Anti-electricity theft analysis method and device

Abstract

The present invention discloses an anti-electricity theft analysis method and device, which relates to the field of anti-electricity theft analysis technology. The method includes: deploying a wideband electromagnetic sensor network to collect magnetic field strength and calculate energy density in real time, dynamically triggering an adaptive notch filter to suppress interference signals, and combining fundamental frequency matching to achieve precise filtering; using strain sensors to monitor box deformation, optimizing deformation calculation through Kalman filtering, and activating an electromagnetic locking device to protect metering equipment; using RFID electronic seals combined with Euclidean distance matching of image feature points to determine seal integrity and trigger a high-sensitivity mode in the event of an anomaly; and in intrusion detection, adjusting the YOLOv5 model confidence threshold, fusing infrared and millimeter-wave radar data to extract motion trajectories, combining Kalman filtering to predict target paths, and generating a blockchain-based protection log. The present invention achieves active defense and rapid response to electricity theft, effectively improving the security of metering equipment and the efficiency of anti-electricity theft.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-electricity theft analysis, and in particular to an anti-electricity theft analysis method and device. Background Art

[0002] With the development of intelligent power systems, electricity theft has become more technical and covert. Existing technologies often use electromagnetic interference devices to disrupt the signals of metering equipment, resulting in distorted energy metering data or equipment failure. However, traditional anti-theft devices often rely on single-sensor monitoring, lacking the ability to dynamically suppress wide-band electromagnetic interference, making them difficult to adapt to the precise detection requirements in complex electromagnetic environments.

[0003] A Chinese patent application with publication number CN104933272B discloses a method and device for analyzing electricity theft prevention, including: obtaining abnormal electricity usage events and corresponding event times for a target user based on records in an electricity usage information collection system. Abnormal electricity usage events include meter operation events and meter abnormality events; when a meter operation event is followed by a meter abnormality event, the target user is determined to be suspected of electricity theft. This method analyzes a large amount of historical data in the electricity usage information collection system to identify specific sequence patterns associated with electricity theft, and then determines whether the user is suspected of electricity theft. This method can improve the effectiveness and pertinence of using the electricity usage information collection system to prevent and investigate electricity theft, fully leveraging the electricity usage information collection system's role in electricity theft prevention and improving data utilization.

[0004] In the above-mentioned prior art, a large amount of historical data in the electricity consumption information collection system is analyzed to find certain specific sequence patterns related to electricity theft behavior, and then determine whether the user is suspected of electricity theft. However, it lacks comprehensive consideration of electromagnetic interference and signal interference. Electricity thieves can use electromagnetic interference equipment to generate strong electromagnetic fields to disrupt the anti-electricity theft detection device, causing it to malfunction or cause metering errors. At the same time, the anti-electricity theft analysis device has limited safety and sealing performance and is easily damaged, and cannot effectively protect the metering equipment. Therefore, it is necessary to provide an anti-electricity theft analysis method and device to solve the above-mentioned problems. Summary of the Invention

[0005] In order to solve the above technical problems, a method and device for anti-electricity theft analysis are provided. This technical solution solves the problem that the existing technology proposed in the above background technology lacks comprehensive consideration of electromagnetic interference and signal interference. Electricity thieves can use electromagnetic interference equipment to generate strong electromagnetic fields, disrupting the anti-electricity theft detection device, causing it to fail to work normally or causing metering errors. At the same time, the anti-electricity theft analysis device has limited safety and sealing performance, is easily damaged, and cannot effectively protect the metering equipment.

[0006] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0007] An anti-electricity theft analysis method, comprising:

[0008] S1 obtains the target user's metering equipment environment information, and the target user's metering equipment environment information as a benchmark for deploying a sensor network;

[0009] S2. The wide-band electromagnetic sensors in the sensor network collect the magnetic field strength of the power supply line in real time, and simultaneously obtain the fundamental frequency of the metering equipment during operation to determine the magnetic field energy density. Then, based on the magnetic field energy density, it is determined whether to trigger the adaptive notch filter. If the adaptive notch filter is triggered, the adaptive notch filter dynamically generates a frequency suppression mask and outputs a signal X. clean (t);

[0010] S3. Receive the signal X output by the adaptive notch filter clean (t), and extract its effective value and phase difference, and then determine whether to activate the box protection system based on the effective value and phase difference;

[0011] S4. Obtain the switch status signal in the box protection system and read the RSSI value and image feature points of the RFID electronic seal. Then, based on the RSSI value and image feature points, determine whether the seal is abnormal. If the seal is abnormal, the intrusion detection module is triggered;

[0012] S5. After the intrusion detection module is triggered, it enters high-sensitivity mode, adjusts the confidence threshold of the YOLOv5 model, and extracts the motion trajectory of the intruder through the fusion perception of the infrared camera and millimeter-wave radar in the sensor network. Based on the motion trajectory of the intruder, a protection log is generated and stored in the blockchain.

