CN-121995298-A - Electric energy meter data acquisition and management method and system based on voltage non-contact anti-electricity-stealing detection

CN121995298ACN 121995298 ACN121995298 ACN 121995298ACN-121995298-A

Abstract

The invention relates to the technical field of data acquisition of electric energy meters and provides a method and a system for acquiring and managing electric energy meter data based on voltage non-contact anti-electricity-stealing detection, wherein the method comprises the steps of communicating with the electric energy meter, reading key data such as freezing events, load curves, voltage loss records and the like in the electric energy meter, and forming a preliminary data set containing line real-time waveforms and data recorded in the electric energy meter; the method comprises the steps of collecting real-time waveforms of lines, carrying out harmonic analysis on the collected real-time waveforms of the lines, identifying abnormal electricity load characteristics, carrying out topology diagnosis, matching actual wiring with 96 models, outputting specific wiring error types and electric quantity correction coefficients, receiving and storing detection reports by a cloud marketing system, and automatically generating or updating inspection work orders. The system comprises a data set generation module, a topology diagnosis module and a report uploading module. The invention forms a full-flow digital management closed loop of detection, diagnosis, inspection and archiving, and the system level integration remarkably improves the anti-electricity-stealing inspection efficiency and the metering anomaly traceability.

Inventors

DENG DONG
FEI GUIHUAI
ZHU JIANBO
GUO WUJUN

Assignees

深圳市先行电气技术有限公司

Dates

Publication Date: 20260508
Application Date: 20260205

Claims (10)

