Bird repelling system and method for wind power plant station

Abstract

The invention provides a bird repelling system and method for a wind power station, and can be applied to the technical field of bird repelling. The bird repelling system comprises a data processing system, a plurality of image acquisition devices and a plurality of bird repelling unmanned aerial vehicles, the image acquisition devices and the bird repelling unmanned aerial vehicles are arranged in a wind power station, the image acquisition devices and the bird repelling unmanned aerial vehicles are in communication connection with the data processing system, and bird repelling devices are carried on the bird repelling unmanned aerial vehicles; the image acquisition equipment is used for acquiring an environment image of the wind power station in real time; the data processing system is used for receiving the environment image sent by the image acquisition equipment and extracting bird information based on the environment image; the data processing system is used for determining a target unmanned aerial vehicle executing a bird repelling task based on the bird information and generating a bird repelling instruction; and the target unmanned aerial vehicle is used for receiving the bird repelling instruction sent by the data processing system and repelling the bird target according to the bird repelling instruction. According to the bird repelling system, bird repelling operation on the wind power plant station can be rapidly and effectively completed, and the bird repelling efficiency and the bird repelling effect of the wind power plant station are improved.

Description

Technical Field

[0001] The present application relates to the field of bird-repelling technology, and in particular to a bird-repelling system and method for a wind farm station. Background Art

[0002] A wind farm is a site that uses wind power to arrange multiple wind turbines into an array according to the terrain and main wind direction to form a group of machines to supply power to the power grid. It is also called a wind power station or wind farm.

[0003] Because wind farms are built in sparsely populated wilderness areas, home to a variety of birds, birds flying at low altitudes are prone to colliding with wind turbine blades. The high-speed rotation of the blades can cause direct damage to birds, resulting in casualties. Wind farms may also interfere with bird migration routes and habitats, impacting their survival and reproduction. Furthermore, bird activities within wind farms, such as nesting and foraging, can damage electrical equipment within the wind farm, impacting normal power generation and operation. Therefore, bird repellent measures at wind farms are particularly important.

[0004] Bird repelling measures can reduce bird casualties and avoid the impact of birds on wind farms. However, existing wind farms have the problem of poor bird repelling effects. Summary of the Invention

[0005] The present application provides a bird-repelling system and method for a wind farm station, which are used to solve the technical problem that the existing wind farm station has a poor bird-repelling effect.

[0006] According to a first aspect disclosed in the present application, the present application provides a bird-repelling system for a wind farm station, comprising a data processing system, and a plurality of image acquisition devices and a plurality of bird-repelling drones deployed in the wind farm station, wherein the image acquisition devices and the bird-repelling drones are respectively communicatively connected to the data processing system, and the bird-repelling drones are equipped with bird-repelling devices;

[0007] The image acquisition device is used to acquire environmental images of the wind farm in real time;

[0008] The data processing system is used to receive the environmental image sent by the image acquisition device and extract bird information based on the environmental image; wherein the bird information includes the number and coordinates of bird targets;

[0009] The data processing system is used to determine a target drone for performing a bird-repelling mission based on the bird information and generate a bird-repelling instruction; wherein the target drone corresponds to one or more of the bird-repelling drones;

[0010] The target UAV is used to receive the bird-repelling instruction sent by the data processing system and drive away the bird target according to the bird-repelling instruction.

[0011] In a feasible implementation, the bird-repelling device includes a high-decibel sound wave bird-repelling module, an ultrasonic wave bird-repelling module, and a laser bird-repelling module.

[0012] In a feasible implementation manner, the image acquisition device is installed on a nacelle of a wind turbine in the wind farm.

[0013] In a feasible implementation manner, a rotating platform is further provided on the nacelle of the wind turbine, and the image acquisition device is installed on the rotating platform.

[0014] In a feasible embodiment, the bird-repellent drone is installed on a nacelle of a wind turbine located in the middle of a wind turbine cluster; wherein the wind farm station includes multiple wind turbine clusters, and the wind turbine cluster includes a preset number of adjacent wind turbines.

