Intelligent electric meter operation error state evaluation method and system, electronic equipment and storage medium
By using a smart meter operation error status assessment system, combined with multi-dimensional data analysis and machine learning, accurate error assessment and predictive maintenance of smart meters are achieved, solving the problem of low efficiency in traditional detection methods and improving the operation and maintenance management level of power companies.
Patent Information
- Application Number
- CN202511231138.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-30
- Publication Date
- 2025-12-19
AI Technical Summary
In actual operation, smart meters are easily affected by changes in ambient temperature and humidity and the aging of components, leading to metering errors. Traditional detection methods are inefficient and difficult to detect potential faults in a timely manner. Existing online monitoring systems lack comprehensive analysis capabilities, resulting in misjudgments or omissions. Power companies face the challenge of optimizing massive equipment maintenance resources.
The system employs a smart meter operation error status assessment system, which includes modules for data acquisition, preprocessing, historical database, error assessment, early warning, predictive maintenance, and resource optimization. It combines wireless communication, environmental sensors, machine learning, and multi-dimensional data analysis to achieve accurate error assessment, intelligent early warning, and predictive maintenance.
It improves the accuracy and efficiency of smart meter operation and maintenance management, reduces false alarms and missed alarms, quickly locates problems, optimizes maintenance response strategies, reduces operation and maintenance costs, improves management convenience, and forms a closed loop of full life cycle management.
Smart Images
Figure CN121167162A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of smart meters, and in particular to a smart meter operation error state evaluation method and system, an electronic device and a storage medium. BACKGROUND
[0002] As a key terminal device for power metering and user power information collection, the operation accuracy of a smart meter directly affects the economic benefits of a power enterprise and the fairness of user power consumption. However, in actual operation, the smart meter is affected by factors such as changes in environmental temperature and humidity, aging of components, and is prone to problems such as metering error drift. Traditional manual periodic detection methods are not only inefficient, but also difficult to find potential faults in a timely manner. Existing online monitoring systems are mostly limited to single error threshold alarms and lack comprehensive analysis capabilities for historical data trends and environmental factors, which can easily lead to misjudgment or missed judgment. At the same time, power enterprises are facing the problem of optimizing maintenance resources for a large number of meter devices, and there is an urgent need to establish an intelligent management system that can realize accurate error evaluation, intelligent early warning and predictive maintenance to improve operation and maintenance efficiency and ensure metering accuracy. SUMMARY
[0003] To solve the above problems, the present application provides a smart meter operation error state evaluation method and system, an electronic device and a storage medium.
[0004] The smart meter operation error state evaluation method and system, the electronic device and the storage medium provided by the present application adopt the following technical solutions: The smart meter operation error state evaluation system comprises a data acquisition module, an output end of the data acquisition module is electrically connected with a data preprocessing module, an output end of the data preprocessing module is electrically connected with a historical database, an output end of the historical database is electrically connected with an error evaluation module, an output end of the error evaluation module is electrically connected with a warning module, an output end of the warning module is electrically connected with a user interface module, an output end of the user interface module is electrically connected with a predictive maintenance module, an output end of the predictive maintenance module is electrically connected with a resource optimization module, and an output end of the resource optimization module is electrically connected with a user notification module.
[0005] As a preferred technical solution of the present application, the data acquisition module is used for real-time acquisition of metering data and environmental parameters of the smart meter, the data preprocessing module is used for filtering and format conversion processing of the collected data, the historical database is used for storing historical operation data of the smart meter, the error evaluation module is used for comparing the current data with the historical data and calculating the operation error rate, the early warning module is used for issuing an alarm when the error rate exceeds a preset threshold, the user interface module is used for displaying the smart meter state report and early warning information, the predictive maintenance module predicts the remaining service life of the meter through historical error data and environmental parameters, the resource optimization module dynamically adjusts the meter replacement plan according to the prediction result, and the user notification module is used for notifying relevant users before the planned replacement.
[0006] As a preferred technical solution of the present application, the data acquisition module is used for real-time acquisition of metering data and environmental parameters of the smart meter, the data preprocessing module is used for filtering and format conversion processing of the collected data, the historical database is used for storing historical operation data of the smart meter, the error evaluation module is used for comparing the current data with the historical data and calculating the operation error rate, the early warning module is used for issuing an alarm when the error rate exceeds a preset threshold, the user interface module is used for displaying the smart meter state report and early warning information, the predictive maintenance module predicts the remaining service life of the meter through historical error data and environmental parameters, the resource optimization module dynamically adjusts the meter replacement plan according to the prediction result, and the user notification module is used for notifying relevant users before the planned replacement.
