2436results about "Electrical measurements" patented technology

The invention discloses a high-precision current detection circuit calibration system, and belongs to the technical field of current detection circuit calibration. Comprising a shunt resistor, a Hall sensor array, a chopping amplifier, a low-noise operational amplifier, a temperature sensor, an ADC acquisition unit, an RC low-pass filter, a signal conditioning unit, a main control unit, an environmental parameter adaptive compensation module and a gain adaptive adjustment module. After an original signal is obtained from a multi-channel sensor, a dynamic error model is constructed in combination with temperature data, a long-term imbalance trend is extracted by using a moving average or median filtering algorithm, and real-time compensation is performed through a pre-calibrated temperature-drift function, so that the system maintenance period is remarkably prolonged.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

An adaptive calibration method and system for an electric energy meter, relating to the technical field of electric energy meter calibration. The method comprises: acquiring basic data and additional data of an electric energy meter (S1); performing time series data alignment, and performing period division on an environment data set to generate a time series segmentation result (S2); acquiring device information of the electric energy meter, performing key feature analysis within a period, and establishing an interference weight factor for each feature (S3); building a performance degradation model (S4); acquiring usage environment data of the electric energy meter by means of a monitoring sensor (S5); then generating an adaptive calibration result on the basis of the performance degradation model (S6); and finally performing electric energy meter calibration management (S7). The present invention solves the technical problem in the prior art where the accuracy of electric energy metering is affected due to untimely calibration caused by an inability to monitor performance degradation of an electric energy meter in real time, thereby implementing effective calibration management for the electric energy meter, ensuring the long-term accuracy and reliability of the electric energy meter, and improving the efficiency of calibration work.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to the field of power equipment state monitoring, in particular to an electric meter box running state monitoring method and system. An electric meter box operation state monitoring system comprises a state sensing module, an intelligent analysis module, a diagnosis and evaluation module and an operation and maintenance decision module. According to the method, the personalized dynamic health reference model of the electric meter box is constructed, and the real-time acquisition of the data of the multi-dimensional sensor is combined, so that the accurate modeling and dynamic adaptation of the running state of the electric meter box are realized, the misjudgment problem of fixed threshold monitoring under different seasons, time periods and load conditions can be effectively eliminated, and the safety of the electric meter box is improved. Therefore, the accuracy of abnormal recognition and the environment adaptability of the system are improved.

The invention relates to the technical field of electronic timer calibration, in particular to an ammeter clock intelligent calibration method and system based on multi-modal fusion, and the method comprises the steps: firstly obtaining ammeter clock voltage and environment data, carrying out the preprocessing, and generating an instantaneous disturbance sequence and a steady-state voltage time sequence; respectively calculating a voltage chronic influence factor, a voltage acute influence factor and an environment acceleration factor; determining the influence coefficient of each factor through historical data regression of similar ammeters, carrying out fusion calculation on total life loss, and carrying out iteration to generate a dynamic correction period adaptive to individual damage; and finally, constructing a same-batch and same-environment electric meter reference group, screening similar clusters through clustering, comparing corrected time consistency, judging unconventional abnormity, and outputting a targeted calibration strategy. According to the method, the multi-dimensional error factors are quantified through multi-modal fusion, the differentiation model is constructed, the dynamic correction period is generated through iteration, the traditional fixed period calibration defect is overcome, and the long-term timing precision stability of the electric meter is improved.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention discloses an electricity meter terminal temperature anomaly identification method, system and device and a storage medium, and relates to the technical field of intelligent electricity meter monitoring and anomaly detection.The method comprises the steps that currents of a live line loop and a zero line loop of an intelligent electricity meter are sampled, and the deviation rate of the live line current relative to the zero line current is calculated; estimating the temperature of the terminal of the electric energy meter based on the deviation rate and by using the resistance characteristic metering error change trend of the manganese-copper shunt; and when the terminal temperature estimated value of the electric energy meter exceeds a preset threshold value, determining that the temperature of the live wire terminal is abnormal, and recording an alarm event. The method realizes real-time acquisition of current difference characteristics, provides a reliable data basis for subsequent terminal temperature estimation, realizes a method of indirectly reflecting terminal temperature rise through electrical characteristics, ensures that alarm can be triggered and a complete tracing chain can be formed when temperature abnormity occurs, and improves the accuracy of terminal temperature estimation. Therefore, the controllability of the operation safety of the electric energy meter and the completeness of post analysis are improved.

