Airport bird detecting and repelling integrated system and method

Abstract

The invention relates to the technical field of airport safety protection, and discloses an airport bird detecting and repelling integrated system, which comprises a multi-band phased array radar detection module for scanning the peripheral area of an airport in real time to obtain the initial positioning information of a bird target; the photoelectric tracking and monitoring module is used for providing the preliminary positioning information according to the radar detection module, and the invention further discloses an airport bird detecting and repelling method which comprises the following steps: detecting the peripheral area of an airport through the multi-band phased array radar to obtain the preliminary positioning information of birds. According to the invention, by adopting a cooperative detection technology of the multi-band phased array radar, the photoelectric tracking equipment and the infrared thermal imager, real-time monitoring and dynamic tracking of bird targets in an airport area are realized, and the defects of limited detection range and multiple monitoring blind areas in the prior art are overcome; meanwhile, a pulse type laser radar is linked with an FOD detection radar, and a closed-loop mechanism for quickly repelling birds and cleaning foreign matters is established.

Description

Technical Field

[0001] The present invention relates to the technical field of airport safety protection, and in particular to an airport bird detection and bird repelling integrated system and method. Background Art

[0002] In modern airport operations, bird strikes are a key problem affecting aviation safety. The uncertainty of bird activities and the complexity of the airport area environment make existing technologies face multiple challenges in bird detection and bird repelling. For example, the current single detection method usually relies on single-band radar or optical equipment, which has obvious limitations in coverage, detection accuracy and environmental adaptability, especially in low light conditions or bad weather environments, making it difficult to accurately identify and track targets. This limitation directly leads to the existence of detection blind spots, making it impossible to achieve efficient monitoring of the entire airport area.

[0003] Existing bird-repelling technologies also have shortcomings. Many traditional bird-repelling methods rely on sound waves, gas explosions, and other methods. Although these bird-repelling methods have a certain effect on birds in a short period of time, their impact range is limited, and birds quickly adapt to such stimulation, resulting in the gradual weakening of the bird-repelling effect. In addition, bird-repelling and subsequent cleaning work are mostly handled manually, and usually cannot form a linked closed loop. Once bird debris or other foreign objects are left on the runway surface, the cleaning response time is long, which is likely to affect the normal progress of subsequent flight operations and may even cause secondary safety hazards.

[0004] At the same time, existing bird detection and bird repelling systems generally lack coordination capabilities. Detection equipment and repelling means usually operate independently, making it difficult to form real-time data sharing and linkage operations. This technology separation model is inefficient when bird targets move quickly or in groups, making it difficult for airports to respond to complex bird activity dynamics in a timely manner. In addition, traditional systems have weak adaptability to new bird behavior patterns and environmental changes, lack a dynamic update mechanism, and the system performance decreases after long-term operation, increasing the difficulty of maintenance and optimization.

[0005] In summary, existing technologies cannot fully cope with the complexity and dynamic requirements of bird strike prevention and control at airports, and there are problems such as detection blind spots, unsustainable bird repelling effects, low cleaning efficiency, and lack of system linkage. There is an urgent need for an integrated system that can achieve all-weather, all-day, efficient bird detection, bird repelling, and cleaning operations to fundamentally improve the safety and efficiency of airport operations. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides an integrated system and method for airport bird detection and bird repelling, which solves the problems in the prior art that bird detection, bird repelling and foreign object cleaning are separated, and have low efficiency and poor adaptability.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an airport bird detection and bird repelling integrated system, comprising: Multi-band phased array radar detection module, used to scan the area around the airport in real time and obtain preliminary positioning information of bird targets; An optoelectronic tracking and monitoring module is used to perform high-precision observation of the target area and obtain high-definition images and movement trajectories of birds based on the preliminary positioning information provided by the radar detection module; An artificial intelligence analysis module is used to analyze the data obtained by the optoelectronic tracking and monitoring module to determine the type, quantity, flight posture, speed and flight trajectory of the birds, and output threat assessment results; A laser expulsion module, used to expel birds from a target area by adjusting laser emission parameters according to the threat assessment result output by the artificial intelligence analysis module; FOD foreign object detection and cleaning module, used to detect and locate foreign objects that may be left after laser expulsion, and complete foreign object cleaning through cleaning equipment; The data collaboration and update module is used to integrate data from various modules of the system and dynamically update the artificial intelligence model.

[0008] Preferably, the multi-band phased array radar detection module includes: A radar transmitting unit, used for transmitting multi-band radar waves; A radar receiving unit, used for receiving radar wave signals reflected by birds; A signal processing unit, used to process the received radar wave signal and calculate the reflection characteristics, position, speed and direction of the bird; Control unit, used to dynamically adjust the radar's transmission power and frequency.

[0009] Preferably, the signal processing unit calculates the speed of the bird using a Doppler frequency shift formula, which is as follows: Among them, f d is the Doppler shift, v is the relative speed of the bird, and λ is the radar wavelength; The signal processing unit calculates the bird reflection characteristics by using the target reflection cross-sectional area formula, which is as follows: Where σ is the bird reflection cross-sectional area, R is the target distance, |E s | 2 is the reflected wave intensity, |E i | 2 is the incident wave intensity.

[0010] Preferably, the photoelectric tracking and monitoring module includes: High-resolution CCD camera unit for high-definition imaging of the target area located by the radar; Infrared thermal imaging unit to detect the thermal radiation signature of birds at night or in low light conditions; A tracking control unit that adjusts the angle and focus of the camera and thermal imaging device based on the radar positioning data; Data output unit for delivering captured high-definition images and videos to the artificial intelligence analysis module.

