An intelligent anti-theft electricity detection method for an electric energy metering box

Abstract

This invention relates to the field of electricity metering and monitoring technology, and particularly to an intelligent anti-theft detection method for electricity meter boxes. Using the radio frequency front-end in an existing HPLC communication module, impedance characteristic data of the physical channel is reported through a manufacturer-defined field reserved in the heartbeat message; a dynamic impedance baseline that changes with load is established; when the impedance of a node shows a significant decrease relative to the baseline without a corresponding change in load, it is marked as a suspected electricity theft node, and the system switches to active frequency scanning mode for multi-frequency point verification; simultaneously, adjacent electricity meters within the same meter box are scheduled to perform collaborative impedance acquisition as a spatial reference, and the presence of pre-meter bypass electricity theft is determined by comparison. This invention provides an intelligent anti-theft detection method for electricity meter boxes that can effectively identify pre-meter low-resistance bypass electricity theft without adding independent detection hardware, and also has a certain degree of suppression effect on false alarms caused by legitimate topology changes in the transformer area.

Description

Technical Field

[0001] This invention relates to the field of electricity metering and monitoring technology, and in particular to an intelligent anti-theft detection method for electricity meter boxes. Background Technology

[0002] With the advancement of automation and informatization in power distribution, smart meters have been widely deployed in low-voltage distribution areas. As terminal equipment for electricity trade settlement, the metering accuracy and operational safety of electricity meters directly affect the revenue management of power supply companies. Among the various causes of electricity loss, human intervention in metering has always been a key focus of anti-theft efforts. In recent years, common methods such as strong magnetic interference and short-circuiting current loops have been gradually identified by evidence collection methods. They have been replaced by a more concealed type of pre-meter bypass theft: a low-resistance wire is privately connected between the meter's incoming line and the neutral line, allowing part of the load current to bypass the meter's internal metering circuit, thus physically circumventing conventional electrical metering.

[0003] Existing anti-electricity theft technologies can be broadly categorized into two types: passive monitoring based on electrical parameters and active detection using independent detection hardware. For example, patent CN117092405A attempts to capture transient waveform changes at the moment of electricity theft by increasing the voltage sampling frequency and introducing differential operations. However, in actual power distribution areas, the contact resistance of the bypass before the meter is usually small, having limited impact on the user-end voltage and easily masked by normal grid fluctuations and harmonics generated by nonlinear loads. Furthermore, higher sampling frequencies and differential operations consume computational resources of the meter's main control MCU, hindering long-term monitoring under low-power conditions.

[0004] To address the limitations of passive monitoring methods, some studies have attempted to introduce active detection mechanisms. For example, patent CN111983304B, which involves adding a dummy load or independent impedance measurement sensor to the electricity meter to poll and detect the line status. While this approach improves sensitivity, it increases the hardware cost per meter, hindering upgrades to existing equipment. Furthermore, because detection is limited to a single node, legitimate reactive power compensation switching or on-load tap regulation in the distribution area can easily lead to misinterpretations of impedance changes at a single node as electricity theft. Simultaneous alarms from multiple meters in the same distribution area can also cause clustered false alarms, increasing the workload of on-site inspections.

[0005] In summary, reducing the false alarm rate of electricity theft detection and improving the ability to detect concealed pre-meter bypasses without significantly increasing the hardware modification cost of individual meters is a practical engineering challenge currently faced by the power distribution side. Given the widespread deployment of high-speed broadband power line carrier (HPLC) communication modules, how to achieve multi-node collaborative detection and reduce blind spots and false alarms in single-node detection by utilizing the characteristics of existing communication modules without adding new hardware is a problem that needs to be solved in the field of anti-electricity theft. Summary of the Invention

