A smart fault monitoring method and system for an electric energy meter

Abstract

This invention relates to the field of electricity meter fault monitoring, and discloses a smart fault monitoring method and system for electricity meters. The smart fault monitoring method for electricity meters includes collecting metering output, power quality parameters, internal clock deviation, and communication link feedback status; for the data streams generated by multi-level monitoring channels, combining terminal load inertia constraints to extrapolate and repair the operating status during periods of missing data, obtaining continuously traceable terminal operation process description information; extracting abnormal coupling parameters that violate physical constraints based on the physical constraints of the electricity meter metering circuit and the communication protocol interaction logic; using the abnormal coupling parameters to screen abnormal candidate electricity meter terminals, deriving the abnormal propagation path and impact range; and dynamically adjusting the monitoring sampling rhythm and alarm triggering conditions based on the abnormal propagation path and real-time operating parameter change trends to generate fault response control information. This invention has the advantage of improving the accuracy of fault identification.

Description

Technical Field

[0001] This invention relates to the field of electricity meter fault monitoring, specifically to a smart fault monitoring method and system for electricity meters. Background Technology

[0002] In existing power distribution systems, electricity meters, as key terminal devices for electricity metering and status sensing, are widely deployed on the residential user side, industrial and commercial user side, and distribution substation side. They are used to collect basic metering data such as voltage, current, power, and electricity consumption, and upload the data to the main station system through the communication network to support metering settlement and operation monitoring.

[0003] However, in actual operation, the on-site environment of electricity meters is complex. Affected by factors such as carrier communication interference, wireless signal attenuation, electromagnetic noise, and network congestion, the data communication link between the electricity meter and the master station is prone to intermittent interruptions or delays, resulting in missing or abnormally fluctuating data received by the master station system. In existing technologies, the master station system usually determines the fault of the electricity meter based on data loss, numerical mutation, or threshold judgment rules, but it is difficult to distinguish between communication link anomalies and metering or hardware failures of the electricity meter itself. When communication anomalies are misjudged as metering failures or power outages, false alarms are easily triggered, increasing the burden of on-site inspections for maintenance personnel and reducing the efficiency of fault handling. Conversely, when true metering anomalies are masked by communication fluctuations, the risk of metering inaccuracies may not be detected in time, affecting the accuracy of electricity metering and the safety of power operation.

[0004] Therefore, it is necessary to design a smart fault monitoring method and system for electricity meters that improves the accuracy of fault identification. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a smart fault monitoring method and system for electricity meters, which has the advantage of improving the accuracy of fault identification and solves the problems mentioned in the background technology.

[0006] To achieve the aforementioned goal of improving the accuracy of fault identification, this invention provides the following technical solution: a smart fault monitoring method for electricity meters, comprising the following steps: Collect metering output, power quality parameters, internal clock deviation, and communication link feedback status, and form multi-level monitoring channels based on the importance of metering services and the difference in communication reliability; For the data streams generated by multi-level monitoring channels, the concentrator polling rhythm and the metering service execution rhythm are introduced as time references. The rhythm of asynchronously collected data is rearranged, and the operating status of the missing measurement period is deduced and repaired by combining the terminal load inertia constraints, so as to obtain continuous and traceable terminal operation process description information. Based on the terminal operation process description information, and according to the interaction logic of the physical constraints of the electricity meter metering circuit and the communication protocol, the coordinated consistency relationship between the metering output behavior and the communication feedback behavior is analyzed, and abnormal coupling parameters that violate physical constraints are extracted. By using abnormal coupling parameters to screen abnormal candidate energy meter terminals, and combining the wiring relationship of the distribution area, the concentrator forwarding entries and the geographical deployment information of the terminals, the abnormal propagation path and the scope of impact are deduced. Based on the abnormal propagation path and the real-time operating parameter change trend, the monitoring sampling rhythm and alarm triggering conditions are dynamically adjusted to generate fault response control information.

[0007] Preferably, the process of forming multi-level monitoring paths based on the difference between the importance of metering services and communication reliability is as follows: It acquires voltage, current, active power, reactive power, and cumulative electrical energy to reflect the real-time operating status of the electricity meter's metering circuit. Collect voltage fluctuation amplitude, frequency offset, harmonic content and three-phase imbalance information, obtain the time deviation and rate of change between the local clock of the energy meter and the time reference of the concentrator, and use it to describe the synchronization status of the terminal clock. The carrier signal-to-noise ratio, link packet loss rate, retransmission count, and communication delay information are obtained to indicate the feedback status of the communication link. Based on the different roles of metering output parameters, power quality parameters, internal clock deviation, and communication link feedback status in metering operations, priority levels for data acquisition and processing are set to form a multi-level monitoring pathway.

[0008] Preferably, the process of rhythm rearranging asynchronously acquired data is as follows: Obtain the concentrator polling scheduling cycle parameters and the master station metering service execution scheduling cycle parameters to construct a time reference framework corresponding to the multi-level monitoring channels; Establish a time mapping relationship between the concentrator polling cycle and the metering service execution rhythm to describe the time correlation constraints between different monitoring channels; Based on the time mapping relationship, the timestamps of data from different monitoring channels are recalibrated, and the recalibrated data is subjected to rhythmic rearrangement processing to form an operational data arrangement record based on a unified time rhythm.

[0009] Preferably, the process of obtaining continuously traceable terminal operation process description information is as follows: Based on the operational data arrangement records, historical load power curves and electricity consumption behavior patterns are extracted to construct a terminal load inertia constraint model; Curve fitting and trend extrapolation are performed on the load status change trend before and after the missing measurement period in the operation data arrangement record to form the state prediction boundary of the missing measurement section. Based on the terminal load inertia constraint model, the cumulative state of power, current and energy during the missing measurement period is extrapolated and calculated. Perform cross-cycle consistency verification on the simulation results and correct the constraints on the simulation results that violate the physical constraints of the metering loop; The revised simulation operation status data is merged with the operation data arrangement record to form a continuous and traceable description of the terminal operation process.