[0013] In an optional embodiment, step S2 specifically includes:

[0014] The magnetic field strength B(t) of the power supply line is collected in real time through a wide-band electromagnetic sensor array, and the fundamental frequency f0 of the metering equipment during operation is simultaneously obtained;

[0015] The wide-band electromagnetic sensor collects the magnetic field strength of the power supply line in real time to obtain the magnetic field energy density E(t);

[0016] When the magnetic field energy density E(t)>E threshold At (f0), the adaptive notch filter is triggered to synchronously acquire the original electrical signal, and then the original electrical signal is input into the adaptive notch filter for filtering;

[0017] Get the frequency suppression mask M(f) dynamically generated by the adaptive notch filter and synchronously determine the output signal X of the adaptive notch filter clean(t), where E threshold (f0) is the threshold function;

[0018] The calculation formula of the magnetic field energy density E(t) is:

[0019] Where E(t) is the magnetic field energy density of the power supply line at time t, is the magnetic field strength of the power supply line at time t, is the integral variable, representing the arrive Every moment within the time period, is the preset time window length;

[0020] The threshold function E threshold The expression formula of (f0) is:

[0021] Where, and are frequency band coefficients, It is the fundamental frequency of the measuring equipment when it is in operation;

[0022] The output signal X of the adaptive notch filter clean The expression formula of (t) is:

[0023] ;

[0024] Where, is the signal after interference suppression output by the adaptive notch filter at time t, is the original electrical signal input into the adaptive notch filter at time t, is the number of interference frequency bands, is the frequency band index, For the The filtered value of the original electrical signal that needs to be filtered, For the first A binary function of the filtered value of the original electrical signal to be filtered.

[0025] In an optional embodiment, step S3 specifically includes:

[0026] Receive the signal X output by the adaptive notch filter clean (t), through , extract X clean (t) The corresponding effective value ,in, is the equivalent DC voltage value of the original electrical signal on the resistor, is the period for calculating the effective value;

[0027] pass , extract X clean (t) The corresponding phase difference ,in, is the active power corresponding to the original electrical signal at time t, is the reactive power corresponding to the original electrical signal at time t;

[0028] Setting the effective value limit threshold using the anti-theft analysis device , Phase difference limit threshold and box shape variable threshold , when the anti-theft analysis device detects The fluctuation range exceeds or phase difference Greater than When the cabinet protection system is activated;

[0029] Obtaining the box shape variables through strain sensors in the sensor network , if the box shape variable Greater than the box shape variable threshold , then the electromagnetic locking device is started and an encrypted control instruction is sent to the actuator;

[0030] Among them, the box shape The calculation formula is:

[0031] Where, is the deformation length, is the original length.

[0032] In an optional embodiment, step S4 specifically includes:

[0033] Get the switch status signal S in the box protection system lock , and read the RSSI value and image feature points of the RFID electronic seal;

[0034] The RSSI limit threshold and seal abnormality judgment threshold are set by the anti-theft analysis device. When the switch state signal is equal to 1 and the RSSI value of the RFID electronic seal is less than the RSSI limit threshold set by the anti-theft analysis device, the Euclidean distance of the image feature point is obtained;

[0035] If the Euclidean distance corresponding to the image feature point is greater than the seal abnormality judgment threshold, the seal is determined to be abnormal and a seal abnormality signal is issued simultaneously. When the anti-theft analysis device receives the seal abnormality signal, the intrusion detection module is activated and the high-sensitivity mode is used;

[0036] The calculation formula of the Euclidean distance of the image feature points is:

[0037] ; Where d is the geometric distance between two feature points in space, is the i-th dimension coordinate of the seal feature point to be measured, is the i-th dimension coordinate of the database template feature point, and n is the number of dimensions of the feature point.