The invention discloses an electricity meter adaptive error compensation method, system and device based on multi-physical coupling and hidden variable modeling and a storage medium, and relates to the technical field of electric energy metering and detection, and the method comprises the steps: constructing a multi-physical coupling basis vector containing a single variable and a cross variable based on a sensing output signal; hidden variable joint modeling is carried out based on the multi-physical coupling basis vector and parasitic capacitance change, magnetic conductivity drift and shunt resistance offset, and a compensation mapping relation of electromagnetic, thermal and stress coupling is formed; generating a compensation coefficient by adopting the compensation mapping relation, and applying the compensation coefficient in an ammeter metering link to complete an error compensation process; according to the method, voltage, current and phase compensation coefficients are generated through multi-physical coupling basis vector and hidden variable modeling, dynamic description and self-adaptive correction of error sources are achieved, dynamic accurate correction of voltage, current and phase is achieved, and it is ensured that the ammeter keeps high-precision metering for a long time under the complex environment and the device drift condition.

The invention relates to the technical field of mutual inductor tests, and provides a mutual inductor test abnormal data automatic filtering analysis method and system, and the method comprises the steps: obtaining magnetic field data, electric field data and thermal field data, respectively carrying out the feature extraction and weighted fusion, and obtaining a multi-physical field fusion feature vector; constructing an electromagnetic induction chain type propagation graph, calculating the weight of an edge in the graph, constructing a magnetic flux conservation constraint graph convolutional neural network, learning propagation and evolution modes of transformer test abnormity, and obtaining a coupling abnormity feature vector; the coupling anomaly feature vectors are classified, normal data, single physical field abnormal data and multi-physical field coupling abnormal data are recognized, and corresponding data are judged as abnormal data and filtered; and carrying out physical mechanism analysis on the filtered abnormal data to obtain an abnormal detection report. According to the invention, high-precision automatic identification, filtering analysis and physical mechanism traceability diagnosis of transformer test abnormal data are realized.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention relates to the field of data processing, in particular to an electric energy meter abnormity monitoring method and system based on digital twinning, and the method comprises the steps: obtaining a historical data time sequence of an electric energy meter, and carrying out the preprocessing; dividing the historical data time sequence of each dimension to obtain a plurality of historical data sets containing historical data time sequence segments of each dimension, and training an LSTM model based on the historical data sets; acquiring a current data time sequence of the electric energy meter and preprocessing the current data time sequence to obtain a current data set, inputting the current data set into the trained LSTM model, outputting predicted values of a plurality of target dimensions at the next moment, obtaining measured values of the plurality of target dimensions at the next moment, calculating a judgment index based on the predicted values, the measured values and a historical data set, and determining the current data time sequence of the electric energy meter. And judging the abnormal condition of the electric energy meter based on the judgment index. According to the method, noise residual errors generated when the self-cognition uncertainty of the model is high can be automatically inhibited, meanwhile, a real state deviation signal during cognition determination is amplified, and the accuracy of anomaly judgment is improved.

The invention belongs to the technical field of intelligent electric meters, and discloses an intelligent electric meter system with an abnormal power consumption behavior recognition function. The system is composed of a data acquisition module, an electric energy quality monitoring module, a data preprocessing module, a power utilization behavior feature extraction module, a short-time behavior monitoring module, an anomaly detection and diagnosis module, a behavior trend analysis module, an alarm and visualization module and a remote collaborative management module. Through deep mining and learning of historical power utilization data of a user, the system can accurately grasp power utilization habits, time period change rules and load characteristics of the user, along with dynamic evolution of power utilization conditions, the system can automatically optimize an anomaly detection threshold value, limitation brought by a traditional fixed threshold value is abandoned, and through the personalized and adaptive design, the power utilization efficiency of the user is improved. The system can exert the optimal efficiency in different regions and different types of users, and the application range and the practicability of the system are remarkably improved.