[0010] Preferably, the process of analyzing the coordination and consistency relationship between measurement output behavior and communication feedback behavior is as follows: Based on the terminal operation process description information, a physical constraint rule base for metering loops is constructed, including the relationship constraints between voltage, current and power, phase angle consistency constraints, and energy accumulation monotonicity constraints. Based on the terminal operation process description information, a communication protocol interaction logic rule base is constructed. The metering output behavior data and communication feedback behavior data in the terminal operation process description information are synchronously correlated and analyzed to calculate the time-series consistency index and numerical consistency index between metering behavior and communication behavior. Based on the time series consistency index and the numerical consistency index, a comprehensive evaluation of the coordination and consistency between metering output behavior and communication feedback behavior is conducted to generate a metering and communication coordination and consistency evaluation result.

[0011] Preferably, the process of extracting anomalous coupling parameters that violate physical constraints is as follows: Based on the results of the consistency evaluation of metering and communication, identify abnormal metering behaviors that violate the physical constraints of the metering loop and abnormal communication behaviors that violate the interaction logic of the communication protocol. Calculate the coupling deviation quantification index between abnormal measurement behavior and abnormal communication behavior, and map the coupling deviation quantification index to abnormal coupling parameters.

[0012] Preferably, the process of screening abnormal candidate energy meter terminals using abnormal coupling parameters is as follows: Statistical analysis was performed on the abnormal coupling parameters corresponding to each electricity meter terminal, and a terminal abnormality score value was calculated to quantify the degree of coupling between metering abnormalities and communication abnormalities. A terminal anomaly ranking list is constructed based on the terminal anomaly score, and the electricity meter terminals are initially screened according to the preset anomaly score threshold to obtain anomaly candidate electricity meter terminals.

[0013] Preferably, the process of deriving the anomaly propagation path and scope of impact is as follows: Using the abnormal candidate energy meter terminal as the abnormal source node, obtain the wiring topology information of the transformer area, including feeder connection relationship, branch node relationship and terminal access level relationship; Obtain concentrator forwarding table entries to determine the data forwarding path and communication link topology, and associate the forwarding level position of abnormal candidate energy meter terminals in the communication link; Based on the node position relationship of the abnormal candidate energy meter terminals in the electrical topology and communication link topology, the abnormal propagation path is deduced to identify the spread trajectory of the abnormal along the metering loop path and the communication forwarding path. Calculate the scope of impact of abnormal propagation based on the propagation path simulation results, determine the set of affected terminals, and generate description information of abnormal propagation relationships.

[0014] Preferably, the process of generating fault response control information is as follows: Based on the description information of abnormal propagation relationships and the deduction results of abnormal propagation paths, the abnormal risk diffusion trend index is calculated, and the abnormal risk diffusion trend index is dynamically updated in combination with the real-time operating parameter change trend. Based on the abnormal source node, propagation path level and affected terminal set in the abnormal propagation relationship description information, the electricity meter terminals are adjusted in a graded manner. The results of monitoring sampling rhythm adjustment, polling strategy adjustment, and alarm threshold adjustment are merged and processed to generate fault response control information for linkage between master station strategy adjustment and on-site operation and maintenance.

[0015] This invention also discloses another technical solution: a smart fault monitoring system for electricity meters, comprising: Multi-level monitoring module: Collects metering output, power quality parameters, internal clock deviation, and communication link feedback status to form a multi-level monitoring path; Rhythm Rearrangement Module: Rearranges asynchronously collected data in time according to the concentrator polling rhythm and the execution rhythm of metering services, and deduces and repairs the operating status of missing measurement periods; Collaborative verification module: Combining the physical constraints of the metering loop with the communication protocol interaction logic, it analyzes the collaborative consistency relationship between metering output behavior and communication feedback behavior; Derivation and propagation module: Uses abnormal coupling parameters to screen abnormal candidate energy meter terminals and derives the abnormal propagation path and impact range; Response control module: Dynamically adjusts the monitoring sampling rhythm and alarm triggering conditions to generate fault response control information.

[0016] Compared with the prior art, the present invention provides a smart fault monitoring method and system for electricity meters, which has the following beneficial effects: This invention constructs a multi-level monitoring path and introduces the concentrator polling cycle and metering service execution rhythm as a unified time benchmark. This achieves time alignment and rhythm rearrangement between asynchronous metering data, communication data, and service scheduling rhythm, enabling the reconstruction of continuous and traceable terminal operation process description information under conditions of missing data and unstable communication, thereby significantly improving the integrity of metering data. By combining terminal load inertia constraints to extrapolate and repair missing data periods, it avoids metering data interruption caused by communication packet loss or terminal anomalies. Based on the physical constraints of metering loops and the interaction logic of communication protocols, a collaborative consistency analysis mechanism is established between metering output behavior and communication feedback behavior. This mechanism can extract abnormal coupling parameters that violate physical constraints and protocol logic, enabling refined identification of metering anomalies, communication anomalies, and their collaborative anomalies, improving the accuracy of anomaly identification and anti-interference capability. By using abnormal coupling parameters to score and screen candidates for anomalies in energy meter terminals, and combining the distribution area wiring topology, concentrator forwarding entries, and terminal geographical deployment information, it deduces the anomaly propagation path and impact range, realizing the propagation pattern of abnormal behavior in both electrical and communication topologies. Based on the abnormal propagation path and the changing trend of real-time operating parameters, the terminal monitoring sampling rhythm, concentrator polling strategy, and alarm triggering conditions are dynamically adjusted to form adaptive fault response control information. This enhances the sensitivity of monitoring and alarms in the early stage of abnormal propagation and automatically restores the default strategy in the stage of abnormal stabilization or dissipation. This reduces the occupation of communication resources and the risk of alarm storms, realizing the transformation of electricity meter fault monitoring from passive alarm to active perception, propagation analysis, and adaptive response control, and improving the operational safety of the electricity metering system. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 As shown in the figure, a smart fault monitoring method for electricity meters in an embodiment of the present invention includes the following steps: S1: Collects metering output, power quality parameters, internal clock deviation, and communication link feedback status, forming multi-level monitoring channels based on the importance of metering services and the difference in communication reliability.