[0038] In an optional embodiment, step S5 specifically includes:

[0039] After triggering the intrusion detection module, enter the high-sensitivity mode and adjust the confidence threshold of the YOLOv5 model: ,in, is the Euclidean distance of the image feature points, is the adjustment coefficient, is the default threshold, is the adjusted threshold;

[0040] Through the fusion perception of infrared camera and millimeter wave radar, the motion trajectory (x(t), y(t)) of the intrusion target is extracted;

[0041] According to the motion trajectory (x(t), y(t)) of the intrusion target, the moving speed v(t) of the intrusion target is obtained;

[0042] The anti-theft analysis device sets the number of intrusion judgment reference frames N and the movement speed limit threshold. If N consecutive frames satisfy v(t) greater than the movement speed limit threshold, the following is executed:

[0043] S5.1. Send an alarm message to the target user's mobile terminal, the alarm message includes the intrusion target coordinates (x curr ,y curr ) and predicted path ;

[0044] S5.2. Activate the sound and light deterrent device and remotely lock the meter communication port, generating a protection log ID event =Hash(S lock ,RSSI,d), and store the protection log in the blockchain;

[0045] The calculation formula of the moving speed v(t) is:

[0046] ;

[0047] Where, is the x-axis coordinate value of the intrusion target at time t in the image perceived by the infrared camera and millimeter-wave radar fusion, is the y-axis coordinate value of the intrusion target at time t in the image perceived by the infrared camera and millimeter-wave radar.

[0048] In an optional embodiment, the frequency band coefficients of the threshold function are determined by offline training and satisfy:

[0049] in, is the peak energy of electromagnetic interference during the i-th training, is the total number of training times.

[0050] Furthermore, an anti-electricity theft analysis device is proposed, for implementing any of the above analysis methods, comprising:

[0051] An electromagnetic interference processing unit comprising a wideband sensor array and an adaptive notch filter, supporting dynamic generation of frequency suppression masks;

[0052] A physical protection controller, which integrates a strain sensor and an electromagnetic locking device, is used to receive the signal output by the adaptive notch filter and extract its effective value and phase difference. Then, based on the effective value and phase difference, it determines whether to activate the box protection system;

[0053] The seal verification module is equipped with an RFID reader and an image processor to obtain the switch status signal in the box protection system and read the RSSI value and image feature points of the RFID electronic seal. It then determines whether the seal is abnormal based on the RSSI value and image feature points;

[0054] An intelligent processing terminal equipped with a millimeter-wave radar and an edge computing chip to extract the movement trajectory of the intruder;

[0055] The intrusion detection module is used to generate a protection log based on the movement trajectory of the intrusion target and store the protection log in the blockchain.

[0056] In an optional embodiment, the electromagnetic interference processing unit communicates with the physical protection controller via a PCIe bus, with a transmission delay of <1ms, meeting the real-time requirement of the signal output by the adaptive notch filter in step S2.

[0057] In an optional embodiment, the seal verification module has a built-in security chip, and performs SM4 encryption on the protection log generated in step S5, and the key update cycle is 24 hours.

[0058] Furthermore, an anti-electricity theft system is proposed, for implementing any of the above devices, specifically comprising:

[0059] An interference prediction unit, which uses historical data of magnetic field energy density on a cloud platform to train an interference prediction model and outputs a threshold function for a future period;

[0060] A secondary verification unit, after the mobile terminal receives the alarm information in step S5, can remotely trigger the RFID electronic seal in step S4 to perform a secondary verification instruction.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] This proposal proposes an anti-electricity theft analysis method and device. Using a wide-band sensor array to collect magnetic field strength signals in real time, the device simultaneously acquires the grid's fundamental frequency parameters, calculates the magnetic field energy density, and dynamically generates a frequency suppression mask. This precisely shields against strong electromagnetic interference injected by electricity theft devices, enabling real-time monitoring of the power line's magnetic field strength and efficient filtering of interference signals. Dynamically matching the interference frequency band with a threshold function avoids metering errors or failures caused by electromagnetic interference.

