An unmanned aerial vehicle bird strike risk intelligent early warning and repelling method based on air-ground linkage

Abstract

The application discloses a kind of unmanned aerial vehicle bird strike risk intelligent early warning and repelling method of air-ground linkage, by deploying ground mother station, high-rise substation and airborne substation trinity distributed monitoring repelling node network, real-time monitoring and collecting bird activity data, the double model risk assessment algorithm of attack tendency bird individual and ordinary bird group is constructed, avoids single, indiscriminate monitoring and driving, corresponding different risk assessment grades, establishes multilevel early warning and multistage repelling mechanism, generates accurate risk grade, early warning grade and executes accurate repelling strategy.The realization of safe, efficient, intelligent bird strike risk early warning and repelling provides security guarantee for the large-scale application of unmanned aerial vehicle in urban low-altitude environment, with great practical value and industrialization prospect.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of low-altitude flight safety and ecological environment protection, and particularly relates to a method for intelligent early warning and avoidance of bird strike risk of unmanned aerial vehicles in ground-air linkage. BACKGROUND

[0002] With low-altitude economy becoming a national strategic emerging industry, Shenzhen, as a core city of the Guangdong-Hong Kong-Macao Greater Bay Area and a national highland of unmanned aerial vehicle industry, is at the forefront of the industry development. As of the end of 2023, the number of unmanned aerial vehicle enterprises in Shenzhen has exceeded 1,200, and the industry scale has reached 65.8 billion yuan. The number of unmanned aerial vehicles in Shenzhen exceeds 120,000, of which 65,000 are commercial and professional operation unmanned aerial vehicles, and the daily take-off and landing frequency exceeds 15,000 times. By 2035, the unmanned aerial vehicle landing points will increase from 483 in 2024 to about 1,500. At the same time, Shenzhen is an important wintering ground and resting place on the migratory route of "East Asia-Australia" migratory birds, and records more than 280 species of birds each year, with a cumulative passing amount of more than 100,000 migratory birds. Compared with 2016, the total number of water birds recorded in the Pindling Futian National Nature Reserve in Guangdong increased from 27,392 to 41,217 (an increase of 50.5%), and the number of water bird species increased from 53 to 78 (an increase of 47.2%). With the substantial increase in low-altitude flight frequency and density, the conflict between low-altitude flight and bird activity space is intensifying, and the influence of bird activity on flight safety is becoming more and more significant, and low-altitude flight safety is facing multiple challenges.

[0003] For high-density cities with super high-rise buildings, bird flight activities are frequent and complex. Among low-altitude aircraft, unmanned aerial vehicle application scenarios are close to the natural environment, and the flight height overlaps with the migration height of small songbirds (within 300 meters), and the flight height of unmanned aerial vehicles overlaps with the main activity height of shorebirds (10-50 meters), waders (20-80 meters), gulls (30-120 meters), and raptors (50-300 meters). At the same time, unlike the occasional bird strike mechanism of airplanes and large aircraft, small and medium-sized unmanned aerial vehicles are small in size, easy to be disturbed by aggressive interference of magpies, crows, pigeons, and raptors, and the structure of small and medium-sized unmanned aerial vehicles is relatively fragile, light in weight, and components are exposed, etc. Bird strikes can seriously threaten low-altitude flight safety, and light ones can cause flight path deviation, and heavy ones can lead to loss of control and crash events. Reducing the incidence of flight accidents caused by bird strikes has important value for ensuring the safety of low-altitude aircraft flight, and is of great significance for the coordinated and win-win development of current low-altitude economy and ecological protection.

[0004] The current bird activity early warning and repelling technology research mainly concentrates in three traditional fields of power grid, building and airport, and single monitoring and repelling are carried out through the monitoring device and the repelling device fixed in the monitoring and repelling station. The repelling device carried on the unmanned aerial vehicle is also started to repel when the bird enters the preset range. However, some birds such as sparrows and house swallows do not have strong attack tendency, and single repelling is easy to cause excessive interference to ordinary non-attack birds, thereby destroying the ecological balance. Therefore, it is urgent to design different risk intelligent early warning and repelling schemes for attack tendency and ordinary birds. SUMMARY

[0005] Therefore, the application discloses a ground-air linkage unmanned aerial vehicle bird collision risk intelligent early warning and repelling method, a distributed monitoring and repelling node network of a ground mother station, a high-rise substation and an airborne substation is deployed, bird activity data of a take-off point and a flight path of the unmanned aerial vehicle are monitored and collected in real time, a double-model risk assessment algorithm of bird individuals with attack tendency and ordinary bird groups is constructed, a multi-level early warning and multi-level repelling mechanism is established corresponding to different risk assessment levels, accurate risk levels, early warning levels and accurate repelling strategies are generated, and safety is provided for large-scale application of the unmanned aerial vehicle in the urban low-altitude environment.

