Internet of Things electric energy meter capable of online verification

Abstract

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things electric energy meter capable of online verification, which comprises a data monitoring module, an anomaly detection module, a performance analysis module, a calibration optimization module, a health monitoring module and a maintenance planning module. According to the invention, through the Internet of Things technology, the remote monitoring and automatic adjustment capability of the electric energy meter is improved, real-time data monitoring and anomaly detection are adopted to realize instant feedback and rapid anomaly identification of the operation data of the electric energy meter, so that the timeliness and accuracy of data processing are improved, the energy efficiency loss and fault risk are reduced, and the multi-dimensional data analysis is more accurate. The electric meter performance state and potential defects can be evaluated and predicted more accurately, calibration parameters are adjusted and optimized in time, the operation efficiency of the electric meter is improved, a long-term maintenance strategy is optimized, performance optimization and fault prevention can be continuously provided for the electric meter through automatic health monitoring and maintenance planning, and the safety of the electric meter is improved. And the stability of the power grid is greatly enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of Internet of Things, and in particular to an Internet of Things electric energy meter capable of online verification. Background Art

[0002] Internet of Things technology refers to a technical system that connects physical objects to the Internet through embedded sensors, software and other technologies. These connected objects can collect and exchange data to achieve automation and smarter operations. In the application of electricity meters, Internet of Things technology enables electricity meters to transmit consumption data to data management systems in real time, while accepting control instructions from the central system to perform remote functional operations such as setting, monitoring and troubleshooting. This technology not only improves the efficiency and accuracy of data collection, but also reduces labor costs and response time through remote monitoring and maintenance, making power grid management more intelligent and efficient.

[0003] Among them, the IoT electricity meter that can be calibrated online involves the implementation of electricity meters through the IoT technology, which can be verified and calibrated without the need for on-site visits. Its main purpose is to measure and manage electric energy in the power system to ensure the accuracy and compliance of measurement. The online verification function allows operation and maintenance personnel to remotely monitor and adjust the performance of the electricity meter, and promptly discover and correct possible metering errors or equipment failures, thereby improving the operation and maintenance efficiency of the electricity meter and the reliability of the power grid. It is an important part of the intelligent management of modern power grids.

[0004] Existing technologies rely on periodic maintenance processes, which result in high maintenance costs and long cycles in large power grid systems, resulting in low efficiency. In existing systems, data processing is delayed, affecting the timeliness of fault response and failing to effectively prevent problems. Lack of efficient data analysis makes it difficult to achieve comprehensive real-time monitoring of meter performance, resulting in slow response to maintenance plans and the inability to accurately adjust and predict. These limitations lead to inaccurate meter management and slow maintenance response. Summary of the invention

[0005] In order to solve the problem that the existing technology relies on periodic maintenance processes, resulting in high maintenance costs and long cycles in the huge power grid system, and low efficiency, in the existing system, there is a delay in data processing, which affects the timeliness of fault response, and cannot effectively prevent the occurrence of problems. Lack of efficient data analysis makes it difficult to achieve comprehensive real-time monitoring of meter performance, resulting in slow response to maintenance plans, inability to accurately adjust and predict, and these limitations lead to inaccurate meter management and slow maintenance response. The embodiment of the present invention provides an Internet of Things electric energy meter that can be calibrated online. The technical solution is as follows:

[0006] On the one hand, an IoT electric energy meter capable of online verification is provided, comprising:

[0007] The data monitoring module records the data sequence and the working status of the electric energy meter based on the real-time operation data of the electric energy meter, and uses the Internet of Things technology to evaluate the data integrity and obtain integrity verification data;

[0008] The anomaly detection module performs real-time data stream analysis based on the integrity verification data, calculates outliers and deviations in the operating data through the IoT communication interface, and performs quality assessment based on the anomaly data to obtain an anomaly index record;

[0009] The performance analysis module performs multi-dimensional data analysis based on the abnormal index records, combined with historical calibration records and performance indicators, analyzes the operating efficiency and accuracy of the meter, and evaluates the problems in the performance log to obtain performance diagnosis results;