[0020] The process of forming multi-level monitoring channels in S1 based on the difference between the importance of metering services and communication reliability is as follows: The system acquires voltage, current, active power, reactive power, and cumulative energy to reflect the real-time operating status of the electricity meter's metering circuit. Within the metering circuit, instantaneous voltage and current data are periodically collected through voltage sampling channels, current transformer sampling channels, and a power calculation module. Based on synchronous sampling points, the corresponding active and reactive power values ​​are calculated. The active and reactive energy are then integrated over time using an energy integration module to generate cumulative energy data. The sampling period is configured according to the metering service type, preferably set to a fixed time interval of 100ms to 1s. A continuous sequence of sampled data is stored in an internal buffer to form a set of basic metering output parameters reflecting the real-time operating status of the electricity meter's metering circuit. The system collects information on voltage fluctuation amplitude, frequency offset, harmonic content, and three-phase imbalance. It obtains the time deviation and rate of change between the local clock of the energy meter and the time reference of the concentrator to describe the synchronization status of the terminal clock. Based on continuously collected voltage and current waveform data, it statistically calculates the change amplitude of the effective voltage value within adjacent time windows to obtain the voltage fluctuation amplitude index. It calculates the actual grid frequency through zero-crossing detection and performs differential calculation with the rated frequency reference to obtain the frequency offset. It performs fast Fourier transform on the sampled waveform to extract the amplitude of each harmonic component and calculates the total harmonic distortion rate to describe the harmonic content level. It calculates the three-phase imbalance index based on the amplitude and phase difference of the three-phase voltage or three-phase current. The local clock of the energy meter obtains the time reference of the concentrator by periodically receiving the broadcast timestamp of the concentrator. It performs differential calculation between the local time and the concentrator time and obtains the rate of change of the time deviation through continuous time window fitting to represent the stability of the terminal clock synchronization status. The system acquires carrier signal-to-noise ratio (SNR), link packet loss rate, retransmission count, and communication delay information to represent the communication link feedback status. The physical layer modulation and demodulation unit outputs the carrier SNR index in real time and records the success and failure status of data frame transmission at the link layer. It also counts the number of packet losses and successful receptions within a unit time window to calculate the link packet loss rate. The communication protocol stack records the number of retransmission triggers for each data frame to form a retransmission count statistical index. A timestamp is inserted between the sending request and receiving response to calculate the time difference between the request and response, obtaining communication delay information. The communication feedback status parameters are aggregated according to a fixed statistical period to form a set of communication link status parameters. Based on the different roles of metering output parameters, power quality parameters, internal clock deviation, and communication link feedback status in metering operations, priority levels for data acquisition and processing are set to form a multi-level monitoring path. Based on the importance of metering operations and communication reliability requirements, different acquisition and processing priority levels are assigned to metering output parameters, power quality parameters, internal clock deviation parameters, and communication link feedback parameters. Voltage, current, active power, reactive power, and cumulative energy are set as primary monitoring parameters to support real-time billing and abnormal metering identification. Voltage fluctuation amplitude, frequency offset, harmonic content, and three-phase imbalance are also prioritized. The following parameters are set as secondary monitoring parameters for power quality assessment and metering reliability analysis: the local clock deviation of the electricity meter and its rate of change are set as tertiary monitoring parameters for time synchronization anomaly detection; and the carrier signal-to-noise ratio, link packet loss rate, retransmission count, and communication delay are set as quaternary monitoring parameters for communication reliability assessment. Different acquisition cycles, caching strategies, and reporting strategies are configured for different parameters according to their priority levels, forming a multi-level monitoring path structure. This allows high-priority parameters to be reported first when communication resources are limited, while low-priority parameters are reported supplementarily when the link is stable, thereby achieving coordinated optimization of metering service reliability and communication resource utilization. S2: For the data streams generated by multi-level monitoring channels, the concentrator polling rhythm and the metering service execution rhythm are introduced as time references. The rhythm of asynchronously collected data is rearranged, and the operating status of the missing measurement period is deduced and repaired by combining the terminal load inertia constraints, so as to obtain continuous and traceable terminal operation process description information.