[0007] As a preferred technical solution of the present application, the electronic device according to the smart meter operation error state evaluation system comprises a processor, an output end of the processor is electrically connected with a memory, an output end of the memory is electrically connected with a communication interface, the memory internally stores a computer program, and the communication interface is used for communication with the smart meter.
[0008] As a preferred technical solution of the present application, the storage medium according to the smart meter operation error state evaluation system comprises a computer readable storage medium, and the computer readable storage medium internally stores a computer program.
[0009] As a preferred technical solution of the present application, the smart meter operation error state evaluation method according to the smart meter operation error state evaluation system comprises the following steps: Step one, real-time collection of metering data and environmental parameters of the smart meter; Step two, pre-processing of the collected data, including filtering and format conversion; Step three, comparison and analysis of the pre-processed data with reference data in the historical database; Step four, calculation of the meter operation error rate based on multi-cycle data change trend; Step five, generation of early warning information and notification of relevant personnel when the error rate exceeds the preset threshold; Step six, dynamic adjustment of the meter maintenance plan according to the evaluation result; Step seven, establishment of a meter health state scoring system, quantification of the evaluation result, and implementation of a differentiated maintenance strategy based on the scoring result; Step eight, continuous optimization of the evaluation algorithm parameters through a feedback mechanism, and generation of a meter full life cycle management report.
[0010] As a preferred technical solution of the present application, in step eight, after the generation of the meter full life cycle management report, the power company staff is allowed to remotely view and confirm the evaluation result, the evaluation process and conclusion are displayed in the form of intuitive charts, typical abnormal cases are added to the system knowledge base, and the algorithm model is optimized based on the historical evaluation result.
[0011] As a preferred technical solution of the present application, in step one, the metering data uploaded by the smart meter is periodically received through a wireless communication network, the temperature and humidity parameters of the meter installation environment are monitored in real time, abnormal data is marked and instant collection is triggered; In step three, the deviation degree of the current data from the historical average value is calculated, the data change trend of continuous multiple cycles is analyzed, then the rationality of the data abnormality is evaluated in combination with the environmental parameter change, and the meter operation state is comprehensively evaluated by using a weighted algorithm; In step four, more specifically, the metering error model is established based on time series analysis, and it is noted that the influence coefficient of the environmental parameters on the measurement accuracy needs to be considered when establishing the metering error model, and then a machine learning algorithm is introduced to dynamically adjust the error judgment threshold. When judging the metering error threshold, the comprehensive error index needs to be calculated instead of a single error value. In step five, more specifically, a multi-level early warning mechanism is set according to the error severity to automatically identify and associate the abnormal conditions of multiple meters in the same area, and then a detailed report containing specific abnormal data and repair suggestions is generated and the warning information is sent through multiple communication channels at the same time. In step six, more specifically, the remaining service life of the meter is predicted based on the error evaluation result, the replacement order and replacement time of the meter are optimized according to the remaining service life, and the optimal maintenance route and resource allocation scheme are automatically generated. After generating the optimal maintenance route and resource allocation scheme, the maintenance plan database is dynamically updated.
[0012] In summary, the present application includes the following at least intelligent meter operation error state evaluation method, system, electronic device and storage medium: The present application improves the accuracy and efficiency of intelligent meter operation and maintenance management through intelligent error evaluation and predictive maintenance mechanism. The system uses a multi-dimensional data analysis method, combines an environmental compensation model and a machine learning algorithm, effectively distinguishes between temporary interference and device degradation, reduces false positives and false negatives, and uses a hierarchical early warning mechanism and regional anomaly detection function to quickly locate problems, optimize maintenance response strategies, and dynamically evaluate the health status of the device. The system realizes the transition from regular maintenance to predictive maintenance, optimizes resource allocation, reduces operation and maintenance costs, and continuously optimizes algorithm parameters through a feedback mechanism, making the system self-adapting to different types of meter evaluation needs. The visual interface and remote management function improve the convenience of management, forming a complete device lifecycle management closed loop. BRIEF DESCRIPTION OF DRAWINGS
[0013] Figure 1 is a distribution diagram of the intelligent meter operation error state evaluation system of the present application; Figure 2 is a schematic diagram of the electronic device of the present application; Figure 3 is a schematic diagram of the storage medium of the present application; Figure 4 is a flowchart of the intelligent meter operation error state evaluation method of the present application. DETAILED DESCRIPTION
[0014] The following will be described in detail in combination with the accompanying Figures 1-4 The present application will be further described in detail.
