Power transmission line protection system and method for preventing line hanging of large unmanned aerial vehicle

Abstract

The invention relates to the technical field of power transmission line safety protection, and discloses a power transmission line protection system and method for preventing line hanging of a large unmanned aerial vehicle, and the system comprises a multi-source sensing module, an intelligent recognition and decision module, an early warning interception module, and a data storage and evidence obtaining module. The multi-source sensing module adopts a frequency spectrum, vision and track multi-source sensing fusion technology; the intelligent identification and decision module is used for target identification, electronic identity verification and flight risk level evaluation; the early warning interception module executes grading processing according to the risk grade; and the data storage and evidence obtaining module is used for data archiving. The invention provides a corresponding method. The method comprises the steps of multi-source data acquisition, authorization verification and multi-mode identification, risk assessment and grading disposal, disposal effect verification and the like. According to the method, a closed-loop protection process of identification, early warning, interception and evidence obtaining is constructed, authorized and unauthorized large unmanned aerial vehicles can be accurately distinguished, and line hanging accidents are effectively prevented while legal operation is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power transmission line safety protection technology, specifically to a power transmission line protection system and method for preventing large drones from snagging power lines. Background Technology

[0002] Currently, the application of large drones in agriculture, logistics and other fields is surging. These drones are large and have high payloads. If they accidentally enter the corridor of power transmission lines, they can easily cause snagging or collisions, leading to line tripping or even large-scale power outages, threatening the safety of the power system.

[0003] Among existing airspace protection technologies for power transmission lines, some solutions rely on optical monitoring for visual inspection, while others use radar scanning to detect targets. During response, when a threat is detected, some devices will activate radio frequency jamming devices, forcing drones to return to base or land via broadband signals.

[0004] Existing technologies have shortcomings. Single identification methods are susceptible to severe weather and struggle to distinguish target types, frequently misidentifying birds or small drones. Furthermore, they cannot verify electronic identities, potentially intercepting legitimate power line inspection drones. The response methods are simplistic; the widely used broad radio frequency suppression lacks specificity and cannot effectively deter low- to medium-risk targets, potentially causing drones to malfunction or generate electromagnetic pollution. The lack of a data loop, with monitoring, identification, and response processes fragmented, hinders effective recording of the entire intrusion incident and impedes subsequent tracing and accountability.

[0005] Therefore, the present invention provides a power line protection system and method for preventing large drones from snagging power lines, in order to overcome the shortcomings of the prior art. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a power line protection system and method for preventing large drones from snagging power lines, which solves the problems of existing technologies such as limited drone protection measures, insufficient identification accuracy, susceptibility to electromagnetic interference, and lack of tiered handling capabilities.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The first aspect of this invention provides a power line protection system for preventing large drones from snagging power lines, comprising:

[0009] The multi-source sensing module is used to perceive airspace targets around the transmission line in real time and collect multi-source sensing data.

[0010] The intelligent identification and decision-making module is used to receive the multi-source perception data, perform target identification and flight risk level assessment, and generate decision instructions;

[0011] The early warning and interception module is used to receive the decision instructions and execute graded handling measures according to the flight risk level;

[0012] The data storage and evidence collection module is used to store identification results and processing records in real time.

[0013] By adopting the above technical solution, this invention constructs a closed-loop protection system encompassing perception, identification, decision-making, interception, and evidence collection. The four modules work collaboratively to achieve precise protection against high-risk targets while avoiding unnecessary interference with non-threatening targets, thus improving the intelligence level of airspace protection for transmission lines.

[0014] Preferably, the multi-source sensing module includes: a spectrum detection unit, employing software-defined radio technology, for capturing the communication signals of the UAV and resolving the UAV's identification; a visual monitoring unit, employing a dual-mode configuration of a high-definition visible light camera and an infrared thermal imager, for acquiring high-definition visible light images and infrared thermal images; and a trajectory tracking unit, based on a Kalman filter algorithm, for fusing spectrum signals and visual coordinates to calculate the three-dimensional position, flight speed, heading, and straight-line distance to the power transmission line in real time.

