A radar layout method for bird invasion detection

Abstract

The present invention provides a radar layout method applied to bird intrusion detection, comprising the following steps: dividing the environment to be laid out into regions, obtaining the environmental parameters of each region, and setting the radar layout parameters of each region; calculating and determining the key regions for bird intrusion according to the environmental parameters of each region, and calculating and determining the radar layout evaluation values of each region according to the radar layout parameters of each region; determining the radar layout regions to be optimized based on the key regions for bird intrusion and the radar layout evaluation values of each region; determining the fitness function of the genetic algorithm based on the radar layout parameters of the radar layout regions to be optimized, and performing genetic algorithm iteration using the fitness function until a preset optimization condition is reached, so as to obtain the optimal radar layout parameters of the radar layout regions to be optimized. This method combines the genetic algorithm to determine the optimal radar layout of the region, avoiding the situations of insufficient layout coverage and layout repetition, and improving the accuracy of bird intrusion detection.

Description

Technical Field

[0001] The present invention relates to the technical field of radar optimized layout, and particularly relates to a radar layout method applied to bird intrusion detection. Background Art

[0002] Currently, with the changes in climate, the increasing speed of species reproduction and population, and the influence of human activities, large-scale migration and diffusion of bird populations have occurred, resulting in the invasion of some alien species into new areas. And these birds may cause damage to the ecosystem, interfere with human activities, and damage crops, etc. Therefore, in order to avoid the damage caused by bird intrusion and cause unnecessary impacts, it is becoming increasingly important to monitor bird intrusion efficiently and at low cost.

[0003] In order to effectively respond to bird intrusion, many regions monitor and manage bird intrusion through various technical means. With the rapid development of network information technology, bird intrusion detection work has become more and more intelligent. By using a perfect video monitoring system and image recognition technology for remote monitoring, signs of birds are captured in real time and quickly recognized, which greatly improves the work efficiency of bird intrusion detection. However, due to the small size of birds and the fact that the video monitoring system is easily affected by environmental factors, the images captured by the video monitoring cameras are often difficult to be accurately recognized. Therefore, radar is introduced to detect birds. However, the existing radar layout has problems such as insufficient coverage and repeated layout, resulting in low detection efficiency and inaccurate detection of bird intrusion by radar.

[0004] Therefore, it is of great value to study a reliable and efficient radar layout method for bird intrusion detection. Summary of the Invention

[0005] The purpose of the present invention is to provide a radar layout method applied to bird intrusion detection, so as to solve the problems of insufficient coverage and repeated layout in the existing radar layout for bird intrusion detection, resulting in low detection efficiency and inaccurate detection of bird intrusion by radar.

[0006] In order to solve the above technical problems, the present invention provides a radar layout method applied to bird intrusion detection, including the following steps:

[0007] S1, divide the environment to be laid out into regions, obtain the environmental parameters of each region, and set the radar layout parameters of each region;

[0008] S2, calculate and determine the key regions of bird intrusion according to the environmental parameters of each region, and calculate and determine the radar layout evaluation value of each region according to the radar layout parameters of each region;

[0009] S3. Determine the radar layout area to be optimized based on the key areas of bird invasion and the radar layout evaluation values of each area;

[0010] S4. Determine the fitness function of the genetic algorithm based on the radar layout parameters of the radar layout area to be optimized, and use the fitness function to perform genetic algorithm iterations until the preset optimization conditions are met to obtain the optimal radar layout parameters of the radar layout area to be optimized.

[0011] In one embodiment, the step S4 specifically includes the following steps: S41. Determine the fitness function based on the radar layout parameters of the radar layout area to be optimized; S42. Divide the radar layout area to be optimized into multiple sub-areas to be optimized; S43. Use the fitness function to calculate the fitness values of all radars in each sub-area to be optimized, and select the two radar points with the smallest fitness values in each sub-area to be optimized as the parents of the sub-area; S44. Set a probability value, and cross-pair the parent radar points of each sub-area to be optimized with the set probability to obtain new child radar points, calculate their fitness values, and replace the radar points with lower fitness values with the radar points with higher fitness values; S45. Use the fitness function to calculate the overall fitness value of each sub-area to be optimized and the total fitness value of the radar layout area to be optimized; S46. Loop S41 to S45 until the overall fitness value of each sub-area to be optimized and the total fitness value of the radar layout area to be optimized reach the highest, and obtain the optimal radar layout parameters of the radar layout area to be optimized.

