A drone intrusion detection and protection method and system based on panoramic thermal imaging

Abstract

The present invention provides a drone intrusion detection and protection method and system based on panoramic thermal imaging, which belongs to the field of drone technology. Specifically, the method and system include: determining the detection deviation in different distance intervals based on the detection data of different historical intrusion targets by a thermal imaging device of a pan-tilt platform, and combining the movement data of different historical intrusion targets to determine that the thermal imaging device cannot be used for intrusion detection processing, determining the deviation of radar detection data at different angles through the analysis results of the radar detection data of the pan-tilt platform, and when it is determined that there is an interference angle based on the deviation of the radar detection data, determining the setting strategy of the pan-tilt platform in the target area based on the distribution data of the interference angle, and using the thermal imaging device and radar device of the pan-tilt platform to detect and process the intrusion drone, thereby improving the accuracy of drone intrusion detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of drones, and in particular relates to a drone intrusion detection and protection method and system based on panoramic thermal imaging. Background Art

[0002] With the rapid development of drone technology, drones often intrude into confidential areas or prohibited areas, which makes how to detect and process drone intrusions a technical problem that needs to be solved urgently.

[0003] Existing technical solutions often use radar or images to detect drone intrusion. Specifically, invention patent applications CN202411815495.3, "A method for detecting small drone intrusions based on improved YOLOv8," and CN202311207429.3, "Method, device, and equipment for detecting illegal intrusion targets in forests based on drones," both provide technical solutions for detecting intrusion drones based on infrared images or images. However, the above technical solutions all have the following problems:

[0004] When performing drone intrusion detection and processing, due to the high flying speed of drones, the time left for the gimbal to perform drone intrusion detection is very short. Therefore, how to generate differentiated intrusion detection setting strategies based on the intrusion risk of drones in the target area and improve the efficiency of detection and processing of intrusion drones has become a technical problem that needs to be solved urgently.

[0005] In response to the above technical problems, the present application specifically provides a drone intrusion detection and protection method and system based on panoramic thermal imaging. Summary of the Invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for detecting and protecting drone intrusions based on panoramic thermal imaging, which specifically includes:

[0008] S1 determines the historical intrusion data of drones in the target area based on the analysis results of the gimbal's detection data, and proceeds to the next step when it is determined that the detection processing busyness of the target area meets the requirements based on the analysis results of the historical intrusion data;

[0009] S2 determines the detection deviation in different distance intervals based on the detection data of the thermal imaging device of the pan-tilt system on different historical intrusion targets, and combines the movement data of different historical intrusion targets to determine that the thermal imaging device cannot be used for intrusion detection processing, and then proceeds to the next step;

[0010] S3 determines the deviation of the radar detection data at different angles based on the analysis results of the radar detection data of the PTZ, and when it is determined that there is an interference angle based on the deviation of the radar detection data, determines the setting strategy of the PTZ in the target area based on the distribution data of the interference angle;

[0011] S4 utilizes the thermal imaging device and radar device of the pan-tilt platform to detect and process intruding drones, and when a drone is detected, performs intrusion protection processing according to the analysis results of the drone's coordinates.

[0012] The beneficial effects of the present invention are:

[0013] Based on the analysis results of the historical intrusion data, it is determined whether the detection processing busyness of the target area meets the requirements, thereby realizing the determination of the intrusion detection processing busyness of the target area from the perspective of the historical intrusion data in the target area, and then realizing the generation of differentiated intrusion detection processing strategies based on the differences in the detection processing busyness of the target area, which greatly improves the detection processing efficiency of areas with higher detection processing busyness, and also reduces the risk of drone intrusion.

[0014] Based on the distribution data of the interference angle, the setting strategy of the gimbal in the target area is determined, which avoids the technical problem of a high degree of interference in radar detection due to the presence of interference objects at certain angles, and determines the setting strategy of the gimbal in a targeted manner. On the basis of reducing the setting cost of the gimbal, the efficiency and reliability of the detection and processing of intruding drones in the target area are improved.

[0015] A further technical solution is that the historical invasion data of the drone includes the number of historical invasions on different dates and the number of drones with different historical invasion times.

[0016] A further technical solution is that the historical intrusion data is determined based on a reading result of historical records in the monitoring data of the UAV within a preset time period.

[0017] A further technical solution is to determine whether the detection processing busyness of the target area meets the requirements, specifically including:

[0018] Determining the number of historical intrusions of the target area in a preset time period based on the analysis results of the historical intrusion data;

[0019] Determining the number of identification difficulties in the historical intrusion counts based on the number of drones with different historical intrusion counts;

[0020] Whether the busyness of the detection processing of the target area meets the requirement is determined according to the number of recognition difficulties.