[0015] By adopting the above technical solutions, SDR technology improves the accuracy of spectrum identification; the dual-mode visual configuration ensures all-weather visual acquisition capabilities; and the application of the Kalman filter algorithm provides high-precision trajectory data for subsequent risk assessment.

[0016] Preferably, the intelligent identification and decision-making module includes: a whitelist management subunit, used to verify whether the drone's identity is an authorized target; a multimodal fusion identification subunit, used to construct a deep learning model, used to fuse multi-source features, and determine whether the target is a large drone; and a risk assessment subunit, used to generate low, medium, and high flight risk levels based on the motion trajectory data and whether it is an authorized target, and to enter risk level alert when the distance of the drone is less than a first preset distance threshold.

[0017] By adopting the above technical solutions, the whitelist mechanism exempts legitimate operations; the deep learning model improves the recognition accuracy of large drones; and the 50m distance threshold provides a quantitative trigger boundary for risk warning.

[0018] Preferably, the early warning and interception module includes: a dynamic electronic fence subunit, used to send obstacle avoidance instructions via aviation signal broadcasting equipment when the risk is medium; a high-volume broadcast warning subunit, used to play warning voice messages using directional speakers when the risk is medium or higher; and a directional ion-drive unit, used to emit directional interference signals using radio frequency interference technology when a high-risk target enters the warning range of the third preset distance threshold, and has built-in interference threshold control logic to automatically stop interference when the authorized UAV is detected.

[0019] By adopting the above technical solutions, the dynamic electronic fence achieves flexible obstacle avoidance guidance; the directional speaker achieves precise voice warnings; and the directional interference technology is activated only within a 20m core danger range and can automatically avoid authorized drones, reducing electromagnetic collateral effects.

[0020] A second aspect of the present invention provides a method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs), comprising the following steps:

[0021] S1. System initialization and parameter configuration, completing parameter calibration and whitelist data import;

[0022] S2. Real-time acquisition of multi-source data: The multi-source sensing module continuously monitors and acquires spectrum signal data, visual image data, and motion trajectory data.

[0023] S3. Authorized target verification and multimodal recognition: The intelligent recognition and decision-making module verifies the electronic identity and performs multimodal recognition on unauthorized targets to determine whether they are large drones.

[0024] S4. Risk Assessment and Tiered Response: The intelligent identification and decision-making module generates a risk level, and the early warning and interception module executes tiered response accordingly.

[0025] Low risk: Only data is recorded, without triggering intervention;

[0026] Medium risk: Initiate early warning and evacuation measures;

[0027] High risk: Initiate mandatory intervention;

[0028] S5. Verification of treatment effects and data archiving: Continuously monitor the target's movement status and store treatment process data in real time.

[0029] By adopting the above technical solution, this method provides a complete closed-loop protection process. The graded handling steps in S4 precisely match the risk level with the handling methods, achieving resource optimization and minimizing interference. The effect verification and archiving in S5 ensure that the threat is eliminated and provide a data chain for post-event traceability.

[0030] Preferably, in step S4, the medium-risk handling is triggered when the distance of the unauthorized large drone is less than a first predetermined distance and greater than a second predetermined distance; the high-risk handling is triggered when the distance of the unauthorized large drone is less than the second predetermined distance, wherein the first predetermined distance is greater than the second predetermined distance.

[0031] By adopting the above technical solutions, distance thresholds of 30m to 50m and less than 30m provide a quantitative basis for handling medium and high risks, ensuring the timeliness of the handling.

[0032] Preferably, in step S3, when the verification result conflicts with the result of the multimodal recognition, a secondary verification is triggered, and the motion trajectory data is called to determine whether the target conforms to the authorized operation trajectory.

[0033] By adopting the above technical solution and performing secondary verification using trajectory data, the problem of conflicting multi-source perception data is solved, and the misjudgment rate of authorized targets is reduced.

[0034] Preferably, in step S4, when the system detects multiple large drones intruding simultaneously, they are handled in order of risk level, with priority given to driving away the target closest to the power transmission line.

[0035] By adopting the above technical solutions and sorting by risk level and distance, the multi-target intrusion scenario was resolved, ensuring that the highest threat targets were dealt with first.