[0006] The technical scheme of the application is a ground-air linkage unmanned aerial vehicle bird collision risk intelligent early warning and repelling method, and the method comprises the following steps:

[0007] Step 1, obtaining bird information data entering a monitoring area based on a distributed monitoring and repelling node network system of a ground mother station deployed on the ground, a high-rise substation of a high-rise building and an airborne substation of an unmanned aerial vehicle;

[0008] Step 2, performing identity matching and identification on the bird in step 1 based on a bird database, and identifying the bird as an attack tendency bird analysis target or an ordinary bird analysis target;

[0009] Step 3, constructing a double-model risk assessment algorithm of the attack tendency bird and the ordinary bird, when the bird is identified as the attack tendency bird analysis target, running an attack tendency bird algorithm model to calculate and output a risk value Ra, and when the bird is identified as the ordinary bird analysis target, running an ordinary bird algorithm model to calculate and output a risk value Rb;

[0010] Step 4, outputting a risk level according to the risk value Ra and the risk value Rb, outputting an early warning level based on the level of the risk level and the distance value between the bird and the unmanned aerial vehicle, performing a corresponding early warning action of the early warning level, outputting a repelling level based on the level of the early warning level, and performing a corresponding repelling action of the repelling level;

[0011] If the number of the flying birds identified as the aggressive flying birds is more than one third of the total number of the flying birds entering the monitoring area, the flying birds are determined as the aggressive flying bird analysis targets; otherwise, the flying birds are determined as the common flying bird analysis targets.

[0012] Further, the calculation formula of the aggressive flying bird algorithm model is:

[0013] Ra=[w1·S+w2·(1 / D)²+w3·V_rel+w4·A_p+w5·T_min-1]×M×H_a;

[0014] wherein S is a flying bird species threat coefficient, D is a straight-line distance (meters) between the flying bird and the unmanned aerial vehicle, V_rel is an approaching speed (meters / second) of the flying bird relative to the unmanned aerial vehicle, A_p is an attack probability estimate based on historical data of the flying bird, T_min is a minimum available decision time (seconds) for the unmanned aerial vehicle to avoid collision, M is a number multiplier of the flying bird, H_a is a historical attack frequency weighting factor of the flying bird species, w1 is an attack tendency influence parameter, taking a value (0, 0.4), representing a weight value of the flying bird species threat coefficient S, w2 is a straight-line distance influence parameter, taking a value (0, 0.4), representing a weight value of the straight-line distance D between the flying bird and the unmanned aerial vehicle, w3 is an approaching speed influence parameter, taking a value (0, 0.3), representing a weight value of the approaching speed V_rel of the flying bird relative to the unmanned aerial vehicle, w4 is an attack probability influence parameter, taking a value (0, 0.2), representing a weight value of the attack probability estimate A_p of the flying bird historical data, w5 is a decision time influence parameter, taking a value (0, 0.2), representing a weight value of the minimum available decision time T_min for the unmanned aerial vehicle to avoid collision, and w1+w2+w3+w4+w5=1 is required to be satisfied.

[0015] Further, the calculation formula of the common flying bird algorithm model is:

[0016] Rb=[α·(D_scale)+β·(V_rel_scale)+γ·(Θ_scale)+δ·(T_avail_scale)]×F_size×F_density;

[0017] Wherein, D_scale is the distance ratio factor of the bird and the UAV, V_rel_scale is the relative speed ratio factor of the bird and the UAV, Θ_scale is the angle ratio factor of the bird and the UAV, T_avail_scale is the available reaction time ratio factor of the UAV, F_size is the bird size coefficient, F_density is the population density factor, α is the distance influence parameter, the value of which is (0, 0.5), representing the weight value of the distance ratio factor D_scale of the bird and the UAV, β is the relative speed influence parameter, the value of which is (0, 0.4), representing the weight value of the relative speed ratio factor V_rel_scale of the bird and the UAV, γ is the flight angle influence parameter, the value of which is (0, 0.3), representing the weight value of the flight angle ratio factor Θ_scale of the bird and the UAV, δ is the reaction time influence parameter, the value of which is (0, 0.2), representing the weight value of the available reaction time ratio factor T_avail_scale of the UAV, and α + β + γ + δ = 1.