The invention discloses a method for detecting abnormal metering performance of an intelligent electric energy meter, which belongs to the technical field of electric energy metering equipment and comprises the following steps of: 1, establishing a reference response curved surface of parasitic parameters of a voltage sampling resistor and a current transformer in the electric energy meter relative to temperature and humidity; step 2, during the operation period of the electric energy meter, acquiring the real-time measurement value of the parasitic parameter in situ; and step 3, matching the real-time measurement value with the reference response curved surface. According to the method, an active matching model library can be constructed by fusing an underlying physical model of a component and a mapping rule set for a complex environment with sudden change of plateau outdoor temperature and humidity, the internal mechanism that parasitic parameters change along with temperature and humidity is explained from the physical essence level, and the limitation of a pure data statistical method is made up; the dynamic error judgment threshold value can adapt to environmental stress changes in real time, and the accuracy of metering performance anomaly detection and the complex environment adaptability are effectively improved.

The invention relates to the technical field of voltage transformers, in particular to a voltage transformer real-time monitoring system based on broadband and edge calculation, which comprises the steps of collecting multiple paths of voltage transformer data, extracting peak values and zero cross points, comparing jump rising edge time differences, unifying time sequences, extracting extreme values and slope identification disturbance sections, and monitoring the voltage transformers in real time. Measuring amplitude continuous change to judge the offset trend, analyzing trend direction classification waveform fragments, and combining the three indexes to generate an operation state label. According to the method, a unified time sequence is constructed by comparing cross-channel jump rising edge time differences, multi-channel jump process alignment is achieved, the synchronous anomaly recognition capability is improved, the number of extreme values and the slope direction are extracted to recognize a disturbance dense section, continuous anomaly is judged by combining statistics of overrun, and the fluctuation trend is judged by tracking the deviation trend between extreme values. And based on the three-index combination classification operation state, the anomaly identification precision is improved.

The invention provides a fault detection method and system based on an intelligent electric meter, and relates to the technical field of fault detection and diagnosis of the intelligent electric meter, and the key points of the technical scheme are that three-dimensional data are obtained, and the three-dimensional data comprise intelligent electric meter data, environment perception data, power grid topology data and line load data; predicting a normal data range of the next one minute in real time according to the three-dimensional data, and judging whether actual data exceed the normal data range or not; when the actual data exceed the normal data range, uploading all the data to a cloud end to analyze and detect faults; and collecting fault data in real time and generating a fault early warning list. The fault detection method and system based on the intelligent electric meter have the advantages that the fault false alarm rate is reduced, the fault detection response efficiency is enhanced, and fault risk early warning is achieved.

The invention discloses an electricity meter metering error correction method and system based on time sequence anomaly detection and an electricity meter, relates to the technical field of electricity meter metering error correction, and provides an intelligent anomaly detection method integrating multi-source data preprocessing, online learning and federation cooperation for a power system in which distributed energy and diversified loads coexist. The method comprises the following steps of: 1, dynamically filtering and caching original data by adopting a suspicious degree function, and outputting high-quality input; secondly, online learning is carried out through multi-algorithm fusion and expert knowledge constraint, and accurate judgment of traditional and emerging modes is achieved; 3, realizing multi-region cooperative gain based on difference parameter sharing and global aggregation; and 4, deploying a lightweight model at the edge end by using feedback evaluation and model distillation, thereby improving the real-time performance and reliability. In conclusion, the false alarm rate and the missing report rate can be remarkably reduced, various changes of load behaviors are effectively adapted, the operation and maintenance efficiency of a power grid is improved, and meanwhile data privacy safety and collaboration are guaranteed.

The invention relates to a self-diagnosis method for a state terminal of a substation measurement system, which comprises the following steps of: comparing signal values at different sampling nodes of signals from the same physical source in each measurement channel through the state terminal, and judging that the channels have physical chain degradation or poor contact; performing perturbation modulation on a measurement sampling process based on a clock signal controlled by a crystal oscillator in the state terminal, and if response deviation abnormity is detected, judging that a sampling time base has drift, time sequence disorder or cache backlog so as to identify a time base health state of a sampling chain in the terminal; in the operation process of the transformer substation, response differences generated by the excitation in different measurement channels are collected, and if response delay, amplitude distortion or jitter starting point deviation is found, it is judged that the anti-interference capacity of the corresponding channel is degraded; and according to the electric energy change rhythm sensed by the terminal in real time, adaptively adjusting the sensitivity threshold value of the jump response, and if frequent misjudgment or slow response of the jump response is detected, marking that the behavior sensitivity of the terminal sampling system is abnormal.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

