and the core data of the abnormality (such as abnormal voltage / current value).

[0095] The method for processing main grid energy meter data provided in this application embodiment obtains the current and voltage of the main grid energy meter and initially screens out the first abnormal main grid energy meter based on preset undervoltage / undercurrent judgment rules. Then, combining the wiring configuration information of the first abnormal main grid energy meter, the energy data of the first abnormal energy meter is corrected using an energy metering conversion formula to eliminate metering deviations caused by differences in wiring methods or transformer ratio parameters. Next, the corrected data is compared with historical energy data from the same period to further identify and lock in the true second abnormal main grid energy meter, effectively eliminating false anomalies caused by instantaneous fluctuations in the power grid or normal load changes. Finally, alarm information containing the anomaly type and detailed data is generated and pushed to the terminal device through an integrated application programming interface. This method significantly improves the accuracy and reliability of main grid energy meter anomaly detection, avoids false alarms caused by instantaneous fluctuations, wiring differences, or historical normal fluctuations, and provides effective support for the safe and stable operation of the power grid.

[0096] Figure 3 A flowchart illustrating a method for processing main grid electricity meter data provided in this application. Figure 3 ,like Figure 3 As shown, based on the above embodiment, in S205: pushing alarm information to the terminal device through the application programming interface specifically includes:

[0097] S301: Pushes alarm information to terminal devices via application programming interface.

[0098] S302: Determine whether the alarm information was successfully pushed.

[0099] If not, execute S303; if yes, execute S05.

[0100] S303: Store alarm information in a message queue and re-push it after a preset interval.

[0101] In other words, if the application programming interface fails to push alarm information, the alarm information will be stored in the message queue and pushed again after a preset interval.

[0102] Message queues are distributed data storage middleware designed based on the first-in, first-out (FIFO) principle. They are used to implement asynchronous message transmission and temporary storage in distributed systems, decoupling message senders and receivers, preventing message loss or system blockage due to network fluctuations or interface failures, and ensuring the reliability and stability of message transmission. The preset duration refers to the retry interval (typically 3-10 seconds in mainnet scenarios) pre-set based on mainnet operation and maintenance response requirements and interface communication characteristics. This balances retry efficiency with system resource consumption, avoiding frequent retries that could put pressure on the interface and system.

[0103] For example, when pushing alarm information to the terminal device through the elink API interface, the response status of the interface is monitored in real time (such as the returned Hypertext Transfer Protocol (HTTP) status code, interface communication receipt, etc.). If the interface times out or returns an error status code, or if the push fails, the alarm information is stored in a pre-configured message queue. After a preset interval (such as 3 seconds), the alarm information is automatically retrieved from the message queue, and the elink API interface is called again to initiate the push request. During the retry process, the interface response status is continuously monitored.

[0104] S304: Determine whether the retry push was successful within the preset number of retries.

[0105] If not, continue to execute S303; if yes, execute S305.

[0106] S305: Store alarm information and push success logs to the local database.

[0107] In other words, if the retry push is successful within the preset number of retries, the alarm information will be removed from the message queue, and the alarm information and push success log will be stored in the local database.

[0108] Correspondingly, if the push fails within the preset number of retries, the process of "temporary queue - interval waiting - retry push" will be repeated until the push is successful or the preset maximum number of retries is reached (usually 3-5 times in the mainnet scenario). In other words, when the preset number of retries is reached, the push failure log information will be stored in the local database. At the same time, a backup reminder mechanism (such as SMS push or platform in-site notification) will be triggered, and the final failure log will be recorded for maintenance personnel to troubleshoot interface failures later, ensuring that alarm information can be stably and timely transmitted to the terminal devices of maintenance personnel, and ensuring the timeliness of anomaly handling.

