Method for dispersing bird behavior migration in humanization airport based on anti-adaptive continuous ultrasonic neural interference

Abstract

The invention relates to the technical field of airport bird strike prevention and control and intelligent acoustic interference, in particular to a humanization airport bird behavior migration dispersing method based on anti-adaptive continuous ultrasonic neural interference, and the method comprises the steps: obtaining the number, position, flight path, moving speed and other dynamic data of bird targets in real time through a multi-dimensional sensor; according to the data and the environmental parameters, ultrasonic signals with variable parameters are directionally transmitted; under the action of ultrasonic waves, behavior responses of the birds are continuously monitored, and the key behavior parameter variable quantity serves as a feedback signal; when it is detected that bird response is weakened, an anti-adaptive algorithm is started instantly, and pseudo-random dynamic adjustment is conducted on ultrasonic parameters; by controlling the sound pressure level and the transmitting direction of the ultrasonic signals, the birds are induced to move outwards safely along the preset migration corridor. Precise dispelling, long-acting dispelling and humanization guiding of birds are achieved, and intelligence, effectiveness and safety of bird prevention and control of the airport are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of airport bird strike prevention and intelligent acoustic interference, and specifically relates to a humane airport bird behavior migration dispersing method based on anti-adaptive continuous ultrasonic neural interference. BACKGROUND

[0002] Development profile and technical evolution: With the rapid growth of China's civil aviation transportation industry, bird activities in the airport flight area and surrounding wetlands, lawns, and water areas have significantly increased, and bird strike incidents pose a persistent threat to flight safety and operational order, with seasonal migration peaks and year-round normalization coexisting. Traditional bird dispersal methods (fireworks, artificial patrol, reflective / scare devices, and falconry) have inherent shortcomings in terms of sustainability, coverage, and controllability, making it difficult to meet the operational requirements of "all-weather, grid-based, non-injurious, and quantifiable evaluation." The bird dispersal method based on acoustic stimulation has become a focus of research and engineering deployment in recent years due to its advantages of being friendly to humans, causing little electromagnetic environmental interference, and being capable of long-term automatic operation. Among them, the continuous sound pressure disturbance scheme represented by 23-30 kHz high-frequency ultrasonic signals can induce avoidance behavior by applying non-injurious stimulation to the vestibular-auditory system of birds, thereby reducing their clustering and staying probability in the runway, taxiway, apron, and green area interface. Around the engineering landing, the industry technology path is evolving from "single-point, fixed-frequency, fixed-process control" static emission to "multi-point coordination, variable-frequency modulation, rhythm management, threshold control, and energy autonomy" systematic solutions: On the one hand, through grid-based multi-directional radiation and upper-lower layer combined emission to form a three-dimensional sound field coverage, reducing dead angles and blind areas; on the other hand, combining solar / wind energy and intelligent control to realize unattended operation and self-checking alarm, and switching frequency combinations and emission rhythms according to rules in different seasons / time periods to suppress habituation and maintain long-term effectiveness. The above engineering practice shows that the effectiveness of continuous ultrasonic interference not only depends on frequency band selection and sound pressure threshold setting, but also depends on the comprehensive modulation capability of spectral complexity, phase / envelope rhythm, duty cycle, directivity, and device group coordination strategy, thereby constructing a "non-injurious, auditable, and maintainable" airport bird prevention line under the premise of safety compliance.

[0003] Deficiency and improvement demand of prior art: In the prior art, CN111972394B takes DQN as the core, reads the state through Doppler radar, and updates the Q network with a reward signal, aiming to adaptively select a "more effective" bird repelling frequency within a specified frequency band, thereby overcoming the problem of "fixed frequency leading to habituation and random occurrence of ineffective frequency points"; CN112237183A constructs a closed-loop device for monitoring, identifying, feeding back, and frequency / power adjustment, which can automatically adjust the transmission parameters according to image feedback. The above two cases have positive significance in the direction of "closed loop, self-adaptation, non-injury", but still have three limitations: first, the parameter dimension is mainly expanded in the "frequency and power" two-axis, and the key modulation dimensions such as phase, envelope rhythm, pulse duty cycle, cross-cycle pseudo-random disturbance, directivity / beam space-time switching that affect the "novelty" of neural stimulation are still lack of systematic disclosure, making it difficult to continuously inhibit bird learning-habituation to a single stimulation mode from the mechanism; second, the response trigger logic is mainly a single-cycle loop with "poor effect, readjustment", lacking a rapid linkage mechanism of "triggering anti-adaptive algorithm as soon as behavior response attenuation is detected", and the strategy coupling of guiding corridor and group dispersion quantitative indicators (such as departure rate, trajectory curvature, and scattering coefficient) aiming at "behavior migration" has not been established; third, the definition of "continuous ultrasonic signal" on the engineering side is more equivalent to "continuous emission or frequency conversion according to the program", and the "continuous neural interference flow" model with pseudo-random dynamics as the core under the joint disturbance of frequency-intensity-phase-pulse mode multi-parameters has not been established, and there is a lack of linkage upper / lower limit control and regional energy autonomy-multi-point collaborative operation guarantee under the safety threshold constraint. In summary, the existing ultrasonic bird repelling technology still has deficiencies in the depth of anti-habituation, pseudo-random linkage dimension, behavior migration guidance, and engineering autonomy guarantee. The problem solved by the present application is "under the constraint of non-injury, through multi-parameter pseudo-random linkage and behavior feedback triggered continuous neural interference, realizing the controllable migration and regional migration of airport bird groups", which belongs to the field of airport bird strike prevention and control and intelligent acoustic interference technology. SUMMARY

[0004] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification of the present application to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the following technical problems in the prior art: under the constraint of non-injury, through multi-parameter pseudo-random linkage and behavior feedback triggered continuous neural interference, realizing the controllable migration and regional migration of airport bird groups.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a humane airport bird behavior migration dispersing method based on anti-adaptive continuous ultrasonic wave neural interference, comprising,

