Distribution line bird repelling method and system based on Internet of Things edge calculation

Abstract

The invention discloses a power distribution line bird repelling method and system based on Internet of Things edge calculation, and relates to the technical field of power distribution network safety. The system comprises: a bird condition acquisition module configured to obtain a bird audio and a bird image to be detected; a model identification module configured to identify the bird audio by using a bird sound identification model and identify the bird image by using a bird image identification model; the controller module is configured to integrate the bird audio recognition result and the bird image recognition result to obtain the type of the bird; the intelligent decision-making module is configured to generate a bird repelling strategy according to the types of the birds; and the bird repelling module is configured to repel birds according to the bird repelling strategy. According to the invention, birds are identified by utilizing edge calculation, so that different bird repelling modes are adopted for different birds, and intelligent and effective modern bird repelling is realized.

Description

Technical Field

[0001] The present invention relates to the field of power distribution network safety technology, and in particular to a method and system for driving away birds from power distribution lines based on edge computing of the Internet of Things. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the strengthening of human awareness of natural environment protection in recent years, the number of birds has gradually increased. Many birds build nests and reproduce on distribution line towers, which has caused great harm to the safe operation of distribution lines. Statistics in recent years show that line failures caused by bird activities are second only to lightning damage and external force damage. Line tripping failures caused by bird damage have become an important factor affecting power safety production.

[0004] At present, bird-repellent facilities on distribution lines are not smart enough and not effective enough. In order to ensure the reliability of distribution lines and prevent birds from damaging power lines due to activities such as playing, nesting, and predation, traditional bird-repellent methods mostly rely on manual inspections and visual repellent (such as colorful flags and windmills). These methods are not as real-time and efficient as modern scientific and technological equipment. For example, manual inspections require personnel to be present in person, which is not only time-consuming and labor-intensive, but also difficult to achieve all-weather and all-round coverage; although visual repellent can repel birds to a certain extent, the effect is limited and unsustainable.

[0005] Secondly, it is difficult to achieve good results by destroying bird nests, using bird repellents (such as bird repellent mirrors, bird spikes, etc.) and other measures to drive away birds. Since birds have the characteristic of recognizing their nests, even if the nests are destroyed many times, they will still return to the original nests to build new nests. As for bird deterrents, since birds have a high ability to identify and judge, they lose their fear of bird repellents over time, and the effect of bird repellents disappears. On some towers, birds even use bird deterrents as fulcrums to build nests.

[0006] Some bird-repelling facilities use a single method, using loudspeakers to repel birds. However, bird species are complex and have different characteristics. Some birds are not afraid of the sound of loudspeakers, so they cannot effectively repel birds. In addition, bird-repelling facilities on distribution lines play audio continuously in a loop to repel birds, and cannot implement targeted measures or start and stop playback. The noise impact is no less than the impact of bird damage.

[0007] Therefore, how to achieve intelligent and effective comprehensive bird repellent on distribution lines, especially poles and towers, has become a technical problem that needs to be urgently solved in the existing technology. Summary of the invention

[0008] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a distribution line bird repellent method and system based on edge computing of the Internet of Things, which utilizes edge computing to identify birds, thereby adopting different bird repellent methods for different birds, and realizing intelligent and effective modern bird repellent.

[0009] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0010] The first aspect of the present invention provides a power distribution line bird repelling system based on edge computing of the Internet of Things, comprising:

[0011] A bird information collection module is configured to obtain audio and images of birds to be detected;

[0012] a model recognition module configured to recognize bird audio using a bird sound recognition model and recognize bird images using a bird image recognition model;

[0013] The controller module is configured to integrate the bird audio recognition result and the bird image recognition result to obtain the bird species;

[0014] An intelligent decision-making module is configured to generate a bird-repelling strategy according to the type of bird;

[0015] The bird repelling module is configured to repel birds according to the bird repelling strategy.

[0016] Furthermore, in the bird information collection module, a microphone is used to obtain the audio of the bird to be detected, and a camera and a lighting are used to obtain the image of the bird to be detected.

[0017] Furthermore, the bird-repelling module has a built-in voice speaker, a laser generator and an ultrasonic generator for repelling birds.

