Bird repeller control method and device based on bird recognition, equipment and medium

Abstract

The invention belongs to the technical field of power transmission line operation and maintenance, and particularly relates to a bird repeller control method and device based on bird recognition, equipment and a medium. The method comprises the steps that the recognition model recognizes shape categories of birds; determining the size of the bird by adopting a pixel statistics method; calculating the flight speed of the bird; based on the shape category, size and flight speed of the bird, performing comparative calculation with a preset bird database, and determining possible species category attribution of the bird; judging the flight path of the bird based on the species type attribution of the bird and the shape type, size and flight speed of the bird; according to the flight path of the bird, judging whether the bird has a touch danger to the target position or not; if it is judged that the touch danger exists, the bird repeller is controlled to generate different bird repelling measure instructions according to the types of the birds. According to the bird repelling device, different bird repelling modes are selected according to the types of birds, and damage to the birds is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transmission line operation and maintenance, and specifically relates to a bird repeller control method, device, equipment and medium based on bird recognition. Background Art

[0002] With the protection and improvement of the ecological environment, the number of birds has increased year by year, and their range of activities has also been expanding. However, the frequent activities of birds near power lines have brought a series of safety hazards. Birds nesting, staying or flying on power lines may cause serious accidents such as line short circuits, equipment damage and even large-scale power outages, posing a threat to the stable operation of electric bird repellers.

[0003] At present, there are various bird-proofing measures for power lines, but most of them have limited effects, great harm to birds, and high maintenance costs. Although traditional bird-repelling methods, such as installing bird-repelling spikes, bird-proof nets, sound or ultrasonic bird-repelling devices, can play a certain role in repelling birds, they often lack specificity and flexibility, and cannot accurately identify and repel different types of birds, and may also cause harm to birds.

[0004] In the field of bird prevention and bird repellent for power lines, the existing technology has the following defects and deficiencies: Lack of accurate identification capabilities: Current bird-repelling technology often cannot accurately identify different types of birds, resulting in a lack of targeted bird-repelling measures. This "one-size-fits-all" approach may be ineffective for some birds or even cause harm to them.

[0005] Limited effect and poor durability: Traditional bird repellent methods, such as sound or ultrasonic bird repellents, can temporarily repel birds, but after long-term use, birds may adapt to the sound waves, thus reducing the repellent effect. In addition, windmill-type bird repellents are greatly affected by meteorological conditions and cannot work properly when there is no wind, further limiting their effectiveness.

[0006] Potential harm to birds: Some physical bird-repelling measures, such as bird thorns, can effectively prevent birds from staying, but they may also cause harm to birds that are accidentally touched, which is contrary to the principles of ecological protection.

[0007] High maintenance cost and inconvenient operation: Existing bird-repelling equipment often requires regular maintenance and replacement, which increases operating costs. At the same time, the installation and removal process of some equipment is complicated and requires professional operation, which is not conducive to rapid deployment and adjustment.

[0008] Greatly affected by environmental factors: The effectiveness of many bird repellents is restricted by environmental conditions, such as weather, season, geographical location, etc. For example, in bad weather, the performance of some devices will drop significantly or even fail to work properly.

[0009] Lack of intelligence and adaptability: Most existing bird-repellent technologies lack intelligent functions and are unable to automatically adjust bird-repellent strategies based on bird behavior and environmental changes, and are unable to achieve remote viewing or management, which results in unstable bird-repellent effects and failure to maintain effectiveness over the long term. Summary of the invention

[0010] The purpose of the present invention is to provide a bird repeller control method, device, equipment and medium based on bird identification to solve the technical problem that the existing bird repellers cannot effectively expel common birds without harming the birds.

[0011] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a bird repellent control method based on bird identification, comprising the following steps: Obtain radar image data of a preset area; When bird activity is detected in the preset area, the radar image data containing the bird image is input into a preset recognition model, and the recognition model outputs the shape category of the bird; Based on radar image data, the size of the bird is determined using pixel statistics; Continuously track the bird to obtain the spatial position of the bird at each time; and determine the flight speed of the bird based on the time and spatial position; Based on the shape, size and flying speed of the bird, the bird is compared with the preset bird database to determine the possible species of the bird. Determine the flight path of a bird based on its species, shape, size, and flight speed; Based on the flight trajectory of the bird, it is determined whether the bird poses a risk of contact with the target location; if it is determined that there is a risk of contact, the bird repeller is controlled to generate different bird repeller measures instructions according to the type of bird; if it is determined that there is no risk of contact, an instruction is generated not to start the bird repeller.

