System and method for evaluating running state of intelligent electric energy meter

Abstract

The invention discloses an intelligent electric energy meter operation state evaluation system and method, and relates to the related field of electric variable measurement, and the method comprises the steps: integrating a passive probe assembly in a metering loop, and determining the propagation data of the metering loop through probe type detection; constructing an analog calculation circuit which is isomorphic with the physical topology of the metering loop, loading propagation data of the metering loop to a loop node, executing feature separation and parameter extraction in an analog domain, and determining a physical parameter variation; and according to the physical parameter variation, locally constructing a lightweight digital model and uploading the lightweight digital model to a zone area edge computing node, executing zone area topology propagation and zone area common factor tracing, and determining a state evaluation result. According to the application, the technical problem of insufficient real-time accurate monitoring and efficient fault traceability of the existing intelligent electric energy meter metering loop operation state monitoring is solved, and the technical effects of improving the real-time accurate monitoring capability of the metering loop operation state and realizing district-level fault efficient traceability and batch meter abnormity overall analysis are achieved.

Description

Technical Field

[0001] This application relates to the field of electrical variable measurement, and in particular to a system and method for evaluating the operating status of smart meters. Background Technology

[0002] As the core infrastructure for electricity metering and data collection at the end of the smart grid, the operational stability and health status of smart metering circuits directly affect the accuracy of electricity metering, the fairness of electricity trading, and the quality of operation and maintenance management of the power grid in the distribution area. It is a crucial link in ensuring the stable and efficient operation of the smart grid. Currently, the mainstream technical methods for monitoring and evaluating the operational status of smart metering circuits mainly include periodic manual on-site verification, offline component testing, and single electrical parameter threshold alarms. Specifically, this relies on maintenance personnel carrying standard verification equipment to conduct meter-by-meter testing, periodically replacing expired components, and real-time monitoring of single parameters such as circuit voltage or current, triggering alarms when they exceed preset thresholds. Existing technologies rely on manual offline operations, resulting in long testing cycles, poor real-time performance, and the ability to only make simple over-limit judgments for single electrical parameters. They cannot take into account the temperature change characteristics of components and multi-dimensional signal responses, making it difficult to detect early signs of weak degradation in the metering circuit. Furthermore, they suffer from inaccurate fault location and the inability to comprehensively analyze batch meter anomalies in the distribution area. Additionally, some online testing methods can interfere with the normal metering operation of the electricity meter, leading to low reliability of the test results.

[0003] At present, the monitoring of the operating status of smart energy meter metering circuits suffers from technical problems such as insufficient real-time and accurate monitoring and inefficient fault tracing capabilities. Summary of the Invention

[0004] This application provides a smart meter operation status assessment system and method. By integrating a tunable passive probe component consisting of a weak signal injection unit, a pickup unit, and a reference resistor network into the smart meter metering circuit, it acquires propagation data of the metering circuit through probe-based detection. An analog computing circuit is constructed that isomorphic to the physical topology of the metering circuit. The propagation data is loaded onto the circuit nodes, and feature separation and parameter extraction are performed in the analog domain. This yields the physical parameter changes, including analog computing parameters and fault source signals obtained from finite bandwidth mode component decomposition. Based on these physical parameter changes, a lightweight digital model is constructed locally on the meter and uploaded to the distribution area edge computing node. Through distribution area topology propagation analysis and common fault factor tracing, the smart meter operation status assessment results are obtained. These technical means address the technical problems of insufficient real-time and accurate monitoring and efficient fault tracing capabilities in existing smart meter metering circuit operation status monitoring. This achieves the technical effects of improving the real-time and accurate monitoring capability of the metering circuit operation status, realizing efficient fault tracing at the distribution area level, and comprehensive analysis of batch meter anomalies.

[0005] This application provides a smart energy meter operation status assessment system, comprising: a metering loop propagation data determination module, used to integrate a tunable passive probe component in the metering loop of the smart energy meter to determine the metering loop propagation data through probe-type detection, wherein the passive probe component includes a weak signal injection unit, a pickup unit, and a reference resistor network; a physical parameter change determination module, used to construct an analog computing circuit isomorphic to the physical topology of the metering loop, load the metering loop propagation data to the loop node, perform feature separation and parameter extraction in the analog domain, and determine the physical parameter change, wherein the physical parameter change includes analog computing parameters and fault source signals under finite bandwidth modal component decomposition; and a status assessment result determination module, used to construct a lightweight digital model locally based on the physical parameter change and upload it to the distribution area edge computing node, perform distribution area topology propagation and distribution area common factor tracing, and determine the status assessment result.

[0006] In a possible implementation, a frequency-tunable weak signal injection unit is used to generate multi-frequency probe signals, wherein the weak signal injection unit is coupled to the current sampling circuit of the energy meter and is composed of a DDS and a programmable gain amplifier; a wideband voltage response pickup unit is used to acquire amplitude and phase frequency responses, wherein the pickup unit is coupled to the voltage sampling circuit of the energy meter and is composed of a high-speed ADC and a digital down-converter; a temperature-sensing reference resistor network is used to acquire the temperature distribution of the metering chip, wherein the reference resistor network is arranged at key thermal nodes of the PCB surrounding the metering chip and is composed of a thermistor array.

[0007] In a possible implementation, metering loop propagation data is determined by probe-based detection. The metering loop propagation data determination module includes: a multi-frequency probe signal injection module, used to generate a multi-frequency probe signal with orthogonal frequency division multiplexing characteristics based on the weak signal injection unit, and inject it into the metering loop, wherein the signal frequency band covers the characteristic response frequency band of key components of the energy meter; a response curve acquisition module, used to acquire the amplitude-frequency response curve and phase-frequency response curve of the multi-frequency probe signal after propagation through the metering loop based on the pickup unit; a temperature field distribution data acquisition module, used to acquire the temperature field distribution data of the microenvironment surrounding the metering chip based on the reference resistor network; and a data integration module, used to integrate the amplitude-frequency response curve, phase-frequency response curve, and temperature field distribution data as metering loop propagation data.

[0008] In a possible implementation, the analog computing circuit includes a state-sensing capacitor branch connected in parallel with the internal electrolytic capacitor of the energy meter, a state-sensing resistor branch connected in series with the sampling resistor of the energy meter, and a frequency drift monitoring branch coupled to the crystal oscillator circuit of the energy meter; wherein, the capacitance value of the state-sensing capacitor branch is in a fixed proportion to the main electrolytic capacitor, and the resistance value of the state-sensing resistor branch is in a fixed proportion to the main sampling resistor.