[0007] S1, real-time detection and identification of bird targets in the airport airspace, obtaining the number, position, flight trajectory and moving speed of the bird targets;

[0008] S2, generating and directing emitting a continuous ultrasonic wave signal by the bird dispersing system according to the real-time state and environmental data of the bird targets, the frequency, intensity, phase and pulse mode of the ultrasonic wave signal are variable;

[0009] S3, continuously monitoring the behavior response of birds under the action of the ultrasonic wave signal, including but not limited to flight height, trajectory change and dispersion degree, and taking the monitoring data as feedback signal;

[0010] S4, based on the feedback signal, when the bird's stimulation response to the ultrasonic wave signal is detected to be weakened, anti-adaptive algorithm is started immediately, and the parameters of the ultrasonic wave signal are pseudo-randomly dynamically adjusted;

[0011] S5, controlling the frequency and sound pressure level of the ultrasonic wave signal, so that it only produces non-injurious stimulation and interference to the bird's auditory and nervous system, and induces the bird targets to move out along the predetermined migration corridor and leave the designated area.

[0012] The beneficial effects of the present application are: the present application takes "monitoring and identification, directional variable parameter continuous ultrasonic wave, behavior response quantification, anti-adaptive pseudo-random linkage, non-injurious threshold and migration corridor guidance" as the main line, emphasizes the immediacy of anti-adaptive triggered by "ethology feedback" and the novel maintenance mechanism of "multi-parameter joint modulation", and realizes humane induced migration under the constraint of sound pressure level, which is positioned in the coordinated optimization of airport ecological safety and operation safety. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0014] Figure 1 The present application is a method flowchart. DETAILED DESCRIPTION

[0015] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0016] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure aspects of the present application.

[0017] Second, as used in this description and in the claims, the phrase "one embodiment" or "an embodiment" as used herein does not necessarily refer to the same embodiment, although it may. Furthermore, the term "embodiment" does not require that all

[0018] Third, the application is described in relation to block diagrams which are intended to facilitate understanding of the application. In some instances, detailed

[0019] Embodiment 1

[0020] With reference to Figure 1 The embodiment provides a humane airport bird behavior migration dispersal method based on anti-adaptive sustained ultrasonic wave neural interference, and the method comprises the following steps.

[0021] S1, real-time detection and identification of bird targets in the airport airspace, obtaining the number, position, flight trajectory and moving speed of the bird targets. The step specifically comprises,

[0022] Real-time detection and identification of bird targets in the airport airspace, comprising,

[0023] Deploying a multi-dimensional sensor, the sensor comprising an optical camera, a millimeter wave radar and an acoustic sensor array, forming a three-dimensional monitoring network;

[0024] Capturing the visual image of the bird target through the optical camera, and using a deep learning model to identify the bird species and count the individuals in the image;

[0025] Obtaining the accurate three-dimensional position coordinates (x k ,y k ,z k ) and real-time speed vector

[0026] Identifying and positioning the bird's call through the acoustic sensor array, assisting in judging the species, activity and approximate position of the bird flock;

[0027] Fusion of heterogeneous data from different sensors, through multi-sensor data fusion algorithm, to construct a comprehensive three-dimensional motion model of bird targets, and to update the number, position, flight trajectory and moving speed of bird targets in real time.

[0028] For example, the method for acquiring the number and species identification of bird targets includes:

[0029] This step is realized by deploying optical cameras in the airport airspace. The optical cameras capture visual images of bird targets in real time. The images are input into a pre-trained deep learning model (for example, YOLO series based on convolutional neural network). The deep learning model detects targets in the images and outputs the bounding box, class (bird species) and confidence of each detected bird target.

[0030] The number N of bird targets is the total number of bird individuals detected by the model in a single frame of image.

[0031]

[0032] Where: 1 (.) is an indicator function, which is 1 when the condition in the parentheses is true, and 0 otherwise; k is the total number of all detected targets in the image; C i is the class of the i-th detected target, is a pre-defined set of bird classes, which is {magpie, sparrow, crow,..., hawk} in this embodiment.

[0033] Further, for the acquisition of three-dimensional position and speed, it includes:

[0034] This step is realized by deploying millimeter wave radar in the airport airspace. The millimeter wave radar measures the distance, azimuth and elevation of the bird target relative to the radar by transmitting and receiving radio waves, using the principle of Doppler effect and phase difference, and calculates the radial velocity. Through triangulation or multi-baseline measurement, these measurements can be converted into accurate three-dimensional position coordinates (x k ,y k ,z k ) and real-time three-dimensional velocity vector

[0035] Further, multi-sensor data fusion and comprehensive three-dimensional motion model construction include:

[0036] This step is the core of this embodiment, aiming to fuse heterogeneous data from optical cameras (providing number and species) and millimeter wave radar (providing position and speed) to construct a high-precision comprehensive three-dimensional motion model of bird targets. This embodiment uses extended Kalman filter algorithm as the core data fusion algorithm.

[0037] The state vector X of the bird target at time k is defined k for its position, velocity and acceleration in three-dimensional space.

[0038]

[0039] wherein:

[0040] x k y k z k is the three-dimensional position coordinate of the bird target at time k;

[0041] is the three-dimensional velocity component of the bird target at time k;

[0042] is the three-dimensional acceleration component of the bird target at time k.

[0043] The prediction process is: based on the state vector X k-1 at the last time, the state vector X

[0044]

[0045] P k = FP k-1 F T + Q

[0046] wherein:

[0047] is the predicted state vector at the current time; F is the state transition matrix, describing how the state evolves over time; u k is the control input vector, which can be ignored or set to a zero vector in this embodiment; P k is the predicted state covariance matrix at the current time, indicating the uncertainty of the prediction; Q is the process noise covariance matrix, indicating the uncertainty of the model itself, P k-1 is the state covariance matrix at the last time.