[0021] The process of rhythm rearrangement for asynchronously acquired data in S2 is as follows: The concentrator polling scheduling cycle parameters and the main station metering service execution scheduling cycle parameters are obtained to construct a time reference framework corresponding to the multi-level monitoring channels. On the concentrator side, the current polling strategy configuration parameters are read, including the single terminal polling cycle, group polling cycle, and polling task queue trigger interval, which are used to describe the concentrator's communication scheduling rhythm for each terminal. On the main station side, the service execution cycle parameters are obtained from the metering service scheduling module, including the metering data entry cycle, the anomaly analysis task execution cycle, and the billing settlement trigger cycle. The concentrator polling scheduling cycle parameters and the main station metering service execution scheduling cycle parameters are converted to a unified time unit and stored as a time reference parameter set to construct a time reference framework corresponding to the multi-level monitoring channels, so that the terminal-side acquisition rhythm, the concentrator-side polling rhythm, and the main station service processing rhythm are in an alignable time scale system. A time mapping relationship is established between the concentrator polling cycle and the metering service execution rhythm to describe the time correlation constraints between different monitoring channels. Time reference parameters for the concentrator polling cycle and the main station metering service execution cycle are collected, including polling start time, service start time, and cycle length. Based on the relationship between the polling cycle and the service execution cycle, the proportional relationship between the two is calculated. If the polling cycle and the service execution cycle are integer multiples of each other, the polling time point directly corresponds to the service processing window. If the proportion is not an integer, the polling time point is mapped to the nearest service processing window through interpolation or rounding. To ensure accurate mapping, the polling time series and the service execution cycle are compared. The time series execution process is aligned to the whole cycle. The whole cycle alignment includes: determining the least common multiple of the polling cycle and the business cycle; aligning the polling time point with the business execution window within this cycle, so that each polling time point corresponds to a unique business processing time slice; dividing the business execution cycle into several business time slices of equal length; and determining the business time slice index to which each polling cycle belongs. The number and length of the business time slices are set according to the data collection frequency and business processing requirements of the monitoring channel, forming a time correlation constraint rule between the polling time series and the business execution time series, ensuring the time synchronization of data from different monitoring channels in cross-level processing flow. Based on the time mapping relationship, the timestamps of data from different monitoring channels are recalibrated. The recalibrated data undergoes rhythmic rearrangement processing to form a record of operational data based on a unified time beat. The original acquisition timestamp and corresponding acquisition channel identifier for each monitoring data point are recorded at the concentrator. According to the established time mapping relationship, the timestamps of each asynchronously acquired data point are recalibrated to a unified operational time beat coordinate system. The recalibrated data is grouped according to the operational time slice index, dividing the data into corresponding operational time slices. Within each operational time slice, rhythmic rearrangement processing is performed according to the following rules: data is arranged according to acquisition priority, with higher priority data receiving higher priority data. Data is prioritized and arranged chronologically under the same priority level, ensuring that data collected earlier comes first. After rearrangement, a record of operational data based on a unified time frame is formed. Through a clear time mapping table construction method and business time slice division rules, asynchronously collected data can be accurately mapped to a unified business time frame coordinate system. The calculation method for the ratio between the polling cycle and the business execution cycle and the whole cycle alignment processing steps are clearly defined, ensuring the reproducibility of the correspondence between the polling time point and the business processing window. The rhythmic rearrangement processing rules for data within the business time slice are clear, enabling cross-monitoring channel data to form a consistent and prioritized operational data record under a unified time frame.

[0022] The process of obtaining continuously traceable terminal operation description information in S2 is as follows: Based on the operational data records, historical load power curves and electricity consumption patterns are extracted to construct a terminal load inertia constraint model. Based on the operational data records, segmented statistical analysis is performed on the terminal load power time series according to continuous operating cycles. Each time segment can be divided into daily, hourly, or minute units, with the specific granularity determined based on load characteristics and data collection frequency. For each time series segment, the following features are extracted: intraday load variation curves, including power peaks, troughs, and stable periods; periodic load characteristics, such as daily peaks and troughs, weekly peaks and troughs, and the amplitude of periodic load fluctuations; and typical load start-up and shutdown behavior patterns, including time series of sharp increases in start-up power and gradual decreases in shutdown power. For loads with recurring patterns, typical behaviors are identified by statistical frequency and duration, forming standardized load behavior templates. These templates are then combined with equipment rated capacity, circuit topology, and load type identifiers to match historical power curves with electricity consumption behavior, identifying load operating states (e.g., startup, stable operation, shutdown, idle). Recurring operating states are clustered to form a set of typical electricity consumption behavior patterns for inertial constraint modeling. During pattern extraction, time series clustering or sliding window analysis methods are used, with power change rate, duration, peak amplitude, and other indicators as feature inputs to classify and identify load behavior, thus extracting historical load characteristics. Based on curves and electricity consumption patterns, load inertia constraint rules are defined: the range of power change per unit time, limiting the acceptable change of load power in a short period of time; the rate of current rise or fall, ensuring that the load start-up or shutdown process conforms to the physical characteristics of the equipment; and the continuity of energy accumulation, ensuring that the load energy consumption changes smoothly within a continuous operating cycle. The parameter thresholds of the constraint model are determined based on the load type and historical data statistics. For example, the average power rise rate of the starting load is taken as the upper limit of the historical starting curve, the rate of power fall of the shut-off load is taken as the lower limit of the historical fall curve, and the daily power fluctuation range is taken as the statistical distribution interval of the historical fluctuation amplitude (e.g., 90% coverage). In the model construction, fitting methods (such as least squares fitting, smooth curve fitting, or moving average fitting) are used to smooth and extract trends from historical power curves, so that the inertial constraint rules can reflect the real physical characteristics of the load, rather than single-point fluctuations. The load inertial constraint model is combined with the extracted electricity consumption behavior patterns to generate continuous and traceable terminal operation process description information, including: load power change curves, typical start-up and shutdown modes, power change rate range, and continuous interval of energy accumulation change. This information is used for load anomaly detection, energy consumption analysis, and cross-cycle operation trend prediction, ensuring that data analysis and subsequent regulation can be based on real physical characteristics. Curve fitting and trend extrapolation are performed on the load state change trend before and after the missing measurement period in the operation data arrangement record to form the state prediction boundary of the missing measurement section. For the time section marked as missing measurement in the operation data arrangement record, the power, current and energy time series in the adjacent time windows before and after the missing measurement section are extracted. The load state change trend curve is constructed by piecewise linear fitting. Based on the fitting results, trend extrapolation is performed into the missing measurement section. Based on the extrapolation results, the upper and lower limit prediction boundaries of the load state in the missing measurement section are generated to limit the feasible change range of the load state in the missing measurement period, thereby avoiding abnormal fluctuations in the extrapolation results that are significantly inconsistent with historical operation patterns. Based on the terminal load inertia constraint model, the power, current and energy accumulation status during the missing measurement period are extrapolated and calculated. Under the boundary constraints of the missing measurement section status prediction, the terminal load inertia constraint model is called to extrapolate and calculate the power, current and energy accumulation status during the missing measurement period step by step. The power extrapolation result is used to calculate the corresponding current extrapolation value. The energy accumulation status is obtained by time integration of the extrapolated power. During the extrapolation process, the power change rate, current change amplitude and energy accumulation continuity are constrained and controlled to ensure that the extrapolated operating status conforms to the physical inertia characteristics of the terminal load and the operating law of the metering circuit. Cross-cycle consistency verification is performed on the simulation results, and constraint corrections are applied to simulation results that violate the physical constraints of the metering loop. Cross-cycle consistency verification is performed on the simulation results and the known data before and after the missing measurement section, including daily cycle energy balance consistency verification, load peak-valley timing consistency verification, and cumulative energy continuity verification. When the simulation results violate the physical constraints of the metering loop, constraint corrections are applied to the simulation results. The physical constraints include the upper limit of the rated current of the energy meter, voltage level constraints, power factor range constraints, and monotonically increasing energy accumulation constraints. For data points that violate the constraints, amplitude clipping, trend smoothing, or re-simulation processing are performed to ensure that the simulation results conform to the physical operating state that the metering loop can achieve. The corrected simulated operational status data is fused with the operational data arrangement record to form a continuous and traceable description of the terminal's operational process. The constrained simulated operational status data is inserted into the operational data arrangement record according to the time index to fill in the original missing time slices and retain the original missing data identifier and simulated identifier fields, so as to achieve distinguishable storage of simulated data and measured data. Based on the fused time series, a continuous time index structure and traceability mark chain are constructed so that the terminal's operational status at any time point can be traced back to the original collected data or the simulated supplementary data, thereby forming a continuous and traceable description of the terminal's operational process.