[0011] Preferably, the artificial intelligence analysis module includes: A data input unit, used for receiving image and video data provided by the optoelectronic tracking and monitoring module; An image feature extraction unit, used to extract bird species, appearance features, quantity and flight posture information through a convolutional neural network; A time series analysis unit, which is used to predict the flight speed of birds and their flight trajectory in the next few seconds based on a recurrent neural network; A threat assessment unit is used to determine the threat level of bird activities to flight safety based on the analysis results; The output unit is used to transmit the analysis results and threat assessment results to the laser expulsion module.

[0012] Preferably, the laser expulsion module comprises: A laser emitting unit, used for emitting pulsed laser; A laser tracking unit, which is used to adjust the direction and angle of laser emission in real time according to the target position and trajectory provided by the artificial intelligence analysis module; Laser control unit, used to adjust the power and emission frequency of the laser to meet the needs of expelling different bird targets; The expulsion feedback unit is used to record the expulsion effect and transmit the information to the FOD foreign object detection and cleaning module.

[0013] Preferably, the FOD foreign object detection and cleaning module includes: A foreign object detection unit, which detects foreign objects that may be left behind after laser expulsion using millimeter-wave radar; A foreign body locating unit, used to mark the location of foreign bodies and generate a cleaning path; A cleaning execution unit, used to complete the cleaning task of foreign matter through automated cleaning equipment; The data output unit is used to transmit the cleaned information to the data coordination and update module.

[0014] A bird detection and bird repelling method at an airport comprises the following steps: Use multi-band phased array radar to detect the area around the airport and obtain preliminary positioning information of birds; According to the radar positioning information, the optoelectronic tracking and monitoring module is activated to obtain high-definition images and movement trajectories of birds; The acquired data is input into the artificial intelligence analysis module to identify the bird species, quantity, flight posture, speed, and predict future flight trajectories; The laser expulsion module is activated based on the AI ​​analysis results, and the laser emission parameters are adjusted to expel birds from the target area; The FOD foreign object detection and cleaning module detects and cleans the target area after laser expulsion to ensure ground operation safety; Dynamically update AI models through data collaboration and update modules.

[0015] The present invention provides an integrated system and method for detecting and repelling birds at an airport, which has the following beneficial effects: 1. The present invention adopts the combined detection technology of multi-band phased array radar, optoelectronic tracking equipment and infrared thermal imager. By rationally arranging these devices in key areas of the airport and establishing a unified monitoring and data processing center, the real-time detection, continuous monitoring and dynamic tracking of bird targets are realized. Compared with the problems of limited detection range and more monitoring blind spots of single equipment in the prior art, the present invention solves the problems of insufficient detection coverage and weak real-time monitoring through the collaborative work of multiple devices and efficient data interaction, and achieves the technical effect of seamless monitoring of the entire airport area. Especially at both ends of the runway and around the taxiway, the equipment is linked through a high-speed network, which improves the overall response speed and accuracy of the system.

[0016] 2. The present invention adopts the bird-repelling and cleaning linkage technology of pulsed laser radar combined with FOD detection radar, and establishes a closed-loop protection mechanism for rapid bird-repelling and foreign object cleaning. The laser radar is used to accurately expel the flock of birds while maintaining dynamic tracking. Once a foreign object is found and shot down, the FOD radar quickly locates and guides the cleaning equipment to remove the foreign object. Compared with the prior art solution in which bird-repelling and cleaning work are separated and the response is delayed, the problem of slow cleaning speed and residual foreign objects affecting airport operations is solved. The present invention achieves the technical effect of rapid response and efficient cleaning, and significantly improves the safety of the airport operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system framework diagram of the present invention; Figure 2 This is a framework diagram of a multi-band phased array radar detection module of the present invention; Figure 3 The photoelectric tracking and monitoring module framework diagram of the present invention; Figure 4 This is a framework diagram of the artificial intelligence analysis module of the present invention; Figure 5This is a framework diagram of the laser expulsion module of the present invention; Figure 6 This is a framework diagram of the FOD foreign matter detection and cleaning module of the present invention; Figure 7 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Please see attached Figure 1 The embodiment of the present invention provides an airport bird detection and bird repelling integrated system, including: Multi-band phased array radar detection module, used to scan the area around the airport in real time and obtain preliminary positioning information of bird targets; An optoelectronic tracking and monitoring module is used to perform high-precision observation of the target area and obtain high-definition images and movement trajectories of birds based on the preliminary positioning information provided by the radar detection module; An artificial intelligence analysis module is used to analyze the data obtained by the optoelectronic tracking and monitoring module to determine the type, quantity, flight posture, speed and flight trajectory of the birds, and output threat assessment results; A laser expulsion module, used to expel birds from a target area by adjusting laser emission parameters according to the threat assessment result output by the artificial intelligence analysis module; FOD foreign object detection and cleaning module, used to detect and locate foreign objects that may be left after laser expulsion, and complete foreign object cleaning through cleaning equipment; The data collaboration and update module is used to integrate data from various modules of the system and dynamically update the artificial intelligence model.

[0020] Specifically, in this embodiment, the multi-band phased array radar detection module is mainly used to cover the area around the airport, perform real-time, all-round rapid scanning of bird targets, and complete the preliminary positioning of the targets.