[0021] A further technical solution is that the number of identification difficulties is the number of historical intrusions in which the number of drones is greater than a preset number of drones.

[0022] A further technical solution is that, when the number of identification difficulties is not within a preset difficulty number range, it is determined that the busyness of the detection processing of the target area does not meet the requirements.

[0023] A further technical solution is that when the detection processing busyness of the target area does not meet the requirements, the gimbals of the target area are set according to a preset number, and the angle ranges of intrusion detection processing of different gimbals are evenly divided according to the preset number, and the detection and processing of intrusion drones are performed based on the thermal imaging device and radar device of the gimbal.

[0024] A further technical solution is that the method for determining the setting strategy of the pan / tilt platform in the target area is:

[0025] determining the number of the interference angles based on the distribution data of the interference angles;

[0026] Determining angle deviations between different adjacent interference angles based on distribution data of different interference angles, and determining a distribution dispersion coefficient of the interference angles based on a ratio of an average value of the angle deviations between different adjacent interference angles to a preset angle deviation amount;

[0027] Based on the number of the interference angles and the distribution dispersion coefficient of the interference angles, a detection deviation coefficient of the pan-tilt platform is determined, and a setting strategy of the pan-tilt platform in the target area is determined using the detection deviation coefficient of the pan-tilt platform.

[0028] A further technical solution is that the method for determining the detection deviation coefficient of the pan / tilt platform is:

[0029] Determining a basic detection deviation coefficient of the gimbal based on the product of the number of interference angles and a preset proportional factor;

[0030] The detection deviation coefficient of the pan / tilt platform is determined according to the product of the basic detection deviation coefficient and the distribution dispersion coefficient of the interference angle.

[0031] A further technical solution is to use the detection deviation coefficient of the pan / tilt platform to determine the setting strategy of the pan / tilt platform in the target area, specifically including:

[0032] When the detection deviation coefficient of the gimbal is greater than a preset deviation coefficient threshold, the gimbals in the target area are set according to a preset number, and the angle ranges of intrusion detection processing of different gimbals are evenly divided according to the preset number, and the thermal imaging device and radar device of the gimbal are used to detect the intrusion drone;

[0033] When the detection deviation coefficient of the pan-tilt platform is not greater than a preset deviation coefficient threshold, the thermal imaging device and radar device of the single pan-tilt platform are used to perform intrusion detection processing on the drone.

[0034] A further technical solution is to use the thermal imaging device and radar device of the pan / tilt platform to detect and process intruding drones, specifically including:

[0035] When the radar detection data of the radar device of the pan-tilt platform determines that there is a suspected intrusion target, the image analysis results of the thermal imaging device of the pan-tilt platform are used to detect and process the intrusion drone;

[0036] When the radar detection data of the radar device of the pan-tilt platform determines that there is a suspected intrusion target, the preset frequency and the image analysis result of the thermal imaging device of the pan-tilt platform are used to perform detection processing on the intrusion drone.

[0037] A further technical solution is to perform intrusion protection based on the analysis results of the drone's coordinates, specifically including:

[0038] According to the analysis results of the coordinates of the drone, the coordinates of the drone are sent to the protection drone in real time;

[0039] The protection drone is used to perform intrusion protection processing on the drone.

[0040] In a second aspect, the present invention provides a drone intrusion detection and protection system based on panoramic thermal imaging, which adopts the above-mentioned drone intrusion detection and protection method based on panoramic thermal imaging, specifically comprising:

[0041] Busyness assessment module, detection result analysis module, setting strategy determination module, protection processing module;

[0042] The busyness evaluation module is responsible for determining whether the detection processing busyness of the target area meets the requirements based on the analysis results of historical intrusion data;

[0043] The detection result analysis module is responsible for determining whether the thermal imaging device can be used for intrusion detection processing;

[0044] The setting strategy determination module is responsible for determining the setting strategy of the PTZ in the target area;

[0045] The protection processing module is responsible for performing intrusion protection processing based on the analysis results of the drone's coordinates.

[0046] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other features and advantages of the present invention will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings;

[0049] Figure 1 It is a flow chart of a drone intrusion detection and protection method based on panoramic thermal imaging;

[0050] Figure 2 It is a flow chart to determine whether the detection processing busyness of the target area meets the requirements;

[0051] Figure 3 It is a flow chart for determining that a thermal imaging device cannot be used for intrusion detection processing;

[0052] Figure 4 is a flow chart of a method for determining an interference angle;

[0053] Figure 5 is a flow chart of a method for determining a setting strategy of a PTZ in the target area;

[0054] Figure 6 This is a framework diagram of a drone intrusion detection and protection system based on panoramic thermal imaging. DETAILED DESCRIPTION

[0055] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0056] In this application, the frequency of intrusion drones in the target area and the distribution data of interference objects during the gimbal radar detection process are used to generate differentiated strategies for detecting and processing intrusion drones, thereby improving the efficiency of detecting and processing intrusion drones.