[0006] To achieve the above-mentioned objectives, this invention provides an intelligent anti-theft detection method for electricity meter boxes, which aims to solve or at least reduce the problems of traditional solutions' reliance on additional hardware, large computing power consumption, and false alarms caused by single-node detection being easily affected by legitimate topology changes.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent anti-theft detection method for an electricity meter box, comprising the following steps: Collect impedance characteristic data of the physical channel of the power line where the electricity meter is located; Establish a dynamic impedance baseline for the energy meter, align the load data of the current period with historical data, and compare the collected impedance characteristic data with the dynamic impedance baseline in real time. If the comparison results show a step decrease in the impedance characteristic data, the electricity meter is marked as a suspected electricity theft node. The impedance data of adjacent energy meters in the same metering box is collected synchronously and used as spatial reference data. By comparing the impedance change characteristics of the suspected electricity theft node with the spatial reference data, it is determined whether there is any electricity theft behavior via pre-meter bypass.

[0008] To further realize the present invention, the following technical solutions may be preferred: Preferably, the acquisition and multiplexing of the impedance characteristic data utilizes the RF front-end with directional coupling measurement capability or its automatic gain control feedback in an existing HPLC communication module, including: The main control unit of the electricity meter periodically sends communication heartbeat messages according to the networking protocol; While the HPLC communication module transmits the heartbeat message, it collects the forward and reverse power envelopes through the directional coupling branch, or calculates the impedance characteristic data of the current physical channel through automatic gain control feedback and channel quality indicator parameters.

[0009] Preferably, the method further includes an impedance data reporting step: The calculated impedance characteristic data is compressed and encoded, then written into the manufacturer-defined field reserved in the heartbeat message, and reported to the metering box concentrator along with the heartbeat message, without occupying a separate communication time slot.

[0010] Preferably, the step of establishing a dynamic impedance baseline includes: The system continuously receives historical impedance characteristic data reported by the energy meter and aligns it with the active power and reactive power data of the same period. The recursive least squares method with a forgetting factor is used to fit the impedance baseline that varies with the load, and the normal fluctuation range of the node under each load segment is estimated simultaneously.

[0011] Preferably, the condition for determining that the impedance characteristic data shows a step decrease is: The currently acquired impedance characteristic data deviates from the dynamic impedance baseline by more than a preset threshold, and no load change matching this magnitude is detected within the corresponding time slice.

[0012] Preferably, after marking the electricity meter as a suspected electricity theft node, the method further includes a state switching step: The metering concentrator pauses the normal data acquisition process of the suspected electricity theft node and issues an instruction to switch its HPLC communication module to active frequency sweep mode.

[0013] Preferably, the active frequency sweep mode specifically includes: The suspected electricity theft node injects several test carriers with different center frequencies into its inbound branch line in sequence, and collects impedance response data at each frequency point to form multi-frequency impedance data, so as to avoid misjudgment caused by the resonance of user-side electrical appliances in a single frequency band.

[0014] Preferably, the spatial reference data acquisition step includes: When the suspected electricity theft node executes the active frequency sweep mode, the metering box concentrator sends a collaborative acquisition command containing a reference timestamp and a node-specific offset time slice to the adjacent electricity meters in the same metering box that have not been marked as abnormal. When the adjacent electricity meters reach their assigned dedicated offset time slot, they each trigger an impedance acquisition action and report the results to the concentrator to obtain the spatial reference dataset within the meter box.

[0015] Preferably, the step of determining whether electricity theft via pre-meter bypass has occurred specifically includes: The multi-frequency impedance data of the suspected electricity theft nodes are compared with the spatial reference dataset; If only the suspected electricity theft node shows a significant impedance drop across multiple frequency points, while the impedance of the adjacent electricity meters remains within the normal fluctuation range of their respective baselines, then it is determined to be electricity theft via pre-meter bypass, and an alarm record containing a timestamp is generated.

[0016] Preferably, the method further includes a baseline correction step: If the comparison results show that the adjacent electricity meter also shows an impedance decrease that is synchronous in time and consistent in trend with the suspected electricity theft node, it is determined to be a legitimate topology change or external interference in the public power grid of the distribution area. Remove the markings on the suspected electricity theft nodes and incorporate the data on the current collective impedance change into the correction of the dynamic impedance baseline; release each node from its suspended state and resume normal data acquisition procedures.