[0036] Preferably, in step S4, if the flight trajectory of the unauthorized large drone points to the power transmission line conductor, the situation is directly upgraded to a high-risk situation.

[0037] By adopting the above technical solutions, a risk assessment dimension based on trajectory and heading is added, and the response speed to malicious or out-of-control targets is improved by judging the intrusion intent.

[0038] Preferably, in step S5, if the high-risk target is not evacuated within the set repeated treatment cycle, the mandatory intervention is triggered repeatedly.

[0039] By adopting the above technical solutions, a repeated treatment cycle is set for targets where a single intervention is ineffective, ensuring the closed-loop and effectiveness of the treatment until the threat is eliminated.

[0040] This invention provides a power line protection system and method for preventing large drones from snagging power lines. It has the following beneficial effects:

[0041] 1. This invention integrates a spectrum detection unit, a visual monitoring unit, and a trajectory tracking unit through a multi-source sensing module to comprehensively acquire airspace target data. Based on this, the intelligent identification and decision-making module, through a whitelist management subunit, a multimodal fusion identification subunit, and a risk assessment subunit, achieves accurate identification and dynamic risk assessment of large unmanned aerial vehicles (UAVs). This enables the system to quickly distinguish between legitimate operational UAVs and potential threats, and dynamically adjust the risk level according to the threat severity, providing an accurate basis for subsequent graded handling and effectively avoiding misjudgments and unnecessary interference.

[0042] 2. This invention employs a tiered, coordinated response mechanism based on real-time risk levels, utilizing an early warning and interception module. In medium-risk situations, the dynamic electronic fence subunit sends obstacle avoidance commands via aviation signal broadcasting equipment, while the high-volume broadcast warning subunit plays warning messages to dissuade the target from entering. When a high-risk target enters the warning range, the directional ion-drive unit uses radio frequency interference technology to emit directional interference signals and incorporates built-in interference threshold control logic to automatically cease interference upon detecting an authorized drone. This invention further refines the response logic for risk trigger distance, trajectory escalation, and repeated mandatory intervention. This tiered response mechanism, from flexible warnings to rigid interception, ensures the accuracy and safety of the response, effectively intervening in drone operations without causing physical damage and safeguarding power transmission lines.

[0043] 3. The data storage and evidence collection module of this invention is used to store identification results and handling records in real time. Combined with dual backups on local hard drives and the cloud, and AES-256 encryption, the system can completely record data from target perception, identification, decision-making to handling. This invention further emphasizes continuous monitoring of target movement and real-time storage of handling process data. This provides reliable and objective evidence for post-event review, liability determination, legal evidence collection, and system performance evaluation and optimization, ensuring the transparency and auditability of the entire protection process. Attached Figure Description

[0044] Figure 1 This is a functional diagram of the power transmission line protection system of the present invention;

[0045] Figure 2 This is a flowchart of the power transmission line protection method of the present invention. Detailed Implementation

[0046] The following is in conjunction with the appendix Figure 1 -Appendix Figure 2 The present invention will be further described in detail below.

[0047] See attached document Figure 1 This invention provides a power line protection system to prevent large drones from snagging power lines, comprising: a multi-source sensing module, an intelligent identification and decision-making module, an early warning and interception module, and a data storage and evidence collection module.

[0048] The multi-source sensing module is used to perceive airspace targets around power transmission lines in real time, and to collect the target's spectrum signal data, visual image data, and motion trajectory data.

[0049] The intelligent identification and decision-making module is connected to the data output end of the multi-source perception module. It is used to receive the collected data, perform target electronic identity verification, multimodal feature recognition, flight risk level assessment, and generate decision instructions based on the assessment results.

[0050] The early warning and interception module is connected to the instruction output terminal of the intelligent identification and decision-making module. It is used to receive decision instructions and, according to the corresponding risk level, execute graded disposal measures such as dynamic electronic fences, high-volume broadcast warnings, or directional radio frequency interference to drive away the vehicle.

[0051] The data storage and evidence collection module is connected to the aforementioned multi-source sensing module, intelligent identification and decision-making module, and early warning and interception module. It is used to store multi-source sensing data, identification results, decision records, and handling records in real time, and supports encrypted backup of audio and video data for post-event tracing and legal evidence collection.