[0012] In one embodiment, the step S41 specifically includes the following steps: S410. Calculate the overlap degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost based on the radar layout parameters of the radar layout area to be optimized; S411. Determine the fitness function of the genetic algorithm based on the overlap degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost.

[0013] In one embodiment, the fitness function is:

[0014] max G(R, S, C) = ω 1 (-g 1 (R)) + ω 2 g 2 (S) + ω 3 (-g 3 (C))

[0015]

[0016]

[0017]

[0018] wherein, g 1 (R), g 2 (S), g 3 (C) are respectively the coincidence degree of the radar detection range, the signal-to-noise ratio of the radar, and the total cost calculation function, ω 1 is the weight of the coincidence degree of radar detection, ω 2 is the weight of the signal-to-noise ratio of the radar, ω 3 is the comprehensive cost weight, s′ ij is the area of the detection range where the j-th radar in area i overlaps with other radars, s ij is the detection range of the j-th radar in area i, P s is the received signal power, P n is the background noise power, n is the total number of layout radars, c 1 is the cost of a single radar, c 2 is other costs, including human resource costs.

[0019] In one embodiment, in the step S44, the parent radar points are cross-matched with a set probability to obtain new child radar points, including: using the range and direction information covered by the parent radar points, through multi-point crossover operation, exchanging and selecting the position information of the parent radar points to obtain the child radar points.

[0020] In one embodiment, in the step S45, the following steps are further included: using the simulated annealing algorithm to optimize the radar layout of each partition to be optimized.

[0021] In one embodiment, in the step of using the simulated annealing algorithm to optimize the radar layout of each partition to be optimized, the following steps are specifically included: selecting the radar point with the lowest fitness function value in the partition to be optimized, and randomly moving the position of the radar; calculating the overall fitness value of the partition to be optimized after the movement, and determining the acceptance probability according to the acceptance probability selection function, and accepting the new radar layout with the acceptance probability.

[0022] In one embodiment, the calculation formula of the acceptance probability selection function is:

[0023]

[0024] wherein, P i is the acceptance probability, F 1 , F 2 respectively represent the overall fitness values of this area before and after moving the radar, k represents the Boltzmann constant, and T is the control parameter.

[0025] In one embodiment, in step S2, it specifically includes the following steps: S20, determining the key areas vulnerable to bird invasion by weighting the influencing factors of each area vulnerable to bird invasion; S21, calculating the radar layout evaluation value of each area based on the radar layout parameters of each area and the ratio of the actual detection range to the theoretical detection range of the radar; wherein, the influencing factors vulnerable to bird invasion include the bird distribution range, environmental suitability, means of transportation, and known invasion areas.

[0026] In one embodiment, in step S1, it specifically includes the following steps: dividing the environment into regions according to the rotating bounding box algorithm; deploying radars at the vertices of the bounding box according to the shape of the bounding box and the maximum detection range of the radar, and deploying another radar every other vertex until the radar detection range covers the entire region.

[0027] The beneficial effects of the present invention are as follows:

[0028] In this method, since the key areas vulnerable to bird invasion and the radar layout evaluation value are introduced as the determining factors for the area where the radar layout needs to be optimized, the radar layout can be adjusted for the key areas vulnerable to bird invasion and the areas where the radar layout is unreasonable at the same time, and the area where the radar layout needs to be carried out can be judged more accurately; and since the genetic algorithm is combined to determine the optimal radar layout of the area, the optimal radar layout for bird invasion detection can be achieved, avoiding the situations of insufficient coverage and duplicate layout, and greatly improving the efficiency and accuracy of bird invasion detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for implementation will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 is the overall flow schematic diagram provided by the preferred embodiment of the present invention;

[0031] Figure 2 is the schematic diagram of the rotating bounding box algorithm provided by the preferred embodiment of the present invention;

[0032] Figure 3 is the schematic diagram of determining the area to be optimized and the genetic algorithm flow provided by the preferred embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.