[0015] Reference should be made to Figure 1, the intelligent electric meter operation error state evaluation system, comprising: a data acquisition module, the output end of the data acquisition module is electrically connected with a data preprocessing module, the output end of the data preprocessing module is electrically connected with a historical database, the output end of the historical database is electrically connected with an error evaluation module, the output end of the error evaluation module is electrically connected with a warning module, the output end of the warning module is electrically connected with a user interface module, the output end of the user interface module is electrically connected with a predictive maintenance module, the output end of the predictive maintenance module is electrically connected with a resource optimization module, and the output end of the resource optimization module is electrically connected with a user notification module.
[0016] The data acquisition module is used for acquiring the metering data and environmental parameters of the intelligent electric meter in real time, the data preprocessing module is used for filtering and format conversion processing of the collected data, the historical database is used for storing the historical operation data of the intelligent electric meter, the error evaluation module is used for comparing the current data with the historical data and calculating the operation error rate, the warning module is used for issuing an alarm when the error rate exceeds a preset threshold, the user interface module is used for displaying the intelligent electric meter state report and warning information, the predictive maintenance module predicts the remaining service life of the electric meter through the historical error data and environmental parameters, the resource optimization module dynamically adjusts the electric meter replacement plan according to the prediction result, and the user notification module is used for notifying the relevant users before the planned replacement.
[0017] The internal integration of the data acquisition module has a wireless communication unit, the output end of the wireless communication unit is electrically connected with an environmental sensor interface unit, the output end of the environmental sensor interface unit is electrically connected with a data buffer unit, the wireless communication unit is used for receiving real-time data sent by the smart meter through a wireless network, the environmental sensor interface unit is used for receiving temperature and humidity environmental parameters, and the data buffer unit is used for temporarily storing the collected original data, the internal integration of the error evaluation module has a data comparison unit, the output end of the data comparison unit is electrically connected with a threshold judgment unit, the output end of the threshold judgment unit is electrically connected with an error calculation unit, the output end of the error calculation unit is electrically connected with a state evaluation unit, the data comparison unit is used for comparing the current period data with the historical average value, the threshold judgment unit is used for judging whether the data deviation exceeds the set threshold value, the error calculation unit is used for calculating the meter operation error rate based on the multi-period data, and the state evaluation unit is used for evaluating the meter health state by comprehensively evaluating the environmental parameters and the metering data, the internal integration of the early warning module has a multi-channel alarm unit, the output end of the multi-channel alarm unit is electrically connected with a hierarchical early warning unit, the output end of the hierarchical early warning unit is electrically connected with a warning record unit, the multi-channel alarm unit can alarm through short message, email and system, the hierarchical early warning unit can set different early warning levels according to the error severity, and the warning record unit is used for storing all early warning events and processing states, the internal integration of the user interface module has a meter state visualization unit, the output end of the meter state visualization unit is electrically connected with a warning management unit, the output end of the warning management unit is electrically connected with a report generation unit, the output end of the report generation unit is electrically connected with a remote control unit, the meter state visualization unit displays the meter operation state in the form of a chart, the warning management unit is used for viewing and processing early warning information, the report generation unit is used for automatically generating a meter health state report, and the remote control unit allows authorized users to remotely set system parameters; When the environmental sensor detects a sudden temperature change, the environmental sensor interface unit triggers a high-frequency data acquisition mode, the data comparison unit compares the current metering data with the historical reference value under the same temperature condition, avoids comparing the global average value, and the state evaluation unit establishes an environmental compensation model by analyzing the correlation between the temperature change curve and the metering error, so that the model can distinguish between temporary environmental interference and permanent device degradation, and false alarms are avoided. In the application, the influence of temperature on electronic components is described by using thermodynamic equation, and the influence of temperature gradient on metering accuracy is quantified by solving partial differential equation; The error calculation unit adopts a sliding window algorithm to accumulate and calculate the slight deviation of a plurality of billing periods in succession. When the accumulated error reaches a first threshold value, the hierarchical early warning unit only marks the abnormality but does not actively alarm. When the accumulated error reaches a second threshold value, the device inspection suggestion is triggered. When the accumulated error exceeds a final threshold value, the replacement process is started. The mathematical principle of the application is based on the ARIMA model of time series. The hidden trend change is captured through the autoregressive integral moving average algorithm. This progressive response ensures early detection of problems and avoids excessive maintenance. When an error occurs, the early warning module automatically retrieves the operating state of all electric meters in the surrounding area. The multi-channel alarm unit adopts a spatial clustering algorithm to identify whether a regional anomaly occurs. The application uses the community discovery algorithm