[0015] According to a second aspect disclosed in the present application, the present application provides a bird-repelling method for a wind farm station, which is applied to a bird-repelling system for a wind farm station. The bird-repelling system includes a data processing system, and multiple image acquisition devices and multiple bird-repelling drones deployed in the wind farm station. The image acquisition devices and the bird-repelling drones are respectively communicated with the data processing system, and the bird-repelling drones are equipped with a bird-repelling device. The method includes:

[0016] The image acquisition device acquires environmental images of the wind farm in real time;

[0017] The data processing system receives the environmental image sent by the image acquisition device and extracts bird information based on the environmental image; wherein the bird information includes the number and coordinates of bird targets;

[0018] The data processing system determines a target drone for performing a bird-repelling task based on the bird information and generates a bird-repelling instruction; wherein the target drone corresponds to one or more of the bird-repelling drones;

[0019] The target UAV receives the bird-repelling instruction sent by the data processing system, and drives away the bird target according to the bird-repelling instruction.

[0020] In a feasible implementation, extracting bird information based on the environment image includes:

[0021] The data processing system obtains the contour information of the moving target in the environmental image based on the inter-frame difference algorithm;

[0022] The data processing system extracts and classifies the moving targets according to the contour information, and obtains bird targets among the moving targets and the number of the bird targets;

[0023] The data processing system obtains the two-dimensional plane coordinates of the bird target based on the pixel point division of the environmental image;

[0024] The data processing system converts the two-dimensional plane coordinates into three-dimensional space coordinates to obtain the coordinates of the bird target.

[0025] In a feasible implementation, obtaining the contour information of the moving target in the environment image based on an inter-frame difference algorithm includes:

[0026] The data processing system performs inter-frame difference operation on two adjacent frames of the real-time environmental image to obtain a grayscale difference image;

[0027] The data processing system binarizes the grayscale difference image to obtain a binary image containing only the foreground and the background;

[0028] The data processing system extracts the foreground of the binary image and obtains the contour information of the moving target.

[0029] In a feasible implementation, determining a target drone for performing a bird-repelling mission based on the bird information includes:

[0030] The data processing system selects the bird-repelling UAV closest to the bird target as the first UAV based on the coordinates of the bird target;

[0031] The data processing system uses the deployment location of the first UAV as the spatial origin and divides the surrounding space into four spatial quadrants according to the spatial orientation;

[0032] If the number of the bird targets is not greater than a preset number and the bird targets are located in the same spatial quadrant, the data processing system determines that the first UAV is the target UAV.

[0033] In a feasible embodiment, the method further includes:

[0034] If the number of the bird targets is greater than a preset number, or the bird targets are located in multiple spatial quadrants, the data processing system selects other bird-repelling drones around the first drone as second drones, and determines the first drone and the second drone as target drones.

[0035] Compared with the existing technology, this application has the following beneficial effects:

[0036] The present application provides a bird-repelling system and method for a wind farm station. Multiple image acquisition devices deployed within the wind farm station collect environmental images of the wind farm station in real time, and then transmit the environmental images to a data processing system for processing. The data processing system obtains the number of flying birds and the coordinates of the birds in the received environmental images to form bird information. Based on the bird information, the system then determines the target drone to perform the bird-repelling mission from among multiple bird-repelling drones deployed within the wind farm station, and generates a bird-repelling instruction based on the bird information. The bird-repelling instruction is then sent to the target drone. After receiving the bird-repelling instruction, the target drone flies and moves to the vicinity of the bird target according to the instruction, and uses the bird-repelling device onboard the drone to repel the bird target.

[0037] Compared to traditional bird-repelling methods, this application combines a data processing system with image acquisition equipment and bird-repelling drones. The system can quickly identify bird targets entering a wind farm and use drones to accurately repel them. Drones can flexibly fly in the air, unrestricted by the ground, and can reach high altitudes that are difficult for traditional bird-repelling devices to reach, improving the effectiveness of bird-repelling devices. It can also quickly cover a wider area and increase bird-repelling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] Figure 1 A schematic structural diagram of a bird repellent system for a wind farm provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the installation structure of an image acquisition device, a bird-repelling drone, and a wind turbine provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of the structure of a bird-repelling drone provided in an embodiment of the present application;

[0042] Figure 4 A schematic flow chart of a bird-repelling method for a wind farm provided in an embodiment of the present application;

[0043] Figure 5 A schematic flow chart of another bird-repelling method for a wind farm provided in an embodiment of the present application.