[0012] Further, obtaining radar image data of a preset area specifically includes: Determining a target location, where a radar device is installed; The radar device at the target position is started, and the radar device scans a preset spatial range of the area where the target position is located to obtain radar image data of the preset area.

[0013] Furthermore, when bird activity is detected in the preset area, radar image data containing bird images is input into a preset recognition model, and the recognition model outputs the shape category of the bird, including: Use radar equipment to continuously scan and monitor the preset area; when a moving object is detected in the preset area, determine that there is bird activity in the preset area; The radar image data containing the moving objects are sent to a pre-trained recognition model, and the recognition model recognizes the radar image data to determine the shape categories corresponding to the birds displayed in the radar image data; wherein the shape categories include: small passerine birds, large birds of prey and long-legged birds.

[0014] Furthermore, based on the radar image data, the size of the bird is determined by using pixel statistics, including: Convert radar image data into image data represented by pixels; extracting a bird outline from the image data; Perform pixel counting on the outline of the bird to obtain the pixel area corresponding to the bird; The shape and size of the bird are estimated based on the pixel area.

[0015] Furthermore, the bird is continuously tracked to obtain the spatial position of the bird at each time; and based on the time and spatial position, the flight speed of the bird is determined, specifically including: Use radar equipment to continuously track the bird, record the bird's position P1 and P2 at consecutive time points t1 and t2 (t2>t1), respectively; calculate the straight-line distance from P1 to P2 as the displacement of the corresponding period; calculate the time difference between time points t1 and t2; Based on the time difference and displacement, the average velocity during the time periods t1 and t2 is calculated.

[0016] Furthermore, based on the shape, size and flying speed of the bird, a comparison is performed with a preset bird database to determine the possible species of the bird, including: Based on the shape, size and flying speed of the bird, the similarity is calculated and compared with the corresponding data in the bird database; According to the comparison result and the preset threshold, the species of the current bird and one or more species matched in the bird database are determined.

[0017] Furthermore, based on the species of the bird, as well as the shape, size and flying speed of the bird, the flight trajectory of the bird is determined, including: The species of the bird, as well as the shape category, size and flight speed of the bird, are input into a pre-trained trajectory prediction model, and the trajectory prediction model outputs the flight trajectory of the bird; wherein the trajectory prediction model is constructed and trained based on a deep reinforcement learning model.

[0018] In a second aspect, the present invention provides a bird repellent control device based on bird identification, comprising: A data acquisition module, used to acquire radar image data of a preset area; A shape category recognition module, for inputting radar image data containing bird images into a preset recognition model when bird activity is detected in the preset area, and the recognition model outputs the shape category of the bird; A size recognition module is used to determine the size of the bird based on radar image data using pixel statistics; The flight speed calculation module is used to continuously track the bird and obtain the spatial position of the bird at each time; and determine the flight speed of the bird based on the time and spatial position; A data module is used to compare and calculate the bird's shape, size and flying speed with a preset bird database to determine the possible species of the bird; A comparison module is used to determine the flight trajectory of a bird based on the species of the bird, as well as the shape, size and flight speed of the bird; The control judgment module is used to judge whether the bird poses a risk of contact with the target position according to the bird's flight trajectory; if it is judged that there is a risk of contact, the bird repeller is controlled to generate different bird repeller measures instructions according to the type of bird; if it is judged that there is no risk of contact, an instruction is generated not to start the bird repeller.

[0019] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned bird repellent control method when executing the computer program.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which implements the above-mentioned bird repellent control method when executed by a processor.

[0021] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention obtains radar image data, identifies the species of birds in an area and predicts their flight trajectories based on the radar image data, thereby driving away the birds before they come into contact with electrical equipment. Different bird-driving methods are selected according to the species of the birds to avoid causing harm to the birds.