[0048] The update process is: at time k, the system obtains the measurement value Z k from the millimeter wave radar and the optical camera, as follows:

[0049]

[0050] wherein: x k,radar y k,radar z k,radar is the three-dimensional position measured by the millimeter wave radar; is the three-dimensional velocity measured by the millimeter wave radar; Nk,camera counting the number of birds for the optical camera.

[0051] The Kalman filter utilizes the measurement value Z k The prediction result is corrected to obtain a more accurate posterior state vector

[0052]

[0053] wherein h(·) is an observation function that maps the state vector to the measurement space;

[0054] y k is the measurement residual, i.e., the difference between the actual measurement value and the predicted measurement value;

[0055] H k is the Jacobian matrix of the observation matrix, describing the nonlinear relationship between the state vector and the measurement value;

[0056] R is the measurement noise covariance matrix, representing the uncertainty of the measurement value;

[0057] S k is the residual covariance;

[0058] K k is the Kalman gain, determining the degree of correction;

[0059] is the updated state vector;

[0060] is the updated state covariance matrix.

[0061] Through the above prediction and update cycle, the system can dynamically and continuously correct and optimize the information of the number, position and speed of the bird target, organically combine the accuracy of the millimeter wave radar and the target recognition ability of the optical camera, and thus construct a smooth and high-precision integrated three-dimensional motion model. The data output of the model serves as the input of the subsequent steps, ensuring the precision and effectiveness of the entire dispersing method.

[0062] Further, the flight trajectory T of the bird target can be obtained by connecting the updated three-dimensional position coordinate points at historical time points.

[0063]

[0064] wherein is the updated three-dimensional position coordinate at time point k, is the corresponding three-dimensional coordinate value. The trajectory data provides an intuitive basis for the subsequent ultrasonic directional emission and dispersing effect evaluation.

[0065] In summary, the specific process is: fusion of heterogeneous data from different sensors, construction of a comprehensive three-dimensional motion model of bird targets through multi-sensor data fusion algorithm, including,

[0066] a) pre-processing and time synchronization of raw data from optical cameras, millimeter wave radars and acoustic sensor arrays, ensuring that different sensors are on the same time reference;

[0067] b) using Kalman filter algorithm, taking the accurate three-dimensional position and velocity vector provided by millimeter wave radar as the main state input, and taking the bird individual count obtained by optical image recognition and the approximate position obtained by acoustic sensor positioning as auxiliary observation information;

[0068] c) constructing a multi-dimensional state vector, which includes the position (x k ,y k ,z k ), velocity and acceleration

[0069] d) through the prediction and update process of Kalman filter, the noise measurement data from different sources are fused, the state vector of bird target is dynamically corrected and optimized, a smooth and high-precision three-dimensional motion model is constructed, and the number, position, flight trajectory and moving speed of birds are updated in real time.

[0070] This step is the basis of the whole dispersing method, which provides a real-time, accurate and comprehensive "bird situation awareness" system, which overcomes the identification blind area and data inaccuracy problem that may occur in traditional single sensor (such as single radar or camera) in bad weather or complex environment; by constructing a smooth and high-precision three-dimensional motion model, the present application can provide reliable and accurate data input for subsequent ultrasonic directional emission and dispersing effect evaluation. This ensures the accuracy and effectiveness of the whole dispersing system decision, which is the premise of realizing precise dispersing and humane guidance.

[0071] S2, according to the real-time state and environmental data of the bird target, a continuous ultrasonic signal is generated and emitted by the bird dispersing system, and the frequency, intensity, phase and pulse mode of the ultrasonic signal are variable. The specific need to be explained in this step is,

[0072] According to the real-time state and environmental data of the bird target, a continuous ultrasonic signal is generated and emitted by the bird dispersing system, including:

[0073] According to the number of bird targets and the distance from the bird dispersing system, the required initial sound pressure level L p-initial of the ultrasonic signal is calculated;

[0074] Real-time acquisition of environmental data, including but not limited to wind speed Vw , wind direction θ w , temperature T and humidity H, and according to the attenuation formula of sound wave propagation in air, the initial sound pressure level is corrected to calculate the actual emission sound pressure level L p-actual ;

[0075] The attenuation formula can be expressed as: L p_actual = L p_initial + α(f)·d

[0076] Wherein, α(f) represents the air absorption attenuation coefficient related to frequency f, temperature T and humidity H, and its specific value can be obtained by table or calculated by empirical formula; d is the straight-line distance between the bird repelling system and the bird target;

[0077] The bird repelling system generates ultrasonic signals using a digital signal processor, and emits ultrasonic signals through a high-power ultrasonic transducer array, and adjusts the emission angle θ w of the transducer array in real time according to the wind direction θ emit , so as to ensure that the ultrasonic signals accurately act on the bird target.

[0078] The real-time number N and three-dimensional position coordinates of the bird target have been obtained from step S1. The bird repelling system first generates the initial parameters of the ultrasonic signal according to these real-time data.

[0079] Further, in order to ensure that the ultrasonic signal can produce a preset effective sound pressure level when it reaches the bird target, the system first calculates an initial emission sound pressure level L p-initial ; The initial sound pressure level is dynamically adjusted according to the real-time number N of the bird target and its straight-line distance d from the bird repelling system. Its calculation can be based on the following relationship:

[0080] L p,initial = L p,target + 20log 10 (d / d0)+ΔL p,N

[0081] Wherein:

[0082] L p-initial is the initial emission sound pressure level required by the bird repelling system at the reference point (1 meter away from the sound source in this embodiment); L p,target is the effective sound pressure level expected to be reached by the ultrasonic signal at the bird target position, which is a preset value and should be within the non-injury range; d is the straight-line distance between the bird repelling system and the bird target; d0 is the reference distance, usually 1 meter; ΔL p,NThe correction gain for the sound pressure level according to the target number of birds N aims to address the group effect that may occur when the size of the bird group increases, and its value can be obtained according to a lookup table or an empirical formula constructed based on experimental data. For example, when the number of birds is large, the sound pressure level is appropriately increased to ensure effective stimulation of all individuals.