[0021] Generally, the module includes a radar transmitting unit, a radar receiving unit, a signal processing unit and a control unit. The radar transmitting unit adopts multi-band transmission technology and supports a wide frequency range of 2GHz to 18GHz to meet the reflection characteristics of different bird sizes. The radar receiving unit can receive echo signals in real time, process multipath scattering signals, and filter out environmental noise.

[0022] Specifically, the signal processing unit calculates the body characteristics of the bird using the radar reflection cross-sectional area formula, which is as follows: Where σ represents the radar reflection cross-sectional area, R is the distance between the bird and the radar, |E s | 2 and |E i | 2 represent the electric field strength of the reflected wave and the incident wave respectively.

[0023] As a possible implementation method, the control unit combines Doppler frequency shift technology to calculate the movement speed of the bird. The specific calculation formula is: Among them, f d represents Doppler frequency shift, v is the target speed, and λ is the radar emission wavelength. By calculating the speed and direction in real time, the radar module can quickly determine the movement trend and potential threat of birds.

[0024] In some embodiments, the module has a scanning radius of 5 km to 15 km and a scanning frequency of 50 to 300 times per second, which can quickly capture the initial activity range of birds.

[0025] In this embodiment, the optoelectronic tracking and monitoring module achieves high-precision observation of the radar positioning target area through high-resolution optical and thermal imaging equipment.

[0026] In one possible implementation, the optoelectronic module includes a high-resolution CCD camera unit, an infrared thermal imaging unit, a tracking control unit, and a data output unit. The CCD camera unit has a pixel count of 10 million to 20 million, a focal length range of 20 mm to 1000 mm, and supports high-speed continuous shooting of 5 to 60 frames per second to ensure the capture of the fast flying movements of birds. The infrared thermal imaging unit works at night, with a detection wavelength range of 3 microns to 12 microns and a thermal sensitivity better than 0.05°C.

[0027] Specifically, the tracking control unit can adjust the angle and focal length of the optical device according to the positioning data provided by the radar to maintain continuous tracking of the target area. Generally, the data output unit will directly transmit the high-definition image and trajectory data to the artificial intelligence analysis module for further processing.

[0028] As an option, the optoelectronic module can be used in conjunction with a background modeling algorithm to eliminate noise interference in complex scenes and achieve clear identification of targets.

[0029] In this embodiment, the artificial intelligence analysis module performs intelligent analysis on the species, quantity, flight posture, speed and trajectory of birds based on deep learning technology, and outputs threat assessment results.

[0030] In a possible implementation, the analysis module is composed of a data input unit, an image feature extraction unit, a time series analysis unit, a threat assessment unit and an output unit. The data input unit receives the image and trajectory data acquired by the photoelectric module, and the image feature extraction unit uses a convolutional neural network to extract features such as the bird's shape contour and feather texture.

[0031] The time series analysis unit predicts the future trajectory of birds through a recurrent neural network. The prediction formula is: in, represents the target position at time t+1, represents the target position at time t, h t is the hidden state at time t.

[0032] Specifically, the threat assessment unit calculates the threat level of bird activities to flight safety based on the target speed, trajectory and danger level of the airport area, and transmits the analysis results to the laser expulsion module.

[0033] In some embodiments, the module can identify 500 to 1000 species of birds, and the flight trajectory prediction time range is 5 seconds to 30 seconds.

[0034] In this embodiment, the laser expulsion module achieves accurate expulsion of birds in the target area by adjusting laser parameters according to the results of artificial intelligence analysis.

[0035] The wavelength of the laser emission unit is 532 nanometers (visible green light) or 1064 nanometers (near infrared), the pulse energy range is 0.1 joule to 5 joule, and the repetition frequency is 10 Hz to 100 Hz. The power density of the laser beam satisfies the following formula: Where E represents energy density, P t is the laser emission power, and A is the laser action area.

[0036] As an option, the laser tracking unit combines the target trajectory data provided by the analysis module to adjust the direction and angle of laser emission in real time to ensure efficient action on bird targets.

[0037] Generally, the module can also record the feedback data after expulsion and pass it to the foreign object detection module.

[0038] In this embodiment, the FOD foreign object detection and cleaning module is responsible for detecting whether there are foreign objects left in the target area after laser expulsion, and completing the cleaning through automated equipment.

[0039] The foreign object detection unit uses millimeter wave radar, with a detection depth of 1 cm to 5 cm below the surface and a positioning accuracy of ±1 cm to ±5 cm. The foreign object positioning unit generates a cleaning path based on the detection results and guides the cleaning equipment to complete the task.

[0040] Specifically, the cleaning execution unit can complete the recovery of foreign objects through automated vehicles or robotic arms to ensure the safe operation of the runway and surrounding environment.

[0041] As a possible implementation method, this module forms a closed loop with the feedback data of the laser expulsion module to further optimize the detection and cleaning process.

[0042] In this embodiment, the data collaboration and update module is responsible for integrating the data flows of each module in the system and dynamically optimizing the artificial intelligence model through incremental learning technology.

[0043] Generally, the module will regularly collect new bird data, including images, videos and trajectory information. Through online learning or incremental learning technology, the analysis module can adapt to changes in bird activities around the airport and improve recognition accuracy and prediction accuracy.

[0044] As an option, the module can also perform statistical analysis of historical data to support airport bird strike prevention and control strategies.