[0022] The present invention combines radar equipment detection technology, image recognition technology and intelligent repelling strategy, and has remote centralized management and data analysis functions, aiming to achieve accurate identification and effective repelling of birds near power lines, thereby ensuring the safe and stable operation of the power bird repellent. The present invention not only improves the efficiency of bird repelling, but also reduces the harm to birds, reflecting the concept of harmonious coexistence between man and nature. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic diagram of a bird repeller control method based on bird identification according to an embodiment of the present invention; Figure 2 This is a flow chart of a bird repeller control method based on bird identification according to an embodiment of the present invention; Figure 3 It is a structural diagram of a bird repeller in an embodiment of the present invention; Figure 4 This is a structural block diagram of a bird repeller control device based on bird recognition according to an embodiment of the present invention; Figure 5 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0025] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0026] The Xception model, named after "Extreme Inception", is a model that is expanded and improved based on the Inception architecture.

[0027] Example 1 like Figure 1 and Figure 2 As shown, Embodiment 1 of the present invention provides a bird repeller control method based on bird identification, comprising the following steps: S1. Obtain radar image data of a preset area; S2. When bird activity is detected in the preset area, the radar image data containing the bird image is input into a preset recognition model, and the recognition model outputs the shape category of the bird; S3, based on radar image data, using pixel statistics method to determine the size of the bird; S4, continuously tracking the bird to obtain the spatial position of the bird at each time; and determining the flight speed of the bird based on the time and spatial position; S5. Based on the shape, size and flying speed of the bird, the bird is compared with a preset bird database to determine the possible species of the bird. S6. judging the flight trajectory of the bird based on the species of the bird, as well as the shape, size and flight speed of the bird; S7. Determine whether the bird poses a risk of contact with the target location based on the flight trajectory of the bird; if it is determined that there is a risk of contact, control the bird repeller to generate different bird repeller measures instructions based on the type of bird; if it is determined that there is no risk of contact, generate an instruction not to start the bird repeller.

[0028] The intelligent bird-repelling method provided by this solution determines whether there is a danger of touching the target location based on the flight trajectory of the bird. For different types of birds, the bird repellent is controlled to adopt different bird-repelling measures. The differentiated bird-repelling strategy protects the rights and interests of birds while ensuring the safety of the target location.

[0029] In step S1, radar image data of a preset area is obtained, which specifically includes: A target position is determined, where a radar device is installed; the radar device at the target position is started, and the radar device scans a preset spatial range of an area where the target position is located to obtain radar image data of the preset area.

[0030] Optionally, radar image data is acquired by a radar device installed on the bird scarer. The radar device is used to monitor and track the activities of surrounding birds in real time. The radar device can rotate 360°, thereby realizing all-round monitoring of bird activities.

[0031] As an example, the radar equipment involved in this solution may be a microwave radar, or other types of radars, such as a lidar.

[0032] In step S2, when bird activity is detected in the preset area, the radar image data containing the bird image is input into a preset recognition model, and the recognition model outputs the shape category of the bird, specifically including: Use radar equipment to continuously scan and monitor the preset area; when a moving object is detected in the preset area, determine that there is bird activity in the preset area; send radar image data containing the moving object to a pre-trained recognition model, and the recognition model recognizes the radar image data to determine the shape category corresponding to the bird displayed in the radar image data; wherein the shape category includes: small passerine birds, large birds of prey and long-legged birds, etc.

[0033] In this scheme, birds are roughly classified mainly based on their shapes.

[0034] Optionally, the recognition model can use a deep learning-based convolutional neural network and perform classification through the Xception model, using depthwise separable convolution to reduce the number of network parameters and computational complexity while maintaining excellent performance. These networks are able to learn features in images and accurately classify birds.

[0035] Depthwise separable convolution is the core of the Xception model, which is divided into two steps: depthwise convolution and pointwise convolution.

[0036] Depthwise Convolution: Applies a separate convolution filter to each input channel.

[0037]

[0038] in, It is the output result of the depthwise convolution operation at position (i, j) and for the kth channel; i and j represent the spatial position index on the output feature map, and k represents the channel index. m and n are the spatial indices of the convolution kernel. k Represents the convolution kernel of the k-th channel, which is convolved only with the k-th input channel. Represents the weight of the convolution kernel of the kth channel at the (m, n) position.

[0039] Pointwise Convolution: Use a 1x1 convolution kernel to combine the output of depthwise convolution.

[0040]

[0041] in, It is the result of the pointwise convolution operation at the spatial position (i, j) on the output feature map; is the weight corresponding to the kth input channel in the 1x1 convolution kernel.