[0063] This proposal proposes an anti-electricity theft analysis method and device that uses strain sensors to monitor the box's deformation in real time. This, combined with a Kalman filter algorithm to optimize deformation calculation, dynamically triggers the electromagnetic locking device. When the box's deformation exceeds a preset threshold, the system immediately activates a protective mechanism, controlling the actuator to lock the device via encrypted instructions. This submillimeter-level deformation monitoring and real-time response improves the box's impact resistance, effectively deterring thieves from damaging the box.

[0064] This solution proposes an anti-electricity theft analysis method and device. By dynamically adjusting the transmission power of the RFID reader and writer and combining it with the Euclidean distance matching of image feature points, it can achieve real-time monitoring of the seal status and identification of tampering traces. By storing the seal status hash value on the blockchain, it ensures that the tamper evidence chain cannot be tampered with, thus solving the technical pain points of traditional seal verification lag and single-modal detection that is easily bypassed. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of an anti-electricity theft analysis method proposed by the present invention;

[0066] Figure 2 A flow chart for dynamically generating a frequency suppression mask and an output signal for the adaptive notch filter of the present invention;

[0067] Figure 3 This is a flow chart for determining whether a seal is abnormal in the present invention;

[0068] Figure 4 This is a module framework diagram of an anti-electricity theft analysis device proposed in the present invention;

[0069] Figure 5 This is a system framework diagram of an anti-electricity theft analysis system proposed in the present invention. DETAILED DESCRIPTION

[0070] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0071] Reference Figure 1 - Figure 5 As shown, an anti-electricity theft analysis method includes:

[0072] S1. Obtain the target user's metering equipment environment information, and deploy the sensor network based on the target user's metering equipment environment information;

[0073] S2. The wide-band electromagnetic sensors in the sensor network collect the magnetic field strength of the power supply line in real time, and simultaneously obtain the fundamental frequency of the metering equipment during operation to determine the magnetic field energy density. Then, based on the magnetic field energy density, it is determined whether to trigger the adaptive notch filter. If the adaptive notch filter is triggered, the adaptive notch filter dynamically generates a frequency suppression mask and outputs a signal X. clean (t);

[0074] S3. Receive the signal X output by the adaptive notch filter clean (t), and extract its effective value and phase difference, and then determine whether to activate the box protection system based on the effective value and phase difference;

[0075] S4. Obtain the switch status signal in the box protection system and read the RSSI value and image feature points of the RFID electronic seal. Then, based on the RSSI value and image feature points, determine whether the seal is abnormal. If the seal is abnormal, the intrusion detection module is triggered;

[0076] S5. After the intrusion detection module is triggered, it enters the high-sensitivity mode, adjusts the confidence threshold of the YOLOv5 model, and extracts the motion trajectory of the intruder target through the fusion perception of the infrared camera and millimeter-wave radar in the sensor network. Based on the motion trajectory of the intruder target, it generates a protection log and stores the protection log in the blockchain.

[0077] Specifically, in step S1, the target user's metering equipment's environmental information is obtained. Based on this information, a sensor network is deployed. Surveyors systematically survey the surrounding electromagnetic, vibration, and temperature parameters of the metering equipment. Furthermore, relevant personnel deploy a multimodal sensing network composed of broadband electromagnetic sensors, vibration acceleration sensors, and infrared thermal imaging modules. This network, combined with LoRaWAN networking technology, enables dynamic data collection, forming a real-time monitoring system covering key areas of the enclosure. This allows for accurate identification of magnetic field interference, abnormal deformation, and intrusion behavior, improving the environmental adaptability and detection reliability of the anti-theft device. Sensors in the sensor network are installed in optimal locations, transmitting data and signals via transmission lines or wireless communications.

[0078] Furthermore, step S2 specifically includes:

[0079] The magnetic field strength B(t) of the power supply line is collected in real time through a wide-band electromagnetic sensor array, and the fundamental frequency f0 of the metering equipment during operation is simultaneously obtained;

[0080] The wide-band electromagnetic sensor collects the magnetic field strength of the power supply line in real time to obtain the magnetic field energy density E(t);

[0081] When the magnetic field energy density E(t)>E threshold At (f0), the adaptive notch filter is triggered to synchronously acquire the original electrical signal, and then the original electrical signal is input into the adaptive notch filter for filtering;

[0082] Get the frequency suppression mask M(f) dynamically generated by the adaptive notch filter and synchronously determine the output signal X of the adaptive notch filter clean (t), where E threshold (f0) is the threshold function;