[0010] The calibration optimization module identifies the calibration parameters that need to be adjusted based on the performance diagnosis results, performs online adjustments, updates the meter calibration standard, and performs the adjusted parameter optimization verification process to obtain the calibration optimization log;

[0011] The health monitoring module monitors the health status of the electric meter based on the calibration optimization log, analyzes the long-term operation data of the electric meter, evaluates the aging and performance degradation trend of the electric meter, continuously updates the health status information of the electric meter, and establishes health status indicators;

[0012] The maintenance planning module predicts future performance problems and maintenance requirements based on the health status indicators, plans maintenance and calibration time, and adjusts the meter maintenance plan according to the predicted time to obtain the optimal configuration of meter maintenance.

[0013] On the other hand, the integrity verification data includes data capture time, status code, and integrity check value; the abnormal index record includes abnormal amplitude, deviation level, and quality control index; the performance diagnosis result includes operating efficiency index, accuracy assessment result, and fault identification information; the calibration optimization log includes adjustment record, calibration process, and optimization index; the health status index includes meter operating time, performance degradation index, and maintenance frequency; the meter maintenance optimization configuration includes maintenance interval time and performance detection standard.

[0014] On the other hand, the data monitoring module includes a data capture submodule, a status monitoring submodule, and an integrity assessment submodule;

[0015] The data capture submodule collects the electricity meter's power and status data at multiple times based on the meter's real-time operating data, and integrates and processes the data to form a meter data set;

[0016] The state monitoring submodule continuously monitors the working state of the meter based on the meter data set, records key operating parameters, including voltage and current, analyzes state changes and monitors meter performance to obtain a state change log;

[0017] The integrity assessment submodule assesses the integrity of the data collected from the electric meter based on the state change log, checks whether the data is missing or wrong, and obtains integrity verification data.

[0018] On the other hand, the anomaly detection module includes a data analysis submodule, an anomaly identification submodule, and a quality assessment submodule;

[0019] The data analysis submodule performs data flow analysis based on the integrity verification data, identifies data patterns and trends, determines that data points deviate from conventional patterns, and obtains data flow analysis results;

[0020] The anomaly identification submodule screens outliers and key deviations based on the data flow analysis results, determines the potential cause of each anomaly point, and evaluates the impact range of the anomaly to obtain an abnormal event log;

[0021] The quality assessment submodule performs quality assessment on the abnormal event log, analyzes data quality issues, calculates and determines the size and impact range of data errors, evaluates the reliability of the data set, and obtains abnormal index records.

[0022] On the other hand, the performance analysis module includes a data fusion submodule and an efficiency analysis submodule;

[0023] The data fusion submodule integrates the historical verification data and the real-time performance indicators based on the abnormal index records, verifies the data consistency through data matching and synchronization, and obtains an integrated performance data set;

[0024] The efficiency analysis submodule analyzes the operating efficiency of the electric meter based on the integrated performance data set, identifies the efficiency change trend by comparing and analyzing the historical and current operating data, and evaluates the reasons for the change in operating efficiency to obtain performance diagnosis results.

[0025] On the other hand, based on the historical and current operation data, the operation efficiency characteristics of the electric meter are extracted according to the formula:

[0026]

[0027] Calculate the operating efficiency η, where P in,i represents the input power of the meter in the i-th period, P loss,i represents the power loss of the electric meter in the i-th period, n P Represents the total number of time periods.

[0028] On the other hand, the calibration optimization module includes a performance monitoring submodule and a parameter adjustment submodule;

[0029] The performance monitoring submodule monitors the key performance parameters of the electric meter based on the performance diagnosis results, records and analyzes the changing trends of the parameters, performs performance deviation mining, and obtains performance monitoring data;

[0030] The parameter adjustment submodule analyzes and determines the calibration parameters that need to be adjusted according to the performance monitoring data, optimizes the calibration process of the meter, updates the meter calibration standard, adjusts each parameter of the meter accordingly, and obtains a calibration optimization log.