[0045] Please see attached Figure 2 , the multi-band phased array radar detection module includes: A radar transmitting unit, used for transmitting multi-band radar waves; A radar receiving unit, used for receiving radar wave signals reflected by birds; A signal processing unit, used to process the received radar wave signal and calculate the reflection characteristics, position, speed and direction of the bird; Control unit, used to dynamically adjust the radar's transmission power and frequency.

[0046] The signal processing unit calculates the speed of the bird using the Doppler frequency shift formula, which is as follows: Among them, f d is the Doppler shift, v is the relative speed of the bird, and λ is the radar wavelength; The signal processing unit calculates the bird reflection characteristics by using the target reflection cross-sectional area formula, which is as follows: Where σ is the bird reflection cross-sectional area, R is the target distance, |E s | 2 is the reflected wave intensity, |E i | 2 is the incident wave intensity.

[0047] Specifically, in this embodiment, the radar transmitting unit is responsible for transmitting multi-band radar waves to the target area, covering S band (2-4GHz), C band (4-8GHz) and X band (8-12GHz) to meet the detection needs of birds of different sizes. Generally speaking, low-frequency radar is more suitable for detecting larger targets, while high-frequency radar has higher resolution for small targets.

[0048] The radar receiving unit is used to receive radar wave signals reflected by birds. The unit supports the capture of multipath signals and can identify real target signals in complex environments. Specifically, the receiving unit ensures the detection accuracy of the radar under high noise conditions through broadband receiving technology and high dynamic range processor.

[0049] In this embodiment, the signal processing unit processes the received radar wave signal and extracts the motion parameters and physical characteristics of the target by analyzing the frequency, amplitude and phase changes of the signal.

[0050] In one possible implementation, the unit calculates the relative speed of the bird using the Doppler frequency shift formula. The formula is as follows: Among them, f d is the Doppler shift, v is the relative speed of the bird, and λ is the wavelength of the radar wave.

[0051] Specifically, when a bird target approaches the radar, the frequency of the reflected wave increases; when the target moves away, the frequency of the reflected wave decreases. By comparing the frequency changes of the transmitted and received waves, the signal processing unit can calculate the relative speed of the target in real time.

[0052] In some embodiments, the signal processing unit dynamically adjusts the value of λ in combination with the radar transmission frequency band to improve the speed calculation accuracy in different frequency bands. For example, the X-band (wavelength 3 cm) has a higher speed accuracy in detecting small bird targets, while the S-band (wavelength 10 cm) is suitable for capturing the overall movement trend of large targets.

[0053] In addition, the unit also calculates the radar reflection characteristics of birds using the target reflection cross-sectional area formula, which is as follows: Among them, σ is the target reflection cross-sectional area, R is the distance between the target and the radar, |E s | 2 and |E i | 2 represent the electric field strength of the reflected wave and the incident wave respectively.

[0054] In general, the reflection cross-sectional area σ is a comprehensive reflection of the bird's body shape, feather density and posture. By analyzing the radar echo, the signal processing unit can distinguish the characteristic differences between birds and other targets (such as drones and wind turbine blades). In some specific application scenarios, the unit forms a dynamic characteristic curve of the target by accumulating echo data to further improve the target recognition capability.

[0055] In this embodiment, the control unit is mainly responsible for dynamically adjusting the radar's transmission power and frequency to optimize the target detection effect. Specifically, when the number of birds in the detection area is dense, the control unit will reduce the transmission power to avoid interference superposition of target signals; when the target is far away, the power will be increased to enhance the signal reflection intensity.

[0056] As an option, the unit can also improve the detection resolution of the target by dynamically adjusting the frequency. For example, when the target is a small bird, the control unit will select a higher radar transmission frequency band to improve the radar's ability to distinguish small targets; when the target is a larger flock of birds, it will switch to a lower frequency band to expand the detection range.

[0057] In another possible implementation, the control unit cooperates with the signal processing unit to dynamically adjust the transmission angle through real-time analysis of the echo signal to ensure that the target is always within the coverage of the radar beam. This collaborative mechanism can significantly improve the radar's ability to detect fast-moving targets, especially in an environment with frequent bird activities at an airport.

[0058] Please see attached Figure 3 , the photoelectric tracking and monitoring module includes: High-resolution CCD camera unit for high-definition imaging of the target area located by the radar; Infrared thermal imaging unit to detect the thermal radiation signature of birds at night or in low light conditions; A tracking control unit that adjusts the angle and focus of the camera and thermal imaging device based on the radar positioning data; Data output unit for delivering captured high-definition images and videos to the artificial intelligence analysis module.

[0059] Specifically, in this embodiment, a high-resolution CCD camera unit is used to perform high-definition imaging of the target area located by the radar. Generally, the number of pixels of a CCD camera is between 10 million and 20 million, and supports a resolution of up to 1920×1080 or higher. The unit uses a large zoom ratio lens with a focal length range of 20 mm to 1000 mm, and the imaging ratio can be flexibly adjusted according to the target distance and size.

[0060] Specifically, during the day or in good lighting conditions, the CCD camera unit can capture the detailed features of birds, including feather texture, morphological contours, and movement posture. As a possible implementation method, the unit is equipped with a high-speed continuous shooting function that supports an image capture rate of 5 to 60 frames per second, ensuring that even when the bird is flying fast, its key movement moments can be accurately captured.

[0061] In some embodiments, the high-resolution CCD camera is also equipped with automatic exposure and dynamic range adjustment functions to maintain the stability of imaging quality in a complex background with alternating strong light or shadows.