[0023] S3: Based on the terminal operation process description information, and according to the interaction logic of the physical constraints of the electricity meter metering circuit and the communication protocol, analyze the coordination and consistency relationship between the metering output behavior and the communication feedback behavior, and extract the abnormal coupling parameters that violate the physical constraints.

[0024] The process of analyzing the consistency relationship between measurement output behavior and communication feedback behavior in S3 is as follows: Based on the terminal operation process description information, a physical constraint rule base for the metering loop is constructed, including constraints on the relationship between voltage, current and power, phase angle consistency constraints, and energy accumulation monotonicity constraints. The physical constraint rules include power calculation consistency constraints between voltage, current and power, which are used to limit the power value to meet the voltage and current product relationship; phase angle consistency constraints between three-phase voltage, current and phase angle, which are used to limit the phase relationship and power factor range of the three-phase system; and energy accumulation monotonicity constraints, which are used to limit the accumulated energy to increase monotonically with time and the rate of change to be constrained by the rated capacity. The physical constraint rule base is stored in the form of rule entries, and each rule is configured with a trigger threshold and anomaly judgment conditions for real-time or offline constraint verification of the terminal metering output behavior. Based on the terminal operation process description information, a communication protocol interaction logic rule base is constructed. Synchronous correlation analysis is performed on the metering output behavior data and communication feedback behavior data in the terminal operation process description information to calculate the timing consistency index and numerical consistency index between metering behavior and communication behavior. Based on the terminal operation process description information, the structure of request frames, response frames, retransmission frames, and acknowledgment frames in the communication protocol interaction process is analyzed to construct a communication protocol interaction logic rule base. This base describes the logical timing relationship between terminal metering data reporting, concentrator polling requests, and master station service responses. Synchronous correlation analysis is performed on the metering output behavior data and communication feedback behavior data. By calculating the time difference between the metering data generation timestamp and the communication data reporting timestamp, the timing consistency index between metering behavior and communication behavior is obtained. By comparing the metering data values ​​and the communication reported data content at the field level, the data field consistency, cumulative energy increment consistency, and power and current consistency are calculated to form a numerical consistency index. The timing consistency index reflects the degree of time coordination between metering behavior and communication feedback, while the numerical consistency index reflects the degree of numerical consistency between the metering loop output and the communication uploaded content. Based on the time-series consistency index and the numerical consistency index, a comprehensive evaluation of the coordination consistency between metering output behavior and communication feedback behavior is conducted to generate a metering and communication coordination consistency evaluation result. Based on the time-series consistency index and the numerical consistency index, a coordination consistency evaluation rule is constructed to comprehensively evaluate the coordination consistency between metering output behavior and communication feedback behavior. According to the importance of metering services and communication reliability requirements, the time-series consistency index and the numerical consistency index are assigned weight coefficients to calculate the coordination consistency score, and the score is mapped to the coordination consistency level to distinguish between normal consistency, weak consistency and abnormal inconsistency. When the coordination consistency score is lower than the preset threshold, the metering communication coordination anomaly flag is triggered, and the time of occurrence of the anomaly, the type of abnormal parameters and the duration of the anomaly are recorded.

[0025] The process of extracting anomalous coupling parameters that violate physical constraints in S3 is as follows: Based on the evaluation results of the consistency assessment between metering and communication, abnormal metering behaviors that violate the physical constraints of the metering loop are identified, as well as abnormal communication behaviors that violate the interaction logic of the communication protocol. Based on the evaluation results of the consistency assessment between metering and communication, abnormal classification and identification are performed on the description information of the terminal operation process. When the metering output parameters violate the power consistency constraint, phase angle consistency constraint, or energy accumulation monotonicity constraint in the rule base of the physical constraint of the metering loop, the corresponding time period is marked as an abnormal metering behavior. When the communication interaction process violates the request and response timing constraint, retransmission strategy constraint, or data field integrity constraint in the rule base of the interaction logic of the communication protocol, the corresponding time period is marked as an abnormal communication behavior. During the identification process, the abnormal behaviors are marked at the time slice level, and the abnormality type, abnormality magnitude, and abnormality duration are recorded to form a set of abnormal metering behaviors and a set of abnormal communication behaviors. The coupling deviation quantification index between abnormal metering behavior and abnormal communication behavior is calculated and mapped to abnormal coupling parameters. Based on the sets of abnormal metering behavior and abnormal communication behavior, abnormal events within the same or adjacent time windows are time-aligned, and the coupling deviation quantification index between abnormal metering behavior and abnormal communication behavior is calculated. These include an abnormal time overlap index, which represents the degree of synchronization between metering and communication anomalies in time; an abnormal amplitude correlation index, which represents the numerical correlation between the abnormal amplitude of metering parameters and the abnormal amplitude of communication links; and an abnormal duration coupling index, which represents the co-evolution relationship between the durations of the two types of anomalies. The coupling deviation quantification index is converted into abnormal coupling parameters according to a preset mapping rule, and a corresponding abnormal coupling type label is configured for each abnormal coupling parameter to distinguish between metering-dominated anomalies, communication-dominated anomalies, and co-coupled anomalies, thereby forming a set of abnormal coupling parameters for metering evidence collection analysis and operation and maintenance decision-making.