[0083] Among them, the calculation formula of magnetic field energy density E(t) is:

[0084] Where E(t) is the magnetic field energy density of the power supply line at time t, is the magnetic field strength of the power supply line at time t, is the integral variable, representing the arrive Every moment within the time period, is the preset time window length;

[0085] Threshold function E threshold The expression formula of (f0) is:

[0086] Where, and are frequency band coefficients, It is the fundamental frequency of the measuring equipment when it is in operation;

[0087] The output signal X of the adaptive notch filter clean The expression formula of (t) is:

[0088] Where, is the signal after interference suppression output by the adaptive notch filter at time t, is the original electrical signal input into the adaptive notch filter at time t, is the number of interference frequency bands, is the frequency band index, For the The filtered value of the original electrical signal that needs to be filtered, For the first A binary function of the filtered value of the original electrical signal to be filtered.

[0089] Furthermore, step S3 specifically includes:

[0090] Receive the signal X output by the adaptive notch filter clean (t), through , extract X clean (t) The corresponding effective value ,in, is the equivalent DC voltage value of the original electrical signal on the resistor, is the period for calculating the effective value;

[0091] pass , extract X clean (t) The corresponding phase difference ,in, is the active power corresponding to the original electrical signal at time t, is the reactive power corresponding to the original electrical signal at time t;

[0092] Setting the effective value limit threshold using the anti-theft analysis device , Phase difference limit threshold and box shape variable threshold , when the anti-theft analysis device detects The fluctuation range exceeds or phase difference Greater than When the cabinet protection system is activated;

[0093] Obtaining the box shape variables through strain sensors in the sensor network , if the box shape variable Greater than the box shape variable threshold , then the electromagnetic locking device is started and an encrypted control instruction is sent to the actuator;

[0094] Among them, the box shape variable The calculation formula is:

[0095] Where, is the deformation length, is the original length.

[0096] Specifically, the raw electrical signal is the source of data for anti-theft system analysis, covering multi-dimensional information such as current, voltage, phase, frequency, and harmonics. Strain sensors (such as resistance strain gauges and fiber optic strain sensors) output electrical signals by measuring tiny deformations (tension or compression) on the surface of an object. Their core data is the deformation variable. The raw data (deformation variable) required by the strain sensor to obtain the box deformation variable needs to be filtered by Kalman filtering, and its state equation is:

[0097] Where, is the process noise when processing the deformation at time k, is the observation noise when processing the deformation at time k, is the final deformation at time k, It is the original deformation directly measured by the strain sensor.

[0098] Furthermore, step S4 specifically includes:

[0099] Get the switch status signal S in the box protection system lock , and read the RSSI value and image feature points of the RFID electronic seal;

[0100] The RSSI limit threshold and seal abnormality judgment threshold are set by the anti-theft analysis device. When the switch state signal is equal to 1 and the RSSI value of the RFID electronic seal is less than the RSSI limit threshold set by the anti-theft analysis device, the Euclidean distance of the image feature point is obtained;

[0101] If the Euclidean distance corresponding to the image feature point is greater than the seal abnormality judgment threshold, the seal is determined to be abnormal and a seal abnormality signal is issued simultaneously. When the anti-theft analysis device receives the seal abnormality signal, the intrusion detection module is activated and the high-sensitivity mode is used;

[0102] Among them, the calculation formula of the Euclidean distance of image feature points is:

[0103] Where, is the geometric distance between two feature points in space, is the i-th dimension coordinate of the seal feature point to be measured, is the i-th dimension coordinate of the database template feature point, and n is the number of dimensions of the feature point.

[0104] Specifically, the database template feature points are the original images of the measuring equipment and electronic seals (such as QR codes). Different measuring equipment and electronic seals will have different templates. These templates are stored in the database. The RFID reader adopts a dynamic power control strategy. The transmission power P RF satisfy:

[0105] in, 30dBm, 20dBm, 10DBm.

[0106] It is understandable that Indicates the minimum received signal strength at which the RFID reader can maintain reliable communication. When the reader detects that the RSSI returned by the tag is lower than this threshold, it considers the communication quality unacceptable (for example, the signal is too weak or the tag is too far away). is between and maximum RSSI value When the intermediate intensity threshold When , the reader may maintain or slightly adjust the transmission power (for example, gradually reduce the power to save energy). When the reader is in a state of low power, the transmission power can be further reduced to reduce interference and power consumption.