[0057] The number of drones that have historically invaded the target area in a preset time period is used to determine the number of historical invasions when the number of drones is greater than the preset number of drones, and this number is used as the number of identification difficulties. When the number of identification difficulties is within the preset difficulty number range, it is determined that the detection processing busyness of the target area meets the requirements.

[0058] The detection deviation of different types of historical intrusion targets in different distance intervals is used to determine the detection success rate in the preset distance interval (i.e., between 2.8km and 3.2km). The type of historical intrusion target with a detection success rate less than 0.9 is used as the identification deviation type. When there is an identification deviation type with an average moving speed greater than the preset speed threshold, it is determined that the thermal imaging device cannot be used for intrusion detection processing.

[0059] Determine the number of detection deviations of the radar detection data of the angle, and determine the radar detection interference coefficient of the angle based on the product of the number of detection deviations of the radar detection data of the angle and 0.01. When the radar detection interference coefficient is greater than 0.3, the angle is determined to be an interference angle.

[0060] Determining the number of interference angles based on the distribution data of the interference angles, determining the angular deviations between different adjacent interference angles based on the distribution data of different interference angles, and determining the distribution dispersion coefficient of the interference angles based on the ratio of the average value of the angular deviations between different adjacent interference angles to a preset angular deviation amount;

[0061] The detection deviation coefficient of the gimbal is determined based on the product of the preset deviation coefficient corresponding to the number of interference angles and the distribution dispersion coefficient of the interference angles. When the detection deviation coefficient of the gimbal is greater than 0.6, the gimbals in the target area are set according to the preset number, and the angle ranges of intrusion detection processing of different gimbals are balanced according to the preset number. The detection of intruding drones is performed based on the thermal imaging device and radar device of the gimbal. When the detection deviation coefficient of the gimbal is not greater than 0.6, the thermal imaging device and radar device of a single gimbal are used to perform intrusion detection of drones.

[0062] Example 1 Figure 1 As shown, in the first aspect, the present application provides a drone intrusion detection and protection method based on panoramic thermal imaging, which specifically includes:

[0063] S1 determines the historical intrusion data of drones in the target area based on the analysis results of the gimbal's detection data, and proceeds to the next step when it is determined that the detection processing busyness of the target area meets the requirements based on the analysis results of the historical intrusion data;

[0064] Furthermore, the historical invasion data of the drone includes the number of historical invasions on different dates and the number of drones with different historical invasion times.

[0065] Specifically, the historical intrusion data is determined based on a reading result of historical records in the monitoring data of the UAV within a preset time period.

[0066] It should be noted that if Figure 2 As shown, determining that the detection processing busyness of the target area meets the requirements specifically includes:

[0067] Determining the number of historical intrusions of the target area in a preset time period based on the analysis results of the historical intrusion data;

[0068] Determining the number of identification difficulties in the historical intrusion counts based on the number of drones with different historical intrusion counts;

[0069] Whether the busyness of the detection processing of the target area meets the requirement is determined according to the number of recognition difficulties.

[0070] Furthermore, the number of identification difficulties is the number of historical intrusions in which the number of drones is greater than a preset number of drones.

[0071] It should be noted that, when the number of recognition difficulties is not within a preset difficulty number range, it is determined that the busyness of the detection processing of the target area does not meet the requirement.

[0072] It should be further explained that when the detection processing busyness of the target area does not meet the requirements, the gimbals of the target area are set according to the preset number, and the angle ranges of intrusion detection processing of different gimbals are evenly divided according to the preset number, and the detection and processing of intrusion drones are performed based on the thermal imaging device and radar device of the gimbal.

[0073] It is understandable that the balanced division of the angle ranges for intrusion detection processing of different pan-tilt platforms according to the preset number specifically includes:

[0074] The angles for intrusion detection processing are divided into equally spaced angle ranges according to the preset number, and different gimbals are responsible for intrusion detection processing of drones in corresponding equally spaced angle ranges.

[0075] Optionally, determining whether the detection processing busyness of the target area meets the requirement specifically includes:

[0076] Determining the number of historical intrusions of the target area in a preset time period based on the analysis results of the historical intrusion data;

[0077] The number of drones detected in the target area on different dates is determined based on the number of drones with different historical intrusion times, and the number is used as the number of intruding drones;

[0078] Whether the detection processing busyness of the target area meets the requirements is determined by the number of intruding drones on different days.

[0079] Furthermore, when there is a date when the number of intruding drones is greater than a preset intrusion number threshold, it is determined that the detection processing busyness of the target area does not meet the requirement.

