1342 results about "Electricity meter" patented technology

The invention provides a multi-source ammeter data intelligent fusion and abnormity identification method. According to the method, original electric parameter data of a plurality of intelligent electric meters are collected under a unified time reference, a cross-equipment measurement data set indexed according to electric meter numbers is constructed, and electric parameter classification and associated information generation are completed in combination with electric wiring information and functional parameters; an electric parameter change curve is extracted through a continuous time window, a value change consistency index is calculated, a response offset feature vector is further introduced for joint judgment, and accurate recognition of an abnormal electric meter and an abnormal time period is achieved; for the intelligent electric meter which is identified as abnormal, constructing an estimated value through data of other electric meters, and calculating residual errors between the estimated value and an original value and between the estimated value and an adjacent estimated value according to a main conductive parameter so as to realize dual residual error verification; judging an anomaly type according to the consistency index and the residual result, and outputting an anomaly recognition result; according to the method, the anomaly detection precision in a multi-table collaboration scene is improved.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

An air conditioner load monitoring and anomaly detection method and system. The method comprises: acquiring user power consumption data and meteorological data, the user power consumption data and the meteorological data comprising the total power of an electricity meter, the power of an air conditioner, and the outdoor temperature, preprocessing the data, and computing electrical characteristics of the user power consumption data; on the basis of the electrical characteristics, learning by using a two-tower neural network so as to obtain operating rules of the air conditioner, and extracting an air conditioner operation curve; on the basis of the air conditioner operation curve, computing electrical characteristics and energy consumption characteristics of the air conditioner, and performing cluster analysis by using a K-means clustering algorithm; and, on the basis of clustering results, comparing the clustering results with clustering results of collected electrical data of the air conditioner under normal and abnormal operating conditions to determine whether the air conditioner is in an abnormal state, so as to implement detection of anomalies in the operation of the air conditioner, thereby improving the precision of load decomposition of the air conditioner, reducing application costs, providing a decision-making reference for equipment maintenance personnel, and prolonging the service life of the equipment.

The invention discloses an electric energy meter error real-time monitoring and intelligent calibration system and method, and relates to the technical field of electric energy meter calibration, and the system comprises a target power data calculation module which is used for collecting voltage and current signals in real time, and carrying out the calculation to obtain target power data; the reference power data calculation module is used for performing real-time calculation to obtain reference power data; the error vector acquisition module is used for acquiring an error vector; the error prediction result generation module is used for carrying out modeling analysis on the error vector and generating an error prediction result; and the target electric energy value output module is used for outputting the target electric energy value. The technical problem that the metering accuracy is insufficient due to the fact that the electric energy meter lacks an effective error analysis and prediction means and a dynamic calibration mechanism in a bidirectional power flow scene in the prior art is solved, and the purposes of real-time monitoring, error analysis and prediction and dynamic calibration of the forward and reverse power data of the electric energy meter are achieved. And the metering accuracy of the electric energy meter in a bidirectional power flow scene is improved.

The invention discloses an intelligent electric meter fault early warning method and system based on multi-parameter synchronous measurement. The method comprises the step of synchronously collecting operation videos, reading data and environmental parameters of the electricity meter. Firstly, illumination compensation optimization is performed on a video, image features are extracted through a convolutional layer and a full connection layer, a comprehensive visual feature vector is constructed in combination with a cross attention mechanism and environmental parameters, and the overall shape of an electric meter is extracted to detail features in a layered manner; the change trend and fluctuation characteristics are extracted from the reading data by using time sequence analysis, and the influence of environmental factors is considered. Then, fusing multi-source features to generate a feature map reflecting the state of the electric meter; normal and fault state features are compared through similarity calculation, and a fault risk is identified and early warning is carried out; and evaluating a result by using ID matching and a machine learning algorithm, and optimizing fault early warning in combination with historical data. By implementing the method, multi-source information can be fused, the speed and accuracy of fault diagnosis can be remarkably improved, and continuous and stable operation of a power system is ensured.

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.