[0044] Description of reference numerals:

[0045] 100-Data processing system;

[0046] 200-fan group;

[0047] 300-wind turbine;

[0048] 301-engine room;

[0049] 400- Image acquisition equipment;

[0050] 500-rotating pan / tilt head;

[0051] 600-bird-scaring drone;

[0052] 601-high decibel sonic bird repellent module;

[0053] 602-Ultrasonic bird repellent module;

[0054] 603-Laser bird repellent module.

[0055] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0056] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0057] A wind farm, also known as a wind power station or wind farm, harnesses wind power by arranging multiple wind turbines in an array based on the terrain and prevailing wind direction to supply power to the grid. Wind energy is a renewable, pollution-free source of energy, and wind farms, as power plants that harness wind energy to generate electricity, are a crucial component of clean energy development.

[0058] Because wind farms are built in sparsely populated wilderness areas, home to a variety of birds, birds flying at low altitudes are prone to colliding with wind turbine blades. The high-speed rotation of the blades can cause direct damage to birds, resulting in casualties. Wind farms may also interfere with bird migration routes and habitats, impacting their survival and reproduction. Furthermore, bird activities within wind farms, such as nesting and foraging, can damage electrical equipment within the wind farm, impacting normal power generation and operation. Therefore, bird repellent measures at wind farms are particularly important.

[0059] Bird repellent measures can reduce bird casualties and minimize their impact on wind farms. Existing wind farms commonly use acoustic or chemical repellents. However, acoustic repellents, due to the long-term use of a single frequency, can easily become accustomed to birds, reducing their effectiveness. Chemical repellents, due to the large size of wind farms, have a limited range of repellents, and residual repellent can also have a certain impact on the ecological environment. Consequently, existing wind farms suffer from poor bird repellency.

[0060] In response to the above technical problems, this application proposes a bird-repellent system for wind farms. By constructing a bird-repellent system based on a data processing system, image acquisition equipment and bird-repellent drones, the bird-repellent operation of the wind farm can be completed quickly and effectively, thereby improving the efficiency and effectiveness of the bird-repellent operation of the wind farm.

[0061] The following is a detailed description of the technical solution of the bird-repelling system for wind farms provided by this application through specific embodiments. It should be noted that the following embodiments can exist independently or in combination with each other, and the same or similar content may not be repeated in different embodiments.

[0062] Figure 1 This is a structural diagram of a bird-repelling system for a wind farm provided in an embodiment of the present application. Figure 2 This is a schematic diagram of the installation structure of an image acquisition device 400, a bird-repelling drone 600 and a wind turbine 300 provided in an embodiment of the present application. Figure 1 and Figure 2 In some embodiments, the bird-repelling system of the wind farm station includes a data processing system 100, and multiple image acquisition devices 400 and multiple bird-repelling drones 600 deployed in the wind farm station. The image acquisition devices 400 and the bird-repelling drones 600 are respectively communicated with the data processing system 100, and the bird-repelling drones 600 are equipped with bird-repelling devices; the image acquisition device 400 is used to acquire environmental images of the wind farm station in real time; the data processing system 100 is used to receive the environmental images sent by the image acquisition device 400, and extract bird information based on the environmental images; the bird information includes the number and coordinates of bird targets; the data processing system 100 is used to determine the target drones that perform the bird-repelling task based on the bird information, and generate bird-repelling instructions; the target drones correspond to one or more of the bird-repelling drones 600; the target drones are used to receive the bird-repelling instructions sent by the data processing system 100, and drive away the bird targets according to the bird-repelling instructions.

[0063] In this embodiment, multiple image acquisition devices 400 deployed within the wind farm station capture real-time images of the wind farm's environment, which are then transmitted to a data processing system 100 for processing. Based on the received images, the data processing system 100 obtains the number of birds and their coordinates in the images to form bird information. Based on the bird information, the system then determines the target drone to perform the bird-repelling mission from among the multiple bird-repelling drones 600 deployed within the wind farm station, and generates a bird-repelling instruction based on the bird information. The bird-repelling instruction is then transmitted to the target drone. Upon receiving the instruction, the target drone flies in accordance with the instruction and drives the bird away using the bird-repelling device onboard the drone.

[0064] Compared to traditional bird-repelling methods, this application utilizes a combination of a data processing system 100, an image acquisition device 400, and a bird-repelling drone 600. The system can quickly identify bird targets entering a wind farm and precisely repel them using the drone. The drone's ability to flexibly fly through the air, unrestricted by the ground, allows it to reach high altitudes difficult for traditional bird-repelling devices to reach, improving the effectiveness of bird-repelling devices and rapidly covering a wider area, thus increasing bird-repelling efficiency.