[0017] The beneficial effects of this invention are: The existing RF front-end with directional coupling measurement capability in the HPLC communication module is reused, or its AGC feedback and CQI parameters are used to calculate impedance, and data is reported through reserved fields in the heartbeat message. Without adding independent detection hardware or changing the original communication timing, a dynamic baseline of "load-time-impedance" is established at the edge. Combined with active frequency scanning of suspected nodes, it has high sensitivity to low-impedance bypass power theft before the meter, reducing the hardware modification cost per meter.

[0018] By synchronously acquiring the impedance of adjacent nodes within the same metering box, single-node monitoring is extended to multi-node collaborative comparison within the box. When legitimate topology changes occur, such as switching of reactive power compensation in transformer areas or starting of large loads, multiple nodes will exhibit a consistent impedance change trend. The system can then cancel alarms and correct baselines accordingly, effectively suppressing false alarms caused by legitimate topology changes and reducing on-site inspection costs. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method logic of the present invention.

[0020] Figure 2 This is a schematic diagram of the overall hardware topology of the present invention. Detailed Implementation

[0021] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This method is implemented based on the existing HPLC communication network in the low-voltage distribution area, requiring no hardware modification to the electricity meter or metering box, nor the installation of a separate impedance measurement sensor. The system operates by leveraging the computing resources of existing equipment and the radio frequency characteristics of the HPLC module. Through state machine scheduling and multi-node data comparison, it achieves the perception and discrimination of changes in the upstream circuit. The following sections describe the hardware architecture, methodology, and experimental verification.

[0024] Example 1 The hardware structure of this invention relies on the HPLC communication network of a standard low-pressure station. For example... Figure 2 As shown, the system hardware includes edge computing nodes deployed on the low-voltage side of the transformer substation or at the aggregation point of multi-meter boxes, and smart energy meters installed at each user's door. A broadband carrier communication network based on OFDM modulation is constructed between the two using a 220V AC power line as the physical medium.

[0025] The mainboard of the electricity meter integrates a dedicated metering chip, a main control MCU, an HPLC communication module, and non-volatile memory. The dedicated metering chip uses a multi-channel high-precision ADC to discretely sample the grid voltage and current, and outputs electrical quantities such as active power, reactive power, RMS voltage, and power factor. The main control MCU runs an embedded real-time operating system, responsible for local data storage, task scheduling, and protocol interaction with the upper-layer network.

[0026] The HPLC communication module is the hardware foundation for this method to obtain physical channel impedance information. Specifically, in the analog RF front-end of the module, an impedance matching network is typically connected in series between the power amplifier (PA) output and the power line coupling transformer. In HPLC chip solutions with directional coupling branches (e.g., using the Zhixinwei TXC043 series or equivalent broadband carrier communication chips with high-speed analog front-ends), the forward and reverse power envelopes can be obtained without adding external components. In solutions without directional coupling branches, the equivalent impedance of the current channel can be indirectly calculated through the chip's own AGC feedback or CQI parameters. Since different manufacturers' HPLC chips differ in their implementation of this part, the implementation of the method of this invention can choose one of the acquisition methods according to the actual hardware capabilities.

[0027] Edge computing nodes are either distribution area concentrators or metering box concentrators, equipped with multi-core processors, DRAM, and Flash storage, enabling them to process concurrent data from multiple nodes. The concentrator maintains a "time-load-impedance" time-series record in memory for each connected energy meter and sends synchronization commands with millisecond-level timestamps to each node via the HPLC downlink channel to align the acquisition times of each node during spatial reference acquisition.

[0028] Example 2 like Figure 1 As shown, the detection process of this method is divided into four stages: impedance data acquisition, dynamic baseline establishment and deviation detection, active frequency sweeping of suspected nodes, and multi-node comparison and judgment. The stages are switched through a state machine.