The invention discloses an electricity consumption information acquisition terminal concurrent meter reading method, device and equipment and a storage medium, and relates to the field of electricity consumption information acquisition. The method comprises the following steps: acquiring a communication routing path and a communication routing hop count from an acquisition terminal to each to-be-acquired electric energy meter in an electric energy meter network topology structure; according to the communication routing path and the hop count of each to-be-collected electric energy meter, the to-be-collected electric energy meters are classified to obtain N concurrent meter reading task groups, and N is larger than or equal to 2; communication routing paths among the to-be-collected electric energy meters in each concurrent meter reading task group are not intersected and communication routing hops are different; and performing concurrent meter reading on the to-be-acquired electric energy meter according to the concurrent meter reading task group to acquire the electricity utilization information. According to the invention, the problems of overweight communication network load and frequent conflicts caused by mutual interference between a downlink reading instruction and an uplink response instruction due to communication channel overlapping and routing conflicts when an existing power utilization information acquisition terminal executes a multi-meter simultaneous reading task are solved.

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 electric meter online monitoring, in particular to an electric energy data intelligent acquisition terminal based on federal learning. The data acquisition and preprocessing module is used for acquiring electric energy data in a power grid in real time and carrying out de-noising and normalization preprocessing on the electric energy data; the model training module based on federated learning is used for training a neural network model for the preprocessed electric energy data by using local data so as to obtain a local model; the homomorphic encryption algorithm application module is used for encrypting and updating data and model parameters in a local data training process by adopting a homomorphic encryption algorithm; and the global model updating module is used for sending the neural network model subjected to multiple times of local data training and encryption updating to a central server, and optimizing and updating the neural network model through a weighted federated average aggregation algorithm. According to the invention, distributed data processing and machine learning technologies are combined to realize intelligent data analysis of electric power data, so that the intelligent level of electric power monitoring and fault early warning is improved.

The invention relates to the technical field of intelligent electric energy meters, in particular to an intelligent electric energy meter system based on an RF net-shaped ad hoc network and a communication method of the intelligent electric energy meter system. The system adopts an intelligent electric energy meter with an integrated gateway function as a gateway node in an RF network and uploads power utilization data and state information to a head end system; independent gateway equipment does not need to be additionally deployed, so that the hardware procurement cost, the installation and debugging cost and the long-term operation and maintenance cost are remarkably reduced; meanwhile, according to the intelligent electric energy meter system communication method based on the RF net-shaped ad hoc network, the optimal main path and the optimal standby path are selected for data transmission by dynamically calculating the comprehensive routing metric value, the reliability of the network is greatly improved through the multi-path redundancy design, and the system has the higher fault self-healing capacity; in addition, the intelligent electric energy meter integrated with the gateway function can monitor and optimize the network topology structure, the data flow distribution and the node resource utilization rate in real time through a built-in network management system, and the data transmission efficiency and the network stability are further improved.

The invention relates to a single-phase load identification intelligent ammeter, which relates to the technical field of ammeters, and comprises an ammeter body, a terminal cover and a fixing piece, the ammeter body is provided with a wiring area, a display area and a drainage channel penetrating through two sides of the ammeter body, and the terminal cover is hinged on the ammeter body and covers the wiring area and the drainage channel. The fixing member is used for detachably fixing the terminal cover on the ammeter body. The plugging part is arranged on the terminal cover, the plugging groove is arranged in the wiring area, the fixing piece is taken down from the plugging part to form an air guide flow channel between the plugging part and the wiring area, and the air guide flow channel is communicated with the drainage channel. Air is supplied to the plugging part through external air supply equipment, dust removal air can be blown into the wiring area along the air guide flow channel and is discharged through the drainage channel, dust in the wiring area can be cleaned under the condition that the terminal cover is not opened, the dust removal efficiency is improved, and the maintenance risk is reduced.

The invention provides an intelligent electric energy meter service life prediction method and device and a readable storage medium, and belongs to the technical field of device service life prediction.The method comprises the steps that multi-dimensional input is constructed by fusing key environment data and an operation time sequence, and the microcosmic, periodic and long-term characteristics of the input are extracted through multi-scale convolution support; the feature combination is dynamically optimized through an adaptive fusion module; constructing a time sequence prediction model, embedding a convolution attention mechanism in an encoder, and compressing redundant information of key features by combining sparse self-attention distillation through key features of feature combination of channel-space double attention dynamic enhancement optimization; the decoder adopts a mask probability sparse attention and iterative prediction strategy to generate an error prediction sequence based on the global features of the encoder to avoid future information interference; and calculating the residual life according to the intersection point of the error prediction sequence and the failure threshold. According to the method, the long sequence prediction precision, the key feature utilization rate and the calculation efficiency are improved, and reliable support is provided for preventive maintenance of the intelligent electric meter.