[0080] Optionally, determining whether the detection processing busyness of the target area meets the requirement specifically includes:

[0081] S11 determines, based on the analysis result of the historical invasion data, dates on which the target area has historical invasions in a preset period, and determines different historical invasion times and the number of drones with different historical invasion times on the dates with historical invasions;

[0082] Optionally, the above step S11 includes the following contents:

[0083] S111 determines the number of historical intrusions of the target area in a preset period based on the analysis result of the historical intrusion data. If the number of historical intrusions of the target area in the preset period is greater than the preset number of intrusions, it is determined that the detection processing busyness of the target area does not meet the requirement. If the number of historical intrusions of the target area in the preset period is not greater than the preset number of intrusions, the process proceeds to step S112.

[0084] S112 determines whether there are historical intrusion dates in the target area within a preset period, and uses them as drone intrusion dates. If the proportion of drone intrusion dates in the preset period is less than the proportion of the preset dates, it is determined that the detection processing busyness of the target area meets the requirement. If the proportion of drone intrusion dates in the preset period is not less than the proportion of the preset dates, the process proceeds to step S113.

[0085] S113 determines the number of historical invasions on dates with historical invasions and the number of drones with different historical invasion numbers. If there is a date with a historical invasion number greater than a preset invasion number threshold, the process proceeds to step S114. If there is no date with a historical invasion number greater than the preset invasion number threshold, the process proceeds to step S12.

[0086] S114 When the proportion of dates with historical intrusion times greater than the preset intrusion times threshold is greater than the proportion of the preset dates, it is determined that the detection processing busyness of the target area does not meet the requirements; when the proportion of dates with historical intrusion times greater than the preset intrusion times threshold is not greater than the proportion of the preset dates, proceed to step S12.

[0087] S12 determines the intrusion detection busy coefficient of the target area on different dates based on the number of historical intrusions of the target area on different dates and the number of drones with different historical intrusion numbers;

[0088] Optionally, the above step S12 includes the following contents:

[0089] S121 determines the intrusion detection busyness coefficient of the target area on different dates based on the number of historical intrusions of the target area on different dates and the number of drones with different historical intrusions. If there is a target area whose intrusion detection busyness coefficient does not meet the requirement, it is determined that the detection processing busyness of the target area does not meet the requirement. If there is no target area whose intrusion detection busyness coefficient does not meet the requirement, the process proceeds to step S122.

[0090] S122 determines an average value of the intrusion detection busy coefficients of the target area on different dates based on the intrusion detection busy coefficients on different dates. If the average value of the intrusion detection busy coefficients on different dates does not meet the requirement, it is determined that the detection processing busyness of the target area does not meet the requirement. If the average value of the intrusion detection busy coefficients on different dates meets the requirement, the process proceeds to step S123.

[0091] S123: When the intrusion detection busy coefficients of different dates are all less than the preset busy coefficient threshold, the process proceeds to step S124; when there is no date with an intrusion detection busy coefficient not less than the preset busy coefficient threshold, the process proceeds to step S13;

[0092] S124 When the sum of the intrusion detection busy coefficients on different dates is less than the preset coefficient threshold, it is determined that the detection processing busyness of the target area does not meet the requirements. When the sum of the intrusion detection busy coefficients on different dates is not less than the preset coefficient threshold, go to step S13.

[0093] S13 determines the area detection busy coefficient of the target area based on the intrusion detection busy coefficients of the target area on different dates, and determines whether the detection processing busyness of the target area meets the requirements according to the area detection busy coefficient.

[0094] S2 determines the detection deviation in different distance intervals based on the detection data of the thermal imaging device of the pan-tilt system on different historical intrusion targets, and combines the movement data of different historical intrusion targets to determine that the thermal imaging device cannot be used for intrusion detection processing, and then proceeds to the next step;

[0095] Furthermore, the distance intervals are divided according to preset distance intervals.

[0096] It should also be noted that the detection deviations in different distance intervals include the number and proportion of drones that failed to be identified as different types of historical intrusion targets in different distance intervals.

[0097] Specifically, the movement data of the historical intrusion targets include movement speeds of different types of historical intrusion targets.

[0098] It can be understood that the types of the historical intrusion targets are divided according to the preset volume intervals corresponding to the volumes of the historical intrusion targets.

[0099] Optional, such as Figure 3 As shown, it is determined that thermal imaging devices cannot be used for intrusion detection, including:

[0100] Determine the detection success rate in a preset distance interval based on the detection deviation of different types of historical intrusion targets in different distance intervals;

[0101] The type of historical intrusion targets with a detection success rate lower than a preset success rate is regarded as the identification deviation type;

[0102] The average moving speeds of the different identification deviation types are determined through the movement data of the different identification deviation types, and according to the average moving speeds of the different identification deviation types, it is determined whether the thermal imaging device can be used for intrusion detection processing.