[0031] On the other hand, the health monitoring module includes a data integration submodule, a trend analysis submodule, and an indicator update submodule;

[0032] The data integration submodule collects historical and real-time operation data of the electric meter based on the calibration optimization log, and merges the information to obtain a healthy data set;

[0033] The trend analysis submodule identifies the aging and performance degradation patterns of the electric meter based on the health data set, and quantifies the aging rate and key indicators of performance degradation to obtain a degradation trend assessment result;

[0034] The indicator updating submodule updates the health status indicator of the electric meter based on the decay trend assessment result, adjusts the health rating, matches the current operating conditions of the electric meter, and obtains the health status indicator.

[0035] On the other hand, the aging and performance degradation patterns of the electric meter are identified, and the key indicators of aging rate and performance degradation are quantified according to the formula:

[0036]

[0037] Calculate the aging rate R, where A i represents the aging index of the i-th monitoring, t i represents the monitoring time, k represents the decay constant, and n represents the number of observations.

[0038] On the other hand, the maintenance planning module includes a performance prediction submodule, a demand analysis submodule, and a plan adjustment submodule;

[0039] The performance prediction submodule analyzes the performance problems and potential maintenance requirements in the future time period based on the health status indicators, and evaluates the potential and time of failure occurrence to obtain performance risk assessment information;

[0040] The demand analysis submodule performs maintenance demand analysis based on the performance risk assessment information, plans key maintenance activities and calibration cycles, and obtains an overview of maintenance needs;

[0041] The plan adjustment submodule adjusts and optimizes the implementation details of the maintenance demand overview, analyzes the matching degree between the predicted maintenance demand and the plan, and obtains the maintenance optimization configuration.

[0042] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0043] Through the Internet of Things technology, the remote monitoring and automatic adjustment capabilities of electricity meters have been improved. Real-time data monitoring and anomaly detection are used to achieve instant feedback on meter operating data and rapid identification of anomalies, thereby improving the timeliness and accuracy of data processing, reducing energy efficiency losses and failure risks, and multi-dimensional data analysis enables more accurate evaluation and prediction of meter performance status and potential defects, and timely adjustment and optimization of calibration parameters, which not only enhances the operating efficiency of meters, but also optimizes long-term maintenance strategies. Through automated health monitoring and maintenance planning, it can continuously provide performance optimization and fault prevention for meters, greatly enhancing the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is intended to represent the electrical energy of the present invention;

[0046] Figure 2 It is a schematic diagram of the electric energy meter framework of the present invention;

[0047] Figure 3 It is a flow chart of the data monitoring module of the present invention;

[0048] Figure 4 is a flow chart of an anomaly detection module of the present invention;

[0049] Figure 5 is a flow chart of the performance analysis module of the present invention;

[0050] Figure 6 A flow chart of the calibration optimization module of the present invention;

[0051] Figure 7 is a flow chart of the health monitoring module of the present invention;

[0052] Figure 8 Flowchart of the maintenance planning module of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0054] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0055] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0056] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0058] The embodiment of the present invention provides an Internet of Things electric energy meter that can be calibrated online, such as Figure 1 As shown, the electric energy meter includes:

[0059] The data monitoring module records the data sequence and the working status of the electric energy meter based on the real-time operation data of the electric energy meter, and uses the Internet of Things technology to evaluate the data integrity and obtain integrity verification data;

[0060] The anomaly detection module performs real-time data stream analysis based on integrity verification data, calculates outliers and deviations in the operating data through the IoT communication interface, and performs quality assessment based on the anomaly data to obtain anomaly index records;

[0061] The performance analysis module conducts multi-dimensional data analysis based on abnormal index records, combined with historical calibration records and performance indicators, analyzes the operating efficiency and accuracy of the meter, and evaluates the problems in the performance log to obtain performance diagnosis results;

[0062] Based on the performance diagnosis results, the calibration optimization module identifies the calibration parameters that need to be adjusted, implements online adjustments, updates the meter calibration standards, and performs the adjusted parameter optimization verification process to obtain the calibration optimization log;

[0063] The health monitoring module monitors the health status of the meter based on the calibration optimization log, analyzes the long-term operation data of the meter, evaluates the aging and performance degradation trend of the meter, and continuously updates the health status information of the meter to establish health status indicators;

[0064] The maintenance planning module predicts future performance issues and maintenance needs based on health status indicators, plans maintenance and calibration time, and adjusts the meter maintenance plan according to the predicted time to obtain the optimal configuration of meter maintenance.