The invention discloses a distributed single-mode small model data synchronization method applied to an intelligent electric energy meter, and the method comprises the steps: S1, building a high-fidelity training environment based on a programmable power supply, dynamically optimizing simulation parameters in combination with end-side feedback, and training an initial power utilization anomaly detection model; s2, modeling the trained model according to an equipment portrait, and generating a lightweight model through bidirectional adaptive compression; s3, deploying a lightweight model at an electric energy meter end and monitoring performance attenuation characteristics; s4, the environment offset is reversely fed back to the cloud through a block gradient return mechanism, and the model parameters are iteratively corrected at the cloud; and S5, triggering cloud secondary training by using feedback data, and circularly updating the model until the precision and power consumption dual-index convergence is achieved. According to the method, a closed-loop optimization link is formed through the end cloud bidirectional data flow, the static defect of traditional one-way deployment is broken through, and continuous self-adaption of the model to the dynamic environment is achieved.

The invention discloses an electricity meter data acquisition method and system based on a DLT698.45-2017 protocol. A dual-mode acquisition mechanism is constructed by expanding an active query communication protocol and a passive monitoring communication protocol in a gateway machine dictionary table, presetting an electricity meter model and data point parameters and developing a message encapsulation and analysis interface. According to the method, data points are grouped and framed according to serial ports and electricity meters, a message sending period is managed through a fixed / temporary sending queue, and PIID automatic self-increasing and FCS dynamic calculation are achieved through a serial port processing thread. The system can generate query messages according to a reading period in an active mode, and can monitor serial port data and filter cross-platform messages in a passive mode. The method can effectively improve the ammeter data acquisition efficiency and accuracy, reduces the system resource consumption, guarantees the data integrity and safety, is suitable for the ammeter data real-time acquisition of a smart power grid and a new energy scene, and is of great significance for the construction and development of the smart power grid.

A meter socket adapter (MSA) for connecting a battery energy storage system (BESS) to a household microgrid through a meter combo load center. The MSA includes: a housing matching an electricity meter and a meter socket in the meter combo load center; a plurality of power conductors that connect among the electricity meter, the meter socket, and BESS; a meter interconnection device (MID) switch for connecting a utility grid or one or more distribution energy resource (DER) devices to supply power to household loads in the household microgrid; a MID driving circuit for driving the MID switch to close or open; and a thermal management circuit couple with a plurality of temperature sensors disposed at each high current connection point to prevent overheat.

The invention relates to the technical field of electric meter data transmission, in particular to an intelligent electric meter multi-mode communication low-power-consumption control and data compression combined processing method. If the ammeter temperature is greater than the temperature threshold, calculating the heat dissipation coefficient of the ammeter according to the heat dissipation parameter; determining the maximum operation power of the ammeter according to the heat dissipation coefficient; judging whether the state of the electricity meter is abnormal or not according to the operation parameters, if the electricity meter works normally, selecting an LPWAN technology for data transmission and selecting a data compression mode according to a data change period, responding to the abnormal work of the electricity meter, adjusting the emission frequency according to the accumulation amount of key data, and after the adjustment of the emission frequency is completed, performing data compression. And determining a data compression and communication mode according to the maximum operation power. By calculating the heat dissipation coefficient and determining the maximum operation power according to the heat dissipation coefficient, the electric meter can operate in a safe power range, so that the operation stability of the electric meter is improved.

The invention relates to the technical field of distribution network management, in particular to a distribution network management electricity meter state analysis method and system. According to the technical scheme, the distribution network management electricity meter state analysis method comprises the following steps that S1, multiple layers of edge computing nodes are deployed in a power distribution network, first-level nodes are deployed in a power distribution station, second-level nodes are deployed in an electricity meter concentrator, and computing resource distribution of the edge nodes is dynamically adjusted based on electricity meter distribution density and real-time loads; s2, through a collaborative acquisition mechanism of the first-level node and the second-level node, acquiring operation data of the electric meter in real time; and S3, transmitting the classified and marked data by adopting a multi-modal compression strategy: compressing the periodic monitoring data by using difference value coding, and extracting the sudden abnormal data by using a feature abstract. By constructing a multi-level edge collaborative acquisition mechanism and a 5G network slice transmission channel, the real-time bottleneck of traditional manual inspection and fixed-period acquisition is broken through.