[0018] The risk level includes four levels, namely, a little risk, low risk, medium risk and high risk, when the value of Ra or Rb is [0, 25), a little risk is output, when the value of Ra or Rb is [26, 50), low risk is output, when the value of Ra or Rb is [51, 75), medium risk is output, and when the value of Ra or Rb is [76, 100), high risk is output.

[0019] The early warning level includes four levels, namely, first level early warning, second level early warning, third level early warning and fourth level early warning, when the risk level is a little risk and the bird enters the monitoring range and the closest distance between the bird and the UAV is greater than 500 meters, first level early warning is output, when the risk level is low risk and the closest distance between the bird and the UAV is less than 300 meters, second level early warning is output, when the risk level is medium risk, the trajectory of the bird intersects with the flight path of the UAV, and the distance between the bird and the UAV is less than 150 meters, third level early warning is output, and when the risk level is high risk, the bird analyzed as the target is of aggressive tendency, the closest distance between the bird of aggressive tendency and the UAV is less than 100 meters, or the bird analyzed as the target is ordinary, the closest distance between the ordinary bird and the UAV is less than 75 meters, fourth level early warning is output.

[0020] The drive-away level includes three levels, namely, first level drive-away, second level drive-away and third level drive-away, when the third level early warning is output, first level drive-away is output, when the fourth level early warning is output, second level drive-away is output, and when the value of Ra or Rb is greater than 100 and the closest distance between the bird and the UAV is less than 50 meters,

[0021] The pre-warning action executed by the first-level pre-warning is to maintain the normal monitoring state and record the monitoring events; the pre-warning action executed by the second-level pre-warning is to automatically increase the monitoring frequency, and the ground mother station or the high-rise substation automatically executes the entering of the driving avoidance preparation state, and the unmanned aerial vehicle keeps the original flight line; the pre-warning action executed by the third-level pre-warning is to trigger the first-level driving avoidance, the driving avoidance action of the first-level driving avoidance is that the ground mother station or the high-rise substation enters the alert state, and sends an alert alarm to the unmanned aerial vehicle in the adjacent area; the pre-warning action executed by the fourth-level pre-warning is to trigger the second-level driving avoidance, the driving avoidance action of the second-level driving avoidance is that the ground mother station and the high-rise substation respond synchronously, the ground mother station takes over, sends an avoidance instruction and adjusts the flight line of the unmanned aerial vehicle in the adjacent area; the action executed by the third-level driving avoidance is that the ground mother station forcibly takes over manually, starts the unmanned aerial vehicle protection mode, and the unmanned aerial vehicle hovers in place or starts the emergency avoidance path planning.

[0022] Compared with the prior art, the present application has the following beneficial effects:

[0023] The ground-air linkage unmanned aerial vehicle bird collision risk intelligent pre-warning and driving avoidance method disclosed by the present application is based on the distributed monitoring and driving avoidance node network on the ground mother station on the ground, the high-rise substation of the high-rise building and the airborne substation of the unmanned aerial vehicle. While realizing ordinary monitoring and driving avoidance, a double-model risk assessment algorithm for individual birds with an attack tendency and ordinary bird groups is constructed, the risk value is calculated through the identification of the birds entering the monitoring range and the double-model algorithm formula, so that a more scientific and accurate risk assessment level can be obtained. The problem of excessive interference caused by single and indiscriminate monitoring and driving of flying birds is avoided. A risk quantification and multi-level progressive pre-warning and driving avoidance mechanism is established, the risk assessment result is matched with the multi-level pre-warning and multi-level driving avoidance mechanism, so that a more scientific and accurate pre-warning level and a more scientific and accurate driving avoidance strategy are obtained. Therefore, the present application can provide multi-level risk assessment, multi-level pre-warning and multi-level driving avoidance strategy for the safe flight of the unmanned aerial vehicle in the low-altitude urban environment, and solves the limitations of the existing single pre-warning and driving avoidance scheme in dealing with complex and variable scenes. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The ground-air linkage unmanned aerial vehicle bird collision risk intelligent pre-warning and driving avoidance method of the present application is a step flow chart.