[0034] In response to bird invasions, many regions monitor and manage bird invasions through various technical means. With the rapid development of network information technology, the work of bird invasion detection has become increasingly intelligent. By using a perfect video surveillance system and image recognition technology for remote monitoring, signs of birds can be captured in real time and quickly recognized, greatly improving the work efficiency of bird invasion detection. Due to the small size of birds and the vulnerability of the video surveillance system to environmental factors, the images captured by the video surveillance cameras are often difficult to accurately identify. Therefore, radar is introduced to detect birds. However, the existing radar layout has problems such as insufficient coverage and layout duplication, resulting in low detection efficiency and inaccurate detection of bird invasions.

[0035] To solve the above problems, this solution provides a radar layout method applied to bird invasion detection. Please refer to Figure 1 , including the following steps:

[0036] In the embodiment of the present application, S1, divide the environment to be laid out into regions, obtain the environmental parameters of each region, and set the radar layout parameters of each region.

[0037] Preferably, step S1 includes the following steps:

[0038] S11, divide the environment into regions according to the rotating bounding box algorithm, obtain the divided regions, and set a number for each divided region. The rotating bounding box algorithm can set the shape of the bounding box according to the actual environment, has better adaptability, can more accurately reflect the boundary and divide the entire environmental region, and is more conducive to calculating its overlapping area and coverage range when dividing the region and initializing the radar layout, improving the calculation efficiency. Therefore, the fine division of the environmental region will be more accurate and reliable after adopting the rotating bounding box algorithm.

[0039] Specifically, step S10 includes the following steps:

[0040] S110, initialize the shape of the bounding box.

[0041] S111, align the vertices of the bounding box with the vertices of the environmental boundary, rotate the bounding box, determine the region with the largest coverage area of the bounding box, and number the regions (i = 1, 2,..., n).

[0042] S112, align the vertices of the bounding box with the vertices of the divided region, rotate the bounding box, and take the region with the smallest overlapping area between the bounding box and the divided region as the new divided region.

[0043] S113, determine whether the new divided region covers the initial region. If so, end the region division. If not, execute S112 until the initial region is completely covered.

[0044] S12. According to the shape of the bounding box and the maximum detection range of the radar, deploy radars at the vertices of the bounding box, and deploy one radar every other vertex. This process is as shown in Figure 2 visible, and number the radars (j = 1, 2,..., m) until the detection range of the radars has covered the entire area, and number the radars in sequence. Among them, the detection range of the Doppler radar is 2m to 10m, and the detection area is fan-shaped. After adopting this setting method, the radar layout in each area completes the initial layout of the radar according to the set rules.

[0045] It should be noted that if the detection range of the radar has covered the entire area, the initial layout of the radar is completed. If the detection range of the radar has not covered the entire area, continue to deploy radars at the vertices to cover the entire area.

[0046] In the embodiment of the present application, S2. Calculate and determine the key areas of bird intrusion according to the environmental parameters of each area, and calculate and determine the radar layout evaluation value of each area according to the radar layout parameters of each area. After adopting this setting method, the present method introduces the key areas of bird intrusion and the radar layout evaluation value as the determining factors for the area where the radar layout needs to be optimized, and can adjust the radar layout for the key areas of bird intrusion and the areas where the radar layout is unreasonable at the same time, so that the area where the radar layout needs to be carried out can be judged more accurately.

[0047] Preferably, step S2 includes the following steps:

[0048] S21. Determine the key areas of bird intrusion by weighting the influencing factors that are vulnerable to bird intrusion in each area. Among them, the influencing factors that are vulnerable to bird intrusion include the bird distribution range, environmental suitability, means of transportation, and known intrusion areas.

[0049] Specifically, step S21 includes the following steps:

[0050] S210. Determine the weight coefficients β of the bird distribution range, environmental suitability, means of transportation, and known intrusion areas near each area according to the environmental parameters of each area 1 , β 2 , β 3 , β 4 , where β 1 +β 2 +β 3 +β 4 = 1.

[0051] S211. Construct an evaluation function for the areas that may be invaded by birds. The formula is:

[0052]

[0053] In the formula, f nirespectively represent 4 impact factors near the corresponding area i: the distribution range of birds, the environmental suitability, transportation means, and the known invaded areas.