The invention relates to the technical field of energy storage systems, and discloses a multi-type ammeter mixing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the ammeter types of a plurality of ammeters connected to the same communication port; configuring a communication address for each electricity meter according to the type of the electricity meter and a preset serial number or configuring a communication address for each electricity meter according to an address label; and establishing communication for each electricity meter according to the communication address. According to the invention, the same communication port can be compatible with the access requirements of different types of ammeters, the method is suitable for a complex scene in which multiple ammeters coexist, the tolerance of the energy storage system to different ammeters is improved, and the problem that the same communication port is difficult to adapt to the mixed use requirements of multiple types of ammeters is solved.

The invention relates to the technical field of intelligent detection of power equipment, in particular to a complex illumination low-voltage electricity meter frequency sensing detection method, system and equipment and a medium, and aims to solve the problems of high-frequency noise interference, intra-class feature difference, insufficient edge end energy efficiency and the like existing in electricity meter detection in a high-brightness environment. Proposing a dynamic frequency domain compensation mechanism to suppress light noise and recover signal details, and optimizing dense scene feature consistency in combination with adaptive filtering and a multi-scale sampling strategy; the boundary fuzzy problem is solved through dynamic feature alignment and an edge compensation mechanism, and parallel acceleration of frequency domain decomposition and dynamic resampling is realized by adopting a heterogeneous hardware architecture; a multi-modal data training framework and a cross-domain transfer learning strategy are fused to enhance the generalization ability of the model; according to the invention, collaborative optimization of detection precision, real-time performance and low power consumption is realized in a complex illumination scene, and high-robustness technical support is provided for intelligent power grid inspection.

The invention provides a line loss reason analysis and positioning method and system based on artificial intelligence and a medium, and relates to the technical field of power system management.The method comprises the steps that when the bus loss rate of a target station area exceeds a preset line loss threshold value, an excitation sequence is issued to a general meter, a circuit breaker and a user electricity meter of the target station area, respectively obtaining corresponding transient response data, and further constructing a dynamic topological graph of the target transformer area; and a topological structure is optimized by combining a graph neural network and an attention mechanism, and abnormal connection points are detected. And determining downstream associated user electric meters, and calculating the power utilization abnormity score based on the load data. And injecting a steep front traveling wave signal into the electric meter with the highest score to obtain a reflection characteristic parameter. And performing iterative analysis by using a Double DQN reinforcement learning algorithm, determining a fault point, a line loss reason type and confidence, and realizing efficient and accurate line loss reason positioning and analysis. According to the invention, efficient and accurate troubleshooting of line loss reasons is realized.

The present application relates to a method, apparatus, computer device, and storage medium for determining a smart meter fault. The method includes: obtaining a data curve measured by the smart meter within a preset time period, where the data curve includes first voltage curves of multiple cycles and current curves corresponding to each first voltage curve. Extracting a preset number of first voltage curves from the data curve as target voltage curves, where the target voltage curves are the first voltage curves corresponding to the current curves with current amplitudes less than a preset value. Determining a target value of the voltage measurement error of the smart meter according to the target voltage curves. Thus, the error value of the voltage measurement caused by the fault of the smart meter is determined, and through this error value, the fault condition of the voltage is quantified, thereby realizing the determination of the meter fault. This enables the staff to repair or replace the smart meter in a timely manner, avoiding situations such as incorrect user electricity bill measurement or power outage caused by meter faults.