[0065] Specifically, image acquisition device 400 is a device for capturing images, such as a camera, a video camera, or a still camera. Preferably, image acquisition device 400 can be an industrial high-speed camera. Industrial high-speed cameras are high-performance cameras widely used in the field of industrial automation. They feature high speed, high precision, and high stability, and are capable of capturing multiple consecutive images in a very short period of time, typically thousands of images per second or even higher. This high-speed shooting capability enables them to capture fast-moving objects and produce high-quality images.

[0066] Specifically, the data processing system 100 is a system that uses computers to process information. The data processing system 100 is generally composed of servers, storage devices, and network devices. Among them, the server is one of the core devices for data processing, which provides computing, storage, and management services. According to their use, servers can be divided into web servers, database servers, file servers, etc. These servers are usually deployed in data centers and provide large-scale and efficient data processing capabilities through high-speed network connections. Storage devices provide data storage services for servers to meet data storage needs. Network devices are used for data transmission and transfer, realizing data communication between the data processing system 100 and the external image acquisition device 400 and the bird-repelling drone 600.

[0067] Specifically, the bird-repelling drone 600 uses an onboard bird-repelling device to repel bird targets. Preferably, the bird-repelling drone 600 can be a multi-rotor drone. Equipped with four or more rotors, a multi-rotor drone has the ability to hover, facilitating hovering and repelling bird targets at specific locations within a wind farm. Furthermore, multi-rotor drones are capable of vertical takeoff and landing, allowing them to take off and land in a very small space without the need for a runway or taxiway, providing flexible takeoff and landing capabilities.

[0068] See Figure 3 In some embodiments, the bird repellent device includes a high-decibel sound wave bird repellent module 601 , an ultrasonic wave bird repellent module 602 , and a laser bird repellent module 603 .

[0069] In this embodiment, the bird-repellent drone 600 can carry a variety of bird-repellent modules to perform bird-repellent operations through different bird-repellent means to cope with bird infestation problems in different areas and of different species, as well as reduce the adaptability of birds to a certain bird-repellent means and improve the bird-repellent effect.

[0070] Specifically, the high-decibel sound wave bird-repelling module 601 uses high-frequency, high-decibel sound waves to drive away birds, and generates sound waves that are harsh and uncomfortable to birds, thereby forcing them to leave the wind farm area.

[0071] Specifically, the ultrasonic bird repellent module 602 uses ultrasonic waves (frequency is usually higher than 20kHz and cannot be heard by human ears) to repel birds. Its working principle is to emit ultrasonic pulses to interfere with and stimulate the nervous system and physiological system of birds, causing them to feel uncomfortable and leave the wind farm area.

[0072] Specifically, the laser bird-repellent module 603 is a technology that uses laser beams to repel birds. The laser bird-repellent module 603 can emit a high-intensity laser beam, which shines on the eyes or body of the bird, causing it to have strong visual stimulation and panic, thereby forcing it to leave the wind farm area.

[0073] See Figure 2 In some embodiments, the image acquisition device 400 is installed on the nacelle 301 of the wind turbine 300 in the wind farm.

[0074] In this embodiment, the image acquisition device 400 is installed on the nacelle 301 of the wind turbine 300 , which can increase the installation height of the image acquisition device 400 and expand the field of view of the image acquisition device 400 .

[0075] Specifically, the number of image acquisition devices 400 to be installed can be adjusted according to the actual environment of the wind farm. An image acquisition device 400 can be installed on each wind turbine 300, or an appropriate number of image acquisition devices 400 can be installed to cover the wind farm.

[0076] See Figure 2 Preferably, a rotating platform 500 is further provided on the nacelle 301 of the wind turbine 300 , and the image acquisition device 400 is installed on the rotating platform 500 .

[0077] In this embodiment, the rotating pan-tilt platform 500 provides a stable shooting platform for the image acquisition device 400, reducing shaking during the shooting process and improving image stability. Furthermore, the rotating pan-tilt platform 500 can adjust the shooting angle of the image acquisition device 400 to achieve omnidirectional shooting and comprehensively capture environmental images of the wind farm.

[0078] See Figure 1 and Figure 2 In some embodiments, the bird-repellent drone 600 is installed on the nacelle 301 of the wind turbine 300 located in the middle of the wind turbine group 200; wherein the wind farm station includes multiple wind turbine groups 200, and the wind turbine group 200 includes a preset number of adjacent wind turbines 300.