[0052] The multi-source sensing module includes a spectrum detection unit, a visual monitoring unit, and a trajectory tracking unit.

[0053] Spectrum Detection Unit: Employing SDR technology, covering the 2.4GHz and 5.8GHz frequency bands. This unit is used to capture communication signals and parse electronic IDs, communication protocols, and signal strength. Specifically, the signal acquisition distance is no less than 1km, and the protocol parsing response time is no greater than 0.3s. This unit can use the FFT algorithm combined with a large-scale UAV communication protocol library to achieve rapid protocol matching.

[0054] Visual monitoring unit: Employs a dual-mode configuration of a high-definition visible light camera (4K resolution) and an infrared thermal imager. This unit uses a 360° rotatable gimbal to complete a panoramic scan every 5 seconds. It acquires visual images and incorporates a feature extraction algorithm to identify parameters such as fuselage length and number of rotors. When the fuselage length is not less than 1.5m and the number of rotors is not less than 4, it is preliminarily identified as a large agricultural drone. In one implementation, the unit incorporates a feature template library and uses edge detection and contour matching algorithms for identification.

[0055] Trajectory tracking unit: Based on the Kalman filter algorithm. This unit integrates spectral signal intensity positioning with visual target coordinates to output the target's 3D position, flight speed, heading, and straight-line distance to the power transmission line in real time. When the target distance is no greater than 100m, the trajectory sampling frequency is increased to 20Hz.

[0056] The intelligent identification and decision-making module includes a whitelist management subunit, a multimodal fusion identification subunit, and a risk assessment subunit.

[0057] The whitelist management subunit is used to establish a database for power line inspection drones, storing their electronic IDs, affiliated units, operating hours, and operating airspace. This subunit supports remote dynamic modification of whitelist data, with an update delay of no more than 10 seconds. When an electronic ID is captured, this subunit compares it in real time; if a match is found and the target is within the operating hour, it is determined to be a safe target.

[0058] Multimodal fusion recognition subunit: Constructing a Transformer-based deep learning model. This model takes spectral electronic ID features and visual features as input, strengthens feature weights through an attention mechanism, identifies large drones, and eliminates interference from small drones. Specifically, the model training dataset contains over 1000 large drone samples and over 5000 interfering target samples.

[0059] Risk assessment sub-unit: Based on the distance between the target and the transmission line, the deviation of the flight trajectory, and whether authorization is granted, generate three levels of risk: low, medium, and high.

[0060] Judgment criteria:

[0061] Low risk: Authorized targets, or unauthorized large drones, but at a distance of not less than 50m and with far-fetched trajectories;

[0062] Medium risk: Unauthorized large drones, within 30-50m, or with a close trajectory;

[0063] High risk: Unauthorized large drones, within 30m, or whose flight path points towards a guide wire (path deviation greater than 60°).

[0064] The risk level calculation cycle is no more than 1 second, and the results are output in real time.

[0065] Misjudgment Correction Subunit: In a preferred embodiment, the module further includes a misjudgment correction subunit. When the spectrum conflicts with the visual recognition result, this subunit calls the trajectory tracking data for secondary verification to determine whether the target conforms to the authorized operation trajectory. The verification time is no more than 1 second.

[0066] The early warning and interception module includes a dynamic electronic fence subunit, a high-volume broadcast early warning subunit, and a directional ion-driving unit.

[0067] Dynamic electronic fence sub-unit: Integrates ADS-B signal broadcasting equipment to simulate temporary no-fly zone messages compliant with the RTCADO-260B standard, sending obstacle avoidance instructions to compliant drones within a 5km radius. For example, in medium-risk situations, it automatically generates a virtual no-fly zone with a radius of 50m centered on the power transmission line, and drones will detour after receiving the instruction.

[0068] High-volume broadcast warning subunit: Employs a directional speaker. This subunit adjusts the speaker's direction based on the target's location, playing preset warning messages for high-risk targets, such as "This is a no-fly zone for high-voltage power lines; evacuate immediately," or "This is a no-fly zone for 220kV high-voltage power lines; your drone has entered a dangerous area; please evacuate immediately, or forceful measures will be taken." The voice message lasts at least 30 seconds, and the time and content of the message are recorded simultaneously.