[0103] Furthermore, when there is an identification deviation type in which the average moving speed is greater than a preset speed threshold, it is determined that the thermal imaging device cannot be used for intrusion detection processing.

[0104] It should also be noted that when it is determined that a thermal imaging device can be used for intrusion detection processing, a single pan-tilt thermal imaging device is used to perform intrusion detection processing on the drone.

[0105] Optionally, determining that the thermal imaging device cannot be used for intrusion detection processing includes:

[0106] Determine the detection success rate in a preset distance interval based on the detection deviation of different types of historical intrusion targets in different distance intervals;

[0107] The type of historical intrusion targets with a detection success rate lower than a preset success rate is regarded as the identification deviation type;

[0108] Through the movement data of different identification deviation types, historical intrusion targets with a moving speed greater than a preset speed threshold in different identification deviation types are determined and regarded as fast-moving targets. Based on the total number of the fast-moving targets, it is determined whether the thermal imaging device can be used for intrusion detection processing.

[0109] Furthermore, when the total number of the fast-moving targets is greater than a preset moving target number threshold, it is determined that the thermal imaging device cannot be used for intrusion detection processing.

[0110] Optionally, determining that the thermal imaging device cannot be used for intrusion detection processing includes:

[0111] S21 determines the detection success rate in a preset distance interval based on the detection deviation of different types of historical intrusion targets in different distance intervals, and uses the type of historical intrusion target with a detection success rate lower than the preset success rate as the identification deviation type;

[0112] S22 determines the deviation influence weight coefficients of different recognition deviation types based on the number of historical intrusion targets of different recognition deviation types and the movement speeds of different historical intrusion targets, and determines the error influence factors of different recognition deviation types by multiplying the detection success rates of different recognition deviation types in a preset distance interval by the deviation influence weight coefficients;

[0113] S23 determines a detection reliability coefficient for intrusion detection processing using the thermal imaging device through error influence factors of different recognition deviation types, and uses the detection reliability coefficient to determine whether intrusion detection processing can be performed using the thermal imaging device.

[0114] It should be noted that the detection reliability coefficient is determined based on the difference between a preset value and the sum of error influencing factors of different recognition deviation types.

[0115] It can be understood that when the detection reliability coefficient is greater than a preset reliability coefficient threshold, it is determined that the thermal imaging device can be used for intrusion detection processing.

[0116] For example, using thermal imaging devices for intrusion detection processing includes:

[0117] The thermal imaging gimbal monitors and provides comprehensive drone detection within a 3km radius. Upon detecting a drone, the thermal imaging system transmits the current azimuth and distance to the protection drone. The protection drone automatically flies toward the target based on the converted world coordinates, detecting intruders in real time during flight. While the protection drone is in flight, the thermal imaging detection system updates the intruder's coordinates in real time, allowing it to adjust its flight direction and more quickly locate the intruder.

[0118] Optionally, the above step S21 includes the following contents:

[0119] S211 determines the detection success rate in a preset distance interval based on the detection deviation of different types of historical intrusion targets in different distance intervals. If the detection success rate of different types of historical intrusion targets in the preset distance interval is not less than the preset success rate, it is determined that the thermal imaging device can be used for intrusion detection processing. If there is a type of historical intrusion target with a detection success rate less than the preset detection success rate threshold, the process proceeds to step S212.

[0120] In step S212, the type of historical intrusion targets with a detection success rate lower than a preset success rate is regarded as an identification deviation type. If the sum of the number of historical intrusion targets in different identification deviation types does not meet the requirement, it is determined that the thermal imaging device cannot be used for intrusion detection. If the sum of the number of historical intrusion targets in different identification deviation types meets the requirement, the process proceeds to step S213.

[0121] In step S213, if there is an identification deviation type where the number of historical intrusion targets is greater than the preset intrusion target number threshold, the process proceeds to step S214. If there is no identification deviation type where the number of historical intrusion targets is greater than the preset intrusion target number threshold, the process proceeds to step S22.

[0122] S214 When the number of historical intrusion targets is greater than the preset intrusion target number threshold and the number of identification deviation types is greater than the preset deviation type number, it is determined that the thermal imaging device cannot be used for intrusion detection processing. When the number of historical intrusion targets is greater than the preset intrusion target number threshold and the number of identification deviation types is not greater than the preset deviation type number, proceed to step S22.

[0123] Optionally, the above step S22 includes the following contents:

[0124] S221: When the average value of the moving speeds of historical intrusion targets of different identification deviation types is determined to be greater than a preset moving speed threshold, the process proceeds to step S222. If there is an identification deviation type in which the average value of the moving speeds of historical intrusion targets is not greater than the preset moving speed threshold, the process proceeds to step S223.