[0009] In a possible implementation, the physical parameter change determination module includes: an analog computing coprocessor construction module, used to construct an analog computing coprocessor based on the analog computing circuit, wherein the analog computing circuit is isomorphic to the key components of the metering loop physical topology; and an analog computing parameter determination module, used to import the metering loop propagation data into the analog computing coprocessor, utilize the analog computing physical response, perform feature separation and parameter extraction in the analog domain, determine the analog computing parameters, and add them to the physical parameter change, wherein the physical response includes changes in charge / discharge time constant, resonant frequency shift, and phase delay, and the separated features include the increment of the equivalent series resistance of the electrolytic capacitor, the temperature drift coefficient shift of the sampling resistor, and the change of the crystal oscillator load resonant frequency.

[0010] In a possible implementation, the physical parameter change determination module includes: a VMD decomposition module, used to perform VMD decomposition on the signal points of each loop for the metering loop propagation data, adaptively obtaining K finite bandwidth mode components; a high-dimensional feature space construction module, used to construct a high-dimensional feature space for the K finite bandwidth mode components, using the instantaneous frequency fluctuation and energy entropy of each finite bandwidth mode component as features; and an ICA decomposition module, used to separate statistically independent fault source signals by performing ICA decomposition on the high-dimensional feature space, and determine the physical parameter change based on the simulation calculation parameters and the fault source signals.

[0011] In a possible implementation, a lightweight digital model is constructed locally based on the changes in the physical parameters. The state assessment result determination module includes a lightweight digital model construction module, used to construct a lightweight digital model locally for each energy meter connected to the distribution area. The construction of the lightweight digital model includes: defining a state vector, which includes the electrolytic capacitor, sampling resistor value, crystal oscillator equivalent inductance, and current transformer excitation inductance; defining an input vector, which includes the amplitude-frequency characteristics of the injected probe signal, ambient temperature, and load current; defining an output vector, which includes the energy meter metering error and phase angle deviation; and defining a coefficient matrix, which is determined by the physical topology of the analog computing circuit and initialized through hardware-in-the-loop calibration. The lightweight digital model is constructed based on the state vector, input vector, output vector, and coefficient matrix.

[0012] In a possible implementation, the system performs topology propagation and source tracing of common factors in the distribution area to determine the state assessment result. The state assessment result determination module includes: a lightweight digital model upload module, used by each electricity meter to upload its local lightweight digital model to the edge computing node of the distribution area; a distribution area topology propagation analysis module, used at the edge computing node of the distribution area to locate the operating fault state and perform distribution area topology propagation analysis to determine the state propagation law; a source tracing analysis module, used to identify the similar aging characteristics of the physical components of the electricity meters in the distribution area topology as common factors in the distribution area for source tracing analysis to determine the source of the fault in the distribution area; and a state assessment result generation module, used to use the state propagation law and the source of the fault in the distribution area as the state assessment result.

[0013] In a possible implementation, after determining the status assessment results, the system further includes: an edge management threshold deployment module, used to deploy edge management thresholds according to the operating mode and management plan of the distribution area's electricity meters; an operation and maintenance management instruction generation module, used by the edge management thresholds to read the status assessment results and make matching decisions to generate operation and maintenance management instructions, wherein the operation and maintenance management instructions are identified by the electricity meter code and location; and a management module, used to execute the component automation drive management and personnel and equipment terminal distribution management of the distribution area topology by decoupling the operation and maintenance management instructions.

[0014] This application also provides a method for assessing the operating status of smart meters, including: integrating a tunable passive probe component into the metering circuit of the smart meter to determine the propagated data of the metering circuit through probe-type detection, wherein the passive probe component includes a weak signal injection unit, a pickup unit, and a reference resistor network; constructing an analog computing circuit isomorphic to the physical topology of the metering circuit, loading the propagated data of the metering circuit to the circuit node, performing feature separation and parameter extraction in the analog domain to determine the changes in physical parameters, wherein the changes in physical parameters include analog computing parameters and fault source signals under finite bandwidth mode component decomposition; based on the changes in physical parameters, constructing a lightweight digital model locally and uploading it to the edge computing node of the distribution area, performing distribution area topology propagation and tracing of common factors in the distribution area, and determining the status assessment result.

[0015] The proposed smart meter operation status assessment system and method, as described in this application, integrates a tunable passive probe component into the metering circuit of the smart meter via a metering circuit propagation data determination module. This module uses probe-based detection to determine the propagation data of the metering circuit. The passive probe component includes a weak signal injection unit, a pickup unit, and a reference resistor network. A physical parameter change determination module constructs an analog computing circuit isomorphic to the physical topology of the metering circuit. The propagation data of the metering circuit is loaded onto the circuit nodes, and feature separation and parameter extraction are performed in the analog domain to determine the physical parameter changes. These physical parameter changes include analog computing parameters and fault source signals under finite bandwidth modal component decomposition. A status assessment result determination module constructs a lightweight digital model locally based on the physical parameter changes and uploads it to the distribution area edge computing node. This module performs distribution area topology propagation and common factor tracing to determine the status assessment result. Through this process, the system and method proposed in this application achieve the technical effects of improving the real-time and accurate monitoring capability of the metering circuit operation status, realizing efficient fault tracing at the distribution area level, and comprehensive analysis of batch meter anomalies. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic diagram of the structure of the smart energy meter operation status evaluation system provided in the embodiments of this application.

[0018] Figure 2 This is a flowchart illustrating the smart energy meter operation status assessment method provided in this application embodiment.

[0019] Explanation of reference numerals in the attached diagram: Module 10 for determining data propagation from metering loops, Module 20 for determining changes in physical parameters, and Module 30 for determining the state assessment results. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] This application provides a smart energy meter operation status evaluation system, such as... Figure 1 As shown, the system includes: The metering loop propagation data determination module 10 is used to integrate a tunable passive probe assembly in the metering loop of a smart energy meter to determine the metering loop propagation data through probe-type detection. The passive probe assembly includes a weak signal injection unit, a pickup unit, and a reference resistor network.