[0069] Directional Ion Flooding Unit: Employs radio frequency jamming technology. In a preferred embodiment, this unit uses a phased array antenna and an adjustable power radio frequency module with a beamwidth of no more than 15°, enabling precise focusing on the jamming area. This unit activates only when a high-risk target enters the 20m warning range, directionally emitting a co-channel jamming signal with a jamming power of no more than 1W.

[0070] Dynamic power adjustment: 0.5W at a distance of 10-20m; 1W at a distance of less than 10m, forcing the drone to hover or return without causing physical damage.

[0071] The unit has built-in interference threshold control logic, which automatically stops interference when an authorized drone is detected.

[0072] The data storage and evidence collection module adopts a dual backup mechanism of local hard drive and cloud server to store multi-source sensing data, identification results and processing records.

[0073] The stored data includes:

[0074] Multi-source sensing data: raw spectrum data, visual images or videos, trajectory parameters;

[0075] Identification and decision-making data: whitelist verification results, multimodal identification results, and risk level change records;

[0076] Data to be processed: warning time, method, drive-away initiation time, interference power, etc.

[0077] This module supports encrypted backup of audio and video data, employing AES-256 encryption in one implementation. Data retention is at least 90 days. Stored data can be queried and downloaded by authorized accounts for post-event tracing and evidence collection.

[0078] See attached document Figure 2 This method includes steps S1 to S5, which construct a closed-loop protection process of perception, identification, early warning, expulsion, and evidence collection.

[0079] Step S1: System Initialization and Parameter Configuration

[0080] Before system startup, maintenance personnel complete system configuration and calibration via a remote control platform, including:

[0081] Multi-source sensing module configuration:

[0082] Spectrum detection unit: calibrates the operating frequency band and sets the signal strength threshold.

[0083] Visual monitoring unit: Adjust the focal length, set the gimbal rotation speed (e.g., 30° / s), and set the feature recognition threshold for large drones.

[0084] Identification and Decision Module Configuration:

[0085] Import whitelist data: Import the electronic ID, operating time period, and operating airspace range of legal inspection drones.

[0086] Risk level threshold setting: Set the warning range of the transmission line (e.g., radius 50m), the graded distance thresholds (e.g., 30m and 50m), and the trajectory deviation threshold (e.g., 60°).

[0087] Step S2: Real-time acquisition of multi-source data

[0088] The multi-source sensing module operates continuously, monitoring the airspace surrounding the transmission line in real time and collecting raw data:

[0089] Spectrum detection unit: Continuously captures signals in frequency bands such as 2.4GHz and 5.8GHz, with a data sampling interval of no more than 0.5s. It resolves electronic IDs, communication protocol types (such as DJISDK, XP protocol), and signal strength within an extremely short time (≤0.3s).

[0090] Visual monitoring unit: Provides full-area coverage via a 360° rotatable pan-tilt unit guided by a preset path or spectrum. Dual-mode configuration (visible light / infrared) ensures effective operation under complex weather conditions. Captures images at high speed (10fps), with built-in algorithms extracting visual features in real time.

[0091] Trajectory tracking unit: Based on the Kalman filter algorithm, it integrates spectrum signal strength positioning (signal time difference) and visual target coordinate data to calculate the target's three-dimensional position, velocity, heading, and straight-line distance to the transmission line in real time. The trajectory data is updated every 1 second; when the target approaches (distance ≤ 100m), the sampling frequency is automatically increased to 20Hz.

[0092] Step S3: Authorized Target Verification and Multimodal Recognition

[0093] The intelligent identification and decision-making module performs identity verification and preliminary threat classification on the data collected by S2:

[0094] Authorized target verification: The whitelist management subunit will perform a high-speed comparison between the captured electronic ID and the database.

[0095] Match successful (authorized target): The ID exists and is within the authorized operation period. It is determined to be an authorized target, marked as low risk, and only track monitoring is maintained.

[0096] Matching failed (unauthorized target): ID is not in the whitelist or the authorized time period has expired. Proceed to the multimodal recognition process.