[0065] Integrity verification data includes data capture time, status code, and integrity check value. Abnormal index records include abnormal amplitude, deviation level, and quality control index. Performance diagnosis results include operating efficiency index, accuracy assessment results, and fault identification information. Calibration optimization logs include adjustment records, calibration process, and optimization indicators. Health status indicators include meter operating time, performance degradation indicators, and maintenance frequency. Meter maintenance optimization configuration includes maintenance interval time and performance detection standards.

[0066] like Figure 2 and Figure 3 As shown, the data monitoring module includes a data capture submodule, a status monitoring submodule, and an integrity assessment submodule;

[0067] The data capture submodule collects the electricity meter's power and status data at multiple times based on the meter's real-time operating data, and integrates and processes the data to form a meter data set;

[0068] The electricity data and status information of the electric meter are collected point by point according to the set sampling interval. The electricity data collected each time is stored as a data sequence. At the same time, a timestamp is recorded for each data point to identify the collection time. The status information is recorded as another data sequence through a specific classification coding method. To ensure the validity of the data, the electricity data needs to be cleaned, such as eliminating abnormal data that is below zero or exceeds the rated value of the meter. After cleaning, the timestamp, electricity value and status code are integrated into a data table by row to form an electric meter data set.

[0069] The state monitoring submodule continuously monitors the working state of the meter based on the meter data set, records key operating parameters including voltage and current, analyzes state changes and monitors meter performance to obtain a state change log;

[0070] Extract the meter power value and status data under the time stamp, calculate the working status during actual operation in combination with the technical parameters of the meter, analyze the continuous change process of the meter's operating parameters, focus on whether the current and voltage ranges exceed the rated limits of the equipment, set a reasonable upper and lower limit range for the recorded voltage data, mark the records that exceed the range, and perform trend analysis on parameter changes within a continuous time period, such as whether there are continuous abnormal fluctuations. For the meter's operating status, monitor its discontinuity points and abnormal coding information, and record abnormal conditions in the status change log to trace the time and cause of the problem.

[0071] The integrity assessment submodule assesses the integrity of the data collected from the meter based on the state change log, checks whether the data is missing or wrong, and obtains integrity verification data;

[0072] Conduct integrity assessment on the information extracted from the meter data set, check whether the records in the log are continuous, especially whether there are breakpoints in the time stamp, count the number of missing time records and their proportion in the entire time series, mark missing points as incomplete data items, and check whether abnormal data points related to the meter operation status conform to the normal distribution pattern. Classify and archive the abnormal data points, generate an integrity verification data set, and provide reliable data support for subsequent evaluation reports and analysis.

[0073] like Figure 2 and Figure 4 As shown, the anomaly detection module includes a data analysis submodule, an anomaly identification submodule, and a quality assessment submodule;

[0074] The data analysis submodule verifies the data based on integrity, performs data flow analysis, identifies data patterns and trends, determines data points that deviate from the normal pattern, and obtains data flow analysis results;

[0075] The data time series is segmented and divided into multiple continuous window segments according to the time span. Key parameters such as maximum, minimum and average values ​​are extracted in each segment. By comparing the changes in parameters in each time period, the change rate of data points in each time period is calculated and abnormal fluctuation points are recorded. For the identified data points, they are classified into corresponding pattern categories according to the data value classification. The frequency and distribution range of each category are extracted through statistical analysis methods, and a pattern distribution report for time series data is generated. Data points that exceed the normal range of each category are identified as abnormal pattern points, and the analysis results are integrated to form data flow analysis results.