[0022] Specifically, the metering loop propagation data determination module 10 is the basic data acquisition layer of the system. Its function is to add a dedicated passive probe component to the original metering loop of the smart energy meter. Through non-invasive probe detection technology, it collects electrical signal response data and temperature environment data during the operation of the metering loop, integrates them to form standardized metering loop propagation data, and provides the original data source for subsequent parameter analysis without interfering with the normal metering operation of the energy meter.

[0023] In one possible implementation, a frequency-tunable weak signal injection unit is used to generate a multi-frequency probe signal, wherein the weak signal injection unit is coupled to the current sampling circuit of the energy meter and is composed of a DDS and a programmable gain amplifier; a wideband voltage response pickup unit is used to acquire the amplitude and phase frequency responses, wherein the pickup unit is coupled to the voltage sampling circuit of the energy meter and is composed of a high-speed ADC and a digital down-converter; a temperature-sensing reference resistor network is used to acquire the temperature distribution of the metering chip, wherein the reference resistor network is arranged at key thermal nodes of the PCB surrounding the metering chip and is composed of a thermistor array.

[0024] Specifically, the metering loop propagation data determination module 10 relies on a tunable passive probe component to achieve data acquisition. This component is the core hardware for non-invasive detection and consists of three core units, specifically as follows: The frequency-tunable weak signal injection unit is the active signal generator, responsible for generating multi-frequency probe signals adapted to metering loop detection. The hardware comprises a direct digital frequency synthesizer (DDS) and a programmable gain amplifier connected in series. It is magnetically coupled to the current sampling loop of the energy meter, without damaging the original loop wiring or interfering with normal metering. The signal frequency can be flexibly adjusted by regulating the DDS parameters to adapt to the detection needs of different components; Wideband voltage response... The pickup unit, belonging to the signal acquisition and receiving end, captures the electrical response characteristics of the probe signal after propagation through the metering loop. The hardware consists of a high-speed ADC and a digital down-converter connected in series, linked to the energy meter's voltage sampling loop via a resistor voltage divider coupling method. It supports wideband signal acquisition and can simultaneously extract the amplitude and phase frequency of the signal, two core response data types. The temperature self-sensing reference resistor network, belonging to the environmental sensing auxiliary unit, monitors the operating temperature field of the metering chip and eliminates temperature interference in parameter detection. The hardware consists of a high-precision thermistor array, strategically placed at key thermal nodes on the PCB board surrounding the metering chip, acquiring node temperatures in real time and converting them into quantifiable temperature field data. These three units work together to provide hardware support for signal injection, response acquisition, and temperature acquisition.

[0025] In one possible implementation, metering loop propagation data is determined by probe-based detection. The metering loop propagation data determination module 10 includes: a multi-frequency probe signal injection module, used to generate a multi-frequency probe signal with orthogonal frequency division multiplexing characteristics based on the weak signal injection unit, and inject it into the metering loop, wherein the signal frequency band covers the characteristic response frequency band of key components of the energy meter; a response curve acquisition module, used to acquire the amplitude-frequency response curve and phase-frequency response curve of the multi-frequency probe signal after propagation through the metering loop based on the pickup unit; a temperature field distribution data acquisition module, used to acquire the temperature field distribution data of the microenvironment surrounding the metering chip based on the reference resistor network; and a data integration module, used to integrate the amplitude-frequency response curve, phase-frequency response curve, and temperature field distribution data as metering loop propagation data.

[0026] Specifically, relying on the DDS module of the weak signal injection unit, a multi-frequency discrete signal in orthogonal frequency division multiplexing (OFDM) format is generated according to a preset frequency sequence. The frequency points cover the inherent response frequency bands of the core metering components of the energy meter, such as electrolytic capacitors, sampling resistors, manganese copper sheets, current transformers, and crystal oscillators. The signal amplitude is adjusted to a low amplitude range in the microvolt level by a programmable gain amplifier to avoid interfering with the normal metering and load current transmission of the energy meter. The signal is injected non-intrusively into the current sampling circuit through magnetic coupling. The signal injection timing is synchronized with the energy meter's own metering sampling clock, and the full-frequency signal injection is completed cyclically according to a fixed period without altering the original circuit wiring.

[0027] The high-speed ADC of the pickup unit synchronously acquires signals from the voltage sampling circuit, with sampling parameters adapted to the probe signal frequency band to ensure distortion-free acquisition. The acquired raw signal is processed by a digital down-converter, down-converting it to baseband to filter out power grid interference, environmental electromagnetic noise, and high-order harmonics, extracting a clean probe response signal. For each corresponding frequency point, the signal amplitude and phase data are extracted, sorted from low to high frequency, and fitted to form amplitude-frequency response curves and phase-frequency response curves. After each round of signal injection, a set of curves is acquired and buffered to ensure continuous and traceable data.

[0028] A thermistor array is strategically positioned at key thermal nodes on the PCB surrounding the metering chip. The resistance value of each thermistor is acquired in real-time via a communication bus and converted into the corresponding node temperature value. Temperature data from each node is collected cyclically at a fixed frequency. Then, a spatial interpolation algorithm is used to integrate the discrete temperature data from each point into a two-dimensional temperature field data representation of the microenvironment surrounding the metering chip. Key indicators such as temperature extremes and node temperature differences are labeled, and the timestamps of probe signal acquisition are synchronously linked to provide temperature data for parameter compensation.

[0029] The local MCU of the electricity meter preprocesses the three types of raw data. The response curve data is filtered and denoised to remove random noise and outliers, retaining only valid feature data. The temperature field data is normalized to eliminate dimensional differences and unify the data range. The processed data are then bound to the same timestamp, removing duplicate and redundant data, and compressed into standardized data packets. These packets contain basic identification information and core detection data, which are transmitted to the physical parameter change determination module 20 at fixed intervals, ensuring efficient, complete, and timely data transmission.

[0030] The physical parameter change determination module 20 is used to construct an analog computing circuit that is isomorphic to the physical topology of the metering loop, load the propagation data of the metering loop to the loop node, perform feature separation and parameter extraction in the analog domain, and determine the physical parameter change, wherein the physical parameter change includes the analog computing parameters and the fault source signal under finite bandwidth modal component decomposition.