[0109] The method for processing main grid electricity meter data provided in this application embodiment, after identifying a second abnormal main grid electricity meter and generating alarm information, pushes the alarm information to the terminal device through an integrated application programming interface. When the initial alarm information push fails, it is temporarily stored in a message queue based on the first-in-first-out principle to decouple the sending and receiving processes, avoiding system blockage or message loss. Subsequently, after a preset interval, the message is automatically retrieved from the queue for retrying the push, and the interface response status is continuously monitored until the push is successful within a preset maximum number of retries. If the push is successful, the alarm information and push success log are stored in a local database. If the push fails after reaching the maximum number of retries, the alarm information and push failure log are stored in a local database, and backup reminder mechanisms such as SMS and in-site messages are triggered to ensure that the abnormal information ultimately reaches the maintenance personnel. Through the above method, with the help of a reliable message push and retry mechanism, the stable delivery of alarm information is ensured, improving the timeliness of main grid metering anomaly handling and maintenance efficiency.

[0110] In one specific implementation of this solution, the data processing method for the main grid electricity meter provided in this application achieves the following technical effects:

[0111] 1) It effectively solves the problems of low efficiency and easy misjudgment caused by human error in the monitoring method of main grid electricity meter data that relies on manual inspection and meter reading, reduces labor costs, avoids economic losses caused by metering errors, reduces the cost of handling user complaints and disputes, and improves the economic benefits of power supply companies.

[0112] 2) By controlling the false anomaly rate to within 5%, the reliability of the main grid electricity meter anomaly judgment results is improved, providing accurate anomaly information for operation and maintenance, reducing ineffective defect elimination work, and thus providing accurate metering services for users, improving public satisfaction. Efficient operation and maintenance helps to build a modern power supply service system, ensures the stable operation of the power grid, provides reliable power support for industrial production and residential life, and promotes local economic development.

[0113] 3) It has achieved full automation of the "data acquisition-analysis and processing-anomaly judgment-alarm push" process, standardized the operation and maintenance process, made it easier for operation and maintenance personnel to grasp the metering status, provided data support for decision-making, and optimized the efficiency of power grid management.

[0114] Figure 4 A schematic diagram of a main grid electricity meter data processing device provided in this application is shown below. Figure 4 As shown, the main grid energy meter data processing device 40 provided in this embodiment includes:

[0115] The first processing module 401 is used to acquire the electrical energy data of the main grid electricity meter. The electrical energy data includes the voltage, current, number and monitoring timestamp of the main grid electricity meter.

[0116] The second processing module 402 is used to perform preliminary screening of electrical energy data to identify the first abnormal main grid electricity meter.

[0117] The third processing module 403 is used to perform false anomaly elimination on the first abnormal main grid energy meter and identify the second abnormal main grid energy meter.

[0118] The fourth processing module 404 is used to determine the alarm information based on the second abnormal main grid energy meter and push the alarm information to the terminal device.

[0119] In one possible implementation, the second processing module 402 is specifically used for:

[0120] Based on electrical energy data, the first abnormal main grid energy meter is identified through preset loss of voltage / current judgment rules;

[0121] The rules for determining voltage / current loss include: the voltage is less than a preset voltage threshold and the duration exceeds a preset voltage loss duration threshold, or the current is less than a preset current threshold and the duration exceeds a preset current loss duration threshold.

[0122] In one possible implementation, the voltage threshold is the product of the rated voltage and a first preset ratio;

[0123] And / or,

[0124] The current threshold is the product of the rated current and the second preset ratio.

[0125] In one possible implementation, the third processing module 403 is specifically used for:

[0126] Based on the wiring configuration information of the first abnormal main grid energy meter, the first energy data of the first abnormal main grid energy meter is corrected by using the pre-acquired energy metering conversion formula to obtain the corrected energy data.

[0127] Based on the corrected electrical energy data, a trend comparison analysis was conducted using historical electrical energy data from the same period to identify the second abnormal main grid electricity meter.

[0128] In one possible implementation, the fourth processing module 404 is specifically used for:

[0129] Alarm information is pushed to the terminal device through the application programming interface. The alarm information includes the second energy data of the second abnormal main grid energy meter and the abnormality type.

[0130] In one possible implementation, the fourth processing module 404 is further used for:

[0131] If the application programming interface fails to push alarm information, the alarm information will be stored in the message queue and pushed again after a preset interval.