[0076] The anomaly identification submodule screens outliers and significant deviations based on the data flow analysis results, determines the potential cause of each anomaly point, and evaluates the impact scope of the anomaly to obtain an abnormal event log;

[0077] The marked abnormal point information is extracted from the data flow analysis, and the time stamp and data value of each point are correlated and analyzed. By comparing the normal distribution of similar data points, the deviation amplitude is calculated and the potential causes of deviation are classified, including acquisition equipment errors, external environmental factors, etc. The influence range of each abnormal point is divided into local and global ranges, and the start time and duration of the abnormal event are recorded in a time series manner. The analyzed data point information is recorded as an abnormal event log.

[0078] The quality assessment submodule conducts quality assessment on abnormal event logs, analyzes data quality issues, calculates and determines the size and impact range of data errors, evaluates the reliability of data sets, and obtains abnormal index records;

[0079] Extract the number and distribution characteristics of anomalies in the log, analyze the data source and cause of each anomaly one by one, calculate the impact of the anomaly on the overall data set, and statistically analyze the error distribution characteristics to quantify the data quality. Use the frequency of occurrence and deviation amplitude of the anomaly to classify and evaluate the size of the data error, record the changing trend of the anomaly index in chronological order, and generate an anomaly index record based on the analysis results of the impact range of the data point.

[0080] like Figure 2 and Figure 5 As shown, the performance analysis module includes a data fusion submodule and an efficiency analysis submodule;

[0081] The data fusion submodule integrates historical verification data and real-time performance indicators based on abnormal index records, verifies data consistency through data matching and synchronization, and obtains an integrated performance data set;

[0082] Synchronize historical data with real-time data in time series. First, select the records in the historical calibration data that match the current equipment model and operating status, confirm the validity and consistency of the data source, and time-align the real-time performance indicators. Use data grouping methods to divide the matching data into time periods for segment-by-segment analysis. Calculate the data mean and maximum deviation in each time period, generate a difference report, mark the part with a difference greater than the set threshold as an inconsistent area, then compare and verify the identified inconsistent areas by checking the recorded equipment parameters, and finally integrate the historical calibration data with the real-time performance indicators to form a usable performance data set.

[0083] The efficiency analysis submodule analyzes the operating efficiency of the meter based on the integrated performance data set. By comparing and analyzing historical and current operating data, it identifies the efficiency change trend and evaluates the reasons for the change in operating efficiency to obtain performance diagnosis results.

[0084] Based on historical and current operating data, the operating efficiency characteristics of the meter are extracted according to the formula:

[0085]

[0086] Calculate the operating efficiency η, where P in,i represents the input power of the meter in the i-th period, P loss,i represents the power loss of the electric meter in the i-th period, n P Represents the total number of time periods.

[0087] P in,i It is obtained by collecting input power data through an electric meter, which is kilowatts;

[0088] P loss,i Obtained by comparing the historical loss rate curve with the current load situation, in kilowatts;

[0089] The following data was collected:

[0090] Input power data [P in,1 ,P in,2 ,P in,3 ] = [100, 150, 200] kW;

[0091] Power loss data [P loss,1 ,P loss,2 ,P loss,3 ] = [10, 15, 20] kW;

[0092] Effective power output accumulation:

[0093]

[0094] Input power accumulation:

[0095]

[0096] Calculate operating efficiency:

[0097]

[0098] The result shows that the meter operating efficiency is 0.9, or 90%, which means that the current meter has a 10% loss when processing the input power. This efficiency value can be used as the initial feature input for subsequent efficiency change trend analysis.