[0031] Specifically, the physical parameter change determination module 20 is the core analysis layer of the system. It receives the metering loop propagation data collected by the metering loop propagation data determination module 10. By building an analog computing circuit that is completely matched with the physical structure of the actual metering loop, and combining analog domain feature separation and signal decomposition algorithms, it extracts the physical parameter offset of the core metering components of the energy meter. At the same time, it separates the fault source signal hidden in the signal, forms a quantified physical parameter change, and accurately locates the abnormal states of the metering components such as aging, drift, and fault.

[0032] In one possible implementation, the analog computing circuit includes a state-sensing capacitor branch connected in parallel with the internal electrolytic capacitor of the energy meter, a state-sensing resistor branch connected in series with the sampling resistor of the energy meter, and a frequency drift monitoring branch coupled to the crystal oscillator circuit of the energy meter; wherein the capacitance value of the state-sensing capacitor branch is in a fixed proportion to the main electrolytic capacitor, and the resistance value of the state-sensing resistor branch is in a fixed proportion to the main sampling resistor.

[0033] Specifically, the physical parameter change determination module 20 relies on an analog computing circuit that is isomorphic to the physical topology of the metering circuit for analysis. This circuit is the core of analog domain feature separation, without software algorithm intervention, and completely replicates the physical and electrical characteristics of the actual metering circuit. The core includes three dedicated sensing branches, with the following functions and structures: The state sensing capacitor branch is connected in parallel with the main electrolytic capacitor inside the energy meter. Its function is to replicate the charging and discharging and capacitance drift characteristics of the main capacitor. The branch capacitance is matched with the main electrolytic capacitor in a fixed ratio. By monitoring the branch capacitor response, it indirectly reflects the aging of the main capacitor and changes in the equivalent series resistance. The state sensing resistor branch is connected in series with the main sampling resistor of the energy meter. Its function is to replicate the temperature drift and resistance offset characteristics of the main sampling resistor. The branch resistance is matched with the main sampling resistor in a fixed ratio, avoiding direct modification of the main circuit and accurately capturing changes in resistance parameters. The frequency drift monitoring branch is coupled with the crystal oscillator circuit of the energy meter. Its function is to monitor the resonant frequency drift and load characteristic changes of the crystal oscillator, mirroring the crystal oscillator's working state and realizing early detection of crystal oscillator faults and drift. The three branches work together to realize the analog domain state perception of the three core metering components: capacitor, resistor and crystal oscillator. Based on this circuit and signal decomposition algorithm, the change of physical parameters is extracted.

[0034] In one possible implementation, the physical parameter change determination module 20 includes: an analog computing coprocessor construction module, used to construct an analog computing coprocessor based on the analog computing circuit, wherein the analog computing circuit is isomorphic to the key components of the metering loop physical topology; and an analog computing parameter determination module, used to import the metering loop propagation data into the analog computing coprocessor, utilize the analog computing physical response, perform feature separation and parameter extraction in the analog domain, determine the analog computing parameters, and add them to the physical parameter change, wherein the physical response includes changes in charge / discharge time constant, resonant frequency shift, and phase delay, and the separated features include the increment of the equivalent series resistance of the electrolytic capacitor, the temperature drift coefficient shift of the sampling resistor, and the change of the crystal oscillator load resonant frequency.

[0035] Specifically, the state-sensing capacitor branch, state-sensing resistor branch, and frequency drift monitoring branch are integrated into a dedicated analog processing chip, solidifying the circuit topology. The analog computing coprocessor is directly connected to the electricity meter's metering circuit hardware, enabling synchronous loading of circuit node signals. The key component parameters of the analog computing circuit are mapped to the main components of the actual metering circuit in a fixed ratio, ensuring a high degree of consistency between the analog response and the physical characteristics of the actual circuit. There is no software algorithm correction throughout the process, guaranteeing the authenticity of the physical response.

[0036] The amplitude-frequency response and phase-frequency response data from the metering loop propagation data are loaded onto the signal input nodes of the analog computing coprocessor, while the temperature field distribution data is synchronously loaded onto the temperature compensation node to ensure that the operating temperature field of the analog computing circuit is consistent with that of the actual metering chip. The analog computing circuit, when powered on, replicates the actual loop operating state, simultaneously acquiring three types of core physical responses: the charging and discharging time constant of the state-sensing capacitor branch, the resonant frequency of the frequency drift monitoring branch, and the overall signal phase delay of the analog branch. Based on physical circuit laws, the difference between the acquired physical responses and the preset standard responses is calculated, eliminating the baseline response under normal operating conditions and separating abnormal features caused only by component aging and faults, thus eliminating environmental and load interference. For the separated abnormal features, the incremental equivalent series resistance of the electrolytic capacitor, the temperature drift coefficient shift of the sampling resistor, and the change in the resonant frequency of the crystal oscillator load are calculated using physical formulas and directly used as analog calculation parameters. The three types of quantified parameters are labeled with the correlation between the corresponding components and faults and incorporated into the changes in physical parameters.

[0037] In one possible implementation, the physical parameter change determination module 20 includes: a VMD decomposition module, used to perform VMD decomposition on the signal points of each loop for the metering loop propagation data, adaptively obtaining K finite bandwidth mode components; a high-dimensional feature space construction module, used to construct a high-dimensional feature space for the K finite bandwidth mode components, using the instantaneous frequency fluctuation and energy entropy of each finite bandwidth mode component as features; and an ICA decomposition module, used to separate statistically independent fault source signals by performing ICA decomposition on the high-dimensional feature space, and determine the physical parameter change based on the simulation calculation parameters and the fault source signals.

[0038] Specifically, voltage and current timing signal segments synchronized with the probe signal injection timing are extracted from the data propagated from the metering loop. Power frequency synchronization headers and abnormal spike pulses are removed, and a fixed-length effective signal sequence is extracted. The number of decomposition modes K, penalty factor α, and convergence threshold ε are preset, with parameters fixed according to the signal characteristics of the electricity meter's metering loop. A variational constraint model is built, transforming the signal decomposition problem into a variational solution problem. The alternating direction multiplier algorithm iteratively updates each modal component and its center frequency until the convergence threshold is met. The energy proportion of each modal component is calculated, and noise components with energy proportions below the preset threshold are removed, retaining the effective finite-bandwidth modal components. The decomposition results are then compared with the simulated domain physical response data for consistency verification, ensuring that the components are undistorted and complete.