[0029] Phase 1: Impedance Data Acquisition. During steady-state operation in the area without electricity theft incidents, the main control MCU of the electricity meter periodically sends keep-alive heartbeat messages according to the HPLC networking protocol. Within the millisecond time window when the HPLC module pushes the message to the power line channel via the RF front-end, the MCU reads the forward and reverse power sample values ​​(or AGC feedback values) through the register interface provided by the module. Based on this, it calculates the voltage standing wave ratio or equivalent reflection coefficient of the current channel, and then converts it into the equivalent impedance value using transmission line theory. Alternatively, for modules without directional coupling branches, it uses a pre-calibrated and locally stored mapping lookup table (LUT) between the receiver AGC feedback stage and the equivalent impedance modulus, combined with the read AGC stage and CQI parameters, to calculate the impedance characteristic modulus value within the current physical frequency band using a linear interpolation algorithm. The entire process does not occupy additional message transmission time slots.

[0030] The calculated impedance value, after being compressed and encoded in hexadecimal, is written into the HPLC heartbeat message. Using an object-oriented data exchange protocol (such as DL / T 698.45), it is encapsulated under a reserved custom object identifier (OAD) and reported to the concentrator along with the heartbeat message. Since each acquisition is performed synchronously with the regular heartbeat, it does not trigger independent packet transmission, thus having a minimal impact on the existing network communication load.

[0031] Phase Two: Dynamic Baseline Establishment and Deviation Detection. The concentrator parses the messages reported by each node, extracts the impedance data, and aligns it with the active and reactive power reported by the node within the same time slice. The concentrator runs a recursive least squares algorithm with a forgetting factor for each node, using load as the independent variable and impedance as the dependent variable to establish a first-order linear regression model for fitting, obtaining a dynamic baseline that changes with the load. Due to the switching of nonlinear loads such as switching power supplies, frequency converters, and LED drivers in the existing 220V network, this baseline itself has a certain normal fluctuation range. While fitting the data, the algorithm simultaneously estimates the fluctuation range of the node under different load segments.

[0032] The concentrator periodically subtracts the latest acquired impedance value from the baseline expected value under the corresponding load to obtain the residual. When the absolute value of the residual exceeds a preset threshold (usually several times the baseline fluctuation range) for several consecutive cycles, and no load change matching the residual amplitude is observed during the same period, the concentrator marks the node as a suspected electricity theft node.

[0033] Phase Three: Active Frequency Sweep and Spatial Reference Acquisition. The concentrator sends a switching command to the suspected node via the downlink channel. The MCU of the suspected node suspends its normal message transmission and switches the HPLC module to active frequency sweep mode. The module sequentially injects several test carriers with preset center frequencies into the incoming branch line, measuring the reflection coefficient and calculating the impedance at each frequency point to form a set of multi-frequency impedance data. The purpose of multi-frequency scanning is to avoid misinterpreting impedance anomalies caused by certain household appliances (such as resonant ballasts or certain capacitors) on specific frequency bands as electricity theft.

[0034] While the suspected node performs frequency scanning, the concentrator sends a collaborative acquisition command based on Time Division Multiplexing (TDMA) to adjacent energy meters in the same metering box that are functioning normally. This command includes a global reference timestamp and a millisecond-level offset time slice allocated to each adjacent node. Upon receiving the command, each adjacent node triggers an impedance acquisition action (using the normal acquisition method) when it reaches its designated time slice, to avoid physical channel collisions caused by multiple nodes simultaneously injecting measurement signals. The results are then reported in the next heartbeat cycle. The concentrator aggregates the above data to obtain spatial impedance reference data within the meter box area.