[0042] In step S3, based on the radar image data, the size of the bird is determined by using a pixel statistics method, which specifically includes: The radar image data is converted into image data represented by pixels; the outline of the bird is extracted from the image data; pixel statistics are performed on the outline of the bird to obtain the pixel area corresponding to the bird; and the shape and size of the bird are estimated based on the pixel area.

[0043] Optionally, during the feature extraction stage, pixel statistics are used to determine the size of the bird in the image using the following formula:

[0044] in, The size of the birds in the image is determined by pixel statistics. H is the height of the image in pixels. W is the width of the image in pixels. P ( i , j ) is a binary function used to indicate the pixel ( i , j ) belongs to the outline or body part of a bird (e.g., determined by threshold segmentation or edge detection). If a pixel ( i , j ) belongs to birds, then P ( i , j )=1; otherwise, P ( i , j )=0.

[0045] In the classifier training stage, a linear SVM is used, and the decision function is based on a linear combination of the weight vector and the bias term:

[0046] Among them, w is the weight vector; x is the input feature vector; b is the bias term; T represents the transpose.

[0047] In step S4, the bird is continuously tracked to obtain the spatial position of the bird at each time; and the flight speed of the bird is determined based on the time and spatial position, which specifically includes: The bird was continuously tracked using radar equipment. At consecutive time points t1 and t2 (t2>t1), the bird's positions P1 and P2 were recorded respectively. The straight-line distance from P1 to P2 was calculated as the displacement of the corresponding time period. The time difference between time points t1 and t2 was calculated. Based on the time difference and displacement, the average speed in the time periods t1 and t2 was calculated.

[0048] Optionally, based on the radar continuous tracking data, the bird's position P1 and P2 are recorded at consecutive time points t1 and t2 (t2>t1). The straight-line distance from P1 to P2 is calculated as the displacement (distance) during this period. At the same time, the time difference between time points t1 and t2 is calculated (delta_time=t2-t1). The flight speed is calculated using the formula speed=distance / delta_time. This speed is the average speed during the time period t1 and t2.

[0049] When tracking birds, you can use the YOLO or SSD method to detect and track birds in real time based on radar image data. ‌YOLO (You Only Look Once) algorithm is a target detection system based on a single neural network. Single Shot MultiBox Detector is a target detection algorithm based on deep learning.

[0050] The loss function for bird tracking includes two parts: classification loss and positioning loss. It is expressed as:

[0051] in, λ It is a hyperparameter used to balance the weight between classification loss and localization loss; Classification Loss: Use cross entropy loss to measure the difference between the predicted category and the actual category. Localization Loss: Use mean squared error (MSE) or intersection over union (IoU) loss to measure the difference between the predicted bounding box and the actual bounding box.

[0052] Features N candidate bounding boxes, each bounding box i There is a predicted class probability distribution p i (obtained through the softmax function) and the actual category label u i (is a one-hot encoded vector), the classification loss is expressed as:

[0053] in, C is the total number of categories (including background categories); Indicates i Bounding boxes for categories c The true label (0 or 1); p i c The model predicts i Bounding boxes belong to the class c probability.

[0054] Assume that for each bounding box, the model predicts four values , respectively representing the center coordinates and width and height offsets of the bounding box relative to a reference box (such as an anchor box). At the same time, the real bounding box also has four corresponding values

[0055] Then the positioning loss can be expressed as:

[0056] in, Respectively represent the model for the i The predicted center point horizontal coordinate, center point vertical coordinate, width and height of each bounding box. Indicates i The transformation form of the real (or target) center point horizontal coordinate, center point vertical coordinate, width and height of the bounding box is obtained by transforming the anchor box from the real bounding box coordinates Calculated to match the predicted value Compare on the same scale. SmoothL1 loss function is the loss function used for regression problems.

[0057] In step S5, based on the shape, size and flying speed of the bird, a comparison is performed with a preset bird database to determine the possible species of the bird, which specifically includes: Based on the shape category, size and flying speed of the bird, similarity calculation and comparison are performed with the corresponding data in the bird database; based on the comparison results and the preset threshold, the current bird is judged to belong to one or more species that match the bird database.

[0058] In step S6, the flight trajectory of the bird is determined based on the species of the bird, as well as the shape category, size and flight speed of the bird, specifically including: inputting the species of the bird, as well as the shape category, size and flight speed of the bird into a pre-trained trajectory prediction model, and the trajectory prediction model outputs the flight trajectory of the bird; wherein the trajectory prediction model is constructed and trained based on a deep reinforcement learning model.