[0039] For each effective finite bandwidth modal component, the analytic signal is obtained through Hilbert transform. The instantaneous frequency sequence of the component is calculated, and three sub-features—average fluctuation, maximum fluctuation, and fluctuation variance—are further statistically analyzed to comprehensively reflect frequency stability. The total energy of a single modal component is calculated, and the energy distribution probability of the component's time-domain signal is calculated. The energy entropy is then solved using the information entropy formula to reflect the dispersion of the signal energy distribution. The instantaneous frequency class features and energy entropy features of each component are concatenated in a fixed order to form a single-component feature sub-vector. Then, the sub-vectors of all effective components are concatenated in the decomposition order to form an overall high-dimensional feature vector. The min-max normalization method is used to map all feature values ​​to the [0,1] interval, eliminating the dimensional differences of amplitude, frequency, and entropy values. Multiple sets of normalized feature vectors are summarized to form a high-dimensional feature space with fixed dimensions, and the feature dimensions and corresponding physical meanings are labeled.

[0040] Centering and whitening are performed on high-dimensional feature space data to eliminate linear correlations between features, improving the convergence speed and accuracy of ICA decomposition. A nonlinear function, an upper limit on the number of iterations, and a convergence threshold are set. A nonlinear function adapted to the electricity meter signal is selected, and the iteration parameters are fixed. The negative entropy maximization criterion is used to iteratively solve for independent components. After each iteration, the convergence condition is checked; if it is met, the iteration stops, and all statistically independent components are output. The independent components are compared with a preset normal operation feature template for similarity. Normal components with a similarity higher than 90% are removed, and the remaining components are the fault source signals, labeled with the fault source type. The separated fault source signals are associated one-to-one with the quantization parameters output by the simulation calculation parameter determination module, labeling the fault source and fault location corresponding to the parameter offset, and integrating them to form a complete physical parameter change.

[0041] The status assessment result determination module 30 is used to construct a lightweight digital model locally based on the changes in the physical parameters and upload it to the edge computing node of the transformer area, perform transformer area topology propagation and trace common factors of the transformer area, and determine the status assessment result.

[0042] Specifically, the status assessment result determination module 30 is the system's decision output layer. Based on the physical parameter change determined by the physical parameter change determination module 20, a lightweight digital model is built locally on a single energy meter and simultaneously uploaded to the edge computing node of the distribution area. Combined with the distribution area topology analysis, the fault propagation law is analyzed, the common fault causes of multiple energy meters in the distribution area are traced, and finally, a feasible status assessment result and operation and maintenance instructions are generated, realizing a closed loop from single meter monitoring to overall management of the distribution area.

[0043] In one possible implementation, a lightweight digital model is constructed locally based on the changes in the physical parameters. The state assessment result determination module 30 includes: a lightweight digital model construction module, used to construct a lightweight digital model locally for each energy meter connected to the distribution area; wherein, the construction of the lightweight digital model includes: defining a state vector, wherein the state vector includes electrolytic capacitor, sampling resistor value, crystal oscillator equivalent inductance, and current transformer excitation inductance; defining an input vector, wherein the input vector includes the amplitude-frequency characteristics of the injected probe signal, ambient temperature, and load current; defining an output vector, wherein the output vector includes energy meter metering error and phase angle deviation; defining a coefficient matrix, wherein the coefficient matrix is ​​determined by the physical topology of the analog computing circuit and its initial value is obtained through hardware-in-the-loop calibration; and constructing the lightweight digital model based on the state vector, input vector, output vector, and coefficient matrix.

[0044] Specifically, a lightweight mathematical model is built based on linear state-space equations, adapted to the low computing power and limited storage hardware conditions of the local MCU of the energy meter. A state vector is defined, selecting core component parameters that directly affect metering accuracy, including the equivalent parameters of the electrolytic capacitor, the resistance of the sampling resistor, the equivalent inductance of the crystal oscillator, and the excitation inductance of the current transformer, comprehensively characterizing the core state of the metering circuit. An input vector is defined, selecting external influences and detection-related parameters, including the amplitude-frequency characteristics of the probe signal, ambient temperature, and real-time load current, as the model input excitation. An output vector is defined, selecting core performance evaluation indicators of the energy meter, including metering error and phase angle deviation, directly reflecting the meter's operating status. A coefficient matrix is ​​defined, with its dimensions determined by the dimensions of the state vector and input vector. Initial values ​​are obtained through hardware-in-the-loop calibration, using energy meters of the same model operating normally, with multiple calibrations and average values ​​taken. Substituting the four types of elements into the linear state-space equations, historical normal and fault data are collected, and the coefficient matrix is ​​iteratively updated using the least squares method until the inference error meets the threshold, completing the model construction and achieving real-time quantitative inference of the single meter's operating status.

[0045] In one possible implementation, the system performs topology propagation and source tracing of common factors in the distribution area to determine the state assessment result. The state assessment result determination module 30 includes: a lightweight digital model uploading module, used by each electricity meter to upload its local lightweight digital model to the distribution area edge computing node; a distribution area topology propagation analysis module, used at the distribution area edge computing node to locate the operating fault state and perform distribution area topology propagation analysis to determine the state propagation law; a source tracing analysis module, used to identify similar aging characteristics of the physical components of the electricity meters in the distribution area topology as common factors in the distribution area for source tracing analysis to determine the fault source in the distribution area; and a state assessment result generation module, used to use the state propagation law and the fault source in the distribution area as the state assessment result.

[0046] Specifically, power line carrier communication is prioritized, reusing existing power lines in the distribution area. LoRa wireless communication is used in areas without communication capabilities. The model coefficient matrix, meter number, real-time physical parameter changes, and timestamps are packaged and compressed in a binary format to reduce the amount of data transmitted. A fixed communication rate and transmission period are set, and each meter is assigned a unique communication address to avoid data conflicts. Data is actively uploaded periodically. Edge nodes perform CRC checks upon receipt; if the check fails, a retransmission command is issued until successful reception. Edge computing nodes store model data categorized by meter number, establishing corresponding indexes for easy subsequent retrieval and analysis.