[0079] In this embodiment, since there are a large number of wind turbines 300 in a wind farm, it would be costly to install a drone on each wind turbine 300. Therefore, the wind turbines 300 in the wind farm can be divided into a plurality of wind turbine clusters 200 according to a preset number, with each wind turbine cluster 200 containing a preset number of adjacent wind turbines 300. The drone is then installed on the nacelle 301 of the wind turbine 300 located in the center of the wind turbine cluster 200. This not only meets the range coverage requirements of the drone bird repellent, but also reduces the deployment cost of the drone.

[0080] Specifically, the number of the wind turbine group 200 can be set to 3, 5, etc.

[0081] Specifically, the wind turbine 300 located in the center of the wind turbine group 200 refers to the wind turbine 300 having the smallest sum of straight-line distances to other wind turbines 300 .

[0082] Figure 4 A schematic diagram of a bird-repelling method for a wind farm provided in an embodiment of the present application is provided. Figure 4In some embodiments, the bird-repelling method for a wind farm is applied to a bird-repelling system of the wind farm. The bird-repelling system includes a data processing system, and multiple image acquisition devices and multiple bird-repelling drones deployed in the wind farm. The image acquisition devices and the bird-repelling drones are respectively connected to the data processing system for communication. The bird-repelling drones are equipped with bird-repelling devices. The process of the method includes the following steps:

[0083] S401 , an image acquisition device acquires environmental images of a wind farm in real time.

[0084] Among them, the image acquisition equipment collects environmental images of the wind farm in real time, so that the subsequent data processing system can determine whether there are birds flying into the wind farm through the collected environmental images.

[0085] S402, the data processing system receives the environment image sent by the image acquisition device, and extracts bird information based on the environment image; wherein the bird information includes the number and coordinates of bird targets.

[0086] Among them, after the data processing system receives the environmental image sent by the image acquisition device, it analyzes and processes the environmental image to extract relevant information such as the number and coordinates of bird targets to provide data support for subsequent bird-repelling operations.

[0087] S403, the data processing system determines the target drone for performing the bird-repelling mission based on the bird information and generates a bird-repelling instruction; wherein the target drone corresponds to one or more of the bird-repelling drones.

[0088] Among them, after obtaining bird information, the data processing system can also determine which drone or drones to select from the bird-repelling drones to perform the bird-repelling task based on the bird information. After the target drone to perform the bird-repelling task is determined, the data processing system sends a bird-repelling instruction to the target drone to control it to perform the bird-repelling task.

[0089] Specifically, generating bird-repelling instructions based on the coordinates of the bird target can guide the target drone to accurately reach the corresponding position of the bird target to perform bird-repelling operations, thereby improving the accuracy of bird-repelling.

[0090] S404: The target UAV receives the bird-repelling instruction sent by the data processing system and drives away the bird target according to the bird-repelling instruction.

[0091] Among them, after the target drone receives the bird-repelling command sent by the data processing system, it drives away the bird targets in the wind farm station based on the bird information carried in the bird-repelling command.

[0092] In this embodiment, by combining a data processing system with image acquisition equipment and a bird-repelling drone, it is possible to quickly identify birds entering a wind farm and precisely repel them using the drone. The drone's flexible flight, unrestricted by the ground, allows it to reach high altitudes difficult to reach with traditional bird-repelling devices, improving their effectiveness and rapidly covering a wider area, thus increasing their efficiency.

[0093] exist Figure 4 Based on the embodiment shown below, Figure 5 , further introduces the technical solution of the bird-repelling method of the above-mentioned wind farm station.

[0094] S501: An image acquisition device acquires environmental images of a wind farm in real time.

[0095] S502: The image acquisition device sends an environment image to the data processing system.

[0096] S503: The data processing system obtains the contour information of the moving target in the environment image based on the inter-frame difference algorithm.

[0097] The inter-frame differencing algorithm is a method that extracts the outline of a moving target by performing a differential operation on two adjacent frames (or more frames, such as three) in a video image sequence. The basic idea is to detect moving targets by exploiting the changes in pixel values ​​between adjacent frames due to object motion. When an object moves in the surveillance scene, a significant difference appears between two adjacent frames. By performing a differential operation on these two frames (i.e., pixel-by-pixel subtraction and taking the absolute value), a differential image is generated. The outline of the moving target in this image will appear as a non-zero value, while the stationary portion will appear close to zero, thus allowing the moving target to be extracted from the image.