[0054] S212. Set the evaluation function value of the key area according to the impact factors of different environments. This evaluation function value of the key area is used as a criterion to determine the key area of bird invasion.

[0055] In this embodiment, preferably set the evaluation value of the key bird invasion area to 0.8. Then, by calculating the evaluation values of each area, it can be judged whether this area is a key area of bird invasion, that is, whether it is necessary to focus on arranging radars to more accurately monitor the bird invasion situation. Of course, the evaluation value of the key bird invasion area is set by those skilled in the art.

[0056] S213. Substitute the environmental parameters of each area into the evaluation function of bird invasion respectively, calculate the evaluation function values of each area, and judge whether the evaluation function values of each area are greater than the evaluation function value of the key area. If so, set this area as the key area of bird invasion.

[0057] S22. Calculate the radar layout evaluation value of each area based on the radar layout parameters of each area and the ratio of the actual detection range to the theoretical detection range of the radar.

[0058] Specifically, substitute the radar layout parameters of each area into the formula of the ratio of the actual detection range to the theoretical detection range to calculate the radar layout evaluation value. Among them, the formula of the ratio of the actual detection range to the theoretical detection range is:

[0059]

[0060] In the formula, n is the total number of radars deployed in the area, s is the maximum detection range of a single radar theoretically, and S n is the total detection range of the radars actually.

[0061] In the embodiment of the present application, as Figure 3 shown, S3. Determine the area where the radar layout needs to be optimized based on the key area of bird invasion and the radar layout evaluation values of each area.

[0062] Preferably, S3 includes the following steps:

[0063] S31. Set the evaluation function value of the area layout.

[0064] In this embodiment, preferably set the evaluation function value of the area layout to 0.95. By calculating the radar layout evaluation function of each area and combining the evaluation value of the bird invasion area, it can be judged whether to continue to optimize the layout of this area. Of course, the evaluation function value of the area layout is set by those skilled in the art according to their actual needs.

[0065] S32. Based on the radar layout evaluation value of each region, determine whether it is greater than the region layout evaluation function value. If so, and if the region is not a key area for bird intrusion, end the optimized layout of this region. If not, continue to optimize the radar layout, that is, determine that this region is the region to be optimized for radar layout.

[0066] In the embodiment of the present application, as Figure 3 shown, S4. Based on the radar layout parameters of the region to be optimized for radar layout, determine the fitness function of the genetic algorithm, and use the fitness function to perform genetic algorithm iteration until the preset optimization condition is reached, and obtain the optimal radar layout parameters of the region to be optimized for radar layout. After adopting this setting method, since the genetic algorithm is combined to determine the optimal radar layout of the region, the optimal radar layout for bird intrusion detection can be achieved, avoiding the situation of insufficient coverage area and duplicate layout, and greatly improving the efficiency and accuracy of bird intrusion detection.

[0067] Preferably, S4 includes the following steps:

[0068] S41. Based on the radar layout parameters of the region to be optimized for radar layout, determine the fitness function.

[0069] Specifically, step S41 specifically includes the following steps:

[0070] S410. Based on the radar layout parameters of the region to be optimized for radar layout, calculate the coincidence degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost, where the weights of the influencing factors of the coincidence degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost are ω 1 , ω 2 , ω 3 , ω 1 +ω 2 +ω 3 = 1.

[0071] It should be noted that the radar layout parameters of the region to be optimized for radar layout include the position, direction, range of the radar, and the signal of the radar, etc.

[0072] S411. Based on the coincidence degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost, construct a radar layout optimization model, and use the radar layout optimization model as the fitness function of the genetic algorithm.