[0099] like Figure 2 and Figure 6 As shown, the calibration optimization module includes a performance monitoring submodule and a parameter adjustment submodule;

[0100] The performance monitoring submodule monitors the key performance parameters of the meter based on the performance diagnosis results, records and analyzes the changing trends of the parameters, conducts performance deviation mining, and obtains performance monitoring data;

[0101] By extracting the historical records and current operating data of the key performance parameters of the meter, comparing the changes of the parameters in different time periods, and using the segmented statistical method to calculate the mean and fluctuation range of the parameters, the parameters with larger fluctuations are marked as high-risk parameters, and the change time points of each parameter are further extracted. The operating data characteristics before and after the change are analyzed, and the difference in operating data is used as a potential basis for deviation. A deviation trend table for each parameter is established, recording the number of data points with different deviation amplitudes, and classifying and archiving the periodic and non-periodic characteristics of parameter changes separately, and generating detailed performance monitoring data according to the density of fluctuation points.

[0102] The parameter adjustment submodule analyzes and determines the calibration parameters that need to be adjusted based on the performance monitoring data, optimizes the calibration process of the meter, updates the meter calibration standard, adjusts each parameter of the meter accordingly, and obtains the calibration optimization log;

[0103] Extract the list of calibration parameters marked as high risk, analyze the deviation and calibration requirements of each parameter one by one by comparing the historical parameter data with the calibration standard, determine the calibration parameters that need to be adjusted, update the calibration values ​​in sequence according to the adjustment priority, and optimize the calibration of the meter in steps, including adjusting the input reference value, updating the calibration, and testing the accuracy of the new calibration value in segments. Record the execution time and adjustment range of each step in the calibration adjustment process, generate a calibration optimization log, and store the adjusted parameter values ​​as the latest calibration standard of the meter.

[0104] like Figure 2 and Figure 7 As shown, the health monitoring module includes a data integration submodule, a trend analysis submodule, and an indicator update submodule;

[0105] The data integration submodule collects the historical and real-time operation data of the meter based on the calibration optimization log, and merges the information to obtain a healthy data set;

[0106] Extract the historical operation records and real-time monitoring data of the meter, group and filter the historical data by time period, confirm the parameter range related to the calibration log, synchronize the real-time monitoring data to the corresponding historical data segment by timestamp, check the continuity of the time series and the correspondence of the data points, mark the points that cannot be matched, merge the information after removing invalid data points, merge the statistical results of the historical data with the changing trends of the real-time data, extract key performance parameters such as operating efficiency, power consumption and load change rate, and form a healthy data set that includes operating sequence, key parameters and calibration results.

[0107] The trend analysis submodule identifies the aging and performance degradation patterns of the meter based on the health data set, and quantifies the aging rate and key indicators of performance degradation to obtain the degradation trend assessment results;

[0108] Identify the patterns of meter aging and performance degradation, and quantify the aging rate and key indicators of performance degradation according to the formula:

[0109]

[0110] Calculate the aging rate R, where A i represents the aging index of the i-th monitoring, t i represents the monitoring time, k represents the decay constant, and n represents the number of observations.

[0111] During the calculation process, the k value needs to be determined based on the regression analysis of historical data, and each A i and t i It is obtained by monitoring and recording historical performance data. For example, if the performance index A of an electric meter has i They are 95, 90, 85, 80 and 75 respectively, and the time t is 0, 1, 2, 3 and 4 years. Let k = 0.1 and substitute into the formula:

[0112]

[0113] The results show that after considering the effect of time decay, the average aging rate is 71.11, reflecting the overall performance degradation trend of the meter over time.

[0114] The indicator update submodule updates the health status indicator of the meter based on the decay trend assessment results, makes health rating adjustments, matches the current operating conditions of the meter, and obtains the health status indicator;

[0115] The health status indicators are adjusted in stages, and the health status is dynamically updated by time period. The parameter values ​​and change ranges of each update are recorded. According to the current operating conditions of the meter, the indicator ranges of similar operating states in history are matched. The matching results are used to correct the current health status indicators and generate health status indicators as the basic data for subsequent operation monitoring and status evaluation.