[0098] Preferably, in order to reduce the storage space occupied by the environmental images in the data processing system, after executing the inter-frame difference algorithm, the environmental images that are determined to be unchanged are deleted to minimize the storage space occupied.

[0099] Specifically, based on the inter-frame difference algorithm, the basic process of obtaining bird targets in the environment image includes:

[0100] Step 1: The data processing system performs inter-frame difference operation on two adjacent frames of the real-time environment image to obtain a grayscale difference image.

[0101] The difference operation between two adjacent frames of environmental images is performed to obtain a grayscale difference image, and the value of each pixel in the grayscale difference image is equal to the absolute value of the difference between the pixel value at the corresponding position in the current frame and the pixel value at the corresponding position in the previous frame.

[0102] Specifically, if the environment image is not a grayscale image, it is necessary to convert the environment image into a grayscale image before performing the inter-frame difference operation on two adjacent frames of the environment image in the real-time environment image.

[0103] Step 2: The data processing system binarizes the grayscale difference image to obtain a binary image containing only the foreground and background.

[0104] Binarization is the process of setting the pixel values ​​of an image to 0 or 255 (for 8-bit grayscale images), creating an image consisting only of black and white. Therefore, binarization of a grayscale difference image involves setting a threshold to extract the moving object from the grayscale difference image. The value of each pixel in the difference image is compared to the threshold. If the value is greater than the threshold, the pixel is considered to be foreground (i.e., part of the moving object); otherwise, it is considered to be background.

[0105] Therefore, once the threshold is determined, we can iterate over each pixel of the grayscale difference image and compare it to the threshold. If the pixel value is greater than or equal to the threshold, the pixel is set to 255 (white); if it is less than the threshold, it is set to 0 (black). In this way, the grayscale difference image is binarized according to the threshold processing result, resulting in a binary image containing only the foreground and background.

[0106] The choice of threshold depends on your specific application and the characteristics of the difference image. A common approach is to use a global threshold, such as the mean, median, or a fixed value of the difference image. More advanced methods may include adaptive thresholding, which dynamically selects a threshold based on the local characteristics of the image.

[0107] Preferably, if a more accurate contour of the moving target is desired, morphological processing (such as corrosion, expansion, opening operation, closing operation, etc.) and connected region analysis and other operations may be performed on the binary image.

[0108] Step 3: The data processing system extracts the foreground of the binary image and obtains the contour information of the moving target.

[0109] Finally, the foreground part of the binary image is extracted to obtain the contour information of the moving target.

[0110] S504: The data processing system extracts and classifies the moving targets based on the contour information, and obtains the bird targets among the moving targets, as well as the number of the bird targets.

[0111] Among them, since there are drones used for bird-scaring operations in addition to birds in wind farms, the moving targets obtained are not necessarily bird targets. Further classification and identification are required to extract and classify the features of the moving targets and obtain the bird targets among the moving targets.

[0112] Specifically, feature extraction is a key step in image processing and computer vision. It aims to extract useful information or attributes from image or video data for subsequent classification, recognition, and other tasks. For the moving target outline obtained by the inter-frame difference algorithm, feature extraction methods such as shape features, texture features, and motion features can be used to obtain the moving target's features.

[0113] Specifically, after extracting the features of a moving target, these features can be used for classification to determine whether the target is a bird. Classification methods can employ rule-based classification, machine learning classification, or deep learning classification. For example, rule-based classification uses extracted features and a predefined set of rules to classify a target, for example, determining whether the target is a bird or a drone based on shape features.

[0114] S505 , the data processing system obtains the two-dimensional plane coordinates of the bird target based on the pixel point division of the environmental image.

[0115] The two-dimensional plane coordinates of the bird target are determined based on the pixel position of the bird target on the environment image.

[0116] S506: The data processing system converts the two-dimensional plane coordinates into three-dimensional space coordinates to obtain the coordinates of the bird target.