[0047] Edge computing nodes pre-store the topology map of the transformer substation, marking transformers, main lines, branch lines, the installation locations of each meter, and wiring relationships. Metering error and phase angle deviation fault thresholds are set, and the output data of each meter model is compared to mark meters exceeding the thresholds as faulty meters. The topological location of faulty meters is extracted, and the status of meters on the same branch and main line is screened to determine whether the fault occurs in a continuous area. A distinction is made between individual meter faults and substation-level propagation faults, determining the fault propagation direction, the number of meters involved, and the affected line segments, generating a fault propagation path table, and marking the fault source location, affected area, and propagation level.

[0048] The changes in physical parameters of each meter were extracted, including parameters of three core components: electrolytic capacitors, sampling resistors, and crystal oscillators. A density clustering algorithm was used, with a set cluster radius, to cluster the parameter changes of multiple meters. Common characteristics were identified within clusters containing ≥3 meters, excluding individual meter aging. These common characteristics were mapped to fault types, categorized into three common factors: grid-side, environmental-side, and load-side. Voltage, current, harmonic, and temperature monitoring data for the distribution area were retrieved and compared with operating parameters during the fault occurrence period to pinpoint the fault source, clarifying its type, location, and triggering cause, thus distinguishing between individual and common faults.

[0049] Based on the degree of parameter deviation and the magnitude of measurement error, and according to various judgment criteria, meter status is divided into four levels: normal, attention, abnormal, and fault. The system summarizes meter number, installation location, parameter deviation value, fault type, fault source, propagation range, and impact degree. Based on the impact of the fault on measurement accuracy and the number of meters involved, maintenance priorities are divided into high, medium, and low levels. Evaluation results are generated according to a fixed template, including textual descriptions and data indicators, and the evaluation results are synchronized to the edge computing node database and the maintenance management platform.

[0050] In one possible implementation, after determining the status assessment result, the system further includes: an edge management threshold deployment module, used to deploy edge management thresholds according to the operating mode and management plan of the transformer area's electricity meters; an operation and maintenance management instruction generation module, used by the edge management thresholds to read the status assessment result and make matching decisions to generate operation and maintenance management instructions, wherein the operation and maintenance management instructions are identified by the electricity meter code and location; and a management module, used to execute the component automation drive management and personnel and equipment terminal distribution management of the transformer area topology by decoupling the operation and maintenance management instructions.

[0051] Specifically, the system deploys three levels of thresholds: a warning threshold, a fault trigger threshold, and a maintenance initiation threshold, corresponding to alert, abnormal, and fault states, respectively. Based on national electricity meter verification regulations and the transformer area maintenance plan, specific values ​​for each threshold level are set, supporting remote modification. Each threshold level is linked to status assessment results, and the assessment results are read and matched in real time. Threshold values ​​are fine-tuned according to different operating modes for residential, industrial, and commercial transformer areas. After deployment, different status data are simulated to verify the accuracy of threshold triggering, ensuring no incorrect triggering and no missed triggering.

[0052] Based on the handling method of the fault, instructions are divided into automatic debugging instructions and manual maintenance instructions. Automatic instructions are generated for minor anomalies, while manual instructions are generated for serious faults. Instruction elements are populated, including instruction number, meter code, installation location, fault details, handling method, completion deadline, and priority. Upon threshold triggering, the system automatically matches the corresponding instruction type and content according to the fault level, generates a unique number for each instruction, and pushes the generated instructions to the edge node management unit and the maintenance personnel's terminals.

[0053] The system analyzes maintenance instructions, breaking them down into automatic and manual execution fields, and separating the instruction type, execution target, and operation content. Automatic debugging instructions are sent to the energy meter MCU or distribution area protection device to perform operations such as parameter calibration, temperature compensation, and frequency correction, and the execution results are transmitted back upon completion. Manual instructions, fault locations, and operation procedures are pushed to the maintenance personnel's terminals, and the transmission records are stored. After completing the operation, the maintenance personnel provide feedback on the execution results and handling status through their terminals. Upon receiving feedback, the edge computing node updates the meter status, resets model parameters, and closes the corresponding maintenance instructions, forming a closed loop.

[0054] This application provides a smart energy meter operation status assessment system and method. By integrating a tunable passive probe component consisting of a weak signal injection unit, a pickup unit, and a reference resistor network into the smart energy meter metering circuit, it acquires propagation data of the metering circuit through probe-based detection. An analog computing circuit is constructed with the physical topology isomorphic to the metering circuit. The propagation data is loaded onto the circuit nodes, and feature separation and parameter extraction are performed in the analog domain to obtain the physical parameter changes, including analog computing parameters and fault source signals obtained from finite bandwidth mode component decomposition. Based on these physical parameter changes, a lightweight digital model is constructed locally on the energy meter and uploaded to the distribution area edge computing node. Through distribution area topology propagation analysis and common fault factor tracing, the smart energy meter operation status assessment results are obtained. These technical means solve the technical problems of insufficient real-time accurate monitoring and efficient fault tracing capabilities in existing smart energy meter metering circuit operation status monitoring. This achieves the technical effects of improving the real-time accurate monitoring capability of the metering circuit operation status, realizing efficient fault tracing at the distribution area level, and comprehensive analysis of batch meter anomalies.

[0055] In the above text, refer to Figure 1 A smart energy meter operation status evaluation system according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A method for evaluating the operating status of a smart energy meter according to an embodiment of the present invention is described.

[0056] The smart meter operation status assessment method according to embodiments of the present invention addresses the technical problems of insufficient real-time and accurate monitoring and efficient fault tracing capabilities in existing smart meter metering circuit operation status monitoring. It aims to improve the real-time and accurate monitoring capabilities of the metering circuit operation status, achieving efficient fault tracing at the distribution area level and comprehensive analysis of batch meter anomalies. The smart meter operation status assessment method includes: integrating a tunable passive probe component into the metering circuit of the smart meter; determining the propagated data of the metering circuit through probe-based detection; wherein the passive probe component includes a weak signal injection unit, a pickup unit, and a reference resistor network; constructing an analog computing circuit isomorphic to the physical topology of the metering circuit; loading the propagated data of the metering circuit onto the circuit node; performing feature separation and parameter extraction in the analog domain to determine the changes in physical parameters; wherein the changes in physical parameters include analog computing parameters and fault source signals under finite bandwidth modal component decomposition; and based on the changes in physical parameters, constructing a lightweight digital model locally and uploading it to the distribution area edge computing node; performing distribution area topology propagation and distribution area common factor tracing to determine the status assessment result.