[0116] like Figure 2 and Figure 8 As shown, the maintenance planning module includes a performance prediction submodule, a demand analysis submodule, and a plan adjustment submodule;

[0117] The performance prediction submodule analyzes performance issues and potential maintenance requirements in the future time period based on health indicators, and evaluates the potential and time of failure to obtain performance risk assessment information;

[0118] Combined with the current operating conditions of the meter, the changing trend of each indicator is analyzed by grouping by time period, the rate and direction of indicator change are calculated, the key points that cause performance problems are evaluated one by one, the critical value range of the indicator and the dependence on the potential relationship with the fault are confirmed, the indicators that exceed the critical value are marked, and the trend of each indicator in a certain period of time in the future is predicted through time series analysis. The time range of fault occurrence is extracted according to the trend curve, and the key areas of maintenance requirements are identified at the same time, and the above results are integrated into performance risk assessment information.

[0119] The demand analysis submodule conducts maintenance demand analysis based on performance risk assessment information, plans key maintenance activities and calibration cycles, and obtains an overview of maintenance needs;

[0120] Extract the time period and range of high-risk indicators from the risk information, conduct a comparative analysis of the impact range of similar risk indicators in combination with equipment maintenance records, list the equipment and indicators that require key maintenance according to priority, and plan a list of key maintenance activities, including maintenance goals, operating procedures and execution frequency. At the same time, analyze the rationality of the calibration cycle, redefine the time allocation plan for calibration tasks, and generate an overview of maintenance needs.

[0121] The plan adjustment submodule adjusts and optimizes the implementation details of the maintenance demand overview, analyzes the matching degree between the predicted maintenance demand and the plan, and obtains the maintenance optimization configuration;

[0122] Make detailed adjustments to each planned maintenance task, match the time allocation of maintenance activities with the actual operation of the meter, re-prioritize key maintenance tasks, analyze the differences between predicted maintenance needs and existing maintenance plans, optimize tasks with large differences, adjust calibration cycles to be consistent with actual equipment operation conditions, evaluate the feasibility of the adjusted plans one by one, form a maintenance optimization configuration, and generate an optimized task execution list.

[0123] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0124] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0125] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0127] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0131] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0132] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An Internet of Things electric energy meter capable of online verification, characterized in that: The electric energy meter comprises: The data monitoring module records the data sequence and the working status of the electric energy meter based on the real-time operation data of the electric energy meter, and uses the Internet of Things technology to evaluate the data integrity and obtain integrity verification data; The anomaly detection module performs real-time data stream analysis based on the integrity verification data, calculates outliers and deviations in the operating data through the IoT communication interface, and performs quality assessment based on the anomaly data to obtain an anomaly index record; The performance analysis module performs multi-dimensional data analysis based on the abnormal index records, combined with historical calibration records and performance indicators, analyzes the operating efficiency and accuracy of the meter, and evaluates the problems in the performance log to obtain performance diagnosis results; The calibration optimization module identifies the calibration parameters that need to be adjusted based on the performance diagnosis results, performs online adjustments, updates the meter calibration standard, and performs the adjusted parameter optimization verification process to obtain the calibration optimization log; The health monitoring module monitors the health status of the electric meter based on the calibration optimization log, analyzes the long-term operation data of the electric meter, evaluates the aging and performance degradation trend of the electric meter, continuously updates the health status information of the electric meter, and establishes health status indicators; The maintenance planning module predicts future performance problems and maintenance requirements based on the health status indicators, plans maintenance and calibration time, and adjusts the meter maintenance plan according to the predicted time to obtain the optimal configuration of meter maintenance.

2. The online calibrable IoT electric energy meter according to claim 1, characterized in that: The integrity verification data includes data capture time, status code, and integrity check value; the abnormal index record includes abnormal amplitude, deviation level, and quality control index; the performance diagnosis result includes operating efficiency index, accuracy assessment result, and fault identification information; the calibration optimization log includes adjustment record, calibration process, and optimization index; the health status index includes meter operating time, performance degradation index, and maintenance frequency; the meter maintenance optimization configuration includes maintenance interval time and performance detection standard.