[0117] Among them, in order to convert the two-dimensional plane coordinates into three-dimensional spatial coordinates, it is also necessary to determine the depth information of the bird target. In the prior art, the depth information of the target is generally obtained using stereo structure algorithms, structured light algorithms, monocular vision and deep learning, camera calibration and three-dimensional reconstruction and other technologies. Specifically, this embodiment can adopt camera calibration and three-dimensional reconstruction technology. The basic principle of this technology is to determine the internal parameters (such as focal length, principal point, etc.) and external parameters (camera position and orientation) of the camera, which is completed by shooting a calibration plate of known geometric shape (such as a chessboard). The points on the calibration plate have known three-dimensional coordinates, and the projection of these points on the image provides two-dimensional coordinates. Using these corresponding relationships, the internal and external parameters of the camera can be calculated. Once the camera calibration is completed, the three-dimensional coordinates of the object can be calculated using methods such as triangulation using the matched feature points. Three-dimensional reconstruction algorithms such as direct linear transformation (DLT) and methods based on nonlinear optimization are commonly used technologies.

[0118] S507: The data processing system selects the bird-repelling UAV closest to the bird target as the first UAV based on the coordinates of the bird target.

[0119] Among them, according to the coordinates of the bird target, the bird-repelling UAV closest to the bird target is selected to perform the bird-repelling operation, so as to shorten the flight distance of the bird-repelling UAV as much as possible and improve the bird-repelling efficiency.

[0120] S508: The data processing system uses the deployment position of the first UAV as the spatial origin and divides the surrounding space into four spatial quadrants according to the spatial orientation.

[0121] With the first drone as the spatial origin, the surrounding space is divided into four spatial quadrants based on spatial orientation, with east, west, south, and north each serving as a directional axis. For example, the 90-degree angle between east and north is the first spatial quadrant, the 90-degree angle between east and south is the second spatial quadrant, the 90-degree angle between south and west is the third spatial quadrant, and the 90-degree angle between west and north is the fourth spatial quadrant.

[0122] S509: If the number of bird targets is not greater than a preset number and the bird targets are located in the same spatial quadrant, the data processing system determines that the first UAV is a target UAV.

[0123] If the number of bird targets is no greater than a preset number, it indicates a relatively small number of birds. If the bird targets are located within a single spatial quadrant, it indicates that the birds are flying in roughly the same direction and are densely distributed. If the number of bird targets is small and their distribution is dense, the first drone can complete the bird-repelling mission alone and be designated as the target drone.

[0124] Specifically, the preset number may be 10.

[0125] S510, if the number of bird targets is greater than a preset number, or the bird targets are located in multiple spatial quadrants, the data processing system selects other bird-repelling drones around the first drone as second drones, and determines the first drone and the second drone as target drones.

[0126] If the number of bird targets exceeds a preset number, it indicates a high number of flying birds. If the bird targets are located in multiple spatial quadrants, this indicates that the birds are flying in different directions and are dispersed. If the number of bird targets is large or their distribution is dispersed, the first drone alone will be less effective in repelling birds, and simultaneous bird repelling will not be possible. Therefore, other drones will need to be assigned to coordinate the repelling. In this case, the other drones surrounding the first drone can be identified as second drones, and both the first and second drones will be designated as target drones for the bird repelling mission.

[0127] Preferably, a multi-agent collaborative algorithm can be used to select other bird-scaring drones. Each drone is considered an agent, and multiple agents can interact with each other to jointly make decisions about bird repelling. A multi-agent system (MAS) consists of multiple autonomous or semi-autonomous individuals (agents) that work together to complete tasks or solve problems through communication, cooperation, and competition. Each agent possesses certain perception, decision-making, and action capabilities, and can autonomously perceive the environment, make decisions, and execute actions.

[0128] Therefore, before adopting the multi-agent collaborative algorithm, it is necessary to establish communication connections between bird-repelling drones to exchange information, share knowledge and coordinate actions. In the bird-repelling system, bird-repelling drones can communicate with each other through the data processing system.

[0129] S511, the data processing system generates a bird-repelling instruction based on the bird information.

[0130] Among them, bird-repelling instructions are generated based on bird information. The bird-repelling instructions carry the number and coordinates of bird targets, thereby instructing the bird-repelling UAV to perform bird-repelling operations.

[0131] S512, the data processing system sends the bird-repelling instruction to the target UAV.

[0132] S513: The target UAV drives away the bird target according to the bird-repelling instruction.

[0133] In this embodiment, bird information is obtained and a target drone for performing a bird-repelling mission is determined based on the bird information, so as to achieve precise bird-repelling and further improve the bird-repelling effect.