[0097] Multimodal Recognition: The multimodal fusion recognition subunit is activated. A deep learning model based on an attention mechanism is employed, fusing spectral and visual features to achieve accurate classification and recognition of unauthorized large drones.

[0098] Misjudgment correction: If the spectrum (authorized ID) conflicts with the visual (feature mismatch) recognition result, the misjudgment correction subunit is triggered to perform a second verification, calling the trajectory data to determine whether it conforms to the authorized operation trajectory, with a verification time of ≤1s.

[0099] Identification result classification: If it is determined to be a non-large target, it is marked as low risk; if it is determined to be an unauthorized large drone, it is confirmed as a potential threat and proceeds to step S4.

[0100] Step S4: Risk Assessment and Tiered Response

[0101] The risk assessment subunit (calculation cycle ≤ 1s) continuously analyzes unauthorized large UAV data, generates low, medium, and high risk levels based on target distance (50m / 30m threshold) and trajectory deviation (60° threshold), and executes corresponding actions:

[0102] Low-risk handling:

[0103] Judgment criteria: The target is not a large target as determined by S3, or the authorized target is within the authorized scope.

[0104] Handling measures: No intervention is triggered; data is simply recorded in real time by the data storage module.

[0105] Medium-risk management:

[0106] Judgment criteria: Unauthorized large drones, within a 30-50 meter warning range, or whose trajectories clearly approach power transmission lines.

[0107] Response measures: Activate Level II warning and evacuation notice.

[0108] Dynamic electronic fence: When the dynamic electronic fence sub-unit is activated, it broadcasts via ADS-B signal (compliant with RTCADO-260B) a virtual no-fly zone command centered on the power transmission line with a radius of 50 meters to compliant drones within a 5km radius, guiding them to detour.

[0109] High-volume broadcast warning: The high-volume broadcast warning subunit activates the directional speaker (120dB@10m, directional accuracy ±5°) to play a warning voice message for ≥30 seconds.

[0110] High-risk handling:

[0111] Judgment criteria: Unauthorized large drones that intrude into the core danger zone within 30 meters, or whose trajectories clearly point towards the guide wire (trajectory deviation > 60°).

[0112] Response measures: Medium-risk measures will continue, and the highest level of mandatory intervention will be initiated.

[0113] Targeted deterrence: When the target enters the final warning range (e.g., 20 meters), the directional ion-deterrent unit is activated. A phased array antenna (beamwidth ≤ 15°) is used to transmit a co-channel jamming signal (frequency error ≤ 1MHz) with a power ≤ 1W.

[0114] Dynamic power: Dynamically adjusts power based on distance to suppress the control link, forcing the drone to hover or return to base.

[0115] Security logic: Built-in threshold control automatically stops interference when an authorized drone is detected to have entered the area uninvited.

[0116] Multi-target: When multiple targets are detected simultaneously, they are sorted by risk level, and the highest risk target is dealt with first.

[0117] Step S5: Verification of treatment effects and data archiving

[0118] Forming a closed loop of expulsion and evidence collection, confirming whether the threat has been eliminated, and securely recording the event chain:

[0119] Verification of the treatment effect: The trajectory tracking unit continuously monitors the motion status of the target being treated at a high frequency (≥20Hz).

[0120] Successful determination and dismantling of measures: When a target moves from a medium-risk area (30-50m) to more than 50m, the electronic fence and broadcast will be automatically dismantled; when a target moves from a high-risk area (<30m) to more than 30m, the targeted removal will cease, and if the target continues to move to more than 50m, all warnings will be completely dismantled.

[0121] Failure and Handling: The trajectory tracking unit continuously monitors the target's movement status. If a high-risk target fails to leave the core danger zone (<30m) within 10 minutes, the system will repeatedly trigger directional removal until the target is removed. If the target still fails to leave within a set time (e.g., 20 minutes), an alarm message will be automatically sent to the power operation and maintenance platform to notify manual intervention.

[0122] Data archiving: The data storage and evidence collection module collects and stores data synchronously, using dual backups on local hard drives (≥1TB) and cloud servers, and all data is encrypted using AES-256.