[0057] The device may further include: a frequency-tunable weak signal injection unit for generating multi-frequency probe signals, wherein the weak signal injection unit is coupled to the current sampling circuit of the energy meter and is composed of a DDS and a programmable gain amplifier; a wideband voltage response pickup unit for acquiring amplitude and phase frequency responses, wherein the pickup unit is coupled to the voltage sampling circuit of the energy meter and is composed of a high-speed ADC and a digital down-converter; and a temperature-sensing reference resistor network for acquiring the temperature distribution of the metering chip, wherein the reference resistor network is arranged at key thermal nodes of the PCB surrounding the metering chip and is composed of a thermistor array.

[0058] The determination of metering loop propagation data through probe-type detection can further include: generating a multi-frequency probe signal with orthogonal frequency division multiplexing characteristics based on the weak signal injection unit, and injecting it into the metering loop, wherein the signal frequency band covers the characteristic response frequency band of key components of the energy meter; acquiring the amplitude-frequency response curve and phase-frequency response curve of the multi-frequency probe signal after propagation through the metering loop based on the pickup unit; obtaining the temperature field distribution data of the microenvironment surrounding the metering chip based on the reference resistor network; and integrating the amplitude-frequency response curve, phase-frequency response curve, and temperature field distribution data as metering loop propagation data.

[0059] The circuit may further include: the analog computing circuit includes a state-sensing capacitor branch connected in parallel with the internal electrolytic capacitor of the energy meter, a state-sensing resistor branch connected in series with the sampling resistor of the energy meter, and a frequency drift monitoring branch coupled to the crystal oscillator circuit of the energy meter; wherein the capacitance value of the state-sensing capacitor branch is in a fixed proportion to the main electrolytic capacitor, and the resistance value of the state-sensing resistor branch is in a fixed proportion to the main sampling resistor.

[0060] This may further include: constructing an analog computing coprocessor based on the analog computing circuit, wherein the analog computing circuit is isomorphic to the key components of the metering loop physical topology; importing the metering loop propagation data into the analog computing coprocessor, utilizing the analog computing physical response, performing feature separation and parameter extraction in the analog domain, determining the analog computing parameters and adding the physical parameter changes, wherein the physical response includes changes in the charge / discharge time constant, resonant frequency shift and phase delay, and the separated features include the increment of the equivalent series resistance of the electrolytic capacitor, the temperature drift coefficient shift of the sampling resistor and the change of the crystal oscillator load resonant frequency.

[0061] The fault source signal under finite bandwidth mode component decomposition can further include: performing VMD decomposition on the signal points of each circuit for the propagation data of the metering circuit to adaptively obtain K finite bandwidth mode components; constructing a high-dimensional feature space for the K finite bandwidth mode components, using the instantaneous frequency fluctuation and energy entropy of each finite bandwidth mode component as characteristics; separating statistically independent fault source signals by performing ICA decomposition on the high-dimensional feature space; and determining the change in physical parameters based on the simulation calculation parameters and the fault source signals.

[0062] The construction of a lightweight digital model locally based on the changes in the physical parameters can further include: constructing a lightweight digital model locally for each electricity meter connected to the distribution area; wherein the construction of the lightweight digital model includes: defining a state vector, wherein the state vector includes the electrolytic capacitor, the resistance value of the sampling resistor, the equivalent inductance of the crystal oscillator, and the excitation inductance of the current transformer; defining an input vector, wherein the input vector includes the amplitude-frequency characteristics of the injected probe signal, the ambient temperature, and the load current; defining an output vector, wherein the output vector includes the metering error and the phase angle deviation of the electricity meter; defining a coefficient matrix, wherein the coefficient matrix is ​​determined by the physical topology of the analog computing circuit and its initial value is obtained through hardware-in-the-loop calibration; and constructing the lightweight digital model based on the state vector, the input vector, the output vector, and the coefficient matrix.

[0063] The process of performing topology propagation and tracing common factors in the distribution area to determine the state assessment results may further include: each electricity meter uploading its local lightweight digital model to the edge computing node of the distribution area; at the edge computing node of the distribution area, locating the operational fault state and performing topology propagation analysis to determine the state propagation pattern; identifying similar aging characteristics of the physical components of the electricity meters in the distribution area topology as common factors for source tracing analysis to determine the fault source of the distribution area; and using the state propagation pattern and the fault source of the distribution area as the state assessment results.

[0064] After determining the status assessment results, the process may further include: deploying edge management thresholds based on the operating mode and management plan of the distribution area's electricity meters; the edge management thresholds reading the status assessment results and making matching decisions to generate operation and maintenance management instructions, wherein the operation and maintenance management instructions are identified by the electricity meter code and location; and by decoupling the operation and maintenance management instructions, executing the automated component-driven management and personnel / equipment-side distribution management of the distribution area topology.

[0065] The smart energy meter operation status assessment system provided in this embodiment of the invention can execute the smart energy meter operation status assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0066] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart energy meter operation status evaluation system, characterized in that, The system includes: A metering loop propagation data determination module is used to integrate a tunable passive probe assembly in the metering loop of a smart energy meter to determine the propagation data of the metering loop through probe-type detection. The passive probe assembly includes a weak signal injection unit, a pickup unit, and a reference resistor network. The physical parameter change determination module is used to construct an analog computing circuit that is isomorphic to the physical topology of the metering loop, load the propagation data of the metering loop to the loop node, perform feature separation and parameter extraction in the analog domain, and determine the physical parameter change, wherein the physical parameter change includes the analog computing parameters and the fault source signal under finite bandwidth modal component decomposition. The status assessment result determination module is used to construct a lightweight digital model locally based on the changes in the physical parameters and upload it to the edge computing node of the transformer area, perform transformer area topology propagation and trace common factors of the transformer area, and determine the status assessment result.

2. The smart energy meter operation status evaluation system as described in claim 1, characterized in that, A frequency-tunable weak signal injection unit is used to generate multi-frequency probe signals. The weak signal injection unit is coupled to the current sampling circuit of the energy meter and is composed of a DDS and a programmable gain amplifier. A wideband voltage response pickup unit is used to acquire amplitude and phase frequency responses. The pickup unit is coupled to the voltage sampling circuit of the energy meter and consists of a high-speed ADC and a digital down-converter. A temperature-sensing reference resistor network is used to collect the temperature distribution of the metering chip. The reference resistor network is arranged at key thermal nodes of the PCB surrounding the metering chip and is composed of a thermistor array.