[0062] In this embodiment, the infrared thermal imaging unit is mainly used to detect the thermal radiation characteristics of birds at night or in low light conditions. The detection wavelength range of the thermal imaging device is 3 microns to 12 microns, and the thermal sensitivity is better than 0.05°C, which can identify bird targets in an environment with large background temperature fluctuations.

[0063] Specifically, the infrared thermal imaging unit captures the temperature difference between birds and the surrounding environment. Birds generally have a higher body temperature, especially at night, and their thermal radiation characteristics are significantly higher than the background temperature. Through thermal imaging images, birds can be clearly distinguished from other low-temperature background objects.

[0064] As an option, the infrared thermal imaging unit can also be used with digital image processing algorithms to enhance thermal imaging images, including edge sharpening and noise suppression, which can significantly improve the identifiability of bird targets under low signal-to-noise ratio conditions.

[0065] In some embodiments, the infrared thermal imaging unit supports temperature analysis and area calibration functions of remote targets to assist in determining the activity status and threat level of bird targets.

[0066] In this embodiment, the tracking control unit is used to dynamically adjust the angle and focal length of the camera and thermal imaging device according to the radar positioning data to ensure that the target is always within the observation range. The unit realizes the high-precision tracking capability of the device through the linkage of the servo motor and the angle adjustment mechanism.

[0067] Specifically, the tracking control unit receives the target location information provided by the radar, including the target's distance, speed and direction data. Based on this data, the unit calculates the optimal observation angle and focal length of the device in real time, and automatically adjusts the direction and optical parameters of the imaging device.

[0068] In one possible implementation, the unit also has a predictive control function. By combining the speed and trajectory information of the target, the device's observation direction is adjusted in advance to avoid observation deviations caused by the target's rapid movement.

[0069] In some embodiments, the tracking control unit also supports a multi-target switching function. When the radar locates multiple bird targets, the tracking control unit can quickly switch the observation direction and focal length of the device between different targets to achieve observation of multiple targets in turn.

[0070] In this embodiment, the data output unit is used to transmit the captured high-definition images and videos to the artificial intelligence analysis module for subsequent target type identification, trajectory prediction and threat assessment. The unit supports both wired and wireless transmission modes.

[0071] Generally, the data output unit is connected to the analysis module via optical fiber or high-speed Ethernet, with a transmission bandwidth of 1Gbps to 10Gbps, ensuring real-time transmission of high-definition video data. In scenarios that require flexible deployment, wireless transmission technologies such as 5G or Wi-Fi6 can be used, with a transmission distance of up to hundreds of meters.

[0072] As a possible implementation method, the unit also has a data compression function, which can losslessly compress high-definition video data, reduce the pressure of data storage and transmission, and ensure data quality.

[0073] In some embodiments, the data output unit also supports edge computing functions, which can perform preliminary processing on the image and video data on the device side, such as denoising, cropping, and target calibration, and transmit the streamlined data to the analysis module, thereby improving the overall efficiency of the system.

[0074] Please see attached Figure 4 , the artificial intelligence analysis module includes: A data input unit, used for receiving image and video data provided by the optoelectronic tracking and monitoring module; An image feature extraction unit, used to extract bird species, appearance features, quantity and flight posture information through a convolutional neural network; A time series analysis unit, which is used to predict the flight speed of birds and their flight trajectory in the next few seconds based on a recurrent neural network; A threat assessment unit is used to determine the threat level of bird activities to flight safety based on the analysis results; The output unit is used to transmit the analysis results and threat assessment results to the laser expulsion module.

[0075] Specifically, in this embodiment, the data input unit is used to receive the image and video data captured by the optoelectronic tracking and surveillance module. Generally, the unit supports high-speed data transmission interfaces, including optical fiber, Ethernet (1Gbps to 10Gbps) or wireless 5G communication modules, to ensure real-time data transmission.

[0076] In a possible implementation, the data input unit can convert the format of the received data, for example, split the video data into continuous frame images and add time stamp information to each frame to facilitate subsequent time series analysis.

[0077] In some embodiments, the data input unit also supports basic pre-processing functions, such as cropping, scaling, denoising, and other operations on image data to improve data processing efficiency and model prediction accuracy.

[0078] In this embodiment, the image feature extraction unit uses a convolutional neural network to extract multi-dimensional information such as the type, appearance characteristics, quantity, and flight posture of the bird. The architecture of the convolutional neural network can select the classic ResNet, VGG or its optimized version to meet the task requirements of bird feature recognition.

[0079] Specifically, the image feature extraction unit extracts the spatial features of the image through layer-by-layer convolution, pooling, and full connection operations. Taking bird species recognition as an example, the unit finally outputs the category probability of the bird through the Softmax classification layer: Among them, P(y=i|x) is the probability that the image belongs to the i-th bird species, z i is the activation value of the i-th category, and k is the total number of bird species.

[0080] In one possible implementation, the image feature extraction unit also supports population estimation based on density maps. Specifically, the unit generates a target density distribution map and calculates the number of birds in the image by integrating the density map: N = ∫ Ω D(x,y)dxdy Where N is the number of birds in the image, and D(x,y) is the target density value of the pixel point (x,y).

[0081] In some embodiments, the unit can also extract flight attitude information, such as gliding, flapping flight, hovering, etc., identify the flight attitude through a specific classification model, and pass the classification results to subsequent units.

[0082] In this embodiment, the time series analysis unit is used to predict the flight speed and future trajectory of the bird based on a recurrent neural network (RNN). The structure of the RNN can select a classic LSTM (Long Short-Term Memory Network) or GRU (Gated Recurrent Unit) architecture to process the time series characteristics of the photoelectric data.