3. The online calibrable IoT electric energy meter according to claim 1 is characterized in that: The data monitoring module comprises: The data capture submodule collects the electricity meter's power and status data at multiple times based on the meter's real-time operating data, and integrates and processes the data to form a meter data set; The state monitoring submodule continuously monitors the working state of the meter based on the meter data set, records key operating parameters, including voltage and current, analyzes state changes and monitors meter performance to obtain a state change log; The integrity assessment submodule assesses the integrity of the data collected from the electric meter based on the state change log, checks whether the data is missing or wrong, and obtains integrity verification data.

4. The online calibrable Internet of Things electric energy meter according to claim 1 is characterized in that: The anomaly detection module comprises: The data analysis submodule performs data flow analysis based on the integrity verification data, identifies data patterns and trends, determines that data points deviate from conventional patterns, and obtains data flow analysis results; The anomaly identification submodule screens outliers and key deviations based on the data flow analysis results, determines the potential cause of each anomaly point, and evaluates the impact range of the anomaly to obtain an abnormal event log; The quality assessment submodule performs quality assessment on the abnormal event log, analyzes data quality issues, calculates and determines the size and impact range of data errors, evaluates the reliability of the data set, and obtains abnormal index records.

5. The online calibrable Internet of Things electric energy meter according to claim 1 is characterized in that: The performance analysis module includes: The data fusion submodule integrates the historical verification data and the real-time performance indicators based on the abnormal index records, verifies the data consistency through data matching and synchronization, and obtains an integrated performance data set; The efficiency analysis submodule analyzes the operating efficiency of the electric meter based on the integrated performance data set, identifies the efficiency change trend by comparing and analyzing the historical and current operating data, and evaluates the reasons for the change in operating efficiency to obtain performance diagnosis results.

6. The online calibrable Internet of Things electric energy meter according to claim 5, characterized in that: Based on the historical and current operating data, the operating efficiency characteristics of the electric meter are extracted according to the formula: Calculate the operating efficiency η, where P in,i represents the input power of the meter in the i-th period, P loss,i represents the power loss of the electric meter in the i-th period, n P Represents the total number of time periods.

7. The online calibrable Internet of Things electric energy meter according to claim 1, characterized in that: The calibration optimization module comprises: The performance monitoring submodule monitors the key performance parameters of the electric meter based on the performance diagnosis results, records and analyzes the changing trends of the parameters, performs performance deviation mining, and obtains performance monitoring data; The parameter adjustment submodule analyzes and determines the calibration parameters that need to be adjusted according to the performance monitoring data, optimizes the calibration process of the meter, updates the meter calibration standard, adjusts each parameter of the meter accordingly, and obtains a calibration optimization log.

8. The online calibrable Internet of Things electric energy meter according to claim 1, characterized in that: The health monitoring module includes: The data integration submodule collects historical and real-time operation data of the electric meter based on the calibration optimization log, and merges the information to obtain a healthy data set; The trend analysis submodule identifies the aging and performance degradation patterns of the electric meter based on the health data set, and quantifies the aging rate and key indicators of performance degradation to obtain a degradation trend assessment result; The indicator updating submodule updates the health status indicator of the electric meter based on the decay trend assessment result, adjusts the health rating, matches the current operating conditions of the electric meter, and obtains the health status indicator.

9. The online calibrable Internet of Things electric energy meter according to claim 8, characterized in that: Identify the aging and performance degradation patterns of the meter and quantify the aging rate and key indicators of performance degradation according to the formula: Calculate the aging rate R, where A i represents the aging index of the i-th monitoring, t i represents the monitoring time, k represents the decay constant, and n represents the number of observations.

10. The online calibrable Internet of Things electric energy meter according to claim 1, characterized in that: The maintenance planning module includes: The performance prediction submodule analyzes the performance problems and potential maintenance requirements in the future time period based on the health status indicators, and evaluates the potential and time of failure occurrence to obtain performance risk assessment information; The demand analysis submodule performs maintenance demand analysis based on the performance risk assessment information, plans key maintenance activities and calibration cycles, and obtains an overview of maintenance needs; The plan adjustment submodule adjusts and optimizes the implementation details of the maintenance demand overview, analyzes the matching degree between the predicted maintenance demand and the plan, and obtains the maintenance optimization configuration.

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