The invention relates to the technical field of power system relay protection, and discloses a relay protection device mutual inductor adjustment system and method, and the system comprises a system connection initialization module, a data collection reference data generation module, a power frequency background signal offset module, a pilot frequency current injection error calibration module, and a calibration result verification output module. The method comprises the following steps: initializing sampling frequency of a to-be-calibrated mutual inductor and calibration source output precision of a calibration system, collecting amplitude and phase data of real-time power frequency current of the mutual inductor, generating offset current with equal amplitude and opposite directions through power electronic compensation based on reference data, optimizing offset current parameters through a closed-loop adjustment algorithm, and adjusting the offset current parameters through a closed-loop adjustment algorithm. Power frequency background signal offset is completed, pilot frequency calibration parameters are set, a difference value between a mutual inductor pilot frequency output signal and a calibration source standard signal is extracted by adopting a zero pointing technology, an optimal calibration coefficient is automatically calculated, verification of a calibration result is completed, and accuracy and reliability of the calibration result are ensured.

A method, apparatus and system for state evaluation of metrological characteristics of a capacitor voltage transformer, relating to the technical field of voltage measurement. The method comprises the following steps: acquiring operation data of a capacitor voltage transformer, establishing a metrological performance evaluation model, and obtaining a statistical control threshold (S1); collecting in real time the operation data of the capacitor voltage transformer, and calculating a statistic of the operation data by means of the metrological performance evaluation model (S2); comparing the statistic of the operation data with the statistical control threshold to determine whether a fault occurs in the capacitor voltage transformer (S3); and detecting a change trend of a primary signal and a secondary signal of the tested capacitor voltage transformer to determine whether metrological characteristics are abnormal (S4). In the method, the concept of time is introduced, by providing a timestamp, the influence of newly obtained data on the metrological performance evaluation model is greater, and by means of real-time update, the metrological performance evaluation model can be updated, so that when in a stable state, the detection time is reduced, and the model calculation is reduced.

The invention discloses an electric energy meter abnormal phenomenon automatic detection and identification method and system, and relates to electric digital data processing, and the method comprises the steps: carrying out the digital signal processing of initial data, the voltage effective value, the total harmonic distortion rate and the analysis of communication message abnormal codes, and generating a structured feature matrix; on the basis of the structured feature matrix, threshold comparison, composite condition judgment and continuous anomaly detection are executed through multi-level rule triggering, and an anomaly event report is generated; comparing the abnormal event report with a historical case, obtaining the similarity between the current abnormal event report and the historical case, and obtaining a similarity TOP-N case set; generating an anomaly diagnosis report by using weighted evidence fusion in combination with rule triggering intensity quantification and case matching credibility conversion; and updating the case library according to the abnormality diagnosis report, and counting and adjusting a rule threshold based on a sliding window. According to the method, the abnormal judgment threshold values of key parameters such as voltage and harmonic waves can be adjusted in real time through dynamic threshold value optimization, and the adaptability is improved.

The invention discloses an automatic calibration method and device for a chip testing machine, and relates to the technical field of chip testing, and the method comprises the steps: forming a four-end-pair closed path through two channels, building a symmetric reciprocal calibration ring, carrying out the zero check, and generating a loop health vector; based on the loop health vector, bidirectional four-quadrant sequencing is performed on the first channel and the second channel, and independent errors of a source chain and a measurement chain are separated to form a channel error map; according to the channel error atlas, slope standard deviation piecewise linearization processing is executed on each measuring range and each frequency point, a middle linear region and nonlinear regions at the two ends are determined, and a sectional calibration table is generated. According to the method, bidirectional four-quadrant sequencing is executed under a healthy loop, and source chain and measurement chain errors are separated, so that an independent error map of each channel is obtained, and error term aliasing in traditional calibration is avoided.

The invention relates to the technical field of intelligent power monitoring, in particular to an electric energy meter data acquisition method and system based on edge computing, and the method comprises the following steps: obtaining a data reading signal and task time of an electric energy meter, collecting a continuous state sequence according to a reference data set, analyzing the variation amplitude, verifying that the task completion is consistent with the time range check amplitude, and judging whether the task completion is consistent with the time range check amplitude. And updating an execution parameter based on the state record adjustment reference value, filling a control configuration activation rhythm signal write-in interface, and obtaining an electric energy meter data acquisition automation control information set. According to the method, real-time verification and self-correction of the acquisition process are realized through benchmark and revision processing of the task triggering process and state fluctuation analysis, so that the execution time and the state change are coordinated and consistent, the acquisition timeliness and stability are effectively improved, and the continuous monitoring capability of the running state is enhanced; adaptive updating of acquisition and control signals is realized, continuous and accurate information acquisition is ensured, and operation efficiency and response flexibility are improved.