[0107] Furthermore, step S5 specifically includes:

[0108] After triggering the intrusion detection module, enter the high-sensitivity mode and adjust the confidence threshold of the YOLOv5 model: ,in, is the Euclidean distance of the image feature points, is the adjustment coefficient, is the default threshold, is the adjusted threshold;

[0109] Through the fusion perception of infrared camera and millimeter wave radar, the motion trajectory (x(t), y(t)) of the intrusion target is extracted;

[0110] According to the motion trajectory of the intrusion target (x(t), y(t)), the moving speed v(t) of the intrusion target is obtained;

[0111] The anti-theft analysis device sets the number of intrusion judgment reference frames N and the movement speed limit threshold. If N consecutive frames satisfy v(t) greater than the movement speed limit threshold, the following is executed:

[0112] S5.1. Send an alarm message to the target user's mobile terminal, including the intrusion target coordinates (x curr ,y curr ) and predicted path ;

[0113] S5.2. Activate the sound and light deterrent device and remotely lock the meter communication port, generating a protection log ID event =Hash(S lock ,RSSI,d), and store the protection log in the blockchain;

[0114] The calculation formula of the moving speed v(t) is:

[0115] ;

[0116] Where, is the x-axis coordinate value of the intrusion target at time t in the image perceived by the infrared camera and millimeter-wave radar fusion, is the y-axis coordinate value of the intrusion target at time t in the image perceived by the infrared camera and millimeter-wave radar.

[0117] Specifically, the intrusion target coordinates (x curr ,y curr ) and predicted path Using the Kalman filter recursive formula:

[0118] ;

[0119] in, is the predicted coordinate value of the intrusion target at time k, is the state transfer matrix at time k, is the Kalman gain at time k, is the observation matrix at time k, is the compensation signal at time k (such as the temperature sensor reading), is the control input matrix, used to associate external control signals (such as temperature compensation), To dynamically adjust the weights of predicted and measured values, The original coordinates directly collected by the sensor.

[0120] It is understandable that the infrared camera and millimeter wave radar fusion perception adopts a weighted fusion strategy:

[0121]

[0122] in, is the data obtained by fusion perception of infrared camera and millimeter wave radar at time t, is the data collected by the infrared camera at time t, is the data collected by the millimeter-wave radar at time t, is the temperature weight;

[0123] The temperature weight is dynamically adjusted according to the ambient temperature. When the temperature at time t is less than 25 degrees, the temperature weight is 0.7. When the temperature at time t is greater than or equal to 25 degrees, the temperature weight is 0.4.

[0124] Furthermore, the frequency band coefficients of the threshold function are determined through offline training, satisfying:

[0125] ;in, is the peak energy of electromagnetic interference during the i-th training, is the total number of training times.

[0126] Furthermore, an anti-electricity theft analysis device is proposed, which is used to implement any of the above analysis methods, including:

[0127] Electromagnetic interference processing unit, which includes a wide-band sensor array and an adaptive notch filter, supporting dynamic generation of frequency suppression masks;

[0128] The physical protection controller integrates a strain sensor and an electromagnetic locking device to receive the signal output by the adaptive notch filter and extract its effective value and phase difference. It then determines whether to activate the box protection system based on the effective value and phase difference.

[0129] Seal verification module, equipped with an RFID reader and image processor, is used to obtain the switch status signal in the box protection system and read the RSSI value and image feature points of the RFID electronic seal. It then determines whether the seal is abnormal based on the RSSI value and image feature points;

[0130] Intelligent processing terminal, equipped with millimeter-wave radar and edge computing chip, is used to extract the movement trajectory of intrusion targets;

[0131] Intrusion detection module,The intrusion detection module is used to generate protection logs based on the movement trajectory of the intrusion target and store the protection logs in the blockchain.

[0132] Furthermore, the electromagnetic interference processing unit communicates with the physical protection controller via the PCIe bus, with a transmission delay of <1ms, meeting the real-time requirement of the signal output by the adaptive notch filter in step S2.

[0133] Furthermore, the seal verification module has a built-in security chip, which performs SM4 encryption on the protection log generated in step S5, and the key update cycle is 24 hours.