[0025] Figure 2 The four-level risk level, four-level pre-warning and four-level driving avoidance mechanism block diagram of the present application. DETAILED DESCRIPTION

[0026] With reference to the accompanying drawings, the technical solutions in the embodiments of the present disclosure will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present disclosure, but not all the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present disclosure.

[0027] Please refer to Figure 1 , Figure 2 The method for intelligent early warning and repelling of bird strike risk of air-ground linkage unmanned aerial vehicle disclosed in the present solution comprises the following steps:

[0028] Step 1: Obtain the bird information data entering the monitoring area based on the distributed monitoring and repelling node network system deployed on the ground mother station, the high-rise building daughter station and the airborne daughter station of the unmanned aerial vehicle.

[0029] Step 2: Perform identity matching and identification on the birds in step 1 based on the bird database, and identify the birds as aggressive tendency bird analysis targets or ordinary bird analysis targets. If more than one-third of the birds entering the monitoring area are identified as aggressive tendency birds, the birds are determined as aggressive tendency bird analysis targets, otherwise, the birds are determined as ordinary bird analysis targets.

[0030] Step 3: Construct a double-model risk assessment algorithm for aggressive tendency birds and ordinary birds. When the birds are identified as aggressive tendency bird analysis targets, run the aggressive tendency bird algorithm model to calculate and output a risk value Ra. When the birds are identified as ordinary bird analysis targets, run the ordinary bird algorithm model to calculate and output a risk value Rb.

[0031] Step 4: Output the risk level according to the numerical values of the risk value Ra and the risk value Rb. Output the early warning level based on the level of the risk level and the distance value between the birds and the unmanned aerial vehicle, execute the early warning action corresponding to the early warning level, output the repelling level based on the level of the early warning level, and execute the repelling action corresponding to the repelling level.

[0032] The calculation formula of the aggressive tendency bird algorithm model is:

[0033] Ra=[w1·S+w2·(1 / D)²+w3·V_rel+w4·A_p+w5·T_min-1]×M×H_a;

[0034] Wherein: S is the threat coefficient of the bird species, D is the straight-line distance between the bird and the UAV (m), V_rel is the approach speed of the bird relative to the UAV (m / s), A_p is the attack probability estimate based on the historical data of the bird, T_min is the minimum available decision time for the UAV to avoid collision (s), M is the number multiplier of the bird, H_a is the historical attack frequency weighting factor of the bird species, w1 is the attack tendency influence parameter, the value is (0, 0.4), which represents the weight value of the bird species threat coefficient S, w2 is the straight-line distance influence parameter, the value is (0, 0.4), which represents the weight value of the straight-line distance D between the bird and the UAV, w3 is the approach speed influence parameter, the value is (0, 0.3), which represents the weight value of the approach speed V_rel of the bird relative to the UAV, w4 is the attack probability influence parameter, the value is (0, 0.2), which represents the weight value of the attack probability estimate A_p of the bird historical data, w5 is the decision time influence parameter, the value is (0, 0.2), which represents the weight value of the minimum available decision time T_min for the UAV to avoid collision, and w1+w2+w3+w4+w5=1.

[0035] The bird species threat coefficient is initially matched according to the existing data of the bird species, and the bird species with the highest species threat coefficient is identified among multiple birds. The value range of the species threat coefficient is 1 to 10, such as the threat coefficients of eagles and gyrfalcons are artificially set to be between 9 and 10 at the initial stage of the system; the threat coefficient of the crow is between 6 and 8; the threat coefficient of the magpie is between 4 and 6; and the threat coefficient of the pigeon is between 2 and 3. In the continuous monitoring process, the species threat coefficient is adjusted according to the probability of the bird repelling event in the database through continuous monitoring and behavior pattern mining. The species threat coefficient = 1 + the probability of repelling the corresponding bird × 9. The probability of repelling the corresponding bird can be represented by F, F = N / T; wherein F is the attack frequency, N is the number of attack events in a specified time period, and T is the total number of flights or the corresponding observation quantity in the time period.

[0036] The definition of the historical attack frequency weighting factor is that the historical attack frequency is the number of attack events of a specific bird on a flight vehicle in a specific time period. This historical attack data is relative, that is, the accumulated data during the entire system operation is also historical attack data. F = N / T; wherein F is the attack frequency, N is the number of attack events in a specified time period, and T is the total number of flights or the corresponding observation quantity in the time period.

[0037] The historical frequency weighting factor H_a: The historical attack frequency is standardized for use in the risk assessment model. This factor can be determined by the following method:

[0038] Normalization:

[0039] w = (F - Fmin) / (Fmax - Fmin) where Fmin and Fmax are the minimum and maximum of the attack frequency in the history record.