[0125] At step S222, when the number of historical intrusion targets with different identification deviation types is within the preset intrusion target number range, it is determined that the thermal imaging device cannot be used for intrusion detection processing. When the number of historical intrusion targets with different identification deviation types is not within the preset intrusion target number range, the process proceeds to step S223.

[0126] S223 determines the deviation impact weight coefficients of different identification deviation types based on the number of historical intrusion targets of different identification deviation types and the movement speeds of different historical intrusion targets. If there is an identification deviation type with a deviation impact weight coefficient greater than a preset weight coefficient threshold, it is determined that intrusion detection processing cannot be performed using the thermal imaging device. If there is no identification deviation type with a deviation impact weight coefficient greater than the preset weight coefficient threshold, the process proceeds to step S224.

[0127] S224 determines the error influence factors of different identification deviation types by multiplying the detection success rate of different identification deviation types in the preset distance interval by the deviation influence weight coefficient. When the number of identification deviation types whose error influence factors are within the preset influence factor interval does not meet the requirements, it is determined that the thermal imaging device cannot be used for intrusion detection processing. When the number of identification deviation types whose error influence factors are within the preset influence factor interval meets the requirements, proceed to step S23.

[0128] S3 determines the deviation of the radar detection data at different angles based on the analysis results of the radar detection data of the PTZ, and when it is determined that there is an interference angle based on the deviation of the radar detection data, determines the setting strategy of the PTZ in the target area based on the distribution data of the interference angle;

[0129] Specifically, such as Figure 4 As shown, the method for determining the interference angle is:

[0130] Determining the number of detection deviations of the radar detection data at the angles based on the deviations of the radar detection data at the angles;

[0131] determining a radar detection interference coefficient of the angle according to the number of detection deviations of the radar detection data of the angle;

[0132] It is determined whether the angle is an interference angle based on the radar detection interference coefficient.

[0133] Furthermore, the radar detection interference coefficient of the angle is determined according to the ratio of the number of detection deviations of the radar detection data of the angle to a preset number threshold.

[0134] It should also be noted that when the radar detection interference coefficient of the angle does not meet the requirement, the angle is determined to be an interference angle.

[0135] It is understandable that when there is no interference angle, the thermal imaging device and radar device of a single gimbal are used to perform intrusion detection processing on the drone.

[0136] Specifically, such as Figure 5 As shown, the method for determining the setting strategy of the PTZ in the target area is:

[0137] determining the number of the interference angles based on the distribution data of the interference angles;

[0138] Determining angle deviations between different adjacent interference angles based on distribution data of different interference angles, and determining a distribution dispersion coefficient of the interference angles based on a ratio of an average value of the angle deviations between different adjacent interference angles to a preset angle deviation amount;

[0139] Based on the number of the interference angles and the distribution dispersion coefficient of the interference angles, a detection deviation coefficient of the pan-tilt platform is determined, and a setting strategy of the pan-tilt platform in the target area is determined using the detection deviation coefficient of the pan-tilt platform.

[0140] Specifically, the method for determining the detection deviation coefficient of the pan / tilt platform is as follows:

[0141] Determining a basic detection deviation coefficient of the gimbal based on the product of the number of interference angles and a preset proportional factor;

[0142] The detection deviation coefficient of the pan / tilt platform is determined according to the product of the basic detection deviation coefficient and the distribution dispersion coefficient of the interference angle.

[0143] It should be noted that the determination of the setting strategy of the pan-tilt platform in the target area by using the detection deviation coefficient of the pan-tilt platform specifically includes:

[0144] When the detection deviation coefficient of the gimbal is greater than a preset deviation coefficient threshold, the gimbals in the target area are set according to a preset number, and the angle ranges of intrusion detection processing of different gimbals are evenly divided according to the preset number, and the thermal imaging device and radar device of the gimbal are used to detect the intrusion drone;

[0145] When the detection deviation coefficient of the pan-tilt platform is not greater than a preset deviation coefficient threshold, the thermal imaging device and radar device of the single pan-tilt platform are used to perform intrusion detection processing on the drone.

[0146] Optionally, a method for determining a setting strategy of the PTZ in the target area is:

[0147] Determining the number of interference angles based on the distribution data of the interference angles, and performing intrusion detection processing on the drone using a single pan-tilt thermal imaging device and a radar device when the number of interference angles is less than a preset number of interference angles;

[0148] When the number of interference angles is not less than the preset number of interference angles:

[0149] When the number of interference angles is greater than a preset value of the number of interference angles, the pan-tilt devices in the target area are set according to the preset number, and the angle ranges of intrusion detection processing of different pan-tilt devices are evenly divided according to the preset number, and the intrusion drone is detected based on the thermal imaging device and radar device of the pan-tilt device;

[0150] When the number of interference angles is not greater than a preset value of interference angles:

[0151] According to the distribution data of different interference angles, the angle deviation between different adjacent interference angles is determined.