[0035] Phase Four: Comprehensive Judgment and Baseline Correction. The concentrator compares the multi-frequency impedance data of suspected nodes with the reference data of adjacent nodes. If only the suspected node shows a significant impedance drop at multiple frequencies, while the impedance of adjacent nodes remains within the normal fluctuation range relative to their own baseline, it is determined to be electricity theft via pre-meter bypass. An alarm record is generated containing information such as the electricity meter asset number, MAC address, impedance change time, and multi-frequency data. After encryption, this record is stored in the concentrator's read-only area for retrieval by the main station's auditing system.

[0036] If the comparison results show that adjacent nodes also exhibit impedance drops that are synchronous in time and similar in magnitude, it is determined to be caused by a legitimate topology change in the public power grid of the distribution area (such as reactive power compensation switching, on-load tap change, large load startup, etc.) or strong external electromagnetic interference. At this time, the concentrator removes the marking of the suspected node, incorporates the collective impedance change data collected in this round as a new sample into the baseline correction, expands the tolerance range of the baseline, and then issues a release command, and each node resumes normal data collection.

[0037] Example 3 Experimental Verification To calibrate the threshold parameters in this method and verify its engineering feasibility, a simplified distribution area verification environment was set up in a low-voltage power distribution laboratory. The environment included a distribution area concentrator connected to a four-position metal metering box containing four single-phase smart meters, numbered A, B, C, and D. The main trunk cable used 16 mm² copper core cable, and the branch cables to each meter used 6 mm² copper core cable, approximately 15 meters in length. HPLC communication operated in the OFDM band from 2.0 MHz to 12.0 MHz.

[0038] It should be noted that the electromagnetic background and load disturbances in the laboratory environment are significantly weaker than those in the actual power grid. The following data are used to illustrate the behavior of this method under relatively controlled conditions. The impedance fluctuation amplitude in the actual transformer area will be larger than that in the experimental environment, as detailed below: I. Baseline Data Acquisition. With only basic test loads connected to each electricity meter, the verification environment was run continuously for approximately 72 hours to accumulate baseline data. Statistical results show that within the 2MHz to 12MHz OFDM band, the mean equivalent impedance and its short-term fluctuations at each node are as follows: Table A: mean approximately 33.8 ohms, short-term fluctuation (1-minute window standard deviation) approximately 0.9 ohms; Table B: mean approximately 32.6 ohms, short-term fluctuation approximately 1.1 ohms; Table C: mean approximately 35.4 ohms, short-term fluctuation approximately 1.0 ohm; Table D: mean approximately 34.3 ohms, short-term fluctuation approximately 0.8 ohms. At longer timescales (hourly), influenced by changes in the skin effect of the distribution network and fluctuations in the dielectric constant of cable insulation caused by temperature and humidity, the baseline exhibits a slow drift of approximately ±2 ohms. This variation has been incorporated into the baseline fitting using a recursive least squares algorithm.

[0039] II. Legal Load Input Test. Around the 75th hour, a resistive electric heating load with a rated power of approximately 2500W was connected to the load side of Table A. The active power reported by Table A increased from a light load state to approximately 2480W (with a measurement deviation of approximately 1% from the nominal value). Its equivalent impedance gradually decreased from around 33.8 ohms to approximately 30.1 ohms within 2-3 heartbeat cycles after connection. Because the impedance decrease and the active power step change were synchronized in time, and their amplitude relationship fell within the baseline range pre-learned by the algorithm, the concentrator did not mark this event as abnormal and included this observation point in the baseline update of Table A.

[0040] III. Meter Bypass Electricity Theft Test. Under experimental conditions, a low-resistance wire of approximately 0.6 meters with a cross-sectional area of ​​4 square millimeters was connected between the inlet and neutral terminals of meter B to simulate meter bypass behavior. During the connection process, the load side of meter B maintained a stable load of approximately 180W. After the bypass wire was connected, the impedance value reported by meter B's subsequent heartbeats dropped from the steady-state baseline (approximately 32.6 ohms) to around 8.5 ohms, a decrease of approximately 24 ohms compared to the baseline, exceeding the algorithm's preset threshold (in this example, a multiple of the baseline standard deviation or a relative amplitude of 30%). Simultaneously, the active power reported by meter B remained around 180W, without a load increase matching this impedance change. Based on this, the concentrator marked meter B as a suspected electricity theft node.