[0026] S4: Use abnormal coupling parameters to screen abnormal candidate energy meter terminals, and combine the wiring relationship of the distribution area, the concentrator forwarding entries and the geographical deployment information of the terminals to deduce the abnormal propagation path and the scope of impact.

[0027] The process of filtering abnormal candidate energy meter terminals using abnormal coupling parameters in S4 is as follows: Statistical analysis is performed on the abnormal coupling parameters corresponding to each electricity meter terminal to calculate the terminal abnormality score value used to quantify the coupling degree between metering abnormalities and communication abnormalities. For each electricity meter terminal, the abnormal coupling parameter sequence within a preset statistical period is summarized, and statistical analysis is performed on the abnormal time overlap index, abnormal amplitude correlation index, and abnormal duration coupling index. The average value, peak value, and cumulative quantified value are calculated respectively, and normalization is performed to eliminate the difference in the dimensions of different parameters. Based on the normalization results, according to the weight configuration strategy of metering service importance and communication reliability weight, weight coefficients are assigned to various coupling indicators, and the terminal abnormality score value is calculated. The terminal abnormality score value is used to quantify the coupling strength and the degree of continuous impact between metering abnormalities and communication abnormalities. The terminal abnormality score value is generated by weighted summation or weighted integration, and the terminal identifier, scoring time window, and scoring result are recorded to form a terminal abnormality score record table. A terminal anomaly ranking list is constructed based on the terminal anomaly score, and the electricity meter terminals are initially screened according to a preset anomaly score threshold to obtain anomaly candidate electricity meter terminals. The anomaly score records of all electricity meter terminals are sorted, and an anomaly ranking list is constructed according to the terminal anomaly score from high to low. Anomaly score level label is attached to the terminals in the ranking list to distinguish high-risk, medium-risk, and low-risk anomaly terminals. The terminal anomaly ranking list is filtered according to the preset anomaly score threshold. When the terminal anomaly score exceeds the threshold, the corresponding electricity meter terminal is marked as an anomaly candidate electricity meter terminal, and the filtering time, anomaly score, and anomaly type label are recorded. When the terminal anomaly score is below the threshold, the terminal is marked as temporarily not abnormal.

[0028] The process of deriving the anomaly propagation path and scope of influence in S4 is as follows: Using candidate abnormal energy meter terminals as abnormal source nodes, the topology information of the transformer area wiring is obtained, including feeder connection relationships, branch node relationships, and terminal access hierarchy relationships. The selected candidate abnormal energy meter terminals are marked as abnormal source nodes, and the topology information of the transformer area wiring is obtained, including feeder and transformer connection relationships, branch node hierarchy relationships, and terminal access hierarchy relationships. The transformer, feeder nodes, branch nodes, and terminal nodes are abstracted into node objects, and the electrical connection relationships are abstracted into topology edges to form an electrical topology graph for abnormal propagation analysis. Each node is configured with a hierarchy number and electrical distance parameters. The concentrator forwarding table entries are obtained to determine the data forwarding path and communication link topology, and the forwarding level position of the abnormal candidate energy meter terminal in the communication link is associated. The communication forwarding table entries are read on the concentrator side to obtain the mapping relationship between the terminal address and the next-hop forwarding node address, the communication routing level, and the link quality parameters, thereby constructing the communication link topology. Based on the mapping relationship between the terminal identifier and the forwarding table entries, the forwarding level position of the abnormal candidate energy meter terminal in the communication link is determined, and the communication nodes are abstracted into communication topology graph nodes, and the forwarding relationship is abstracted into communication topology edges to form a communication link topology graph. Based on the node positional relationship of the candidate abnormal energy meter terminals in the electrical topology and communication link topology, anomaly propagation path deduction is performed to identify the diffusion trajectory of the anomaly along the metering loop path and the communication forwarding path. Based on the node positional relationship of the anomaly source node in the electrical topology and communication topology, anomaly propagation path deduction is performed along the metering loop path and the communication forwarding path, respectively. In the electrical topology path deduction, a topology traversal is performed from the anomaly source node to the upstream feeder node and the downstream branch node to identify the node sequence that may be affected by the physical coupling of the metering loop. In the communication topology path deduction, a step-by-step node traversal is performed from the anomaly source node along the communication forwarding path to identify the node sequence that may be affected by the link anomaly or the data forwarding anomaly. During the deduction process, propagation priority and propagation probability weight are calculated based on the node level, electrical distance, and communication link weight parameters to form the anomaly diffusion trajectory description path. The abnormal propagation impact range is calculated based on the propagation path simulation results, the set of affected terminals is determined, and abnormal propagation relationship description information is generated. Based on the abnormal propagation path simulation results, the impact range of nodes on the path is determined, and the abnormal propagation impact range parameters are calculated, including the propagation level depth, the number of affected nodes, and the propagation coverage area. The set of terminal nodes on the propagation path is marked as the set of affected terminals, and the abnormal propagation path identifier, propagation distance weight, and propagation confidence parameters are recorded for each terminal node, thereby forming abnormal propagation relationship description information.