[0123] Storage content: multi-source sensing data, identification / decision data, and processing data.

[0124] Data retention: Local data is retained for ≥90 days, and cloud data is retained for ≥1 year.

[0125] Reports: Supports automatic generation of daily airspace safety reports for transmission lines and remote transmission to the operation and maintenance platform.

[0126] Specific Implementation Example 1: System Deployment and Parameter Configuration

[0127] This embodiment deploys the system on a 220kV transmission line that passes through farmland and mountainous areas. The specific configuration and parameters are as follows:

[0128] Multi-source sensing module:

[0129] Spectrum detection unit: Uses USRPB210 (SDR) equipment, covering the 2.4-2.5GHz and 5.725-5.875GHz frequency bands.

[0130] Visual monitoring unit: Employs a dual-mode configuration of a 4K high-definition visible light camera and an infrared thermal imager. Features a 360° rotatable gimbal with a rotation speed of 30° / s, completing a panoramic scan every 5 seconds.

[0131] Trajectory tracking unit: Based on the Kalman filter algorithm, the trajectory sampling frequency is increased to 20Hz when the target enters a range of 100m.

[0132] Intelligent recognition and decision-making module:

[0133] Whitelist Management Sub-unit: Import the electronic IDs of 3 inspection drones. The operation period is set from 9:00 to 16:00, Monday to Friday, and the operation airspace is 30m on both sides of the line.

[0134] Early warning and interception module:

[0135] Dynamic electronic fence sub-unit: ADS-B signal broadcasting equipment, broadcasting power 5W, ensuring effectiveness within a 5km range.

[0136] High-frequency broadcast warning subunit: directional loudspeaker, directivity ±5°, sound pressure level 120dB@10m.

[0137] Directional ion drive unit: Phased array antenna, beamwidth 15°. Interference power dynamically adjusted, set from 0.5W to 1W.

[0138] Data storage and forensics module:

[0139] It is equipped with a 2TB local hard drive and connects to the cloud platform via 4G / 5G. All data is encrypted using AES-256.

[0140] Specific Implementation Example 2: Large Drone Intrusion Handling Process

[0141] This embodiment simulates responding to an intrusion threat under the scenario of Embodiment 1.

[0142] Scenario: An agricultural plant protection drone accidentally enters the warning area of ​​a power transmission line.

[0143] System handling process:

[0144] S2. Data Acquisition: The drone is 60m away from the line.

[0145] Spectrum: Capture the signal and parse out the electronic ID and MG dedicated protocol.

[0146] Vision: The target was locked, and the body length was identified as 1.8m, indicating it to be a large drone.

[0147] Trajectory: By fusing data, the speed of 3 m / s and the heading are calculated to indicate the route.

[0148] S3. Identification and Verification: The intelligent identification and decision-making module receives data. Because the electronic ID does not match the whitelist, the system calls the multimodal fusion model, combining the MG protocol and the 1.8m fuselage feature, and determines it to be an unauthorized large drone within 0.8s.

[0149] S4. Risk Assessment and Mitigation:

[0150] Medium-risk response: If a drone approaches within 50 meters, it is classified as a medium-risk situation. Activate the dynamic electronic fence and high-volume broadcast warning.

[0151] High-risk handling: If the drone continues to approach to within 30m, it is considered high-risk. The system will continuously broadcast a high-volume warning. Targeted deterrence: If the drone ignores the warning and further intrudes into the 20m warning range, the system will activate the directional ion-deterrent unit, emitting a 2.4GHz directional jamming signal to suppress its control link.

[0152] Verification of the handling effect: After the drone's control link was suppressed, it hovered for 10 seconds before returning to base. When the trajectory unit detected that the distance had recovered to 50m, the system automatically released the directional drive, while maintaining the electronic fence and broadcast.

[0153] All alert: The drone has moved to a distance of 60m, the threat is over, and all warnings are lifted.

[0154] S5. Data Archiving: The system automatically stores and backs up the spectrum signal, full-process visual video, trajectory data, risk level changes, announcement content, and start and stop times of the expulsion for this event, and generates a unique number 202X0815-001.