[0040] The historical attack frequency weighting factor H_a = 1 + 1.5w. Its value range is [1, 2.5].

[0041] Regarding the calculation of A_p attack probability, different attack probabilities are preliminarily set according to different attack tendencies of birds (as shown in the following table).

[0042] Bird type Attack probability Raptor (eagle, hawk) 0.8 Carrion crow 0.65 Magpie 0.5 Pigeon 0.35

[0043] The system can also calculate the flight feature vector F = (F1, F2,..., F_n) of the flying bird by continuously analyzing its flight trajectory, and match it with the preset attack pattern library to obtain the attack probability A_p. The calculation formula is:

[0044] A_p = max_i(similarity(F, Attack_Pattern_i))

[0045] Regarding the calculation of T_min minimum available decision time

[0046] T_min = (D - D_safe) / (V_rel + V_evasion)

[0047] D_safe is the safety distance threshold, and V_evasion is the maximum achievable evasion speed of the UAV.

[0048] Among them, the feature vector F includes F1: height, unit: meter, indicating the height of the bird flight; F2: lateral speed, unit: meter / second, indicating the lateral movement speed of the bird; F3: longitudinal speed, unit: meter / second, indicating the lifting speed of the bird in the vertical direction; F4: approach angle, unit: degree, indicating the angle of the bird flying towards the UAV; F5: flight path stability, unit: standard deviation, the stability of the flight path calculated by analyzing the trajectory data; F6: distance change rate, unit: meter / second, indicating the distance change rate between the bird and the UAV; F7: behavior state, a categorical variable such as "normal" or "attack" state; F8: group size, unit: quantity, indicating the number of birds; F9: migration pattern, a categorical variable indicating the regular or abnormal behavior of bird migration; F 10 : time characteristics, indicating the time period (such as hours) of bird activity w1, w2, w3, w4, w5 The initial values of w1, w2, w3, w4, w5 are set to the reference values w1:w2:w3:w4:w5=3:3:2:1:1 (after normalization w1=0.3).

[0049] The calculation formula of the common bird algorithm model is:

[0050] Rb=[α·(D_scale)+β·(V_rel_scale)+γ·(Θ_scale)+δ·(T_avail_scale)]×F_size×F_density;

[0051] Wherein: D_scale is the distance scale factor, the calculation formula is D_scale= (300-D) / 225, wherein D≤300 meters, otherwise 0; V_rel_scale is the relative speed scale factor, the calculation formula is V_rel_scale=V_rel / V_max, V_max is the maximum detection speed supported by the system; Θ_scale is the angle scale factor, the calculation formula is Θ_scale= (180-θ) / 180, θ is the flight path angle; T_avail_scale is the available reaction time scale factor, the calculation formula is T_avail_scale= (T_crit - T_avail) / T_crit, when T_avail < T_crit, T_avail_scale otherwise 0; F_size is the bird size coefficient, which is positively correlated with the bird wingspan size; F_density is the population density factor;

[0052] α is the distance influence parameter, the value is (0, 0.5), which represents the weight value of the distance scale factor D_scale of the bird and the unmanned aerial vehicle, β is the relative speed influence parameter, the value is (0, 0.4), which represents the weight value of the relative speed scale factor V_rel_scale of the bird and the unmanned aerial vehicle, γ is the flight angle influence parameter, the value is (0, 0.3), which represents the weight value of the flight angle scale factor Θ_scale of the bird and the unmanned aerial vehicle, δ is the reaction time influence parameter, the value is (0, 0.2), which represents the weight value of the available reaction time scale factor T_avail_scale of the unmanned aerial vehicle, and α + β + γ + δ = 1. The bird size coefficient is positively correlated with the wingspan size of the bird, F_size=W / W max , W is the wingspan of the target bird, Wmax is the maximum wingspan set by the system (the largest bird in the region). The population density factor reflects the density of the bird population in a specific area, F_density=N / N max , N is the number of detected birds, N max is the maximum number of birds in the system design.

[0053] The initial values of α, β, γ, δ are set to the reference value α:β:γ:δ=4:3:2:1 (normalized α=0.4).