[0083] Specifically, this unit predicts the trajectory coordinates of the bird in the next few seconds through the time sequence information of consecutive frame images. The trajectory prediction model can be expressed as: in, is the predicted target position at time t+1, is the target position at time t, h t is the hidden state at time t.

[0084] In one possible implementation, the unit modifies the trajectory based on the target's velocity information, where the velocity is calculated based on the target position change between frames: Where v is the target velocity, Δd is the change distance of the target position between two frames, and Δt is the time interval between two frames.

[0085] In some embodiments, the unit supports parallel prediction of the trajectories of multiple targets and determines whether there is a possibility of flight conflict between birds through trajectory overlap analysis.

[0086] In this embodiment, the threat assessment unit is used to comprehensively determine the threat level of bird activities to flight safety based on the results of the image feature extraction unit and the time series analysis unit. Generally, the unit matches and analyzes the type, number, flight speed, and trajectory of the birds with the dynamic information of the airport flight area.

[0087] Specifically, the threat assessment unit uses the threat assessment formula based on the distance of the bird's activity from the aircraft, its flight direction and speed: T=f(d,v,θ,N) Among them, T is the threat level, d is the distance between the target and the aircraft, v is the target speed, θ is the angle between the target flight direction and the aircraft path, and N is the number of targets.

[0088] In some embodiments, the unit supports custom threat threshold settings. When the threat level of a target exceeds the set threshold, the evaluation unit will determine that the target is high risk and notify the subsequent expulsion module.

[0089] In this embodiment, the output unit is used to transmit the analysis results and threat assessment results to the laser expulsion module. Generally, the output unit transmits information in a standardized data format, including the target's location information, speed, trajectory prediction, and threat level.

[0090] As an option, the output unit supports multimodal communication methods, such as TCP / IP protocol for local data transmission or MQTT protocol for message passing in distributed systems.

[0091] In some embodiments, the output unit may also record historical data and store it in a system database to facilitate subsequent model optimization and analysis of system operation records.

[0092] Please see attached Figure 5 , the laser expulsion module comprises: A laser emitting unit, used for emitting pulsed laser; A laser tracking unit, which is used to adjust the direction and angle of laser emission in real time according to the target position and trajectory provided by the artificial intelligence analysis module; Laser control unit, used to adjust the power and emission frequency of the laser to meet the needs of expelling different bird targets; The expulsion feedback unit is used to record the expulsion effect and transmit the information to the FOD foreign object detection and cleaning module.

[0093] Specifically, in this embodiment, the laser emission unit is mainly used to emit pulsed lasers, and its specific emission parameters are adjusted according to the target characteristics and distance. Generally, the unit can select a laser with a wavelength of 532 nanometers (visible green light) or 1064 nanometers (near infrared) to adapt to the visual sensitivity of different bird targets.

[0094] Specifically, the pulse energy range of the laser emission unit is 0.1 joule to 5 joule, and the repetition frequency is between 10 Hz and 100 Hz. The emission power of the pulsed laser can be dynamically adjusted according to the target distance to ensure that the laser energy density meets the expulsion requirements. The calculation formula for laser energy density is: Where E is the laser energy density, P t is the laser emission power, and A is the beam action area.

[0095] In one possible implementation, the laser emitting unit also has a beam focusing function, which can concentrate the laser beam on a specific small area, thereby improving the expulsion efficiency.

[0096] In some embodiments, the laser emission unit is equipped with multiple lasers to support simultaneous expulsion operations on multiple targets.

[0097] In this embodiment, the laser tracking unit realizes real-time adjustment of the laser emission direction and angle through a servo control system. The unit receives the target position and trajectory information provided by the artificial intelligence analysis module, and dynamically calculates the laser emission path according to the movement trend of the target.

[0098] Specifically, the laser tracking unit uses a dual-axis servo motor system that can achieve precise adjustment in the horizontal and vertical directions, with an angle adjustment accuracy better than 0.1°. The core algorithm of the tracking unit is based on a predictive control model, which can estimate the target position in advance and avoid launch deviations caused by rapid target movement.

[0099] As an option, the laser tracking unit also supports multi-target switching mode. When multiple bird targets enter the expulsion range, the unit can quickly switch the direction of laser emission to expel each target in turn.

[0100] In some embodiments, the laser tracking unit is equipped with a gyro stabilization system for maintaining the stability of laser emission under complex meteorological conditions (such as strong winds or vibrations).

[0101] In this embodiment, the laser control unit is used to adjust the power, frequency and wavelength of the laser to meet the needs of driving away different bird targets. Generally, the unit dynamically adjusts the emission parameters of the laser according to the type, number and distance of the target.

[0102] Specifically, the laser control unit controls the interference intensity of the laser by adjusting the pulse energy and repetition frequency. For example, for smaller bird targets, lower power and frequency can be used to avoid excessive expulsion; while for large bird targets, the energy density of the laser needs to be increased to enhance the expulsion effect.

[0103] In one possible implementation, the laser control unit dynamically selects the laser wavelength based on the visual characteristics of the target. For example, a 532-nanometer wavelength is more visually stimulating to birds and is suitable for close-range expulsion, while a 1064-nanometer wavelength is suitable for long-range effects.

[0104] In some embodiments, the laser control unit also has an energy limiting function to ensure that the laser power always remains within a safe range to avoid causing permanent damage to birds or interference with other airport equipment.