[0132] When the number of retries for push notifications reaches a preset number, the log information of push failures will be stored in the local database.

[0133] In one possible implementation, the main grid electricity meter data processing device 40 further includes:

[0134] The fifth processing module 405 is used to preprocess the electrical energy data to obtain preprocessed electrical energy data; the preprocessing includes at least one of missing value processing, outlier processing and standardization processing.

[0135] The main grid electricity meter data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0136] Figure 5 A schematic diagram of the structure of an electronic device provided in this application, such as... Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0137] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0138] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0139] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0140] The memory may include random access memory (RAM) in high-speed memory, and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0141] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0142] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0143] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0144] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an ASIC. Alternatively, the processor and the readable storage medium can exist as discrete components in a device.

[0145] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0148] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0149] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0150] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing data of a master grid electric energy meter, characterized in that, The method comprises the following steps: acquiring electric energy data of a main grid electric energy meter, wherein the electric energy data comprises voltage, current, number and monitoring time stamp of the main grid electric energy meter; performing preliminary screening processing on the electric energy data to determine a first abnormal main grid electric energy meter; performing false abnormality exclusion on the first abnormal main grid electric energy meter to determine a second abnormal main grid electric energy meter; determining alarm information based on the second abnormal main grid electric energy meter, and pushing the alarm information to a terminal device.

2. The method of claim 1, wherein, The preliminary screening processing on the electric energy data to determine a first abnormal main grid electric energy meter comprises: determining a first abnormal main grid electric energy meter based on the electric energy data by using a preset voltage loss / current loss determination rule. The voltage loss / current loss determination rule comprises that the voltage is less than a preset voltage threshold value and the duration exceeds a preset voltage loss duration threshold value, or the current is less than a preset current threshold value and the duration exceeds a preset current loss duration threshold value.

3. The method of claim 2, wherein, The voltage threshold value is a product of a rated voltage and a first preset proportion. And / or, The current threshold value is a product of a rated current and a second preset proportion.

4. The method according to any one of claims 1 to 3, characterized in that, The false abnormality exclusion on the first abnormal main grid electric energy meter to determine a second abnormal main grid electric energy meter comprises: correcting first electric energy data of the first abnormal main grid electric energy meter based on wiring configuration information of the first abnormal main grid electric energy meter by using a pre-acquired electric energy metering conversion formula to obtain corrected electric energy data; determining the second abnormal main grid electric energy meter by comparing and analyzing trends of the corrected electric energy data and synchronous historical electric energy data.

5. The method according to any one of claims 1 to 3, characterized in that, The pushing of the alarm information to the terminal device comprises: pushing the alarm information to the terminal device through an application programming interface, wherein the alarm information comprises second electric energy data and an abnormal type of the second abnormal main grid electric energy meter.

6. The method of claim 5, wherein, The pushing of the alarm information to the terminal device through the application programming interface comprises: if the application programming interface fails to push the alarm information, storing the alarm information in a message queue and re-pushing after an interval of a preset time duration; when the number of re-pushing reaches a preset number, storing log information of the failed pushing to a local database.

7. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: preprocessing the electric energy data to obtain preprocessed electric energy data, wherein the preprocessing comprises at least one of missing value processing, abnormal value processing and standardization processing.

8. A processing device for master grid electric energy meter data, characterized in that, The method comprises: a first processing module configured to acquire electric energy data of a main grid electric energy meter, wherein the electric energy data comprises voltage, current, number and monitoring time stamp of the main grid electric energy meter; a second processing module configured to perform preliminary screening processing on the electric energy data to determine a first abnormal main grid electric energy meter; a third processing module configured to perform false abnormality exclusion on the first abnormal main grid electric energy meter to determine a second abnormal main grid electric energy meter; a fourth processing module configured to determine alarm information based on the second abnormal main grid electric energy meter, and push the alarm information to a terminal device.

9. An electronic device, comprising: The method comprises: a memory and a processor; the memory stores computer execution instructions. The processor executes computer-executable instructions stored in the memory such that the processor performs the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions which, when executed by a processor, implement the method of any one of claims 1 to 7.

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