[0054] In the present scheme, the risk level includes four levels, namely, slight risk, low risk, medium risk, and high risk. When the value of Ra or Rb is [0, 25), a slight risk is output. When the value of Ra or Rb is [26, 50), a low risk is output. When the value of Ra or Rb is [51, 75), a medium risk is output. When the value of Ra or Rb is [76, 100), a high risk is output. The early warning level includes four levels, namely, first-level early warning, second-level early warning, third-level early warning, and fourth-level early warning. When the risk level is slight risk, and a bird enters the monitoring range, and the closest distance between the bird and the unmanned aerial vehicle is greater than 500 meters, a first-level early warning is output. When the risk level is low risk, and the closest distance between the bird and the unmanned aerial vehicle is less than 300 meters, a second-level early warning is output. When the risk level is medium risk, the bird trajectory intersects the flight path of the unmanned aerial vehicle, and the distance between the bird and the unmanned aerial vehicle is less than 150 meters, a third-level early warning is output. When the risk level is high risk, and an aggressive bird analysis target is identified, the closest distance between the aggressive bird and the unmanned aerial vehicle is less than 100 meters, or a common bird analysis target is identified, the closest distance between the common bird and the unmanned aerial vehicle is less than 75 meters, a fourth-level early warning is output. The avoidance level includes three levels, namely, first-level avoidance, second-level avoidance, and third-level avoidance. When the third-level early warning is output, a first-level avoidance is output. When the fourth-level early warning is output, a second-level avoidance is output. When the value of Ra or Rb is greater than 100, the closest distance between the bird and the unmanned aerial vehicle is less than 50 meters, and the collision prediction time is less than 5 seconds of the emergency threshold, a third-level avoidance is output. The third-level avoidance is also a critical avoidance.

[0055] Specifically, the pre-warning action executed by the first-level pre-warning is to maintain the normal monitoring state and record the monitoring events; the pre-warning action executed by the second-level pre-warning is to automatically increase the monitoring frequency, and the ground mother station or the high-rise sub-station automatically executes the entering of the repellent preparation state, and the unmanned aerial vehicle keeps the original flight route; the pre-warning action executed by the third-level pre-warning is to trigger the first-level repellent, the repellent action of the first-level repellent is that the ground mother station or the high-rise sub-station enters the alert state, sends an alert alarm to the unmanned aerial vehicle in the adjacent area, activates the repellent function of the ground mother station and the high-rise sub-station in the corresponding monitoring area, executes the intensity of 60%, the unmanned aerial vehicle keeps the flight route, and real-time monitoring of the bird reaction, if the trigger condition is no longer met, the first-level repellent is removed; a maximum bird repellent duration threshold is set, if no effect is seen beyond the preset threshold, the second-level repellent is automatically upgraded. The pre-warning action executed by the fourth-level pre-warning is to trigger the second-level repellent, the repellent action of the second-level repellent is that the ground mother station and the high-rise sub-station respond synchronously, the ground mother station takes over, sends an avoidance instruction and adjusts the flight route of the unmanned aerial vehicle in the adjacent area, activates the repellent function of the ground mother station and the high-rise sub-station in the corresponding monitoring area, and executes the intensity to 100%; at the same time, the repellent function on the unmanned aerial vehicle is activated, the control center sends a speed reduction instruction to the unmanned aerial vehicle, and the speed is reduced to 50%, the bird reaction is monitored in real time, if the trigger condition is no longer met, the second-level repellent is removed; a maximum bird repellent duration threshold is set, if no effect is seen beyond the preset threshold, the system is automatically upgraded to the third-level repellent. The action executed by the third-level repellent is that the ground mother station is forced to manually take over, the unmanned aerial vehicle protection mode is started, and the unmanned aerial vehicle hovers in place or starts an emergency avoidance path planning.

[0056] In the present scheme, the distributed monitoring and repelling node network system deployed on the ground mother station, high-rise building child station and unmanned aerial vehicle airborne child station includes that the ground mother station is deployed in the unmanned aerial vehicle taking-off and landing point area, at least one ground mother station is configured for each taking-off and landing point, the coverage radius is at least 500 meters, and the vertical detection range is at least 100 meters. Each ground mother station is equipped with a 24GHz phased array radar, a 1080P high-definition infrared and visible light dual-spectrum camera as a mother station monitoring module; and is equipped with a directional bird warning sound and a natural enemy call sound wave array with a frequency of 2-8kHz adjustable, a low-power 532nm waveband green light laser projector and an electronic bird call generator as a mother station repelling module, responsible for bird monitoring and repelling in the taking-off and landing point area, providing long-distance early monitoring and bird collision prevention. The high-rise child station is deployed in a building with a height of ≥100 meters, the distance between adjacent child stations is ≤2000 meters, and the coverage overlap rate is ≥20%. Each high-rise child station is equipped with a small-sized 24GHz phased array radar and a 4K high-definition zoom camera system as a high-rise child station monitoring module; and is equipped with a small directional sound wave emitter, a low-power green light laser and an intelligent stroboscopic light as a high-rise child station repelling module. The airborne child station is equipped with an ultralight 77GHz millimeter wave MIMO radar and an edge computing enhanced vision system as an airborne child station monitoring module; and is equipped with a 20-40kHz lightweight ultrasonic wave emitter or a high-brightness LED stroboscopic optical bird repelling device as an airborne child station repelling module.