[0155] Conclusion: In this incident, the system took 0.8 seconds from identification to warning and 2 seconds to initiate the removal process, successfully preventing the wire snagging accident and without interfering with surrounding equipment.

Claims

1. A power line protection system for preventing large drones from snagging power lines, characterized in that, include: The multi-source sensing module is used to perceive airspace targets around the transmission line in real time and collect multi-source sensing data. The intelligent identification and decision-making module is used to receive the multi-source perception data, perform target identification and flight risk level assessment, and generate decision instructions; The early warning and interception module is used to receive the decision instructions and execute graded handling measures according to the flight risk level; The data storage and evidence collection module is used to store identification results and processing records in real time.

2. The power line protection system for preventing large drones from snagging power lines according to claim 1, characterized in that, The multi-source sensing module includes: The spectrum detection unit, employing software-defined radio technology, is used to capture the communication signals of the drone and parse its identification identifiers. The visual monitoring unit adopts a dual-mode configuration of a high-definition visible light camera and an infrared thermal imager to acquire high-definition visible light images and infrared thermal imaging images; The trajectory tracking unit, based on the Kalman filter algorithm, is used to fuse spectral signals and visual coordinates to calculate three-dimensional position, flight speed, heading, and straight-line distance from the power transmission line in real time.

3. A power line protection system for preventing large drones from snagging power lines according to claim 1, characterized in that, The intelligent recognition and decision-making module includes: The whitelist management sub-unit is used to verify whether the drone's identity is an authorized target; The multimodal fusion recognition subunit constructs a deep learning model to fuse multi-source features and determine whether the target is a large drone. The risk assessment subunit is used to generate three levels of flight risk (low, medium, and high) based on the motion trajectory data and whether it is an authorized target, and to enter risk level alert when the distance of the UAV is less than a first preset distance threshold.

4. A power line protection system for preventing large drones from snagging power lines, as described in claim 1, is characterized in that... The early warning and interception module includes: The dynamic electronic fence sub-unit is used to send obstacle avoidance instructions via aviation signal broadcasting equipment when there is medium risk. The high-volume broadcast warning subunit is used to play warning voice messages using a directional speaker when the risk level is medium or higher. The directional ion-drive unit is used to emit directional interference signals using radio frequency interference technology when a high-risk target enters the warning range of the third preset distance threshold. It also has built-in interference threshold control logic to automatically stop interference when an authorized drone is detected.

5. A method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: S1. System initialization and parameter configuration, completing parameter calibration and whitelist data import; S2. Real-time acquisition of multi-source data: The multi-source sensing module continuously monitors and acquires spectrum signal data, visual image data, and motion trajectory data. S3. Authorized target verification and multimodal recognition: The intelligent recognition and decision-making module verifies the electronic identity and performs multimodal recognition on unauthorized targets to determine whether they are large drones. S4. Risk Assessment and Tiered Response: The intelligent identification and decision-making module generates risk levels, and the early warning and interception module executes tiered responses. Low risk: Only data is recorded, without triggering intervention; Medium risk: Initiate early warning and evacuation measures; High risk: Initiate mandatory intervention; S5. Verification of treatment effects and data archiving: Continuously monitor the target's movement status and store treatment process data in real time.

6. A method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, In step S4, the medium-risk handling is triggered when the distance of the unauthorized large drone is less than a first predetermined distance and greater than a second predetermined distance; the high-risk handling is triggered when the distance of the unauthorized large drone is less than the second predetermined distance, wherein the first predetermined distance is greater than the second predetermined distance.

7. A method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, In step S3, when the verification result conflicts with the result of the multimodal recognition, a secondary verification is triggered, and the motion trajectory data is called to determine whether the target conforms to the authorized operation trajectory.

8. A method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, In step S4, when the system detects multiple large drones intruding simultaneously, it sorts them by risk level and prioritizes driving away the target closest to the power transmission line.

9. A method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, In step S4, if the flight trajectory of the unauthorized large drone points to the power transmission line conductor, the situation is upgraded to a high-risk operation.

10. A method for protecting power transmission lines from being snagged by large unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, In step S5, if the high-risk target is not evacuated within the set repeated handling cycle, the mandatory intervention is triggered repeatedly.

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