[0083] Since the propagation of ultrasonic waves in air will produce significant absorption attenuation due to environmental factors such as temperature and humidity, this step aims to correct the initial sound pressure level calculated above to determine the final actual emission sound pressure level L p-actual .

[0084] Specifically, the system obtains environmental data in real time through integrated or connected meteorological sensors, including but not limited to temperature T and humidity H.

[0085] According to the attenuation principle of sound waves propagating in air, the attenuation A atm is related to the propagation distance d and the air absorption attenuation coefficient α, and their relationship can be expressed as:

[0086] A atm = α·d

[0087] wherein:

[0088] A atm is the attenuation of ultrasonic waves propagating in air; α is the air absorption attenuation coefficient; this coefficient is closely related to environmental factors such as the frequency f of ultrasonic waves, temperature T, and humidity H, and further, this coefficient α can be obtained through an empirical formula. In the preferred embodiment, the following empirical formula is used for approximate calculation, which takes into account the effects of temperature and humidity:

[0089]

[0090] wherein:

[0091] f is the frequency of ultrasonic waves; P atm is the atmospheric pressure, usually taking the standard atmospheric pressure value; T is the environmental temperature; τ T is the thermal relaxation time constant, τ R is the molecular relaxation time constant, both of which are related to humidity H and temperature T.

[0092] The final actual emission sound pressure level L p-actual is equal to the initial sound pressure level L p-initial plus the attenuation A atm caused by air absorption.

[0093] L p,actual = L p,initial + A atm

[0094] This L p-actualThe final sound pressure level that the bird repelling system needs to emit in the current environment to ensure that the ultrasonic signal can still maintain the preset effective sound pressure level L when it reaches the bird target position p,target .

[0095] Further, the bird repelling system uses a digital signal processor to generate an ultrasonic signal with a specific frequency, intensity, phase, and pulse pattern based on the above calculation results, and the signal is emitted directionally by a high-power ultrasonic transducer array.

[0096] Since wind speed and direction have an impact on the propagation direction of sound waves, the bird repelling system can dynamically adjust the emission angle of the transducer array according to the real-time wind direction data to ensure that the ultrasonic signal can accurately act on the bird target. The transducer array usually has mechanical or electronic beam steering capabilities and can accurately concentrate sound wave energy in the target area according to the relative relationship between the wind direction and the bird target position to maximize the dispersing effect. The specific transducer array and digital signal processor are prior art and will not be described in detail.

[0097] This step realizes the "customization" and "precise delivery" of ultrasonic signals. It not only simply emits ultrasonic waves, but also dynamically adjusts the ultrasonic parameters according to the actual situation of the target bird group (quantity, distance) and the changes in the external environment; ensures that the ultrasonic energy can maintain the preset effective sound pressure level when it reaches the bird target, avoiding the weakening of the dispersing effect due to environmental attenuation. At the same time, directional emission reduces the sound wave interference to non-target areas, improves energy utilization efficiency, and lays a physical foundation for subsequent humane guidance.

[0098] S3, continuously monitor the behavioral response of birds under the action of ultrasonic signals, including but not limited to flight height, trajectory change, and dispersion degree, and use the monitoring data as feedback signals. The need to be explained in this step is that

[0099] Continuously monitor the behavioral response of birds under the action of ultrasonic signals, and use the monitoring data as feedback signals, including:

[0100] After the ultrasonic signal is emitted, continuously track the bird target using an optical camera and a millimeter wave radar;

[0101] Calculate the change in key behavioral parameters of the bird target before and after the action of the ultrasonic wave, including: a) bird flock dispersion rate change ΔD, b) bird flock average flight speed change rate c) bird flock average flight height change rate d) bird flock overall moving direction and predetermined dispersing direction angle change rate Δθ;

[0102] Collect the key behavioral parameter change set as feedback signals and input them into the anti-adaptive algorithm module.

[0103] Further, the key behavior parameter change set is taken as a feedback signal, including,

[0104] a) Record and store the initial value of the bird key behavior parameter within a specific time window before the ultrasonic signal acts;

[0105] b) After the ultrasonic signal is emitted, continuously calculate the real-time values of key parameters such as bird flock dispersity, average flight speed, average flight height, and the angle between the overall moving direction and the predetermined dispersal direction;

[0106] c) Calculate the difference or rate of change between the real-time value and the initial value of the bird key behavior parameter, and compose a multi-dimensional vector of all the above change rate parameters as a feedback signal input to the anti-adaptive algorithm module.

[0107] Further, the implementation of this step is based on the real-time three-dimensional position, speed and trajectory data of the bird target obtained from step S1, and the ultrasonic signal has been emitted according to step S2. After the ultrasonic signal is emitted, the system continues to track the bird target using optical cameras and millimeter wave radars.

[0108] The tracking process uses the comprehensive three-dimensional motion model constructed in step S1 to update the dynamic behavior data of the bird target in real time. This process ensures that the system can accurately capture every subtle change in the behavior of the bird under the stimulation of the ultrasonic wave, providing a reliable data basis for subsequent behavior response evaluation.

[0109] During the monitoring stage under the action of the ultrasonic wave, the system will continuously calculate the change amount of the key behavior parameters of the bird target before and after the action, which will be the core basis for evaluating the dispersal effect and whether the bird has adaptability.