[0134] Furthermore, an anti-electricity theft system is proposed, for implementing any of the above devices, specifically comprising:

[0135] Interference prediction unit: The interference prediction unit uses historical data of magnetic field energy density on the cloud platform to train an interference prediction model and output a threshold function for the future period;

[0136] The secondary verification unit can remotely trigger the RFID electronic seal in step S4 after the mobile terminal receives the alarm information in step S5 to perform a secondary verification instruction.

[0137] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing anti-electricity theft, characterized in that: include: S1 obtains the target user's metering equipment environment information, and the target user's metering equipment environment information as a benchmark for deploying a sensor network; S2. The wide-band electromagnetic sensors in the sensor network collect the magnetic field strength of the power supply line in real time, and simultaneously obtain the fundamental frequency of the metering equipment during operation to determine the magnetic field energy density. Then, based on the magnetic field energy density, it is determined whether to trigger the adaptive notch filter. If the adaptive notch filter is triggered, the adaptive notch filter dynamically generates a frequency suppression mask and outputs a signal X. clean (t); S3. Receive the signal X output by the adaptive notch filter clean (t), and extract its effective value and phase difference, and then determine whether to activate the box protection system based on the effective value and phase difference; S4. Obtain the switch status signal in the box protection system and read the RSSI value and image feature points of the RFID electronic seal. Then, based on the RSSI value and image feature points, determine whether the seal is abnormal. If the seal is abnormal, the intrusion detection module is triggered; S5. After triggering the intrusion detection module, enter high-sensitivity mode, adjust the confidence threshold of the YOLOv5 model, and extract the motion trajectory of the intruder through the fusion perception of the infrared camera and millimeter-wave radar in the sensor network. Based on the motion trajectory of the intruder, a protection log is generated and stored in the blockchain. Step S2 specifically includes: The magnetic field strength B(t) of the power supply line is collected in real time through a wide-band electromagnetic sensor array, and the fundamental frequency f0 of the metering equipment during operation is simultaneously obtained; The wide-band electromagnetic sensor collects the magnetic field strength of the power supply line in real time to obtain the magnetic field energy density E(t); When the magnetic field energy density E(t)>E threshold At (f0), the adaptive notch filter is triggered to synchronously acquire the original electrical signal, and then the original electrical signal is input into the adaptive notch filter for filtering; Get the frequency suppression mask M(f) dynamically generated by the adaptive notch filter and synchronously determine the output signal X of the adaptive notch filter clean (t), where E threshold (f0) is the threshold function; The calculation formula of the magnetic field energy density E(t) is: Where E(t) is the magnetic field energy density of the power supply line at time t, is the magnetic field strength of the power supply line at time t, is the integral variable, representing the arrive Every moment within the time period, is the preset time window length; The threshold function E threshold The expression formula of (f0) is: Where, and are frequency band coefficients, It is the fundamental frequency of the measuring equipment when it is running; The output signal X of the adaptive notch filter clean The expression formula of (t) is: Where, is the signal after interference suppression output by the adaptive notch filter at time t, is the original electrical signal input into the adaptive notch filter at time t, is the number of interference frequency bands, is the frequency band index, For the The filtered value of the original electrical signal that needs to be filtered, For the first A binary function of the filtered value of the original electrical signal to be filtered; step S3 specifically includes: Receive the signal X output by the adaptive notch filter clean (t), through , extract X clean (t) The corresponding effective value ,in, is the equivalent DC voltage value of the original electrical signal on the resistor, is the period for calculating the effective value; pass , extract X clean (t) The corresponding phase difference ,in, is the active power corresponding to the original electrical signal at time t, is the reactive power corresponding to the original electrical signal at time t; Setting the effective value limit threshold using the anti-theft analysis device , Phase difference limit threshold and box shape variable threshold , when the anti-theft analysis device detects The fluctuation range exceeds or phase difference Greater than When the cabinet protection system is activated; Obtaining the box shape variables through strain sensors in the sensor network , if the box shape variable Greater than the box shape variable threshold , then the electromagnetic locking device is started and an encrypted control instruction is sent to the actuator; Among them, the box shape The calculation formula is: Where, is the deformation length, is the original length.