The invention discloses an electric meter historical data restoration method based on a multi-dimensional incidence relation, and relates to the technical field of electric power data management and intelligent restoration, the accuracy and traceability of data restoration are realized by establishing a multi-source exception type library and an element time-varying degradation model, and the accuracy and traceability of data restoration are improved for multi-source data conflicts. The method comprises the following steps: pre-defining a plurality of abnormal mode combinations and corresponding repair strategies, positioning a fault source through mode matching of a real-time index and an abnormal type library, selecting reference data in combination with a historical average deviation rate of a target ammeter and an associated ammeter, avoiding deviation caused by single judgment, and for nonlinear degradation, firstly determining fault elements of a shunt and an ADC chip, a resistance degradation model and a conversion error model are constructed, a theoretical correction value is calculated, a result is optimized through a machine learning model trained by historical normal data, meanwhile, original readings, model parameters and calculation bases are stored in an associated mode, a traceable log is formed, and the repair accuracy is guaranteed.

The invention discloses a low-voltage distribution area topology identification method, system and device and a storage medium, and the method comprises the following steps: deploying a synchronous measurement unit in a low-voltage distribution area, and obtaining the outgoing line current phasor and voltage phasor data with a synchronous time scale of a whole network; constructing an incoming line and outgoing line current phasor set, and performing adaptive descending sorting according to amplitudes; determining that the nodes corresponding to the incoming line current phasors and the found nodes corresponding to the one or more outgoing line current phasors are in a father-son relationship, and iterating the process until all sets are empty, thereby completing the identification of the topological relationship of the power distribution side; and according to the equipment number contained in the obtained voltage phasor data, determining a meter box connected with the user electricity meters, and then clustering the user electricity meters connected to the same meter box according to the voltage phase angle to obtain the connection phase sequence of each user electricity meter. The problem of key information missing is solved from a data source, and accurate and efficient identification of topology is realized by fusing physical constraints.

The invention discloses a data acquisition modeling method for electricity utilization information acquisition, which comprises the following steps of: reading historical electricity meter acquisition records and power grid load data for preprocessing to generate standardized electricity meter response characteristic data; constructing a six-dimensional networking behavior feature vector, and generating an ammeter dynamic partitioning result through a density clustering algorithm; generating a partition-level resource allocation strategy in combination with the ammeter metadata and the data value evaluation model; and a double-layer time wheel scheduling mechanism is constructed, the outer layer manages resource allocation among partitions, the inner layer controls electricity meter decline time slice allocation, and an optimized acquisition scheduling execution plan is generated. The problem that a traditional method neglects power grid topology constraints and data value differences is solved, the data acquisition success rate and the resource utilization efficiency are remarkably improved, and an intelligent technical solution is provided for an electric power information acquisition system.

The invention relates to the technical field of electric energy meter calibration, and provides an intelligent calibration method and system for an electric energy meter. The method comprises the steps that after calibration is triggered, a bus electric energy meter is switched to a bus-level electrical isolation scene through a switching relay; extracting a first group of bus-level metering values after injecting all-condition steady-state signals; comparing the theoretical values to calculate a first group of bus-level errors; judging whether to switch to a branch-level electrical isolation scene or not according to the error distribution characteristics; if switching is carried out, a multi-dimensional disturbance load combination is dynamically loaded on the K branch circuits through the program control load module, and a second group of bus level metering values are synchronously metered; and performing branch-level energy consumption error traceability calculation on the second group of values, and executing closed-loop calibration to solve the technical problems of low calibration precision and incapability of accurately reflecting error distribution caused by incapability of coping with a multi-dimensional disturbance load environment in real time in a traditional electric energy meter calibration process, so as to achieve the purposes of improving the accurate calibration capability of the electric energy meter in multiple scenes and improving the calibration accuracy of the electric energy meter in multiple scenes. And the equipment calibration time is shortened.

The invention provides a verification method and system for reliable tripping of an electric energy meter, and relates to the technical field of electric energy meters, and the verification method for reliable tripping of the electric energy meter comprises the steps: obtaining detection parameters of the electric energy meter; performing parameter analysis on the electric energy meter detection parameters through the initial monitoring model to obtain an analysis result; finely adjusting the initial monitoring model according to the analysis result to obtain an updated model; according to the updating model, updating the tripping threshold monitoring table; wherein the trip-out threshold monitoring table comprises a plurality of trip-out monitoring thresholds and a trip-out mode formed by the trip-out monitoring thresholds; according to the electric energy meter detection parameters and the tripping threshold monitoring table, a corresponding tripping mode is selected to control tripping; in the tripping process, generating a tripping result according to a feedback signal of the tripping process; and comparing the tripping result with the tripping mode. According to the verification method and system provided by the invention, the reliability of tripping mode selection can be effectively ensured, and meanwhile, the reliability of the tripping process can also be ensured.