[0110] Specifically, the system first calculates the dispersity of the bird flock, which is represented by the standard deviation of the centroid distance of each bird target in the bird flock. Let the three-dimensional position vector of the i-th bird in the bird flock before the ultrasonic wave acts be P i,before = [x i,before ,y i,before ,z i,before ] T , where x i,before , y i,before , z i,before are the components in the three-dimensional coordinates, obtained from the three-dimensional position coordinates at the determined time k; the centroid position vector P centroid,before of the bird flock is calculated from the average value of the three-dimensional position vectors of all birds in the bird flock, and the bird flock dispersity σ d,before is:

[0111]

[0112] in:

[0113] N represents the number of birds in the flock.

[0114] ||·|| represents the Euclidean norm, which is the magnitude of a vector.

[0115] After the ultrasound is applied, the system calculates the new bird flock dispersion σ in the same way. d,after The rate of change of flock dispersion ΔD can be defined as:

[0116]

[0117] This rate of change can quantitatively reflect whether a flock of birds tends to disperse or gather under ultrasonic stimulation; a positive value indicates increased dispersion, while a negative value indicates decreased dispersion.

[0118] Furthermore, the calculation of the rate of change of the average flight speed of the bird flock includes, firstly, calculating the average flight speed of the bird flock before and after the ultrasonic wave exposure, as follows:

[0119]

[0120] in:

[0121] v i,before and v i,after Let be the three-dimensional velocity vectors of the i-th bird before and after the ultrasonic wave, respectively, and N be the number of bird targets. and These represent the average flight speeds of the flock before and after the ultrasonic wave exposure.

[0122] Rate of change of average flight speed of bird flocks It can be defined as:

[0123]

[0124] This step aims to assess changes in the overall flight speed of the flock.

[0125] Furthermore, an example of calculating the rate of change of the average flight altitude of a flock of birds is to calculate the average flight altitude of the flock before and after the action of ultrasonic waves.

[0126]

[0127] in:

[0128] z i,before and z i,after Let be the three-dimensional height coordinates of the i-th bird before and after the ultrasonic wave action, and N be the number of bird targets. The average flight altitude of the flock of birds before the ultrasonic waves were applied. This represents the average flight altitude of the flock of birds after the ultrasonic waves have acted upon it.

[0129] Rate of change of average flight altitude of bird flocks It can be defined as:

[0130]

[0131] This step is used to assess the bird flock's behavioral response in the vertical direction.

[0132] Furthermore, as an example, the calculation of the rate of change of the angle between the overall movement direction of the flock and the predetermined dispersal direction includes:

[0133] First, determine the predetermined dispersal direction vector d. predetermined Then, calculate the overall movement direction vector d of the bird flock before and after the ultrasonic wave. flock,before and d flock,after These vectors can be approximated by the displacement vector of the flock's centroid over a period of time.

[0134] Calculate the angle θ between the direction of bird flock movement and the predetermined dispersal direction before and after ultrasonic treatment. before and θ after :

[0135]

[0136] in:

[0137] The dot product of vectors is represented by .

[0138] arccos(·) is the inverse cosine function.

[0139] The rate of change Δθ of the angle between the overall movement direction of the flock and the predetermined dispersal direction can be defined as:

[0140]

[0141] This step is used to assess the directionality of the dispersing effect.

[0142] The set of changes in the key behavioral parameters obtained from the above calculations, namely the rate of change of flock dispersion ΔD and the rate of change of average flock flight speed, are used to... Rate of change of average flight altitude of bird flocks The rate of change of the angle between the overall movement direction of the flock and the predetermined dispersal direction, Δθ, is combined to form a feedback signal vector S. feedback .

[0143]

[0144] Feedback signal vector S feedbackThe vector is then input into the anti-adaptive algorithm module in step S4, serving as the basis for the algorithm to adjust the ultrasonic parameters. Each component of this vector can intuitively reflect the dispersing effect of the ultrasonic signal on the flock of birds. For example, an increase in the ΔD value indicates that the flock of birds begins to disperse, while a decrease in the Δθ value indicates that the flock of birds begins to move in a predetermined direction.

[0145] This step establishes a quantitative evaluation system for the effectiveness of ultrasonic waves, transforming the "effect" of bird deterrence from a vague concept into measurable data. This enables closed-loop control, allowing the system to "sense" the actual responses of the birds, rather than simply mechanically executing preset programs. By quantifying the behavioral responses of the birds, this invention can accurately determine the attenuation of the deterrence effect, providing an immediate and reliable triggering basis for the next step's adaptive algorithm. This significantly improves the system's response speed and deterrence effectiveness.

[0146] S4. Based on the feedback signal, when a weakening response of birds to ultrasonic signals is detected, an anti-adaptive algorithm is immediately activated to dynamically adjust the parameters of the ultrasonic signals using pseudo-random methods, preventing birds from developing habitual adaptations to a single stimulus pattern. This step requires further explanation.

[0147] Based on feedback signals, when a weakened response in birds to ultrasonic signals is detected, an anti-adaptive algorithm is immediately activated, including:

[0148] Set a preset dispel effect threshold T eff This threshold can be quantified based on parameters such as flock dispersion and changes in flight trajectory;

[0149] Continuously calculate the actual dispersal effect index E of bird targets under ultrasonic waves. actual The index can be derived from the following formula:

[0150]

[0151] Among them, k1, k2, and k3 are weighting coefficients, whose values ​​are dynamically adjusted according to bird species and environmental factors. Specifically, for birds that need to be driven away quickly, such as large birds of prey, the weight of the coefficient k2, which is related to their speed, can be appropriately increased; while in windy weather, the weight of the coefficient k3, which is related to their flight trajectory, can be decreased.

[0152] A preset dispel effect threshold T eff This value is set based on the habits of different bird populations and historical bird control data from the airport. When the actual bird deterrence effect index E is continuously monitored during a continuous monitoring period... actual Less than the preset dispersal effect threshold T eff When it is determined that the bird has adapted to the current ultrasonic pattern, the anti-adaptive algorithm is immediately activated.