2. The anti-electricity theft analysis method according to claim 1, characterized in that: Step S4 specifically includes: Get the switch status signal S in the box protection system lock , and read the RSSI value and image feature points of the RFID electronic seal; The RSSI limit threshold and seal abnormality judgment threshold are set by the anti-theft analysis device. When the switch state signal is equal to 1 and the RSSI value of the RFID electronic seal is less than the RSSI limit threshold set by the anti-theft analysis device, the Euclidean distance of the image feature point is obtained; If the Euclidean distance corresponding to the image feature point is greater than the seal abnormality judgment threshold, the seal is determined to be abnormal and a seal abnormality signal is issued simultaneously. When the anti-theft analysis device receives the seal abnormality signal, the intrusion detection module is activated and the high-sensitivity mode is used; The calculation formula of the Euclidean distance of the image feature points is: Where, is the geometric distance between two feature points in space, is the i-th dimension coordinate of the seal feature point to be measured, is the i-th dimension coordinate of the database template feature point, and n is the number of dimensions of the feature point.

3. The anti-electricity theft analysis method according to claim 1, characterized in that: Step S5 specifically includes: After triggering the intrusion detection module, enter the high-sensitivity mode and adjust the confidence threshold of the YOLOv5 model: ,in, is the Euclidean distance of the image feature points, is the adjustment coefficient, is the default threshold, is the adjusted threshold; Through the fusion perception of infrared camera and millimeter wave radar, the motion trajectory (x(t), y(t)) of the intrusion target is extracted; According to the motion trajectory (x(t), y(t)) of the intrusion target, the moving speed v(t) of the intrusion target is obtained; The anti-theft analysis device sets the number of intrusion judgment reference frames N and the movement speed limit threshold. If N consecutive frames satisfy v(t) greater than the movement speed limit threshold, the following is executed: S5.

1. Send an alarm message to the target user's mobile terminal, the alarm message includes the intrusion target coordinates (x curr ,y curr ) and predicted path ; S5.

2. Activate the sound and light deterrent device and remotely lock the meter communication port, generating a protection log ID event =Hash(S lock ,RSSI,d), and store the protection log in the blockchain; The calculation formula of the moving speed v(t) is: ； Where, is the x-axis coordinate value of the intrusion target at time t in the image perceived by the infrared camera and millimeter-wave radar fusion, is the y-axis coordinate value of the intrusion target at time t in the image perceived by the infrared camera and millimeter-wave radar.

4. The anti-electricity theft analysis method according to claim 1, characterized in that: The frequency band coefficients of the threshold function are determined by offline training and satisfy: in, is the peak energy of electromagnetic interference during the i-th training, is the total number of training times.

5. An anti-electricity theft analysis device, used to implement the analysis method according to any one of claims 1 to 4, characterized in that: include: An electromagnetic interference processing unit comprising a wideband sensor array and an adaptive notch filter, supporting dynamic generation of frequency suppression masks; A physical protection controller, which integrates a strain sensor and an electromagnetic locking device, is used to receive the signal output by the adaptive notch filter and extract its effective value and phase difference. Then, based on the effective value and phase difference, it determines whether to activate the box protection system; The seal verification module is equipped with an RFID reader and an image processor to obtain the switch status signal in the box protection system and read the RSSI value and image feature points of the RFID electronic seal. It then determines whether the seal is abnormal based on the RSSI value and image feature points; An intelligent processing terminal equipped with a millimeter-wave radar and an edge computing chip to extract the movement trajectory of the intruder; The intrusion detection module is used to generate a protection log based on the movement trajectory of the intrusion target and store the protection log in the blockchain.

6. The anti-electricity theft analysis device according to claim 5, characterized in that: The electromagnetic interference processing unit communicates with the physical protection controller via the PCIe bus, with a transmission delay of <1ms, meeting the real-time requirement of the signal output by the adaptive notch filter in step S2.

7. The anti-electricity theft analysis device according to claim 5, characterized in that: The seal verification module has a built-in security chip, which performs SM4 encryption on the protection log generated in step S5, and the key update cycle is 24 hours.

8. An anti-electricity theft system, used to implement the device according to any one of claims 5 to 7, characterized in that: Specifically include: An interference prediction unit, which uses historical data of magnetic field energy density on a cloud platform to train an interference prediction model and outputs a threshold function for a future period; A secondary verification unit, after the mobile terminal receives the alarm information in step S5, can remotely trigger the RFID electronic seal in step S4 to perform a secondary verification instruction.

Images