[0018] Furthermore, the bird information collection module and the bird repelling module are integrated in the intelligent sensing terminal, and the model building module, the controller module and the intelligent decision-making module are integrated in the edge computing gateway. The edge computing gateway obtains real-time data from the intelligent sensing terminal, obtains stored data from the bird damage warning system, and uses the obtained data for processing and calculation.

[0019] Furthermore, in the model recognition module, the bird sound recognition model and the bird image recognition model are composed of algorithms stored in the bird damage warning system, which are selected and combined according to the information of the smart terminal and then sent to the edge computing gateway.

[0020] Furthermore, the intelligent sensing terminal is connected to the edge computing gateway in a non-electrical wireless manner, and the edge computing gateway is connected to the bird damage warning system in a non-electrical wireless manner.

[0021] Furthermore, the bird damage warning system includes a bird repelling strategy management module, a sound management module, and an algorithm management module, wherein the bird repelling strategy management module is used to store bird repelling strategies, the sound management module is used to store bird audio data, and the algorithm management module is used to store different recognition algorithms.

[0022] A second aspect of the present invention provides a method for driving away birds from a power distribution line based on edge computing of the Internet of Things, comprising the following steps:

[0023] Obtain audio and images of birds to be detected;

[0024] Use the bird sound recognition model to recognize bird audio, and use the bird image recognition model to recognize bird images;

[0025] Combining the bird audio recognition results and the bird image recognition results, the bird species is obtained;

[0026] Generate a bird repellent strategy according to the type of bird, and repel the birds according to the bird repellent strategy.

[0027] The third aspect of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps in the distribution line bird-repelling method based on Internet of Things edge computing as described in the second aspect of the present invention are implemented.

[0028] The fourth aspect of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for driving away birds from distribution lines based on edge computing of the Internet of Things as described in the second aspect of the present invention are implemented.

[0029] One or more of the above technical solutions have the following beneficial effects:

[0030] The present invention discloses a method and system for driving away birds from distribution lines based on edge computing of the Internet of Things. Cameras and microphones can capture the activities of birds in an area in real time. Intelligent sensing terminals transmit the collected sound and image data to intelligent decision-making modules of edge computing gateways. The intelligent decision-making modules process and analyze the data and trigger an early warning mechanism. This real-time monitoring capability enables the system to promptly detect situations where birds are approaching or entering sensitive areas. Intelligent bird driving is performed by controlling the built-in voice speaker, laser generator and ultrasonic generator through a controller, thereby improving the real-time monitoring and efficient early warning capabilities of bird driving away from power grid line poles.

[0031] The system of the present invention can intelligently identify the types, numbers and behavioral characteristics of birds. This intelligent identification capability enables the system to formulate accurate intelligent bird-repelling strategies for different types of birds or different threat levels, thereby improving the bird-repelling effect.

[0032] The present invention utilizes the edge computing gateway to work in collaboration with the intelligent sensing terminal and the bird-damage warning system, makes decisions on the spot at the edge side, and handles them in a timely manner. An ultrasonic generator can be used to emit ultrasonic waves of a specific frequency to stimulate the nervous system of birds and keep them away. A built-in voice speaker can also be used for voice simulation to simulate the call of an eagle, etc., to scare birds away. A laser generator can be used for strong light strobe, and the birds' fear of flashes can be used to stimulate their visual systems with strong strobe light. The system uses a combination of multiple technologies to achieve real-time monitoring and intelligent early warning and expulsion of birds, thereby ensuring the safety of the power grid.

[0033] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings in the specification, which constitute a part of the present invention, 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 improper limitations on the present invention.

[0035] Figure 1 This is a working flow chart of the power distribution line bird repelling system based on edge computing of the Internet of Things in the first embodiment of the present invention;

[0036] Figure 2 This is a structural diagram of a power distribution line bird repelling system based on edge computing of the Internet of Things in Embodiment 1 of the present invention;

[0037] Figure 3 The figure is a flow chart of the bird-repelling process in the first embodiment of the present invention. DETAILED DESCRIPTION

[0038] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0039] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations;

[0040] Embodiment 1:

[0041] Embodiment 1 of the present invention provides a power distribution line bird repelling system based on edge computing of the Internet of Things, such as Figure 2As shown in the figure, it includes multiple intelligent sensing terminals, edge computing gateways and bird damage warning systems installed on different distribution line towers. Figure 1 As shown, the bird situation collection module and the bird repelling module are integrated in the intelligent sensing terminal, and the model building module, the controller module, the long-distance transmission networking module and the intelligent decision-making module are integrated in the edge computing gateway. The edge computing gateway obtains real-time data from the intelligent sensing terminal, obtains stored data from the bird damage warning system, and uses the obtained data for processing and calculation. The bird damage warning system includes a bird repelling strategy management module, a sound management module and an algorithm management module, wherein the bird repelling strategy management module is used to store bird repelling strategies, the sound management module is used to store bird audio data, and the algorithm management module is used to store different recognition algorithms. The intelligent sensing terminal is non-electrically and wirelessly connected to the edge computing gateway. The edge computing gateway is non-electrically and wirelessly connected to the bird damage warning system.

[0042] In a specific implementation, the bird repellent strategy management module is used for the edge computing gateway to intelligently decide on the relevant bird repellent strategy settings. Various bird repellent strategies can be flexibly configured, and the strategies can be sent to the edge computing gateway in the form of configuration files. The intelligent decision-making module formulates effective bird repellent strategies based on the identification results, which not only improves the accuracy and efficiency of bird repellent measures, but also reduces interference with the environment.

[0043] Specifically, for certain types of birds, such as pigeons and sparrows, sound bird repellent strategies are used to play sounds they don't like, such as the calls of natural enemies or high-frequency noise. For larger birds with better eyesight, such as eagles and falcons, laser bird repellent strategies are used. The laser generator emits high-intensity, short-pulse laser beams to stimulate the birds, making them feel uncomfortable and leaving the target area. For scenes and time periods with high bird density and frequent activities, sound, laser, and ultrasonic bird repellent strategies are used in combination to avoid potential threats to the safety of distribution lines. The bird repellent strategy module can flexibly configure various bird repellent strategies and send the strategies to the edge computing gateway in the form of configuration files. The intelligent decision-making module formulates effective bird repellent strategies based on the recognition results, which not only improves the accuracy and efficiency of bird repellent measures, but also reduces interference with the environment.

[0044] In a specific implementation, the sound management module is used to manage bird-repelling audio, and manages the sounds of natural enemies and sounds that scare birds according to the species of birds.

[0045] Reference Figure 3The sound management module is used to manage bird-repelling audio. It manages the sounds of natural enemies and sounds that scare birds according to the bird species, and sends the audio files to the bird-repelling module through the edge computing gateway for playback. Specifically, the sounds used to repel birds are collected and stored, and classified and labeled according to the calls of natural enemies, artificial sounds, bird screams, etc. Information such as bird species. The calls of natural enemies include the calls of predators (eagles, raptors, etc.), the calls of specific birds (owls, etc.), etc. These sounds can simulate the presence of natural enemies, making birds feel threatened and flee; artificial sounds include gunshots, firecrackers, gongs and drums, etc., which have a loud sound and deterrent effect. Birds will feel frightened and flee after hearing them; bird screams, birds are usually afraid to hear the screams of their own kind because it makes them feel threatened. The collected sounds are sent from the bird damage warning system to the edge computing gateway controller module through the Internet of Things. The controller module also receives the audio bird repellent strategy sent by the intelligent decision-making module. After the audio bird repellent strategy is selected, confirmed and split, the controller module generates a sound playback instruction and sends it together with the bird repellent audio through the local network to the perception terminal bird repellent module. After receiving the sound playback instruction, the bird repellent module plays the specified bird repellent audio through the voice speaker.

[0046] In a specific implementation, the algorithm management module is used to manage intelligent algorithms such as bird image recognition and analysis algorithms and sound recognition and analysis algorithms, and sends appropriate algorithms to the edge computing gateway according to the resource size, computing power card type, and environment of the edge computing gateway. The intelligent decision-making module deploys the algorithm container at the edge, provides reasoning services, and assists in bird pest control decisions.