3. The smart energy meter operation status evaluation system as described in claim 2, characterized in that, The metering loop propagation data is determined by probe-type detection. The metering loop propagation data determination module includes: The multi-frequency probe signal injection module is used to generate a multi-frequency probe signal with orthogonal frequency division multiplexing characteristics based on the weak signal injection unit and inject it into the metering circuit, wherein the signal frequency band covers the characteristic response frequency band of the key components of the energy meter. The response curve acquisition module is used to acquire the amplitude frequency response curve and phase frequency response curve of the multi-frequency probe signal after propagation through the metering loop, based on the pickup unit. The temperature field distribution data acquisition module is used to acquire temperature field distribution data of the microenvironment surrounding the metering chip based on the reference resistor network. The data integration module is used to integrate the amplitude frequency response curve, phase frequency response curve and temperature field distribution data as propagation data of the metering loop.

4. The smart energy meter operation status evaluation system as described in claim 1, characterized in that, The analog computing circuit includes a state-sensing capacitor branch connected in parallel with the internal electrolytic capacitor of the energy meter, a state-sensing resistor branch connected in series with the sampling resistor of the energy meter, and a frequency drift monitoring branch coupled to the crystal oscillator circuit of the energy meter. The capacitance of the state-sensing capacitor branch is in a fixed ratio to the main electrolytic capacitor, and the resistance of the state-sensing resistor branch is in a fixed ratio to the main sampling resistor.

5. The smart energy meter operation status evaluation system as described in claim 4, characterized in that, The physical parameter change determination module includes: An analog computing coprocessor construction module is used to construct an analog computing coprocessor based on the analog computing circuit, wherein the analog computing circuit is isomorphic to the key components of the physical topology of the metering loop; The simulation calculation parameter determination module is used to import the propagation data of the metering loop into the simulation calculation coprocessor, utilize the simulation calculation physical response, perform feature separation and parameter extraction in the simulation domain, determine the simulation calculation parameters and add the physical parameter changes, wherein the physical response includes the change of charging and discharging time constant, resonant frequency shift and phase delay, and the separated features include the increment of the equivalent series resistance of the electrolytic capacitor, the temperature drift coefficient shift of the sampling resistor and the change of the resonant frequency of the crystal oscillator load.

6. The smart energy meter operation status evaluation system as described in claim 5, characterized in that, The physical parameter change determination module includes: The VMD decomposition module is used to perform VMD decomposition on the signal points of each loop for the propagation data of the metering loop, and adaptively obtain K finite bandwidth mode components. The high-dimensional feature space construction module is used to construct a high-dimensional feature space for K finite bandwidth modal components, using the instantaneous frequency fluctuations and energy entropy of each finite bandwidth modal component as features; The ICA decomposition module is used to separate statistically independent fault source signals by performing ICA decomposition on the high-dimensional feature space, and to determine the change in physical parameters based on the simulation calculation parameters and the fault source signals.

7. The smart energy meter operation status evaluation system as described in claim 1, characterized in that, Based on the changes in the physical parameters, a lightweight digital model is constructed locally. The state assessment result determination module includes: A lightweight digital model building module is used to build a lightweight digital model locally for each electricity meter connected to a distribution area; wherein, the construction of the lightweight digital model includes: Define a state vector, wherein the state vector includes the electrolytic capacitor, the sampling resistor value, the crystal oscillator equivalent inductance, and the current transformer magnetizing inductance; Define an input vector, wherein the input vector includes the amplitude-frequency characteristics of the injected probe signal, the ambient temperature, and the load current; Define an output vector, wherein the output vector includes the metering error and phase angle deviation of the energy meter; Define a coefficient matrix, wherein the coefficient matrix is ​​determined by the physical topology of the analog computing circuit and its initial values ​​are obtained through hardware-in-the-loop calibration; The lightweight digital model is constructed based on the state vector, input vector, output vector, and coefficient matrix.

8. The smart energy meter operation status evaluation system as described in claim 1, characterized in that, Perform topology propagation and source tracing of common factors in the transformer area to determine the state assessment results. The state assessment result determination module includes: The lightweight digital model upload module is used by each electricity meter to upload its local lightweight digital model to the edge computing node of the distribution area; The transformer area topology propagation analysis module is used to locate the operational fault state at the edge computing node of the transformer area and perform transformer area topology propagation analysis to determine the state propagation law. The source tracing analysis module is used to identify the similar aging characteristics of the physical components of the electricity meters in the transformer substation topology, and to conduct source tracing analysis as common factors in the transformer substation to determine the source of the fault in the transformer substation. The status assessment result generation module is used to take the status propagation law and the fault source of the transformer area as the status assessment result.

9. The smart energy meter operation status evaluation system as described in claim 1, characterized in that, After determining the state assessment results, the system further includes: The edge management threshold deployment module is used to deploy edge management thresholds based on the operating mode and management plan of the electricity meters in the distribution area; The operation and maintenance management instruction generation module is used for the edge management threshold to read the status assessment result and make matching decisions to generate operation and maintenance management instructions, wherein the operation and maintenance management instructions are identified by the electricity meter code and location; The management module is used to execute automated component-driven management and personnel / equipment-side distribution management of the transformer area topology by decoupling the operation and maintenance management instructions.

10. A method for evaluating the operating status of smart energy meters, characterized in that, The method is implemented by the smart energy meter operation status assessment system according to any one of claims 1-9, and the method includes: In the metering circuit of a smart energy meter, an integrated tunable passive probe assembly is used to determine the data propagated in the metering circuit through probe-type detection. The passive probe assembly includes a weak signal injection unit, a pickup unit, and a reference resistor network. An analog computing circuit is constructed that is isomorphic to the physical topology of the metering loop. The propagation data of the metering loop is loaded into the loop node. Feature separation and parameter extraction are performed in the analog domain to determine the change in physical parameters. The change in physical parameters includes the analog computing parameters and the fault source signal under finite bandwidth modal component decomposition. Based on the changes in the physical parameters, a lightweight digital model is constructed locally and uploaded to the edge computing node of the transformer area. The topology propagation of the transformer area and the tracing of common factors in the transformer area are performed to determine the state assessment results.

Images