[0029] S5: Based on the abnormal propagation path and the real-time operating parameter change trend, dynamically adjust the monitoring sampling rhythm and alarm triggering conditions to generate fault response control information.

[0030] The process of generating fault response control information in S5 is as follows: Based on the description information of abnormal propagation relationships and the inference results of abnormal propagation paths, an abnormal risk diffusion trend index is calculated, and the abnormal risk diffusion trend index is dynamically updated in combination with the real-time operating parameter change trend. Based on the description information of abnormal propagation relationships and the inference results of abnormal propagation paths, statistical analysis is performed on the propagation level depth, number of propagation nodes, and propagation path weight parameters from the abnormal source node to each propagation path node to construct an abnormal risk diffusion trend index, which is used to quantify the diffusion speed and diffusion intensity of anomalies in electrical and communication topologies. The abnormal risk diffusion trend index includes an abnormal propagation level growth rate index, an affected terminal number growth rate index, and a propagation path weight accumulation index, which are used to represent the time evolution trend of anomaly diffusion. The real-time operating parameter change trend is obtained, including the voltage fluctuation amplitude change trend, the communication link packet loss rate change trend, and the terminal anomaly score change trend, and the abnormal risk diffusion trend index is periodically and dynamically updated. Based on the abnormal source node, propagation path level, and affected terminal set in the abnormal propagation relationship description information, the monitoring sampling rhythm, concentrator polling strategy, and alarm trigger threshold of the electricity meter terminal are adjusted in a graded manner. For abnormal source nodes and high propagation level nodes, the terminal sampling cycle is shortened and the data reporting priority is increased. At the same time, a high-priority polling queue is allocated to the corresponding terminal on the concentrator side. For medium and low propagation level nodes, the original sampling rhythm is maintained or lightly encrypted polling is performed, and a medium-priority polling strategy is configured. The alarm trigger threshold is dynamically adjusted according to the abnormal risk diffusion trend indicator. When the abnormal diffusion trend indicator rises, the alarm threshold is reduced to improve alarm sensitivity. When the abnormal diffusion trend indicator stabilizes or decreases, the default threshold is restored to avoid alarm storms or missed alarm risks. The results of monitoring sampling rhythm adjustment, polling strategy adjustment, and alarm threshold adjustment are fused together to generate fault response control information for main station strategy adjustment and on-site operation and maintenance linkage. The results of terminal monitoring sampling rhythm adjustment, concentrator polling strategy adjustment, and alarm threshold adjustment are also fused together to form a unified set of fault response control parameters, including terminal sampling strategy parameters, concentrator polling scheduling parameters, and main station alarm strategy parameters. These parameters are then sent to the concentrator and terminal sides for execution via the strategy distribution interface, generating fault response control information records for operation and maintenance linkage. This enables adaptive monitoring enhancement, communication scheduling optimization, and alarm linkage handling in abnormal scenarios, thereby improving the efficiency of abnormal handling and the security of system operation.

[0031] Example 2: Figure 2 As shown, a smart fault monitoring system for electricity meters includes: Multi-level monitoring module: Collects metering output, power quality parameters, internal clock deviation, and communication link feedback status to form a multi-level monitoring path; Rhythm Rearrangement Module: Rearranges asynchronously collected data in time according to the concentrator polling rhythm and the execution rhythm of metering services, and deduces and repairs the operating status of missing measurement periods; Collaborative verification module: Combining the physical constraints of the metering loop with the communication protocol interaction logic, it analyzes the collaborative consistency relationship between metering output behavior and communication feedback behavior; Derivation and propagation module: Uses abnormal coupling parameters to screen abnormal candidate energy meter terminals and derives the abnormal propagation path and impact range; Response control module: Dynamically adjusts the monitoring sampling rhythm and alarm triggering conditions to generate fault response control information.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart fault monitoring method for electricity meters, characterized in that, Includes the following steps: Collect metering output, power quality parameters, internal clock deviation, and communication link feedback status, and form multi-level monitoring channels based on the importance of metering services and the difference in communication reliability; For the data streams generated by multi-level monitoring channels, the concentrator polling rhythm and the metering service execution rhythm are introduced as time references. The rhythm of asynchronously collected data is rearranged, and the operating status of the missing measurement period is deduced and repaired by combining the terminal load inertia constraints, so as to obtain continuous and traceable terminal operation process description information. Based on the terminal operation process description information, and according to the interaction logic of the physical constraints of the electricity meter metering circuit and the communication protocol, the coordinated consistency relationship between the metering output behavior and the communication feedback behavior is analyzed, and abnormal coupling parameters that violate physical constraints are extracted. By using abnormal coupling parameters to screen abnormal candidate energy meter terminals, and combining the wiring relationship of the distribution area, the concentrator forwarding entries and the geographical deployment information of the terminals, the abnormal propagation path and the scope of impact are deduced. Based on the abnormal propagation path and the real-time operating parameter change trend, the monitoring sampling rhythm and alarm triggering conditions are dynamically adjusted to generate fault response control information.

2. The intelligent fault monitoring method for electricity meters according to claim 1, characterized in that, The process of establishing multi-level monitoring pathways based on the differences in the importance of metering services and communication reliability is as follows: It acquires voltage, current, active power, reactive power, and cumulative electrical energy to reflect the real-time operating status of the electricity meter's metering circuit. Collect voltage fluctuation amplitude, frequency offset, harmonic content and three-phase imbalance information, obtain the time deviation and rate of change between the local clock of the energy meter and the time reference of the concentrator, and use it to describe the synchronization status of the terminal clock. The carrier signal-to-noise ratio, link packet loss rate, retransmission count, and communication delay information are obtained to indicate the feedback status of the communication link. Based on the different roles of metering output parameters, power quality parameters, internal clock deviation, and communication link feedback status in metering operations, priority levels for data acquisition and processing are set to form a multi-level monitoring pathway.