in graph theory to convert the power grid topology structure into a weighted graph. The potential regional fault is found by calculating the node similarity. This group analysis can effectively distinguish between single device failure and substation-level problems. The system inputs historical error data, environmental parameters, operation records, etc. into a deep neural network to output the aging curve of each component. The resource optimization module establishes a maintenance priority queue accordingly. The core algorithm of the resource optimization module is the solution to the knapsack problem with constraints. The overall device reliability is maximized under limited maintenance resources. The physical layer calculates the aging rate of electronic components according to the Arrhenius equation, considering the acceleration effect of environmental stresses such as temperature and humidity. The threshold value is self-adaptively optimized through machine learning. The threshold judgment unit continuously records false positives and false negatives. The reinforcement learning framework is used to adjust the judgment parameters. When a new type of error mode is detected, the derived features are automatically generated and added to the evaluation model. The theoretical basis of the application is the Bayesian optimization framework. The mapping relationship between parameters and evaluation results is established through Gaussian process regression to gradually approach the optimal judgment boundary. This dynamic adjustment mechanism enables the system to adapt to the technical evolution of new smart meters. After maintenance is completed, the user notification module collects the difference between the on-site verification results and the system evaluation. These feedback data are updated to the knowledge base through online learning algorithms. The feature extraction and weight allocation strategies are optimized. The application uses the federated learning framework to aggregate the maintenance experience of multiple power supply stations while protecting data privacy. The system constructs a digital twin model. The effect of algorithm modification is evaluated through simulation testing before actual deployment.
[0018] Referring to Figure 2 , an electronic device includes a processor, an output of the processor is electrically connected with a memory, an output of the memory is electrically connected with a communication interface, the memory internally stores a computer program, and the communication interface is used for communication with a smart meter.
[0019] Referring to Figure 3 , a storage medium includes a computer readable storage medium, and the computer readable storage medium internally stores a computer program.
[0020] See Figure 4 The method for assessing the operational error status of smart meters includes the following steps: Step 1: Collect metering data and environmental parameters from smart meters in real time. Receive metering data uploaded by smart meters periodically via wireless communication network. Monitor the temperature and humidity parameters of the meter installation environment in real time, mark abnormal data, and trigger immediate collection. Step two involves preprocessing the collected data, including filtering and format conversion; Step 3: Compare and analyze the preprocessed data with the benchmark data in the historical database, calculate the deviation between the current data and the historical average, analyze the data change trend over multiple consecutive periods, and then assess the rationality of the data anomalies in conjunction with changes in environmental parameters and use a weighted algorithm to comprehensively evaluate the meter's operating status. Step 4: Calculate the meter operation error rate based on the changing trend of multi-period data, and establish a meter measurement error model based on time series analysis. It should be noted that the measurement error model needs to consider the influence coefficient of environmental parameters on measurement accuracy. Then, a machine learning algorithm is introduced to dynamically adjust the error judgment threshold. When judging the measurement error threshold, a comprehensive error index should be calculated instead of a single error value. Step 5: When the error rate exceeds the preset threshold, generate an early warning message and notify relevant personnel. Set up a multi-level early warning mechanism according to the severity of the error to automatically identify and associate the abnormal situation of multiple meters in the same area. Then generate a detailed report containing specific abnormal data and repair suggestions and send the early warning message simultaneously through multiple communication channels. Step 6: Dynamically adjust the meter maintenance plan based on the evaluation results, predict the remaining service life of the meter based on the error evaluation results, optimize the meter replacement sequence and replacement time based on the remaining service life, and automatically generate the optimal maintenance route and resource allocation plan. After generating the optimal maintenance route and resource allocation plan, dynamically update the maintenance plan database. Step 7: Establish a health status scoring system for electricity meters, quantify the assessment results, and implement differentiated maintenance strategies based on the scoring results; Step 8: Continuously optimize the evaluation algorithm parameters through the feedback mechanism, generate a full life cycle management report for the electricity meter, and allow power company staff to remotely view and confirm the evaluation results after the full life cycle management report is generated. Display the evaluation process and conclusions in an intuitive chart format, add typical abnormal cases to the system knowledge base, and optimize the algorithm model based on historical evaluation results. By collecting metering data and environmental parameters in real time, combining historical benchmark data for multi-dimensional analysis, using weighted algorithm and machine learning to dynamically adjust error judgment threshold, the accurate evaluation of the running state of the electric meter is realized, a complete management system including multi-level early warning mechanism and differentiated maintenance strategy is established, the metering error model is constructed through environmental parameter influence coefficient and time series analysis, and the maintenance plan and resource allocation scheme are optimized based on the evaluation results, the application uses feedback mechanism to continuously optimize algorithm parameters, forms a closed loop of electric meter whole life cycle management, and effectively improves the operation and maintenance efficiency and management level of electric power metering equipment.