[0134] In the above embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a particular embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined in any way. To keep the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present application are indicated by the appended claims.

[0136] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A bird repelling system for a wind farm, characterized in that: It includes a data processing system, and a plurality of image acquisition devices and a plurality of bird-repelling drones arranged in a wind farm station, wherein the image acquisition devices and the bird-repelling drones are respectively connected to the data processing system in communication, and the bird-repelling drones are equipped with bird-repelling devices; The image acquisition device is used to acquire the environmental image of the wind farm in real time; The data processing system is used to receive the environment image sent by the image acquisition device, and extract bird information based on the environment image; wherein the bird information includes the number and coordinates of bird targets; The data processing system is used to determine a target drone for performing a bird-repelling task based on the bird information and generate a bird-repelling instruction; wherein the target drone corresponds to one or more of the bird-repelling drones; The target UAV is used to receive the bird-repelling instruction sent by the data processing system, and to drive away the bird target according to the bird-repelling instruction.

2. The system according to claim 1, characterized in that The bird-repelling device comprises a high-decibel sound wave bird-repelling module, an ultrasonic wave bird-repelling module and a laser bird-repelling module.

3. The system according to any one of claims 1 to 2, characterized in that: The image acquisition device is installed on the nacelle of the wind turbine in the wind farm.

4. The system according to claim 3, characterized in that The nacelle of the wind turbine is also provided with a rotating platform, and the image acquisition device is installed on the rotating platform.

5. The system according to any one of claims 1 to 2, characterized in that: The bird-repellent drone is installed on a nacelle of a wind turbine located in the middle of a wind turbine group; wherein the wind farm station includes a plurality of wind turbine groups, and the wind turbine group includes a preset number of adjacent wind turbines.

6. A method for driving away birds in a wind farm, characterized in that: A bird-repelling system applied to a wind farm station, the bird-repelling system comprising a data processing system, and a plurality of image acquisition devices and a plurality of bird-repelling drones arranged in the wind farm station, the image acquisition devices and the bird-repelling drones are respectively connected to the data processing system for communication, the bird-repelling drones are equipped with a bird-repelling device, and the method comprises: The image acquisition device acquires the environmental image of the wind farm in real time; The data processing system receives the environment image sent by the image acquisition device, and extracts bird information based on the environment image; wherein the bird information includes the number and coordinates of bird targets; The data processing system determines a target drone for performing a bird-repelling task based on the bird information, and generates a bird-repelling instruction; wherein the target drone corresponds to one or more of the bird-repelling drones; The target UAV receives the bird-repelling instruction sent by the data processing system, and drives away the bird target according to the bird-repelling instruction.

7. The method according to claim 6, characterized in that Extracting bird information based on the environment image includes: The data processing system obtains the contour information of the moving target in the environmental image based on the inter-frame difference algorithm; The data processing system extracts and classifies the moving targets according to the contour information, and obtains the bird targets among the moving targets, as well as the number of the bird targets; The data processing system obtains the two-dimensional plane coordinates of the bird target based on the image pixel point division of the environment image; The data processing system converts the two-dimensional plane coordinates into three-dimensional space coordinates to obtain the coordinates of the bird target.

8. The method according to claim 7, characterized in that Based on the inter-frame difference algorithm, the contour information of the moving target in the environment image is obtained, including: The data processing system performs inter-frame difference operation on two adjacent frames of the real-time environment image to obtain a grayscale difference image; The data processing system binarizes the grayscale difference image to obtain a binary image containing only the foreground and the background; The data processing system extracts the foreground of the binary image and obtains the contour information of the moving target.

9. The method according to claim 6, characterized in that Based on the bird information, a target drone for performing a bird-repelling mission is determined, including: The data processing system selects the bird-repelling UAV closest to the bird target as the first UAV based on the coordinates of the bird target; The data processing system uses the deployment position of the first UAV as the spatial origin and divides the surrounding space into four spatial quadrants according to the spatial orientation; If the number of the bird targets is not greater than a preset number and the bird targets are located in the same spatial quadrant, the data processing system determines that the first UAV is the target UAV.

10. The method according to claim 9, characterized in that The method further comprises: If the number of the bird targets is greater than a preset number, or the bird targets are located in multiple spatial quadrants, the data processing system selects other bird-repelling drones around the first drone as second drones, and determines the first drone and the second drone as target drones.

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