[0059] Optionally, different bird repelling measures include playing bird repelling sounds, ultrasonic bird repelling or laser bird repelling.

[0060] Specifically, the bird-repelling sound is the call of the natural enemy of birds or the sound that makes birds feel uncomfortable. There are several types of bird-repelling sounds, and different bird-repelling sounds are used according to the identified species. For example, some birds are sensitive to ultrasound, so ultrasound is released to drive them away, and some birds are sensitive to light, so laser is used to drive them away.

[0061] Specifically, the bird repellent will save all detection data, identification results and bird repelling records, and upload them to the Internet of Things platform in real time through the communication module.

[0062] Managers can remotely monitor the operating status of the equipment, receive alarm information, and adjust equipment parameters or issue control instructions as needed through the IoT platform. This remote management capability greatly improves the flexibility and response speed of the bird repeller.

[0063] like Figure 3 As shown, in an optional embodiment, a microwave radar is used to acquire radar data. Identification and judgment are performed in the AI ​​processing unit, and instructions are generated, and the instructions are sent to the bird-repelling module using the communication module. The bird-repelling module is used to perform different bird-repelling operations according to the received instructions, such as sound, ultrasound, laser, etc.

[0064] In a preferred embodiment, photovoltaic panels can be set up to generate electricity, lithium batteries can be used for power supply, and the power management module can distribute the power of the lithium batteries to each power module to ensure the continuous and stable operation of the equipment in the field environment, with low voltage and low power consumption characteristics to extend the service life of the equipment.

[0065] Optionally, the IoT platform is connected to the bird repeller via a communication module to support remote device status query and control command issuance.

[0066] Example 2 like Figure 4 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a bird repellent control device based on bird identification, comprising: A data acquisition module, used to acquire radar image data of a preset area; A shape category recognition module, for inputting radar image data containing bird images into a preset recognition model when bird activity is detected in the preset area, and the recognition model outputs the shape category of the bird; A size recognition module is used to determine the size of the bird based on radar image data using pixel statistics; The flight speed calculation module is used to continuously track the bird and obtain the spatial position of the bird at each time; and determine the flight speed of the bird based on the time and spatial position; A data module is used to compare and calculate the bird's shape, size and flying speed with a preset bird database to determine the possible species of the bird; A comparison module is used to determine the flight trajectory of a bird based on the species of the bird, as well as the shape, size and flight speed of the bird; The control judgment module is used to judge whether the bird poses a risk of contact with the target position according to the bird's flight trajectory; if it is judged that there is a risk of contact, the bird repeller is controlled to generate different bird repeller measures instructions according to the type of bird; if it is judged that there is no risk of contact, an instruction is generated not to start the bird repeller.

[0067] Example 3 like Figure 5 As shown, the present invention also provides an electronic device 100 for implementing a bird repellent control method based on bird identification; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0068] The memory 101 can be used to store a computer program 103 . The processor 102 implements the steps of the bird repellent control method based on bird identification in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101 .

[0069] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0070] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0071] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a bird repellent control method based on bird identification, and the processor 102 can execute the plurality of instructions to implement: Obtain radar image data of a preset area; When bird activity is detected in the preset area, the radar image data containing the bird image is input into a preset recognition model, and the recognition model outputs the shape category of the bird; Based on radar image data, the size of the bird is determined using pixel statistics; Continuously track the bird to obtain the spatial position of the bird at each time; and determine the flight speed of the bird based on the time and spatial position; Based on the shape, size and flying speed of the bird, the bird is compared with the preset bird database to determine the possible species of the bird. Determine the flight path of a bird based on its species, shape, size, and flight speed; Based on the flight trajectory of the bird, it is determined whether the bird poses a risk of contact with the target location; if it is determined that there is a risk of contact, the bird repeller is controlled to generate different bird repeller measures instructions according to the type of bird; if it is determined that there is no risk of contact, an instruction is generated not to start the bird repeller.