[0021] The application realizes intelligent management of the whole process from data collection to maintenance decision through multi-module cooperation, the system takes the data collection module as the starting point, real-time obtains the metering data and environmental parameters of the electric meter, stores them in the historical database after preprocessing, provides data basis for subsequent analysis, the error evaluation module uses multi-dimensional analysis method, combines time series algorithm and environmental compensation model, compares the current data with the historical benchmark value, calculates the running error rate through sliding window accumulation, and uses ARIMA model to capture potential trend changes, the early warning module uses hierarchical response mechanism, implements differentiated early warning strategy according to the error severity, and uses spatial clustering algorithm to identify regional abnormal patterns, the system introduces machine learning technology, predicts the device aging trend through deep neural network, optimizes maintenance resource allocation based on knapsack problem algorithm, and realizes dynamic adjustment of error threshold through Bayesian optimization framework, the predictive maintenance module constructs a digital twin model, verifies the maintenance scheme through simulation test, and the feedback mechanism continuously optimizes the evaluation algorithm parameters, the whole system uses technical means such as environmental parameter influence coefficient quantification, multi-period trend analysis and group anomaly detection, effectively distinguishes temporary interference and substantive failure, avoids over-maintenance and ensures timely processing of problem equipment, the user interface module provides visual state display and remote control function, and forms a management closed loop with the user notification module, the system upgrades the traditional periodic maintenance to predictive maintenance based on device state, through establishing health score system and differentiated maintenance strategy, the operation and maintenance precision and management efficiency of electric power metering equipment are significantly improved, the establishment of environmental compensation model, the design of progressive early warning mechanism, the application of group anomaly detection algorithm and the realization of self-learning optimization capability provide a complete solution for whole life cycle management of smart meters.
[0022] The above are preferred embodiments of the application, which do not limit the protection scope of the application, therefore: any equivalent changes made on the structure, shape, principle of the application shall be covered within the protection scope of the application.
Claims
1. A system for assessing the operating error state of a smart meter, the system comprising: The utility model relates to an intelligent electric meter error prediction system, comprising: a data acquisition module, the output of the data acquisition module is electrically connected with a data preprocessing module, the output of the data preprocessing module is electrically connected with a historical database, the output of the historical database is electrically connected with an error evaluation module, the output of the error evaluation module is electrically connected with a warning module, the output of the warning module is electrically connected with a user interface module, the output of the user interface module is electrically connected with a predictive maintenance module, the output of the predictive maintenance module is electrically connected with a resource optimization module, and the output of the resource optimization module is electrically connected with a user notification module.
2. The smart meter operational error state assessment system of claim 1, wherein: The data acquisition module is used for acquiring the metering data and environmental parameters of the intelligent electric meter in real time, the data preprocessing module is used for filtering and format conversion processing of the collected data, the historical database is used for storing the historical operation data of the intelligent electric meter, the error evaluation module is used for comparing the current data with the historical data and calculating the operation error rate, the warning module is used for issuing an alarm when the error rate exceeds the preset threshold, the user interface module is used for displaying the intelligent electric meter state report and warning information, the predictive maintenance module predicts the remaining service life of the electric meter through the historical error data and environmental parameters, the resource optimization module dynamically adjusts the electric meter replacement plan according to the prediction result, and the user notification module is used for notifying the relevant user before the planned replacement.