[0073] The radar layout optimization model max G is:

[0074] max G(R, S, C) = ω 1 (-g 1 (R)) + ω 2 g 2(S) + ω 3 (-g 3 (C))

[0075]

[0076]

[0077]

[0078] The fitness function F is as follows:

[0079] F = max G

[0080] wherein, g 1 (R), g 2 (S), g 3 (C) are respectively the coincidence degree of the radar detection range, the signal-to-noise ratio of the radar, and the total cost calculation function, ω 1 is the weight of the coincidence degree of radar detection, ω 2 is the weight of the signal-to-noise ratio of the radar, ω 3 is the weight of the comprehensive cost, s ′ ij is the area of the detection range where the j-th radar in area i overlaps with other radars, s ij is the detection range of the j-th radar in area i, P s is the received signal power, P n is the background noise power, n is the total number of radars in the layout, c 1 is the cost of a single radar, c 2 is other costs, including human resource costs.

[0081] S42. Partition the radar layout area to be optimized to obtain multiple sub-areas to be optimized.

[0082] S43. Use the fitness function to calculate the fitness values of all radars in each sub-area to be optimized, and select the two radar points with the smallest fitness values in each sub-area to be optimized as the parents of the corresponding sub-area.

[0083] S44. Set a probability value, and cross-pair the parent radar points in each sub-area to be optimized with the set probability to obtain new offspring radar points, calculate their fitness values, and replace the radar points with lower fitness values with the radar points with higher fitness values.

[0084] Among them, the probability value set in this embodiment is 0.7. Of course, the set probability value is selected according to the actual needs of those skilled in the art.

[0085] Preferably, the parent radar points are cross - paired with a set probability. The new offspring radar points are obtained by using the range and direction information covered by the parent radar points. Through multi - point crossover operation, the position information of the parent radar points is exchanged and selected to obtain the offspring radar points.

[0086] S45. Calculate the overall fitness value of each partition to be optimized and the total fitness value of the radar layout area to be optimized by using the fitness function.

[0087] Preferably, while calculating the overall fitness value of each partition to be optimized, the radar layout of each partition to be optimized is also optimized by using the simulated annealing algorithm to avoid local optimal layout. After determining the optimal radar layout of the area by combining the genetic algorithm and the simulated annealing algorithm, the radar layout of each partition to be optimized can be accurately optimized.

[0088] Specifically, it includes the following steps:

[0089] S450. Select the radar point with the lowest fitness function value in the partition to be optimized and randomly move the position of this radar.

[0090] S451. Calculate the overall fitness value of the partition to be optimized after the movement, and determine the acceptance probability according to the acceptance probability selection function, and accept the new radar layout with the acceptance probability.

[0091] That is, if the area fitness value after changing the radar position is higher than before, accept the change of the radar with a probability of 1.

[0092] If it is less, then with a probability of Accept. The probability of accepting the new radar point can be controlled by adjusting the size of T.

[0093] Among them, the calculation formula of the acceptance probability selection function is:

[0094]

[0095] In the formula, P i is the acceptance probability, F 1 , F 2 respectively represent the overall fitness values of this area before and after moving the radar. k represents the Boltzmann constant, and T is the control parameter. The parameter value can be changed according to the optimization situation to obtain the optimal solution.

[0096] S46. Loop S41 to S45 until the overall fitness value of each partition to be optimized and the total fitness value of the radar layout area to be optimized reach the highest, and obtain the optimal radar layout parameters of the radar layout area to be optimized, that is, judge whether the fitness values of each area and the total area fitness value reach the highest. If so, obtain the optimal radar layout plan. If not, continue to execute steps S41 to S45.

[0097] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A radar layout method for bird intrusion detection, characterized in that, it includes the following steps: S1. Divide the environment to be laid out into regions, obtain the environmental parameters of each region, and set the radar layout parameters of each region; S2. Calculate and determine the key regions of bird intrusion according to the environmental parameters of each region, and calculate and determine the radar layout evaluation values of each region according to the radar layout parameters of each region; S3. Determine the radar layout region to be optimized based on the key regions of bird intrusion and the radar layout evaluation values of each region; S4. Determine the fitness function of the genetic algorithm based on the radar layout parameters of the radar layout region to be optimized, and use the fitness function to perform genetic algorithm iteration until the preset optimization conditions are met to obtain the optimal radar layout parameters of the radar layout region to be optimized; The S4 step specifically includes the following steps: S41. Determine the fitness function based on the radar layout parameters of the radar layout region to be optimized; S42. Divide the radar layout region to be optimized to obtain multiple sub-regions to be optimized; S43. Use the fitness function to calculate the fitness values of all radars in each sub-region to be optimized, and select the two radar points with the smallest fitness values in each sub-region to be optimized as the parents of the sub-region to be optimized; S44. Set a probability value, cross-pair the parent radar points in each sub-region to be optimized with the set probability to obtain new child radar points, calculate their fitness values, and replace the radar points with lower fitness values with the radar points with higher fitness values; S45. Use the fitness function to calculate the overall fitness value of each sub-region to be optimized and the total fitness value of the radar layout region to be optimized; S46. Loop S41 to S45 until the overall fitness value of each sub-region to be optimized and the total fitness value of the radar layout region to be optimized reach the highest, and obtain the optimal radar layout parameters of the radar layout region to be optimized.