3. The intelligent fault monitoring method for electricity meters according to claim 2, characterized in that, The process of rhythm rearrangement for asynchronously acquired data is as follows: Obtain the concentrator polling scheduling cycle parameters and the master station metering service execution scheduling cycle parameters to construct a time reference framework corresponding to the multi-level monitoring channels; Establish a time mapping relationship between the concentrator polling cycle and the metering service execution rhythm to describe the time correlation constraints between different monitoring channels; Based on the time mapping relationship, the timestamps of data from different monitoring channels are recalibrated, and the recalibrated data is subjected to rhythmic rearrangement processing to form an operational data arrangement record based on a unified time rhythm.

4. The intelligent fault monitoring method for electricity meters according to claim 3, characterized in that, The process of obtaining continuous and traceable terminal operation description information is as follows: Based on the operational data arrangement records, historical load power curves and electricity consumption behavior patterns are extracted to construct a terminal load inertia constraint model; Curve fitting and trend extrapolation are performed on the load status change trend before and after the missing measurement period in the operation data arrangement record to form the state prediction boundary of the missing measurement section. Based on the terminal load inertia constraint model, the cumulative state of power, current and energy during the missing measurement period is extrapolated and calculated. Perform cross-cycle consistency verification on the simulation results and correct the constraints on the simulation results that violate the physical constraints of the metering loop; The revised simulation operation status data is merged with the operation data arrangement record to form a continuous and traceable description of the terminal operation process.

5. A smart fault monitoring method for electricity meters according to claim 4, characterized in that, The process of analyzing the consistency relationship between measurement output behavior and communication feedback behavior is as follows: Based on the terminal operation process description information, a physical constraint rule base for metering loops is constructed, including the relationship constraints between voltage, current and power, phase angle consistency constraints, and energy accumulation monotonicity constraints. Based on the terminal operation process description information, a communication protocol interaction logic rule base is constructed. The metering output behavior data and communication feedback behavior data in the terminal operation process description information are synchronously correlated and analyzed to calculate the time-series consistency index and numerical consistency index between metering behavior and communication behavior. Based on the time series consistency index and the numerical consistency index, a comprehensive evaluation of the coordination and consistency between metering output behavior and communication feedback behavior is conducted to generate a metering and communication coordination and consistency evaluation result.

6. The intelligent fault monitoring method for electricity meters according to claim 5, characterized in that, The process of extracting anomalous coupling parameters that violate physical constraints is as follows: Based on the results of the consistency evaluation of metering and communication, identify abnormal metering behaviors that violate the physical constraints of the metering loop and abnormal communication behaviors that violate the interaction logic of the communication protocol. Calculate the coupling deviation quantification index between abnormal measurement behavior and abnormal communication behavior, and map the coupling deviation quantification index to abnormal coupling parameters.

7. A smart fault monitoring method for electricity meters according to claim 6, characterized in that, The process of screening abnormal candidate energy meter terminals using abnormal coupling parameters is as follows: Statistical analysis was performed on the abnormal coupling parameters corresponding to each electricity meter terminal, and a terminal abnormality score value was calculated to quantify the degree of coupling between metering abnormalities and communication abnormalities. A terminal anomaly ranking list is constructed based on the terminal anomaly score, and the electricity meter terminals are initially screened according to the preset anomaly score threshold to obtain anomaly candidate electricity meter terminals.

8. The intelligent fault monitoring method for electricity meters according to claim 7, characterized in that, The process of deriving the anomaly propagation path and scope of impact is as follows: Using the abnormal candidate energy meter terminal as the abnormal source node, obtain the wiring topology information of the transformer area, including feeder connection relationship, branch node relationship and terminal access level relationship; Obtain concentrator forwarding table entries to determine the data forwarding path and communication link topology, and associate the forwarding level position of abnormal candidate energy meter terminals in the communication link; Based on the node position relationship of the abnormal candidate energy meter terminals in the electrical topology and communication link topology, the abnormal propagation path is deduced to identify the spread trajectory of the abnormal along the metering loop path and the communication forwarding path. Calculate the scope of impact of abnormal propagation based on the propagation path simulation results, determine the set of affected terminals, and generate description information of abnormal propagation relationships.

9. A smart fault monitoring method for electricity meters according to claim 8, characterized in that, The process of generating fault response control information is as follows: Based on the description information of abnormal propagation relationships and the deduction results of abnormal propagation paths, the abnormal risk diffusion trend index is calculated, and the abnormal risk diffusion trend index is dynamically updated in combination with the real-time operating parameter change trend. Based on the abnormal source node, propagation path level and affected terminal set in the abnormal propagation relationship description information, the electricity meter terminals are adjusted in a graded manner. The results of monitoring sampling rhythm adjustment, polling strategy adjustment, and alarm threshold adjustment are merged and processed to generate fault response control information for linkage between master station strategy adjustment and on-site operation and maintenance.

10. A smart fault monitoring system for electricity meters, applied to the smart fault monitoring method for electricity meters as described in any one of claims 1-9, characterized in that, include: Multi-level monitoring module: Collects metering output, power quality parameters, internal clock deviation, and communication link feedback status to form a multi-level monitoring path; Rhythm Rearrangement Module: Rearranges asynchronously collected data in time according to the concentrator polling rhythm and the execution rhythm of metering services, and deduces and repairs the operating status of missing measurement periods; Collaborative verification module: Combining the physical constraints of the metering loop with the communication protocol interaction logic, it analyzes the collaborative consistency relationship between metering output behavior and communication feedback behavior; Derivation and propagation module: Uses abnormal coupling parameters to screen abnormal candidate energy meter terminals and derives the abnormal propagation path and impact range; Response control module: Dynamically adjusts the monitoring sampling rhythm and alarm triggering conditions to generate fault response control information.

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