[0041] After the concentrator issues the switching command, meter B enters active frequency sweep mode. Since the bypass wire primarily exhibits inductive reactance characteristics at high frequencies (inductive reactance increases with frequency), its equivalent impedance after parallel connection with the original line shows significant frequency-varying characteristics. Meter B was checked sequentially at three center frequencies: 3.0MHz, 6.5MHz, and 11.0MHz, with measured equivalent impedances of approximately 9.5 ohms, 17.2 ohms, and 23.8 ohms, respectively. Compared to its original baseline of 32.6 ohms, it shows a significant drop in impedance at low frequencies, while exhibiting a non-linear upward trend at high frequencies. This characteristic of a sharp drop at low frequencies and a non-linear increase in inductive reactance at high frequencies due to parasitic capacitance further confirms from a physics perspective that the anomaly is caused by unauthorized connection of a metal wire leading to pre-meter bypass power theft, resulting in a significant decrease across a wide frequency range. Simultaneously, the concentrator issued a synchronous acquisition command, triggering impedance acquisition for meters A, C, and D at the same time. The returned results were as follows: meter A approximately 29.9 ohms (consistent with its updated baseline, still connected to a 2500W load), meter C approximately 35.1 ohms, and meter D approximately 34.0 ohms, all within the normal fluctuation range of their respective baselines. Combining multi-frequency data and spatial reference data, the concentrator determined that meter B had experienced pre-meter bypass power theft and generated an alarm record.

[0042] IV. Legitimate Topology Change Test. After removing the bypass wire from meter B and allowing the system to return to steady state, a 20kVar reactive power compensation capacitor bank was connected to the low-voltage side of the simulated distribution area. At the moment of connection, the equivalent impedance reported by all four meters showed a significant decrease: meter A dropped to approximately 14.8 ohms, meter B to approximately 13.5 ohms, meter C to approximately 15.7 ohms, and meter D to approximately 14.1 ohms. Due to slight differences in the length of the branch cables and the terminal contact resistance at each meter location, the absolute value of the impedance decrease exhibited a reasonable dispersion, but the four meters were basically synchronized in time (the time difference was within the concentrator's time synchronization accuracy range), and no change in their respective active power matched this magnitude. During the initial screening stage, all four meters were temporarily marked as suspected abnormalities.

[0043] After entering the comparison phase, the concentrator identified a high degree of consistency in the impedance drop of the four meters in terms of time, direction, and relative amplitude, classifying it as a legitimate topology change at the transformer substation level. It then removed the abnormal markers from the four meters and incorporated the data from this collective impedance change into the baseline update, expanding the baseline tolerance for the corresponding load segment. Each node resumed normal data acquisition procedures after receiving the unsuspend command. No electricity theft alarms were output during this round of testing, verifying that this method has a certain false alarm suppression capability under legitimate topology change scenarios.

[0044] In real low-voltage distribution areas, due to the complexity of the background load, the impedance fluctuation amplitude will increase significantly, and various thresholds should be calibrated based on the existing network data.

[0045] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent anti-theft detection of electricity meter boxes, characterized in that, Includes the following steps: Collect impedance characteristic data of the physical channel of the power line where the electricity meter is located; Establish a dynamic impedance baseline for the energy meter, align the load data of the current period with historical data, and compare the collected impedance characteristic data with the dynamic impedance baseline in real time. If the comparison results show a step decrease in the impedance characteristic data, the electricity meter is marked as a suspected electricity theft node. The impedance data of adjacent energy meters in the same metering box is collected synchronously and used as spatial reference data. By comparing the impedance change characteristics of the suspected electricity theft node with the spatial reference data, it is determined whether there is any electricity theft behavior via pre-meter bypass.