[0047] Specifically, the algorithm management module is used to manage the bird image recognition and analysis algorithms and the sound recognition and analysis algorithms, and sends the required algorithms to the edge computing gateway for edge intelligent decision-making. More specifically, the image recognition algorithm, sound recognition algorithm and other intelligent algorithms are uploaded to the bird damage warning system, and the algorithm is labeled with basic information such as the type of edge gateway computing power card that is compatible with the algorithm (such as Rockchip, Horizon, Cambrian, etc.), configuration requirements (such as memory, etc.), and applicable scenarios (such as cities, villages, fields, etc.). The bird damage warning system sends appropriate algorithms to the edge computing gateway according to the resource size, computing power card type, and environment of the edge computing gateway. The intelligent decision-making module deploys the algorithm container on the edge to provide reasoning services and assist in bird damage prevention and control decisions.

[0048] It should be noted that the algorithm in the algorithm management module in this embodiment adopts an existing model algorithm in the prior art, or a model algorithm after parameter adjustment obtained by training an existing model architecture using a local training set.

[0049] Reference Figure 2The long-distance transmission networking module is used to realize remote communication and data transmission between the edge computing gateway and the bird damage warning system, and between the perception terminal and the edge computing gateway, and to build a reliable communication network. In this embodiment, one edge computing gateway and seven perception terminals form a monitoring unit, one intelligent perception terminal is installed on each pole tower, and the edge computing gateway is installed on the middle pole tower. A local network is formed by means of a trusted WLAN to ensure the stable transmission of images, audio, and control instructions between the edge computing gateway and the perception terminal, quickly complete the bird situation collection, intelligent decision-making, and bird driving execution processes, improve the level of bird damage prevention and control, and protect the stable operation of the distribution line. The edge computing gateway and the bird damage warning system are networked through 4G to realize remote configuration of the edge computing gateway and transmission of warning data.

[0050] Reference Figure 1 The bird information acquisition module includes a microphone, a camera and a lighting. In the bird information acquisition module, the microphone is used to obtain the audio of the bird to be detected. Specifically, the microphone captures the sound information such as the bird's call. The camera and the lighting are used to collect the image data of the environment around the tower to obtain the image of the bird to be detected.

[0051] Specifically, the sound picker is mainly used to collect bird calls. It uses a high-sensitivity microphone to accurately capture bird calls in the surrounding environment and maintain clear recording quality even in a noisy environment. The camera can capture bird photos near the power grid poles through a high-definition lens, realizing real-time monitoring and recording of bird activities. The lighting can provide lighting at night so that the camera can better capture bird images at night.

[0052] The model recognition module is configured to recognize bird audio using a bird sound recognition model and recognize bird images using a bird image recognition model.

[0053] The model recognition module is used to process and analyze the sound data monitored by the microphone. The collected bird calls are deeply analyzed using advanced audio processing algorithms. By identifying the call characteristics of different birds, such as frequency, rhythm and pitch, the technology can accurately determine the type, quantity and activity status of birds. Specifically, the audio signal is first subjected to a fast Fourier transform (FFT) to obtain the spectrum of the signal. Then the spectrum is filtered through a Mel frequency filter group, and finally the logarithm of the filtered result is taken and a discrete cosine transform (DCT) is performed to obtain the Mel frequency cepstrum coefficient (MFCC). For bird calls, different types of bird calls will have different performances in the MFCC coefficients. In the classification stage, convolutional neural networks (CNN) are used for processing. When identifying bird calls, the convolution layer may extract features such as frequency modulation patterns that are unique to certain birds, and then through multi-layer feature extraction and fusion, the type and activity status of the bird are finally determined. The second is used to process and analyze the images captured by the camera, and analyze the clear images of bird activities captured by the high-definition camera, including their body shape, feather color, behavioral characteristics, etc. By performing deep learning and pattern recognition on these images, we can accurately determine the species, number, and behavior patterns of birds. Specifically, we use convolutional neural networks to extract bird features in images. The first is the convolution layer, which contains multiple convolution kernels. It slides on the input image to perform convolution calculations and extract features. For bird images, different convolution kernels can capture the outline of the bird's body shape, feather color boundaries, etc. In color images, convolution kernels process multiple channels at the same time, and multiple convolution kernels can extract a variety of rich features. Next is the pooling layer, which reduces the data dimension, halves the size of the feature map, reduces the amount of calculation and prevents overfitting, while enhancing the model's robustness to small changes such as noise and small posture displacement in bird images. Finally, the activation function introduces nonlinear factors. ReLU can avoid the problem of gradient disappearance, so that the model can learn the nonlinear relationship between complex features such as bird feathers and body shape and species and behavior, and enhance feature expression. After the feature extraction and analysis of the previous multi-layer network, the extracted features are mapped to species probability distribution through the fully connected layer. The category with the highest probability is the predicted bird species. The number of birds is determined based on the number of individual bird target boxes identified in the image. The model recognition module has an operating environment for algorithms such as image recognition and sound recognition. It uses containerized deployment and executes the above algorithms through the built-in computing power card of the edge computing gateway to obtain bird activity information.