The invention relates to the technical field of electric energy meter burning prevention, in particular to an electric energy meter burning fault prevention system fusing multi-modal data. The system comprises a data acquisition module used for acquiring an environment temperature sequence and a voltage sequence, a current sequence and an internal temperature sequence of each phase of an electric energy meter; the data normality analysis module is used for acquiring the data normality probability of each phase at each moment; the data anomaly judgment module is used for fitting the voltage sequence and the current sequence of each phase to obtain a fitting curve of each phase, and then judging whether the current moment is a suspected anomaly moment or not; the time segmentation module is used for acquiring different time periods by using the environment temperature sequence; the temperature trend analysis module is used for acquiring the current stability degree, the current approaching degree and the temperature slope of each time period; and acquiring a predicted temperature slope based on the current stability degree, the current approaching degree and the temperature slope of each phase in each time period, and further performing early warning. The electric energy meter can be effectively prevented from being burnt.

The invention discloses a metering performance evaluation method and system for an intelligent electric energy meter, a medium and a product, and the method comprises the steps: employing a standard meter for synchronous operation for a to-be-evaluated intelligent electric energy meter, so as to obtain a field test value of a basic error of the intelligent electric energy meter, inputting the environment detection data and the operation time of the intelligent electric energy meter to be evaluated into the trained basic error prediction model corresponding to the model of the intelligent electric energy meter to be evaluated so as to obtain a model prediction value of a basic error; the basic error prediction model comprises an input layer, a one-dimensional CNN layer, an LSTM layer fused with a time attention mechanism, a full connection layer and a basic error output layer, and the metering performance level of the intelligent electric energy meter is determined according to a field test value and a model prediction value of a basic error. The invention aims to realize metering performance evaluation of the intelligent electric energy meter in a typical environment so as to realize higher operation efficiency and solve the bottleneck problem of long-term dependence on modeling and local feature extraction.

The invention provides an intelligent electric meter HPLC communication module dynamic calibration method suitable for environmental disturbance, and relates to the technical field of dynamic calibration, and the method comprises the steps: collecting multi-source data, collecting time sequence communication data of a communication module in real time, and carrying out the analysis to obtain a communication index. Initial calibration parameters are formulated, dynamic adjustment parameters are calculated, and the initial calibration parameters are adjusted according to the dynamic adjustment parameters to obtain real-time operation parameters. And judging whether the current communication index reaches a preset communication threshold, if so, maintaining the real-time operation parameter, and otherwise, optimizing the dynamic adjustment parameter. And analyzing the environmental disturbance data and the power line channel characteristic data to obtain associated influence data, carrying out feature extraction on the current communication index to obtain a communication abnormal feature, and adjusting the dynamic adjustment parameter in combination with the associated influence data to obtain a calibration correction parameter. According to the invention, intelligence and automation of communication module calibration are realized, manual intervention and experience dependence are reduced, and calibration efficiency and accuracy are improved.

The invention relates to the technical field of resistance simulator calibration, and discloses a self-calibration method and system based on a resistance simulator. The method comprises the steps that real-time operation parameters are collected through a sensor array to form a data stream, multi-scale feature components and residual components are extracted through self-adaptive signal decomposition, an abnormal fluctuation mode is identified through joint analysis, and key influence factors are mined in combination with causal reasoning. A historical calibration database and key factors are integrated, a reference performance trajectory is generated through a regression model, the actual trajectory deviation degree is calculated to evaluate calibration urgency, and multi-dimensional information is fused to generate a calibration strategy through a time sequence prediction model. According to the method, a closed-loop system from data acquisition to strategy generation is constructed, dynamic monitoring and accurate evaluation are realized, calibration automation and decision scientificity are improved, a systematic solution is provided for reliable operation of the resistance simulator under complex working conditions, and the method is suitable for the field of high-precision electronic testing and metering.