[0072] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0073] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A bird repellent control method based on bird identification, characterized in that: The following steps are involved: Obtain radar image data of a preset area; When bird activity is detected in the preset area, the radar image data containing the bird image is input into a preset recognition model, and the recognition model outputs the shape category of the bird; Based on radar image data, the size of the bird is determined using pixel statistics; Continuously track the bird to obtain the spatial position of the bird at each time; and determine the flight speed of the bird based on the time and spatial position; Based on the shape, size and flying speed of the bird, the bird is compared with the preset bird database to determine the possible species of the bird. Determine the flight path of a bird based on its species, shape, size, and flight speed; Based on the flight trajectory of the bird, it is determined whether the bird poses a risk of contact with the target location; if it is determined that there is a risk of contact, the bird repeller is controlled to generate different bird repeller measures instructions according to the type of bird; if it is determined that there is no risk of contact, an instruction is generated not to start the bird repeller.

2. The bird repellent control method according to claim 1, characterized in that: Obtain radar image data for a preset area, including: Determining a target location, where a radar device is installed; The radar device at the target position is started, and the radar device scans a preset spatial range of the area where the target position is located to obtain radar image data of the preset area.

3. The bird repellent control method according to claim 2, characterized in that: When bird activity is detected in the preset area, the radar image data containing the bird image is input into a preset recognition model, and the recognition model outputs the shape category of the bird, specifically including: Use radar equipment to continuously scan and monitor the preset area; when a moving object is detected in the preset area, determine that there is bird activity in the preset area; The radar image data containing the moving objects are sent to a pre-trained recognition model, and the recognition model recognizes the radar image data to determine the shape categories corresponding to the birds displayed in the radar image data; wherein the shape categories include: small passerine birds, large birds of prey and long-legged birds.

4. The bird repellent control method according to claim 1, characterized in that: Based on radar image data, the size of the bird is determined using pixel statistics, including: Convert radar image data into image data represented by pixels; extracting a bird outline from the image data; Perform pixel counting on the outline of the bird to obtain the pixel area corresponding to the bird; The shape and size of the bird are estimated based on the pixel area.

5. The bird repellent control method according to claim 1, characterized in that: The bird is continuously tracked to obtain the spatial position of the bird at each time; and the flight speed of the bird is determined based on the time and spatial position, including: Use radar equipment to continuously track the bird, record the bird's position P1 and P2 at consecutive time points t1 and t2 (t2 > t1), respectively; calculate the straight-line distance from P1 to P2 as the displacement of the corresponding period; calculate the time difference between time points t1 and t2; Based on the time difference and displacement, the average velocity during the time periods t1 and t2 is calculated.

6. The bird repellent control method according to claim 1, characterized in that: Based on the shape, size and flying speed of the bird, the bird is compared with the preset bird database to determine the possible species of the bird, including: Based on the shape, size and flying speed of the bird, the similarity is calculated and compared with the corresponding data in the bird database; According to the comparison result and the preset threshold, the species of the current bird and one or more species matched in the bird database are determined.

7. The bird repellent control method according to claim 1, characterized in that: Based on the bird's species, shape, size and flight speed, the bird's flight trajectory is determined, including: The species of the bird, as well as the shape category, size and flight speed of the bird, are input into a pre-trained trajectory prediction model, and the trajectory prediction model outputs the flight trajectory of the bird; wherein the trajectory prediction model is constructed and trained based on a deep reinforcement learning model.

8. A bird repellent control device based on bird identification, characterized in that: include: A data acquisition module, used to acquire radar image data of a preset area; A shape category recognition module, for inputting radar image data containing bird images into a preset recognition model when bird activity is detected in the preset area, and the recognition model outputs the shape category of the bird; A size recognition module is used to determine the size of the bird based on radar image data using pixel statistics; The flight speed calculation module is used to continuously track the bird and obtain the spatial position of the bird at each time; and determine the flight speed of the bird based on the time and spatial position; A data module is used to compare and calculate the bird's shape, size and flying speed with a preset bird database to determine the possible species of the bird; A comparison module is used to determine the flight trajectory of a bird based on the species of the bird, as well as the shape, size and flight speed of the bird; The control judgment module is used to judge whether the bird poses a risk of contact with the target position according to the bird's flight trajectory; if it is judged that there is a risk of contact, the bird repeller is controlled to generate different bird repeller measures instructions according to the type of bird; if it is judged that there is no risk of contact, an instruction is generated not to start the bird repeller.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the bird repellent control method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the bird repellent control method described in any one of claims 1 to 7 is implemented.

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