[0152] When there is an interference angle whose angle deviation from an adjacent interference angle does not meet the required interference angle, the pan-tilt devices in the target area are set according to a preset number, and the angle ranges of intrusion detection processing of different pan-tilt devices are evenly divided according to the preset number, and the intrusion drone is detected based on the thermal imaging device and radar device of the pan-tilt device;

[0153] If there is no interference angle whose angular deviation from the adjacent interference angle does not meet the requirements:

[0154] When there is an interference angle whose angle deviation from the adjacent interference angle is greater than a preset angle deviation threshold, it is regarded as a distributed discrete angle;

[0155] When the number of distributed discrete angles does not meet the requirement, the pan-tilt devices in the target area are set according to a preset number, and the angle ranges for intrusion detection processing of different pan-tilt devices are evenly divided according to the preset number, and the intrusion drone is detected based on the thermal imaging device and radar device of the pan-tilt device;

[0156] When the number of the distributed discrete angles meets the requirement or there is no interference angle whose angle deviation from the adjacent interference angle is greater than a preset angle deviation threshold;

[0157] Determining a distribution dispersion coefficient of the interference angle based on a ratio of an average value of angle deviations between different adjacent interference angles to a preset angle deviation;

[0158] Based on the number of the interference angles and the distribution dispersion coefficient of the interference angles, a detection deviation coefficient of the pan-tilt platform is determined, and a setting strategy of the pan-tilt platform in the target area is determined using the detection deviation coefficient of the pan-tilt platform.

[0159] S4 utilizes the thermal imaging device and radar device of the pan-tilt platform to detect and process intruding drones, and when a drone is detected, performs intrusion protection processing according to the analysis results of the drone's coordinates.

[0160] Furthermore, the thermal imaging device and radar device of the pan-tilt platform are used to detect and process intruding drones, specifically including:

[0161] When the radar detection data of the radar device of the pan-tilt platform determines that there is a suspected intrusion target, the image analysis results of the thermal imaging device of the pan-tilt platform are used to detect and process the intrusion drone;

[0162] When the radar detection data of the radar device of the pan-tilt platform determines that there is a suspected intrusion target, the preset frequency and the image analysis result of the thermal imaging device of the pan-tilt platform are used to perform detection processing on the intrusion drone.

[0163] Specifically, intrusion protection processing is performed based on the analysis results of the drone's coordinates, including:

[0164] According to the analysis results of the coordinates of the drone, the coordinates of the drone are sent to the protection drone in real time;

[0165] The protection drone is used to perform intrusion protection processing on the drone.

[0166] It should be noted that intrusion protection processing is performed based on the analysis results of the drone's coordinates, specifically including:

[0167] If the gimbal detects a drone intrusion, it proceeds to the next step;

[0168] Track the intruding drone, generate the intruding drone ID, obtain the current gimbal azimuth, obtain the current target size, and estimate the distance based on the target size;

[0169] The current azimuth angle and target distance are sent to the UAV control platform, and the thermal imaging gimbal converts the gimbal's azimuth angle into the world coordinate system;

[0170] The drone control platform determines whether the target sent by the thermal imaging gimbal is a new target or an existing target. If it is a new target, it starts a new protection drone and controls the new attack drone to fly to the current coordinates. It binds the new protection drone ID to the intrusion drone ID and proceeds to step 14.

[0171] If it is an existing target, query the protection drone ID based on the intrusion drone ID;

[0172] Update the location of the intruding drone to the protection drone. When the protection drone approaches the intruding drone, it uses the drone detection algorithm to detect the intruding drone.

[0173] When an intruding drone is detected, the defense drone automatically approaches the intruding drone through the flight control system;

[0174] When the distance between the protection drone and the intrusion drone approaches a certain distance, the protection drone launches the net bag loaded on its fuselage towards the intrusion drone, and the protection drone returns;

[0175] In the second aspect of embodiment 2, Figure 6 As shown, the present invention provides a UAV intrusion detection and protection system based on panoramic thermal imaging, which adopts the above-mentioned UAV intrusion detection and protection method based on panoramic thermal imaging, specifically including:

[0176] Busyness assessment module, detection result analysis module, setting strategy determination module, protection processing module;

[0177] The busyness evaluation module is responsible for determining whether the detection processing busyness of the target area meets the requirements based on the analysis results of historical intrusion data;

[0178] The detection result analysis module is responsible for determining whether the thermal imaging device can be used for intrusion detection processing;

[0179] The setting strategy determination module is responsible for determining the setting strategy of the PTZ in the target area;

[0180] The protection processing module is responsible for performing intrusion protection processing based on the analysis results of the drone's coordinates.