[0105] In this embodiment, the expulsion feedback unit is used to record the effect of the expulsion operation, including key information such as whether the target has left, expulsion time, laser parameters, etc. The unit transmits feedback data to the FOD foreign object detection and cleaning module to provide a reference for subsequent foreign object processing.

[0106] Specifically, the expulsion feedback unit monitors the target changes in the laser action area in real time to determine whether the expulsion effect has achieved the expected result. For example, by monitoring the target's movement trajectory, it can confirm whether the bird has left the key area of ​​the airport.

[0107] As an option, the eviction feedback unit also supports the storage of historical data, which can record the parameters and results of multiple eviction operations. This data can be used for subsequent model optimization and eviction strategy adjustment.

[0108] In some embodiments, the expulsion feedback unit works in conjunction with the artificial intelligence analysis module to support recalibration and analysis of unsuccessful expulsion targets to ensure the overall expulsion effect of the system.

[0109] Please see attached Figure 6 , the FOD foreign object detection and cleaning module includes: A foreign object detection unit, which detects foreign objects that may be left behind after laser expulsion using millimeter-wave radar; A foreign body locating unit, used to mark the location of foreign bodies and generate a cleaning path; A cleaning execution unit, used to complete the cleaning task of foreign matter through automated cleaning equipment; The data output unit is used to transmit the cleaned information to the data coordination and update module.

[0110] Specifically, in this embodiment, the foreign object detection unit detects the target area through millimeter wave radar to identify foreign objects that may be left after laser expulsion. Millimeter wave radar operates in the 24GHz to 77GHz frequency band, has high resolution and strong penetration ability, and can effectively detect foreign objects on the runway surface and shallow underground.

[0111] Specifically, the unit uses the reflection characteristics of radar to quickly capture the location information of foreign objects based on the difference in reflected signals between the material of the foreign object and the background ground. The target detection range is 0.5 km to 5 km, the detection depth can reach 1 cm to 5 cm below the surface, and the positioning accuracy is better than ± 1 cm.

[0112] As an option, the foreign object detection unit uses a multi-antenna array configuration combined with multi-beam scanning technology to achieve rapid coverage of large areas. At the same time, the unit can distinguish between bird remains, conventional objects on the airport ground (such as tools or debris) and irrelevant background noise in combination with a target feature database.

[0113] In some embodiments, the foreign object detection unit also supports dynamic adjustment of radar transmission power to meet the detection requirements in complex environments. For example, when there are metal fragments in the target area, the radar transmission power can be increased to enhance the strength of the echo signal.

[0114] In this embodiment, the foreign object positioning unit is used to mark the specific location of the foreign object according to the radar echo data provided by the detection unit and generate a cleaning path. Generally, the unit combines the high-resolution characteristics of the millimeter-wave radar and calculates the precise coordinates of the foreign object through the spatial distribution of the echo signal.

[0115] Specifically, the foreign object positioning unit uses a target segmentation algorithm to separate the high-reflection area in the radar echo signal from the background signal, and further extracts the geometric shape and position parameters of the target. The positioning accuracy can be controlled within the range of ±1 cm.

[0116] In a possible implementation, the unit uses a path planning algorithm to generate an optimal cleaning path for the cleaning device according to the spatial distribution of the target. For example, based on the A* algorithm or the Dijkstra algorithm, the moving trajectory of the cleaning device is optimized in combination with the number, location and cleaning priority of the target to reduce the cleaning time and energy consumption.

[0117] In some embodiments, the foreign object locating unit also supports dynamic adjustment of target priority. For example, foreign objects close to the centerline of the runway can be set as the priority cleaning target, while foreign objects far away from the critical area can be processed later.

[0118] In this embodiment, the cleaning execution unit completes the cleaning task of foreign matter through an automated cleaning device. The cleaning device can be a robot cleaning vehicle or a mechanical arm with high-precision navigation and multi-functional cleaning capabilities.

[0119] Specifically, the cleaning equipment receives the path planning data provided by the positioning unit and moves along the set path to the location of the foreign object. The robot cleaning vehicle is equipped with an adsorption device, a mechanical gripper and a garbage collection box, and can handle foreign objects of different sizes and materials. The cleaning speed is usually 1 m / s to 3 m / s, and it can complete the cleaning of a large area in a short time.

[0120] As a possible implementation method, the cleaning equipment is also integrated with a real-time monitoring system, which can take images before and after the foreign matter is cleaned during the cleaning process and upload them to the system storage through a data output unit.

[0121] In some embodiments, the cleaning device supports a multi-device collaborative working mode. For example, in the case of a large-scale distribution of foreign matter, multiple robot cleaning vehicles can coordinate cleaning tasks through a central scheduling system to improve cleaning efficiency.

[0122] In this embodiment, the data output unit is used to transmit the information after the cleaning is completed to the data coordination and update module. The output information includes the type, quantity, location, cleaning time and confirmation status of the cleaning completion of the foreign matter.

[0123] Generally, the data output unit is connected to the system database through a standardized interface to support real-time data upload and storage. As an option, the output unit can also transmit the cleaning results to the airport operation management system through a wireless communication module (such as Wi-Fi or 5G) to achieve synchronous monitoring of the cleaning status.

[0124] In some embodiments, the data output unit also supports statistical analysis of historical cleaning data. For example, by analyzing the accumulated data of the cleaning task, the operating parameters of the subsequent foreign object detection and cleaning module can be optimized to further improve the operating efficiency of the system.