[0153] The parameters of the ultrasonic signal are dynamically adjusted using pseudo-random methods, including:

[0154] A pre-built ultrasonic parameter mode library is constructed, containing a variety of preset, optimized, and verified combinations of ultrasonic signal parameters, including frequency f and intensity L. p Phase φ and pulse mode P m For example, the pattern library may contain the following combinations: combination A, a continuous wave with a frequency of 25 kHz; combination B, a square wave with a frequency of 28 kHz and a pulse pattern of 0.5 seconds ON and 0.5 seconds OFF; combination C, a nonlinear frequency sweep between 23 kHz and 30 kHz with a pulse width of a pseudo-random sequence.

[0155] The pseudo-random dynamic adjustment algorithm selects a new combination of parameters from a pattern library in a pseudo-random manner based on the degree of response attenuation indicated in the feedback signal; the pseudo-random selection is based on the following rules:

[0156] a) The system prioritizes the mode that differs most from the current mode in frequency, phase, or pulse pattern to achieve the greatest possible stimulus variation; for example, if the current mode is a continuous wave, the algorithm prioritizes the pulse mode to provide a completely new stimulus.

[0157] b) When adjusting the frequency, the system selects a wideband sweep mode, which involves rapidly sweeping the frequency non-linearly within the 23kHz to 30kHz range of bird hearing sensitivity. This sweep mode can more effectively stimulate the bird's nervous system and prevent it from adapting to a single frequency.

[0158] c) When adjusting the pulse pattern, the system uses pseudo-random sequences to generate non-repeating pulse intervals and pulse widths in order to break the bird's ability to predict behavior.

[0159] After selecting a new combination of ultrasonic parameters, the bird deterrent system will immediately switch to this mode and maintain stable transmission for a certain period of time. The system will continuously monitor the behavioral response of the bird target in this new mode until the response is detected to weaken again, and then repeat the above anti-adaptive algorithm process.

[0160] This step addresses the biggest challenge in traditional sound-based bird deterrence technology—the adaptive behavior of birds to a single stimulus pattern. Through multi-parameter joint modulation and pseudo-random selection, it continuously provides novel and unpredictable stimuli, breaking the birds' predictive abilities and ensuring the invention maintains a long-term deterrence effect. This significantly extends the system's effective lifespan and reduces operation and maintenance costs. The pseudo-random dynamic adjustment mechanism fundamentally overcomes the limitation of "fixed frequency leading to habituation" in existing technologies, achieving true "continuous neural interference."

[0161] S5. Control the frequency and sound pressure level of the ultrasonic signal to produce only non-damaging stimulation and interference to the bird's hearing and nervous system, inducing the bird to move outward along the predetermined migration corridor and leave the designated area. Specifically, this step aims to ensure that the frequency and sound pressure level of the ultrasonic signal only cause physiological discomfort to the bird without inducing permanent hearing damage or organ lesions. This is primarily achieved through strict limitations on the sound pressure level.

[0162] Controlling the frequency and sound pressure level of ultrasonic signals to ensure they only cause non-damaging stimulation and interference to the hearing and nervous system of birds includes: when generating and emitting ultrasonic signals, the bird deterrent system controls the sound pressure level of the ultrasonic signals within a safe range, preferably not exceeding 85 dB, to ensure that the ultrasonic waves only cause physiological discomfort to the birds' hearing and nervous system without causing permanent hearing damage or organ lesions. This threshold is set based on extensive biological research data, aiming to ensure that the ultrasonic signals only cause transient and reversible stimulation and interference to the birds' hearing and nervous system. By strictly limiting the sound pressure level below this non-damaging threshold, this invention achieves humane bird deterrence, avoiding substantial harm to birds, thus demonstrating the social responsibility value of this invention.

[0163] The core of this step lies in how to intelligently guide bird targets to a predetermined safe area through the directional emission of ultrasound.

[0164] Inducing bird targets to move outward along a predetermined migration corridor and leave the designated area includes:

[0165] Based on the real-time location and flight trajectory of the bird target, the system intelligently determines the directional emission direction of the ultrasonic waves, thereby generating a "driving field".

[0166] The direction of the driving force field F repel The driving field always points away from the airport runway and takeoff / landing routes, and towards the designated migration corridor; the intensity of the driving field can be determined based on the distance d between the birds and the designated migration corridor. corridor Adjustments are made to achieve precise guidance;

[0167] Bird deterrence systems can determine the angle θ between the bird target's direction of movement and the predetermined migration corridor direction. angle It dynamically adjusts its own position and ultrasonic emission direction to ensure that the driving field always guides bird targets to a safe area, preventing birds from flying around randomly or rushing into sensitive airspace after being startled.

[0168] Furthermore, it should be noted that a planned migration corridor is not a physical entity, but rather a pre-defined three-dimensional spatial area within an airport geographic information system (GIS). This area is typically a specific path, away from airport runways, taxiways, and takeoff and landing routes, that safely guides birds away from the airport airspace. A planned migration corridor can be represented as one or more three-dimensional vectors or spatial geometries, and its form can be defined as:

[0169] L corr ={P start ,P end}

[0170] Where: L corr A predetermined migration corridor can be defined as starting from point P. start To the destination P end A vector path.

[0171] Based on the real-time location and flight trajectory of the bird target obtained in step S1, the bird deterrence system intelligently determines the directional emission direction of ultrasonic waves, creating an invisible "driving force field" around the bird target. The driving force field always points away from the sensitive airspace of the airport and towards the predetermined migration corridor.

[0172] The intensity of the driving field (characterized by the ultrasonic sound pressure level at the target location) can be adjusted according to the distance between the birds and the predetermined migration corridor to achieve precise guidance rather than indiscriminate dispersal. The intensity of the driving field I... repel Minimum distance from bird target to predetermined migration corridor The relationship between them can be represented as:

[0173]

[0174] Among them: I repel The strength of the driving field is positively correlated with the sound pressure level at the bird's location; I0 is the maximum driving force strength when adjacent to the predetermined migration corridor; d min is the minimum straight-line distance from the bird target to the predetermined migration corridor; c is a positive attenuation coefficient, the value of which can be adjusted according to the bird species and guidance requirements.