1. The electric energy meter data acquisition and management method based on voltage non-contact anti-electricity-stealing detection is characterized by comprising the following steps of: Based on the collected preliminary data set, the transformation ratio and the polarity parameters of the current transformer are combined, the comprehensive errors of the electric energy meter and the current transformer are calculated, the judgment accuracy is achieved, the collected line real-time waveforms are subjected to harmonic analysis, abnormal electricity utilization load characteristics are identified, topology diagnosis is conducted, the actual wiring is matched with 96 models, and specific wiring error types and electric quantity correction coefficients are output.
2. The method for collecting and managing data of the electric energy meter based on voltage non-contact anti-electricity-stealing detection according to claim 1, wherein the process of outputting specific wiring error types and electric quantity correction coefficients comprises the following steps: The method comprises the steps of obtaining a high-frequency waveform sequence in a preliminary data set, taking the high-frequency waveform sequence in the preliminary data set as an independent real power source reference, calculating an instantaneous power theoretical value of a line, calibrating and converting current data in the high-frequency waveform by combining a given current transformer transformation ratio and a polarity parameter, and carrying out point-by-point comparison and integral operation on the calibrated instantaneous power theoretical value and a load curve power value read out from the inside of an electric energy meter in the preliminary data set; The method comprises the steps of performing discrete spectrum transformation on a high-frequency waveform sequence, extracting amplitude, phase and time-varying modes of each subharmonic to form an electricity utilization characteristic spectrum, performing depth matching and deviation calculation on the current electricity utilization characteristic spectrum and a reference spectrum in a historical normal electricity utilization mode, and identifying and generating a characteristic identifier indicating the type of an electricity utilization load; The method comprises the steps of sending the comprehensive error index, the abnormal characteristic identifier, the waveform phase relation in the primary data set and the voltage loss record information into a preset topological reasoning engine, carrying out multidimensional fitting and probability calculation on an actual error mode, harmonic characteristics, event records and 96 models by the topological reasoning engine, and finally outputting a most probable wiring error type and an electric quantity correction coefficient for electric quantity compensation calculation.
3. The method for collecting and managing electric energy meter data based on voltage non-contact anti-electricity-stealing detection according to claim 2, wherein the process of multidimensional fitting and probability calculation of the actual error pattern, harmonic features, event records and 96 models by the topology inference engine comprises the following steps: The core of the topology reasoning engine is a pre-generated and stored wiring error knowledge base which comprises 96 pre-defined models of possible wiring errors, wherein each model describes a physical wiring mode and corresponds to a group of theoretical multidimensional characteristic expressions, namely an error range, a harmonic distortion mode, a phase angle deviation rule and a typical voltage and current event sequence which are expected to occur when the errors occur; The topological reasoning engine executes multidimensional fitting calculation, and performs full-space and multi-attribute synchronous matching on the input multidimensional diagnosis feature vectors and theoretical feature vectors of all 96 models in a wiring error knowledge base; based on the comprehensive matching degree score, the topological reasoning engine starts probability calculation and decision, performs sequencing and probability processing on the matching degrees of all models, calculates posterior probability of each model becoming an actual fault cause, and selects a model with highest posterior probability as a diagnosis conclusion.
4. A method for collecting and managing data of an electric energy meter based on voltage non-contact anti-electricity-theft detection as recited in claim 3, wherein the process of generating a comprehensive matching score for each model comprises the steps of: The method comprises the steps of projecting and comparing a multidimensional diagnosis feature vector with a theoretical expected feature vector of a certain model in a wiring error knowledge base in a high-dimensional feature space formed by error, harmonic wave, phase and event multidimensional; Different weight coefficients are distributed for each type of characteristic attribute according to the diagnostic importance of the characteristic attribute, and the deviation degree of an actual measurement value and a theoretical value in each dimension is calculated respectively; And generating a normalized comprehensive matching degree score aiming at the model by reversely synthesizing all the weighted deviation degrees.
5. The method for collecting and managing electric energy meter data based on voltage non-contact anti-electricity-stealing detection according to claim 4, wherein the process of calculating the deviation of the measured value from the theoretical value in each dimension comprises the following steps: Generating a weight coefficient by learning, training and counting massive historical correct and incorrect wiring cases, and presetting an inherent diagnosis significance coefficient for each type of characteristic attribute; Adopting a difference measurement method based on a statistical confidence interval to calculate the absolute difference between an actual measurement value and a theoretical expected value of the characteristic attribute, and carrying out normalization processing on the difference value and the standard deviation of the characteristic in a history normal fluctuation range to obtain an original deviation degree; the original degree of deviation is multiplied by the assigned weight coefficient to generate a weighted degree of deviation score for the dimension of the current attribute.
6. The method for collecting and managing electric energy meter data based on voltage non-contact anti-electricity-theft detection according to claim 5, wherein the process of calculating the absolute difference between the actual measurement value and the theoretical expected value of the characteristic attribute comprises the following steps: For each type of characteristic attribute, a historical normal fluctuation range is defined, which is represented as a numerical interval taking a mean value as a center and a positive standard deviation and a negative standard deviation as a boundary, and a standard deviation reference value for quantifying the normal fluctuation range of the characteristic; proportional conversion is carried out on the absolute difference value and a standard deviation reference value corresponding to the confidence interval parameter library, and the significance degree of the absolute difference value relative to a normal fluctuation range is estimated; An original deviation characterizing the relative differential intensity is generated by a normalization process based on historical statistics.
7. The method for collecting and managing electric energy meter data based on voltage non-contact anti-electricity-theft detection according to claim 6, wherein the process of scaling the absolute difference value with the corresponding standard deviation reference value in the confidence interval parameter library comprises the following steps: Extracting a dynamic reference index group of target feature attributes from a pre-established confidence interval parameter library, wherein the dynamic reference index group comprises three core elements, namely a historical mean value center point, a normal fluctuation boundary value and a standard deviation amplitude value; The method comprises the steps of obtaining an attribute measurement value through feature extraction of a measured data stream, carrying out algebraic difference operation on the attribute measurement value obtained through feature extraction and an expected value output by a theoretical model to generate an original difference quantity which is not standardized; on the basis of the calibration difference coefficient, carrying out secondary correction by combining the normal fluctuation boundary value recorded by the characteristics in the confidence interval parameter library, starting an amplitude compression mechanism when the calibration difference coefficient exceeds the fluctuation boundary, starting a linear maintaining mechanism when the calibration difference coefficient is within the boundary range, retaining the original proportional relation, and finally outputting the normalized deviation index with environmental adaptability.
8. The method for collecting and managing the data of the electric energy meter based on the voltage non-contact anti-electricity-stealing detection according to claim 1 is characterized by further comprising the steps of collecting real-time voltage and current waveform data on a circuit in a non-contact manner through an insulating rod by using a high-voltage clamp meter, communicating with the electric energy meter through an interface, reading freezing event, load curve and voltage loss record key data inside the electric energy meter, and forming a preliminary data set containing the circuit real-time waveform and the electric energy meter internal record data.
9. The method for collecting and managing the electric energy meter data based on the voltage non-contact anti-electricity-stealing detection according to claim 1, further comprising the steps that the detection terminal uploads a preliminary data set, a wiring error type and an electric quantity correction coefficient to a cloud marketing system through a 4G network, and the cloud marketing system receives and stores reports to form a user electricity file and automatically generates or updates an inspection work order.
10. The utility model provides an electric energy meter data acquisition and management system based on non-contact anti-electricity-stealing of voltage detects which characterized in that contains: The data set generation module is used for acquiring real-time voltage and current waveform data on a circuit in a non-contact manner through an insulating rod by using the high-voltage clamp meter, and is communicated with the electric energy meter through an interface to read freezing event, load curve and voltage loss record key data inside the electric energy meter so as to form a preliminary data set containing the circuit real-time waveform and the electric energy meter internal record data; The topology diagnosis module is used for calculating the comprehensive error of the electric energy meter and the current transformer based on the collected preliminary data set and combining the transformation ratio and the polarity parameter of the current transformer, judging the accuracy, carrying out harmonic analysis on the collected line real-time waveform, and identifying the abnormal electric load characteristics; the report uploading module is used for detecting that the terminal uploads the preliminary data set, the wiring error type and the electric quantity correction coefficient to the cloud marketing system through the 4G network; the cloud marketing system receives and stores the report, forms a user electricity file, and automatically generates or updates the audit work order.