3. The smart meter operational error state assessment system of claim 1, wherein: The internal integration of the data acquisition module has a wireless communication unit, the output end of the wireless communication unit is electrically connected with an environmental sensor interface unit, the output end of the environmental sensor interface unit is electrically connected with a data buffer unit, the wireless communication unit is used for receiving real-time data sent by the smart meter through the wireless network, the environmental sensor interface unit is used for receiving temperature and humidity environmental parameters, and the data buffer unit is used for temporarily storing the collected raw data, the internal integration of the error evaluation module has a data comparison unit, the output end of the data comparison unit is electrically connected with a threshold judgment unit, the output end of the threshold judgment unit is electrically connected with an error calculation unit, the output end of the error calculation unit is electrically connected with a state evaluation unit, the data comparison unit is used for comparing the current period data with the historical average value, the threshold judgment unit is used for judging whether the data deviation exceeds the set threshold, the error calculation unit is used for calculating the meter operation error rate based on the multi-period data, and the state evaluation unit is used for evaluating the meter health status comprehensively based on the environmental parameters and the metering data, the internal integration of the early warning module has a multi-channel alarm unit, the output end of the multi-channel alarm unit is electrically connected with a hierarchical early warning unit, the output end of the hierarchical early warning unit is electrically connected with a warning record unit, the multi-channel alarm unit can alarm through short message, email and system, the hierarchical early warning unit can set different early warning levels according to the error severity, and the warning record unit is used for storing all early warning events and processing states, the internal integration of the user interface module has a meter state visualization unit, the output end of the meter state visualization unit is electrically connected with a warning management unit, the output end of the warning management unit is electrically connected with a report generation unit, the output end of the report generation unit is electrically connected with a remote control unit, the meter state visualization unit displays the meter operation state in the form of a chart, the warning management unit is used for viewing and processing early warning information, the report generation unit is used for automatically generating a meter health status report, and the remote control unit allows authorized users to remotely set system parameters.
4. The electronic device, according to the intelligent electric meter operation error state evaluation system of any one of claims 1-3, characterized in that: The processor is electrically connected with a memory, and the memory is electrically connected with a communication interface.
5. A storage medium for an intelligent meter operation error state evaluation system according to any one of claims 1 to 3, characterized by: The computer readable storage medium stores a computer program.
6. The method for evaluating the operation error state of the smart meter according to any one of claims 1-3, characterized in that: The method comprises the following steps: Step one, real-time acquisition of metering data and environmental parameters of the smart meter; Step two, preprocessing of the collected data, including filtering and format conversion; Step three, comparison and analysis of the preprocessed data with the reference data in the historical database; Step four, calculation of the meter operation error rate based on the multi-period data change trend; Step five, when the error rate exceeds the preset threshold, generating early warning information and notifying relevant personnel; Step six, dynamic adjustment of the meter maintenance plan according to the evaluation result. Step seven, establish the health status score system of the electric meter, quantify the evaluation results, and implement differentiated maintenance strategies based on the score results; Step eight, continuously optimize the evaluation algorithm parameters through the feedback mechanism, and generate the electric meter full life cycle management report.
7. The method of claim 6, wherein: In step eight, more specifically, after generating the electric meter full life cycle management report, the power company staff is allowed to remotely view and confirm the evaluation results, display the evaluation process and conclusion in the form of intuitive charts, add typical abnormal cases to the system knowledge base, and optimize the algorithm model based on historical evaluation results.
8. The method of claim 6, wherein the method further comprises: In step one, more specifically, the metering data uploaded by the smart meter is received regularly through the wireless communication network, the temperature and humidity parameters of the meter installation environment are monitored in real time, and the abnormal data is marked and triggered for immediate collection; In step three, more specifically, the deviation degree of the current data from the historical average value is calculated, and the data change trend of multiple consecutive periods is analyzed, then the rationality of the data anomaly is evaluated combined with the environmental parameter change, and the running state of the meter is evaluated by using the weighted algorithm; In step four, more specifically, the metering error model is established based on time series analysis, it needs to be noted that the influence coefficient of environmental parameters on metering accuracy needs to be considered when establishing the metering error model, then the machine learning algorithm is introduced to dynamically adjust the error judgment threshold, and the comprehensive error index needs to be calculated instead of a single error value when setting the metering error judgment threshold; In step five, more specifically, a multi-level early warning mechanism is set according to the error severity to automatically identify and associate the abnormal conditions of multiple electric meters in the same area, then a detailed report containing specific abnormal data and repair suggestions is generated, and the warning information is sent through multiple communication channels; In step six, more specifically, the remaining service life of the electric meter is predicted based on the error evaluation results, the replacement order and replacement time of the electric meter are optimized according to the remaining service life, and the optimal maintenance route and resource allocation scheme are automatically generated, and the optimal maintenance route and resource allocation scheme are dynamically updated in the maintenance plan database after being generated.