[0057] The present scheme constructs a double-model risk assessment algorithm for individual birds with aggressive tendencies and ordinary bird groups. By identifying birds entering the monitoring range and calculating risk values through the double-model algorithm, a more scientific and accurate risk assessment level can be obtained. The problem of excessive interference caused by indiscriminate monitoring and driving of flying birds is avoided. A risk quantification and multi-level progressive early warning and repelling mechanism is established. The risk assessment results are matched with the multi-level early warning and multi-level repelling mechanism, so that a more scientific and accurate early warning level is obtained, and a more scientific and accurate repelling strategy is executed.

[0058] The above has described various embodiments of the present disclosure, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles, practical applications or improvements to the technology in the market of the embodiments, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. An unmanned aerial vehicle (UAV) bird strike risk intelligent early warning and repelling method based on air-ground linkage, characterized in that, The method comprises the following steps: Step 1, obtaining the flying bird information data entering the monitoring area based on the distributed monitoring and repelling node network system deployed on the ground base station on the ground, the high-rise substation of the high-rise building and the airborne substation of the unmanned aerial vehicle; Step 2, identity matching and identification of the flying birds in step 1 based on the flying bird database, identifying the flying birds as attack-tendency flying birds or ordinary flying birds, wherein if more than one-third of the flying birds entering the monitoring area are identified as attack-tendency flying birds, the flying birds are identified as attack-tendency flying bird analysis targets, otherwise, the flying birds are identified as ordinary flying bird analysis targets; Step 3, constructing an attack-tendency flying bird and ordinary flying bird double-model risk assessment algorithm, when the flying birds are identified as attack-tendency flying bird analysis targets, running the attack-tendency flying bird algorithm model to calculate and output a risk value Ra, and when the flying birds are identified as ordinary flying bird analysis targets, running the ordinary flying bird algorithm model to calculate and output a risk value Rb; Step 4, outputting a risk level according to the numerical values of the risk value Ra and the risk value Rb, outputting a warning level based on the level of the risk level and the distance value between the flying birds and the unmanned aerial vehicle, executing a warning action corresponding to the warning level, outputting a repelling level based on the level of the warning level, and executing a repelling action corresponding to the repelling level.

2. The method of claim 1, wherein the method further comprises: The calculation formula of the attack-tendency flying bird algorithm model is: Ra=[w1·S+w2·(1 / D)²+w3·V_rel+w4·A_p+w5·T_min-1]×M×H_a; Wherein: S is a bird species threat coefficient, D is the straight-line distance between the bird and the unmanned aerial vehicle, unit: meter, V_rel is the approaching speed of the bird relative to the unmanned aerial vehicle, unit: meter / second, A_p is the attack probability estimate based on the historical data of the bird, T_min is the minimum available decision time for unmanned aerial vehicle collision avoidance, unit: second, M is the number multiplier of the bird, H_a is the historical attack frequency weighting factor of the bird species, w1 is the attack tendency influence parameter, the value is (0, 0.4), which represents the weight value of the bird species threat coefficient S, w2 is the straight-line distance influence parameter, the value is (0, 0.4), which represents the weight value of the straight-line distance D between the bird and the unmanned aerial vehicle, w3 is the approaching speed influence parameter, the value is (0, 0.3), which represents the weight value of the approaching speed V_rel of the bird relative to the unmanned aerial vehicle, w4 is the attack probability influence parameter, the value is (0, 0.2), which represents the weight value of the attack probability estimate A_p of the bird historical data, w5 is the decision time influence parameter, the value is (0, 0.2), which represents the weight value of the minimum available decision time T_min for unmanned aerial vehicle collision avoidance, and w1+w2+w3+w4+w5=1 must be satisfied. 3.The UAV bird-strike risk intelligent early warning and repelling method of ground-air linkage according to claim 1, characterized in that, The calculation formula of the ordinary flying bird algorithm model is: Rb=[α·(D_scale)+β·(V_rel_scale)+γ·(Θ_scale)+δ·(T_avail_scale)]×F_size×F_density; Wherein, D_scale is the distance ratio factor of the bird and the UAV, V_rel_scale is the relative speed ratio factor of the bird and the UAV, Θ_scale is the flight angle ratio factor of the bird and the UAV, T_avail_scale is the available reaction time ratio factor of the UAV, F_size is the bird size coefficient, F_density is the population density factor, α is the distance influence parameter, the value is (0, 0.5), which represents the weight value of the distance ratio factor D_scale of the bird and the UAV, β is the relative speed influence parameter, the value is (0, 0.4), which represents the weight value of the relative speed ratio factor V_rel_scale of the bird and the UAV, γ is the flight angle influence parameter, the value is (0, 0.3), which represents the weight value of the flight angle ratio factor Θ_scale of the bird and the UAV, δ is the reaction time influence parameter, the value is (0, 0.2), which represents the weight value of the available reaction time ratio factor T_avail_scale of the UAV, and α + β + γ + δ = 1.