Description

Electric energy meter data acquisition and management method and system based on voltage non-contact anti-electricity-stealing detection Technical Field The invention relates to the technical field of data acquisition of electric energy meters, in particular to an electric energy meter data acquisition and management method and system based on voltage non-contact anti-electricity-stealing detection. Background The traditional electric energy meter data acquisition needs to be opened for checking or lead sealing is damaged, and the problems of high operation risk, low efficiency and easiness in legal disputes exist. The electricity stealing behavior detection relies on manual investigation, and wiring errors, such as 96 error types of three-phase four-wire or internal events, such as voltage loss and uncovering record, are difficult to find in real time. The voltage sampling, the comprehensive error analysis and the event record extraction cannot be synchronously completed on the premise of non-contact, so that the anti-electricity-stealing response is lagged, and the data management is fragmented. In the prior art, the application number CN202311655996.5 discloses an electricity consumption information acquisition system of a special transformer acquisition terminal, which comprises an electric energy meter data acquisition module, a state quantity acquisition module, a pulse quantity acquisition test module and a data transmission module, wherein the electric energy meter data acquisition module acquires electricity consumption data from power equipment of a special transformer user in real time, the pulse quantity acquisition test module can effectively calculate errors during data acquisition again, the errors can be reduced through calculation, the accuracy of the data is further improved, services are provided through encryption services and SSAL services through two different PF_UNIX interfaces, only security management APP can access ESAM after the system is started, and the security starting mainly comprises system security starting and application APP security starting, and the system security starting is carried out through signature and signature verification of a system mirror image. The application APP safety starting effectively improves the safety of data and prevents the leakage of the data through the signature and signature verification inspection of the application APP, but the error detection is single, namely the error is calculated only through a pulse quantity acquisition test module, the comprehensive error evaluation is carried out without combining the transformation ratio, the polarity parameter and the harmonic analysis of the current transformer, and the metering accuracy is difficult to be comprehensively reflected. The topology diagnosis is lacking, the connection topology matching and the electric quantity correction of the electric energy meter are not involved, and the connection errors such as phase sequence misconnection, transformer inverse pole and the like cannot be identified. The data utilization limitation is that although the data safety is emphasized, the collected data is not deeply combined with the cloud management system, and an inspection work order or an optimized electricity file cannot be automatically generated. In the second prior art, application number CN202411252195.9 discloses an electric energy meter operation data processing method and system, the system comprises an electric energy meter circuit measurement data acquisition module, an electric energy meter circuit measurement compensation parameter analysis module and an electric energy meter circuit measurement data processing module, and the circuit instantaneous power consumption measurement superposition compensation quantity parameters are counted through numerical analysis science, so that comprehensive calculation of circuit instantaneous power consumption measurement compensation quantities under different environment temperature states, power supply frequency change states and load current change states is realized. The method and the device improve the accuracy of operation data processing of the electric energy meter, carry out instantaneous actual power consumption numerical statistics of the circuit based on instantaneous power consumption parameters of the circuit and instantaneous power consumption measurement superposition compensation quantity parameters of the circuit, scientifically and efficiently analyze the instantaneous actual power consumption parameters of the circuit and feed the parameters back to the power management platform in real time through the communication network of the Internet of things, improve the quality and reliability of data acquisition of the electric energy meter, but limit environmental compensation, namely carry out instantaneous power consumption compensation only for temperature, frequency and load changes, and do not involve detection of non-tech