2. The intelligent anti-theft detection method for an electricity meter box according to claim 1, characterized in that, The impedance characteristic data acquisition and multiplexing utilizes the RF front-end with directional coupling measurement capability or its automatic gain control feedback in the existing HPLC communication module, including: The main control unit of the electricity meter periodically sends communication heartbeat messages according to the networking protocol; While the HPLC communication module transmits the heartbeat message, it collects the forward and reverse power envelopes through the directional coupling branch, or calculates the impedance characteristic data of the current physical channel through automatic gain control feedback and channel quality indicator parameters.

3. The intelligent anti-theft detection method for an electricity meter box according to claim 2, characterized in that, It also includes the impedance data reporting step: The calculated impedance characteristic data is compressed and encoded, then written into the manufacturer-defined field reserved in the heartbeat message, and reported to the metering box concentrator along with the heartbeat message, without occupying a separate communication time slot.

4. The intelligent anti-theft detection method for an electricity meter box according to claim 1, characterized in that, The steps for establishing a dynamic impedance baseline include: The system continuously receives historical impedance characteristic data reported by the energy meter and aligns it with the active power and reactive power data of the same period. The recursive least squares method with a forgetting factor is used to fit the impedance baseline that varies with the load, and the normal fluctuation range of the node under each load segment is estimated simultaneously.

5. The intelligent anti-theft detection method for an electricity meter box according to claim 4, characterized in that, The condition for determining that the impedance characteristic data shows a step decrease is: The currently acquired impedance characteristic data deviates from the dynamic impedance baseline by more than a preset threshold, and no load change matching this magnitude is detected within the corresponding time slice.

6. The intelligent anti-theft detection method for an electricity meter box according to claim 1, characterized in that, After marking the electricity meter as a suspected electricity theft node, the method also includes a state switching step: The metering concentrator pauses the normal data acquisition process of the suspected electricity theft node and issues an instruction to switch its HPLC communication module to active frequency sweep mode.

7. The intelligent anti-theft detection method for an electricity meter box according to claim 6, characterized in that, The active frequency sweeping mode is specifically as follows: The suspected electricity theft node injects several test carriers with different center frequencies into its inbound branch line in sequence, and collects impedance response data at each frequency point to form multi-frequency impedance data, so as to avoid misjudgment caused by the resonance of user-side electrical appliances in a single frequency band.

8. A method for intelligent anti-theft detection of electricity meter boxes according to claim 1 or 7, characterized in that, The steps for acquiring the spatial reference data include: When the suspected electricity theft node executes the active frequency sweep mode, the metering box concentrator sends a collaborative acquisition command containing a reference timestamp and a node-specific offset time slice to the adjacent electricity meters in the same metering box that have not been marked as abnormal. When the adjacent electricity meters reach their assigned dedicated offset time slot, they each trigger an impedance acquisition action and report the results to the concentrator to obtain the spatial reference dataset within the meter box.

9. A method for intelligent anti-theft detection of an electricity meter box according to claim 8, characterized in that, The steps for determining whether electricity theft via pre-meter bypass have occurred are as follows: The multi-frequency impedance data of the suspected electricity theft nodes are compared with the spatial reference dataset; If only the suspected electricity theft node shows a significant impedance drop across multiple frequency points, while the impedance of the adjacent electricity meters remains within the normal fluctuation range of their respective baselines, then it is determined to be electricity theft via pre-meter bypass, and an alarm record containing a timestamp is generated.

10. The intelligent anti-theft detection method for an electricity meter box according to claim 9, characterized in that, The method also includes a baseline correction step: If the comparison results show that the adjacent electricity meter also shows an impedance decrease that is synchronous in time and consistent in trend with the suspected electricity theft node, it is determined to be a legitimate topology change or external interference in the public power grid of the distribution area. Remove the markings on the suspected electricity theft nodes and incorporate the data on the current collective impedance change into the correction of the dynamic impedance baseline; release each node from its suspended state and resume normal data acquisition procedures.

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