2. The radar layout method for bird intrusion detection according to claim 1, characterized in that, the S41 step specifically includes the following steps: S410. Calculate the coincidence degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost based on the radar layout parameters of the radar layout region to be optimized; S411. Determine the fitness function of the genetic algorithm based on the coincidence degree of radar detection, the signal-to-noise ratio of the radar, and the comprehensive cost.

3. The radar layout method for bird intrusion detection according to any one of claims 1 to 2, characterized in that, the fitness function is: max G(R, S, C) = ω 1 (-g 1 (R)) + ω 2 g 2 (S) + ω 3 (-g 3 (C)) g 3 (C) = nc 1 + c 2 where g 1 (R), g 2 (S), g 3 (C) are the coincidence degree of the radar detection range, the signal-to-noise ratio of the radar, and the total cost calculation function respectively, ω 1 is the weight of the coincidence degree of radar detection, ω 2 is the weight of the signal-to-noise ratio of the radar, ω 3 is the weight of the comprehensive cost, s′ ij is the area of the detection range where the j-th radar in area i overlaps with other radars, s ij is the detection range of the j-th radar in area i, P s is the received signal power, P n is the background noise power, n is the total number of layout radars, c 1 is the cost of a single radar, c 2 is other costs, including human resource costs.

4. The radar layout method for bird intrusion detection according to claim 1, characterized in that, in the S44 step, the parent radar points are cross-paired with the set probability to obtain new child radar points, including: Using the coverage range and direction information of the parent radar points, through multi-point crossover operation, exchange and select the position information of the parent radar points to obtain the child radar points.

5. The radar layout method for bird intrusion detection according to claim 1, characterized in that, in the S45 step, it further includes the following steps: Optimize the radar layout of each partition to be optimized using the simulated annealing algorithm.

6. The radar layout method for bird intrusion detection according to claim 5, characterized in that in the step of optimizing the radar layout of each partition to be optimized using the simulated annealing algorithm, the following steps are specifically included: Select the radar point with the lowest fitness function value in the partition to be optimized, and randomly move the position of this radar; Calculate the overall fitness value of the partition to be optimized after the movement, and determine the acceptance probability according to the acceptance probability selection function, and accept the new radar layout with the acceptance probability.

7. The radar layout method for bird intrusion detection according to claim 6, characterized in that the calculation formula of the acceptance probability selection function is: Where P i is the reception probability, F 1 , F 2 respectively represent the overall fitness values of this area before and after the mobile radar moves, k represents the Boltzmann constant, and T is the control parameter.

8. The radar layout method for bird intrusion detection according to claim 1, characterized in that in step S2, the following steps are specifically included: S20, weight the influencing factors that are vulnerable to bird intrusion in each area to determine the key areas of bird intrusion; S21, calculate the theoretical detection range based on the radar layout parameters of each area, and then determine the radar layout evaluation value of each area based on the ratio of the actual detection range to the theoretical detection range; wherein, the influencing factors that are vulnerable to bird intrusion include the bird distribution range, environmental suitability, means of transportation, and known intrusion areas.

9. The radar layout method for bird intrusion detection according to claim 1, characterized in that in step S1, the following steps are specifically included: Divide the environment into regions according to the rotating bounding box algorithm; According to the shape of the bounding box and the maximum detection range of the radar, deploy radars at the vertices of the bounding box, and deploy one more radar every other vertex until the radar detection range has covered the entire area.

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