[0181] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0182] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0183] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for detecting and protecting drone intrusion based on panoramic thermal imaging, characterized in that: Specifically include: S1 determines historical intrusion data of drones in the target area based on the analysis results of the gimbal's detection data, and when it is determined based on the analysis results of the historical intrusion data that the detection processing busyness of the target area meets the requirements, that is, when the detection processing busyness is low, proceeds to the next step; When the detection processing busyness of the target area does not meet the requirements, a preset number of pan-tilt platforms are set in the target area, and the angle ranges of intrusion detection processing of different pan-tilt platforms are evenly divided according to the preset number, and the thermal imaging device and radar device of the pan-tilt platform are used to detect the intrusion drone; S2 determines the detection deviation in different distance intervals based on the detection data of different historical intrusion targets of the thermal imaging device of the pan-tilt head, and when it is determined that the thermal imaging device cannot be used for intrusion detection processing in combination with the movement data of different historical intrusion targets, proceeds to the next step; S3 determines the deviation of the radar detection data at different angles based on the analysis results of the radar detection data of the PTZ, and when it is determined that there is an interference angle based on the deviation of the radar detection data, determines the setting strategy of the PTZ in the target area based on the distribution data of the interference angle; S4 utilizes the thermal imaging device and radar device of the pan-tilt platform to detect and process intruding drones, and when a drone is detected, performs intrusion protection processing according to the analysis results of the drone's coordinates.

2. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 1, wherein: The historical invasion data of the drone includes the number of historical invasions on different dates and the number of drones with different historical invasion times.

3. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 1, wherein: The historical intrusion data is determined based on a reading result of historical records in the monitoring data of the UAV within a preset time period.

4. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 1, wherein: Determining that the detection processing busyness of the target area meets the requirements specifically includes: Determining the number of historical intrusions of the target area in a preset time period based on the analysis results of the historical intrusion data; Determining the number of identification difficulties in the historical intrusion counts based on the number of drones with different historical intrusion counts; Whether the busyness of the detection processing of the target area meets the requirement is determined according to the number of recognition difficulties.

5. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 1, wherein: The method for determining the setting strategy of the PTZ in the target area is: determining the number of the interference angles based on the distribution data of the interference angles; Determining angle deviations between different adjacent interference angles based on distribution data of different interference angles, and determining a distribution dispersion coefficient of the interference angles based on a ratio of an average value of the angle deviations between different adjacent interference angles to a preset angle deviation amount; Based on the number of the interference angles and the distribution dispersion coefficient of the interference angles, a detection deviation coefficient of the pan-tilt platform is determined, and a setting strategy of the pan-tilt platform in the target area is determined using the detection deviation coefficient of the pan-tilt platform.

6. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 5, wherein: The method for determining the detection deviation coefficient of the pan / tilt platform is as follows: Determining a basic detection deviation coefficient of the gimbal based on the product of the number of interference angles and a preset proportional factor; The detection deviation coefficient of the pan / tilt platform is determined according to the product of the basic detection deviation coefficient and the distribution dispersion coefficient of the interference angle.

7. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 5, wherein: Determining a setting strategy of the pan-tilt platform in the target area by using the detection deviation coefficient of the pan-tilt platform specifically includes: When the detection deviation coefficient of the gimbal is greater than a preset deviation coefficient threshold, a preset number of gimbals are set in the target area, and the angle ranges of intrusion detection processing of different gimbals are evenly divided according to the preset number, and the intrusion drone is detected based on the thermal imaging device and radar device of the gimbal; When the detection deviation coefficient of the pan-tilt platform is not greater than a preset deviation coefficient threshold, the thermal imaging device and radar device of the single pan-tilt platform are used to perform intrusion detection processing on the drone.

8. The method for detecting and protecting drone intrusion based on panoramic thermal imaging according to claim 5, wherein: Intrusion prevention is performed based on the analysis results of the drone's coordinates, including: According to the analysis results of the coordinates of the drone, the coordinates of the drone are sent to the protection drone in real time; The protection drone is used to perform intrusion protection processing on the drone.

9. A UAV intrusion detection and protection system based on panoramic thermal imaging, using the UAV intrusion detection and protection method based on panoramic thermal imaging according to any one of claims 1 to 8, specifically comprising: Busyness assessment module, detection result analysis module, setting strategy determination module, protection processing module; The busyness evaluation module is responsible for determining whether the detection processing busyness of the target area meets the requirements based on the analysis results of historical intrusion data; The detection result analysis module is responsible for determining whether the thermal imaging device can be used for intrusion detection processing; The setting strategy determination module is responsible for determining the setting strategy of the PTZ in the target area; The protection processing module is responsible for performing intrusion protection processing based on the analysis results of the drone's coordinates.

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