The invention discloses a fault identification method and system of an intelligent electric meter, and belongs to the field of fault identification of the intelligent electric meter, and the method comprises the following steps: collecting the electric parameter data of a historical electric meter and the power consumption parameter of each electric device, and obtaining the standby power consumption parameter of the electric device; setting standby power consumption base lines in different time periods including working days and rest days, and triggering early warning when the deviation between the real-time power consumption and the base lines exceeds a preset threshold value; performing fault prediction according to the triggered early warning to obtain a specific fault; obtaining an abnormal reason of an abnormal point in the historical electricity meter electric parameter data, carrying out correlation analysis on the historical abnormal point and a predicted fault, obtaining the abnormal reason of the abnormal point with the highest correlation, and taking the abnormal reason as a fault reason after verification and outputting the abnormal reason; according to the invention, through obtaining the ammeter data, the power consumption of each device and the standby power consumption, the personalized baseline is established in combination with different time periods, time and seasons, the abnormality of the ammeter is accurately identified, and fault misjudgment caused by high standby power of the device is effectively avoided.

The present invention provides a control method, device and equipment for a software electronic seal of a smart electricity meter, and relates to the technical field of smart electricity meters. The present invention realizes real-time monitoring and processing of electricity theft by the smart electricity meter by setting an electronic seal in a software manner in the smart electricity meter, and combining it with the event record monitoring of the smart electricity meter. When suspected electricity theft is discovered, the system sends abnormal alarm information to the monitoring center, receives instructions from the monitoring center, and processes whether the smart electricity meter is powered off, thereby realizing remote monitoring and processing of electricity theft by the smart electricity meter. The present invention solves the problem of difficulty in identifying electricity theft when threaded seals and snap-on seals are damaged or imitated through electronic seals, and can timely discover and process electricity theft.

The invention discloses an electric energy metering method of a high-voltage electric energy meter. The electric energy metering method comprises the following steps that a main meter and an auxiliary meter are powered on and initialized; the main meter operates the first metering module and sends a communication handshake signal to the auxiliary meter, and the auxiliary meter operates the second metering module and responds to the communication handshake signal sent by the main meter to establish a communication link; the main meter and the auxiliary meter respectively read register values of the first metering module and the second metering module, and calculate an A-phase electric parameter and a C-phase electric parameter; the main meter acquires a C-phase electric parameter from the auxiliary meter regularly, and calculates a combined-phase electric parameter according to the A-phase electric parameter and the C-phase electric parameter; the main meter sequentially writes the combined-phase register value and the A-phase register value into a first metering module, and calculates combined-phase electric quantity and A-phase electric quantity according to the combined-phase register value and the A-phase register value respectively; and the auxiliary meter synchronously writes the C-phase register value into the second metering module and calculates the C-phase electric quantity according to the C-phase register value. According to the invention, the technical problem of how to optimize the existing high-voltage electric energy meter and realize high-precision measurement and metering of three-phase electric parameters of a high-voltage power grid is solved.

The utility model provides an electric energy meter calibrating device comprising a calibrating box body, the calibrating box body comprises a first plate and a second plate which are arranged at intervals along a first direction, and at least one of the first plate and the second plate is provided with at least one first air port; the second air opening is formed in the plate body for connecting the first plate and the second plate; the supporting partition plate is arranged in the verification box body in the first direction, and the space in the verification box body is divided into a first cavity and a second cavity by the supporting partition plate; the plurality of direct-current circuit boards are connected with the supporting partition plate, the direct-current circuit boards are arranged in the first cavity, and at least one direct-current circuit board is provided with a first heating piece; the at least one first fan is arranged on the supporting partition plate, and the first fan and the first heating piece are oppositely arranged; and the plurality of alternating-current circuit boards are connected with the supporting partition plate and are arranged in the second cavity. According to the technical scheme, the direct current circuit board and the alternating current circuit board are arranged in a layered mode, the heat dissipation capacity of the device is remarkably improved, and the temperature of the whole system is kept stable.