4. The UAV bird-strike risk intelligent early warning and repelling method of ground-air linkage according to claim 1, characterized in that, The risk level includes four levels, namely, a small risk, a low risk, a medium risk, and a high risk, when the Ra or Rb value is [0, 25), a small risk is output, when the Ra or Rb value is [26, 50), a low risk is output, when the Ra or Rb value is [51, 75), a medium risk is output, and when the Ra or Rb value is [76, 100), a high risk is output.

5. The UAV bird-strike risk intelligent early warning and repelling method of ground-air linkage according to claim 4, characterized in that, The warning level includes four levels, namely, a first-level warning, a second-level warning, a third-level warning, and a fourth-level warning, when the risk level is a small risk and the bird enters the monitoring range and the closest distance between the bird and the UAV is greater than 500 meters, a first-level warning is output; when the risk level is a low risk and the closest distance between the bird and the UAV is less than 300 meters, a second-level warning is output; when the risk level is a medium risk, the bird trajectory intersects the UAV flight path, and the distance between the bird and the UAV is less than 150 meters, a third-level warning is output; when the risk level is a high risk and an aggressive bird analysis target is identified, the closest distance between the aggressive bird and the UAV is less than 100 meters, or a common bird analysis target is identified, the closest distance between the common bird and the UAV is less than 75 meters, a fourth-level warning is output.

6. The UAV bird-strike risk intelligent early warning and repelling method of ground-air linkage according to claim 5, characterized in that, The repelling level includes three levels, namely, a first-level repelling, a second-level repelling, and a third-level repelling, when the third-level warning is output, a first-level repelling is output, when the fourth-level warning is output, a second-level repelling is output, and when the Ra or Rb value is greater than 100, the closest distance between the bird and the UAV is less than 50 meters, and the collision prediction time is less than 5 seconds of the emergency threshold, a third-level repelling is output.

7. The UAV bird-strike risk intelligent early warning and repelling method of ground-air linkage according to claim 6, characterized in that, The first-level warning executes a warning action of maintaining a normal monitoring state and recording a monitoring event. The pre-warning action executed by the secondary pre-warning is to automatically increase the monitoring frequency, and the ground mother station or high-rise sub-station automatically executes into a driving avoidance preparation state, and the unmanned aerial vehicle maintains the original flight route; the pre-warning action executed by the tertiary pre-warning is to trigger a first-level driving avoidance, and the driving avoidance action of the first-level driving avoidance is that the ground mother station or high-rise sub-station enters an alert state and sends an alert alarm to the unmanned aerial vehicle in the adjacent area; The pre-warning action executed by the fourth-level pre-warning is to trigger a second-level driving avoidance, and the driving avoidance action of the second-level driving avoidance is that the ground mother station and the high-rise sub-station synchronously respond, the ground mother station takes over, sends an avoidance instruction and adjusts the flight route of the unmanned aerial vehicle in the adjacent area; The action executed by the tertiary driving avoidance is that the ground mother station forcibly takes over manually, starts the unmanned aerial vehicle protection mode, and the unmanned aerial vehicle hovers in place or starts the emergency avoidance path planning.

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