[0175] The bird deterrence system can dynamically adjust its position and ultrasonic emission direction according to the angle between the bird target's movement direction and the predetermined migration corridor direction, so as to ensure that the driving force field always guides the bird target to a safe area and prevents the bird from flying around randomly or rushing into sensitive airspace after being startled.

[0176] Let V be the direction vector of the bird target's movement. bird The predetermined migration corridor direction vector is V. corr The bird deterrent system needs to adjust the ultrasonic wave emission direction vector as V. emitThe system calculates the angle between the two. The launch direction is dynamically adjusted, with the aim of aligning the bird target's movement direction vector V with the target's direction of movement. bird Gradually approaching the predetermined migration corridor direction vector V corr .

[0177] V emit =V bird +k θ ·(V corr -V bird )

[0178] Where: k θ The gain is adjusted for direction, and its value depends on the size of the included angle θ. When θ is large, the gain can be increased appropriately to achieve rapid correction; when θ is small, the gain can be decreased to achieve smooth guidance.

[0179] This step embodies the core of the invention's "humanitarian" concept. It not only focuses on the effectiveness of bird dispersal but also emphasizes the protection of birds. By combining dispersal with precise guidance, it prevents birds from being startled and flying erratically or entering sensitive airspace, achieving controllability in the dispersal process. This step achieves synergistic optimization of airport ecological safety and flight operation safety. It ensures that the bird dispersal process causes no substantial harm to birds, while intelligent guidance reduces the risk of secondary bird strikes, thus improving the overall safety of airport airspace.

[0180] In summary, this invention, based on a complete closed-loop control chain of "real-time perception - directional launch - quantitative feedback - intelligent adaptation - humane guidance," constructs a systematic, intelligent, and humane airport bird control solution that addresses the shortcomings of existing solutions in terms of anti-habituation depth, pseudo-random linkage dimension, behavior migration guidance, and engineering autonomy assurance.

[0181] The overall beneficial effects of this invention are as follows: by using an instant anti-adaptive mechanism triggered by behavioral feedback and multi-parameter (frequency, intensity, phase, pulse) joint pseudo-random modulation, the habitual adaptation of birds is fundamentally suppressed, ensuring the high dissipation efficiency of the system during long-term operation.

[0182] Strict control of ultrasonic sound pressure levels ensured the non-invasiveness of the bird deterrence process. At the same time, the controllable migration of bird flocks was achieved through the concept of "driving force field" and the guidance of "pre-determined migration corridors," avoiding secondary threats to the airport's sensitive airspace after birds were startled.

[0183] Multi-sensor data fusion provides the entire system with high-precision situational awareness, while quantified behavioral feedback provides a reliable basis for intelligent decision-making. This enables the system to respond accurately and adaptively based on the real-time status of the flock and changes in the environment, rather than simply operating in a programmed manner.

[0184] In summary, this invention provides a comprehensive solution for airport bird strike prevention that is highly efficient, long-term, humane, and safe, and has significant practical value and technological innovation.

[0185] It should be understood that numerous specific implementation decisions can be made during the development of any practical implementation, such as in any engineering or design project. Such development efforts may be complex and time-consuming, but for those skilled in the art who benefit from this disclosure, the development effort will be a routine work of design, manufacturing, and production without requiring much experimentation.

[0186] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference, characterized in that: include, S1. Real-time detection and identification of bird targets in airport airspace, and acquisition of the number, location, flight trajectory and speed of the bird targets; S2. Based on the real-time status and environmental data of the bird target, a continuous ultrasonic signal is generated and directionally emitted by the bird deterrent system, wherein the frequency, intensity, phase, and pulse mode of the ultrasonic signal are variable; S3. Continuously monitor the bird's behavioral response to the ultrasonic signal, including but not limited to flight altitude, trajectory changes and dispersion, and use the monitoring data as a feedback signal; S4. Based on the feedback signal, when it is detected that the bird's response to the ultrasonic signal is weakened, the anti-adaptive algorithm is immediately activated to perform pseudo-random dynamic adjustment of the parameters of the ultrasonic signal. S5. Control the frequency and sound pressure level of the ultrasonic signal so that it only produces non-damaging stimulation and interference to the bird's hearing and nervous system, inducing the bird target to move outward along the predetermined migration corridor and leave the designated area.

2. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 1, characterized in that: The real-time detection and identification of bird targets within airport airspace includes, Deploy multi-dimensional sensors, including optical cameras, millimeter-wave radar, and acoustic sensor arrays, to form a three-dimensional monitoring network; The optical camera captures visual images of bird targets, and a deep learning model is used to identify bird species and count individuals in the images. The millimeter-wave radar acquires the precise three-dimensional position coordinates (x, y) of the bird target. k ,y k ,z k ) and real-time velocity vector The acoustic sensor array is used to identify and locate bird calls, helping to determine the species, activity level, and approximate location of bird flocks. By fusing heterogeneous data from different sensors and employing a multi-sensor data fusion algorithm, a comprehensive three-dimensional motion model of bird targets is constructed, and the number, location, flight trajectory, and movement speed of the bird targets are updated in real time.

3. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 2, characterized in that: The step of generating and directionally emitting a continuous ultrasonic signal based on the real-time status and environmental data of the bird target includes: Based on the number of bird targets and their distance from the bird deterrence system, calculate the required initial ultrasonic signal sound pressure level L. p-initial ; Real-time acquisition of environmental data, including but not limited to wind speed V w Wind direction θ w The initial sound pressure level (L) is calculated by taking temperature (T) and humidity (H) as the parameters, and correcting for the attenuation of sound waves in air. p-actual ; The attenuation formula can be expressed as: L p_actual =L p_initial +α(f)·d Where α(f) represents the air absorption attenuation coefficient related to frequency f, temperature T and humidity H, and its specific value can be obtained from a table or calculated by an empirical formula; d is the straight-line distance between the bird deterrent system and the bird target; The bird deterrent system uses a digital signal processor to generate ultrasonic signals, which are then emitted directionally through a high-power ultrasonic transducer array, and the signal is determined based on the wind direction θ. w Real-time adjustment of the transducer array's emission angle θ emit .

4. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 3, characterized in that: The continuous monitoring of the bird's behavioral response to the ultrasonic signal, using the monitoring data as a feedback signal, includes: After the ultrasonic signal is emitted, the optical camera and millimeter-wave radar are used to continuously track bird targets; Calculate the changes in key behavioral parameters of bird targets before and after ultrasonic exposure. These key behavioral parameters include: the rate of change in flock dispersion ΔD and the rate of change in average flock flight speed. Rate of change of average flight altitude of bird flocks The rate of change of the angle between the overall movement direction of the flock and the predetermined dispersal direction, Δθ; The set of changes in the key behavioral parameters is used as a feedback signal and input to the anti-adaptive algorithm module.

5. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 4, characterized in that: Based on the feedback signal, when a weakened response of the bird to the ultrasonic signal is detected, an anti-adaptive algorithm is immediately activated, including: Set a preset dispel effect threshold T eff This threshold can be quantified based on parameters such as flock dispersion and changes in flight trajectory; Continuously calculate the actual dispersal effect index E of bird targets under ultrasonic waves. actual The index can be derived from the following formula: Where k1, k2, and k3 are weighting coefficients; the actual dispersal effect index E is calculated over a continuous monitoring period. actual Less than the preset threshold T eff When it is determined that the bird has adapted to the current ultrasonic pattern, the anti-adaptive algorithm is immediately activated.

6. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 5, characterized in that: The pseudo-random dynamic adjustment of the parameters of the ultrasonic signal includes: Construct an ultrasonic parameter mode library, which contains a variety of preset combinations of ultrasonic signal parameters, such as frequency f and intensity L. p Phase φ and pulse mode P m ; The pseudo-random dynamic adjustment algorithm selects a new parameter combination from the pattern library in a pseudo-random manner based on the degree of response attenuation indicated in the feedback signal; the pseudo-random selection is based on the following rules: a) Prioritize the mode that differs most from the current mode in frequency, phase, or pulse pattern to achieve the maximum stimulation variation; b) When adjusting the frequency, a wideband sweep mode is used, that is, a fast non-linear sweep in the range of 23kHz to 30kHz; c) When adjusting the pulse pattern, a pseudo-random sequence is used to generate non-repeating pulse intervals and pulse widths in order to break the birds' ability to predict their habits. The parameters of the ultrasonic signal are switched to a new combination and maintained for a certain period of time until a weakening response is detected again.

7. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 6, characterized in that: Controlling the frequency and sound pressure level of the ultrasonic signal to produce only non-damaging stimulation and interference to the bird's hearing and nervous system includes: controlling the sound pressure level of the ultrasonic signal within a safe range, wherein the sound pressure level is not higher than 85 dB, to ensure that the ultrasonic waves only cause physiological discomfort to the bird's hearing and nervous system without causing permanent hearing damage or organ lesions.

8. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 7, characterized in that: Inducing bird targets to move outward along a predetermined migration corridor and leave the designated area includes: The direction of the driving force field F repel The driving field always points away from the airport runway and takeoff / landing routes, and towards the designated migration corridor; the intensity of the driving field can be determined based on the distance d between the birds and the designated migration corridor. corridor Adjustments are made to achieve precise guidance; Bird deterrence systems can determine the angle θ between the bird target's direction of movement and the predetermined migration corridor direction. angle It dynamically adjusts its own position and ultrasonic emission direction to ensure that the driving field always guides the bird target to a safe area, preventing the bird from flying around randomly or rushing into sensitive airspace after being startled.

9. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 8, characterized in that: By fusing heterogeneous data from different sensors and employing a multi-sensor data fusion algorithm, a comprehensive three-dimensional motion model of a bird target is constructed, including: a) Preprocess and time-synchronize raw data from optical cameras, millimeter-wave radar, and acoustic sensor arrays to ensure that different sensors are on the same time reference; b) Using the Kalman filter algorithm, the precise three-dimensional position and velocity vectors provided by the millimeter-wave radar are used as the main state inputs, and the bird individual counts obtained by optical image recognition and the approximate positions obtained by acoustic sensor positioning are used as auxiliary observation information; c) Construct a multidimensional state vector containing the position (x, y) of the bird target in three-dimensional space. k ,y k ,z k ),speed and acceleration d) Through the prediction and update process of Kalman filtering, noisy measurement data from different sources are fused to dynamically correct and optimize the state vector of bird targets, construct a smooth and high-precision three-dimensional motion model, and update the number, position, flight trajectory and movement speed of birds in real time.

10. The humane airport bird behavior migration and dispersal method based on anti-adaptive continuous ultrasonic neural interference according to claim 9, characterized in that: The set of changes in the key behavioral parameters is used as a feedback signal, including: a) Record and store the initial values ​​of key bird behavioral parameters within a specific time window before the ultrasonic signal is applied; b) After the ultrasonic signal is transmitted, continuously calculate the real-time values ​​of key parameters such as flock dispersion, average flight speed, average flight altitude, and the angle between the overall movement direction and the predetermined dispersal direction. c) Calculate the difference or rate of change between the real-time values ​​and the initial values ​​of the key bird behavior parameters, and combine all the above rate of change parameters into a multi-dimensional vector as a feedback signal, which is then input into the anti-adaptive algorithm module.

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