[0125] Please see attached Figure 7, a bird detection and bird repelling method at an airport, comprising the following steps: Use multi-band phased array radar to detect the area around the airport and obtain preliminary positioning information of birds; According to the radar positioning information, the optoelectronic tracking and monitoring module is activated to obtain high-definition images and movement trajectories of birds; The acquired data is input into the artificial intelligence analysis module to identify the bird species, quantity, flight posture, speed, and predict future flight trajectories; The laser expulsion module is activated based on the AI ​​analysis results, and the laser emission parameters are adjusted to expel birds from the target area; The FOD foreign object detection and cleaning module detects and cleans the target area after laser expulsion to ensure ground operation safety; Dynamically update AI models through data collaboration and update modules.

[0126] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An airport bird detection and bird repelling integrated system, characterized in that: include: Multi-band phased array radar detection module, used to scan the area around the airport in real time and obtain preliminary positioning information of bird targets; An optoelectronic tracking and monitoring module is used to perform high-precision observation of the target area and obtain high-definition images and movement trajectories of birds based on the preliminary positioning information provided by the radar detection module; An artificial intelligence analysis module is used to analyze the data obtained by the optoelectronic tracking and monitoring module to determine the type, quantity, flight posture, speed and flight trajectory of the birds, and output threat assessment results; A laser expulsion module, used to expel birds from a target area by adjusting laser emission parameters according to the threat assessment result output by the artificial intelligence analysis module; FOD foreign object detection and cleaning module, used to detect and locate foreign objects that may be left after laser expulsion, and complete foreign object cleaning through cleaning equipment; The data collaboration and update module is used to integrate data from various modules of the system and dynamically update the artificial intelligence model.

2. The integrated bird detection and bird repelling system for airports according to claim 1, characterized in that: The multi-band phased array radar detection module includes: A radar transmitting unit, used for transmitting multi-band radar waves; A radar receiving unit, used for receiving radar wave signals reflected by birds; A signal processing unit, used to process the received radar wave signal and calculate the reflection characteristics, position, speed and direction of the bird; Control unit, used to dynamically adjust the radar's transmission power and frequency.

3. The integrated bird detection and bird repelling system for airports according to claim 2, characterized in that: The signal processing unit calculates the speed of the bird using the Doppler frequency shift formula, which is as follows: Among them, f d is the Doppler shift, v is the relative speed of the bird, and λ is the radar wavelength; The bird reflection characteristics are calculated in the signal processing unit by the target reflection cross-sectional area formula, which is as follows: Where σ is the bird reflection cross-sectional area, R is the target distance, |E s | 2 is the reflected wave intensity, |E i | 2 is the incident wave intensity.

4. The integrated bird detection and bird repelling system for airports according to claim 1, characterized in that: The optoelectronic tracking and monitoring module comprises: High-resolution CCD camera unit for high-definition imaging of the target area located by the radar; Infrared thermal imaging unit to detect the thermal radiation signature of birds at night or in low light conditions; A tracking control unit that adjusts the angle and focus of the camera and thermal imaging device based on the radar positioning data; Data output unit for delivering captured high-definition images and videos to the artificial intelligence analysis module.

5. The integrated bird detection and bird repelling system for airports according to claim 1, characterized in that: The artificial intelligence analysis module includes: A data input unit, used for receiving image and video data provided by the optoelectronic tracking and monitoring module; An image feature extraction unit, used to extract bird species, appearance features, quantity and flight posture information through a convolutional neural network; A time series analysis unit, which is used to predict the flight speed of birds and their flight trajectory in the next few seconds based on a recurrent neural network; A threat assessment unit is used to determine the threat level of bird activities to flight safety based on the analysis results; The output unit is used to transmit the analysis results and threat assessment results to the laser expulsion module.

6. The integrated bird detection and bird repelling system for airports according to claim 1, characterized in that: The laser expulsion module includes: A laser emitting unit, used for emitting pulsed laser; A laser tracking unit, which is used to adjust the direction and angle of laser emission in real time according to the target position and trajectory provided by the artificial intelligence analysis module; Laser control unit, used to adjust the power and emission frequency of the laser to meet the needs of expelling different bird targets; The expulsion feedback unit is used to record the expulsion effect and transmit the information to the FOD foreign object detection and cleaning module.

7. The integrated bird detection and bird repelling system for airports according to claim 1, characterized in that: The FOD foreign object detection and cleaning module includes: A foreign object detection unit, which detects foreign objects that may be left behind after laser expulsion using millimeter-wave radar; A foreign body locating unit, used to mark the location of foreign bodies and generate a cleaning path; A cleaning execution unit, used to complete the cleaning task of foreign matter through automated cleaning equipment; The data output unit is used to transmit the cleaned information to the data coordination and update module.

8. A method for detecting and repelling birds at an airport, characterized in that: The following steps are involved: Use multi-band phased array radar to detect the area around the airport and obtain preliminary positioning information of birds; According to the radar positioning information, the optoelectronic tracking and monitoring module is activated to obtain high-definition images and movement trajectories of birds; The acquired data is input into the artificial intelligence analysis module to identify the bird species, quantity, flight posture, speed, and predict future flight trajectories; The laser expulsion module is activated based on the AI ​​analysis results, and the laser emission parameters are adjusted to expel birds from the target area; The FOD foreign object detection and cleaning module detects and cleans the target area after laser expulsion to ensure ground operation safety; Dynamically update AI models through data collaboration and update modules.

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