[0054] In a specific implementation, in the model recognition module, the bird sound recognition model and the bird image recognition model are both selected and combined by the storage algorithm in the bird damage warning system according to the information of the smart terminal and then sent to the edge computing gateway. The specific selection and combination is determined by the controller module.

[0055] The controller module is configured to integrate the bird audio recognition result and the bird image recognition result to obtain the bird species.

[0056] In a specific implementation, the controller module is also used to select and split bird-repelling strategies, and control the actions of the voice speaker, laser generator and ultrasonic generator according to the bird-repelling strategies to drive away birds and prevent bird damage.

[0057] Specifically, the controller module is used to control the actions of the voice speaker, laser generator and ultrasonic generator according to the bird-repelling strategy to drive away birds and prevent bird damage.

[0058] The controller module receives the bird-repelling strategy generated by the intelligent decision-making module, analyzes the strategy and splits it into different execution instructions, such as the type, frequency, and duration of the sound played by the voice speaker, the scanning speed, angle, and power of the laser beam of the laser generator, and the frequency and duration of the ultrasonic wave. The split single strategy is distributed to the bird-repelling module in the form of control instructions to achieve the effect of bird repelling.

[0059] The intelligent decision-making module is configured to generate a bird-repelling strategy according to the type of bird and transmit it to the controller module for execution.

[0060] When generating a bird-repelling strategy, the environmental factors of the sensing terminal are taken into account. When the environment is in a residential area, avoid using sound and laser methods to reduce sound and light interference to the residential area.

[0061] The bird repelling module is configured to repel birds according to the bird repelling strategy.

[0062] In a specific embodiment, the bird-repelling module has a built-in voice speaker, a laser generator and an ultrasonic generator for driving away birds. The voice speaker is used to randomly and intermittently output various sounds that scare birds, the laser generator is used to emit a laser beam with a very obvious threat intensity and color to birds, and the ultrasonic generator is used to generate ultrasonic waves of a specific frequency that are very harsh and uncomfortable to birds.

[0063] Specifically, the controller module sends bird-repelling instructions to the bird-repelling module, and instructs its voice speaker to play the calls of their natural enemies or sounds they don't like according to the sound type, frequency, and duration instructions to create panic and drive away the birds. Instructs its laser generator to emit a laser beam with a very obvious threat intensity and color to birds according to the scanning speed, angle, and power of the laser beam. Instructs its ultrasonic generator to generate ultrasonic waves of a specific frequency that are very harsh and uncomfortable to birds according to the frequency, duration, and other instructions.

[0064] The bird-repelling measures played by the bird-repelling module in this embodiment can be adjusted in terms of method, time and size according to the recognition result, and will not be played continuously, thereby reducing noise pollution and waste of resources.

[0065] Embodiment 2:

[0066] Embodiment 2 of the present invention provides a method for driving away birds from a power distribution line based on edge computing of the Internet of Things, comprising the following steps:

[0067] Step 1: Obtain the bird audio and bird image to be detected.

[0068] Step 2: Use the bird sound recognition model to recognize the bird audio, and use the bird image recognition model to recognize the bird image.

[0069] Step 3: Combining the bird audio recognition results and the bird image recognition results to obtain the bird species.

[0070] Step 4. Generate a bird repelling strategy according to the bird species, and repel the birds according to the bird repelling strategy.

[0071] Embodiment three:

[0072] Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps of the method for driving away birds from distribution lines based on edge computing of the Internet of Things as described in Embodiment 1 of the present invention are implemented.

[0073] Embodiment 4:

[0074] Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for driving away birds from distribution lines based on edge computing of the Internet of Things as described in Embodiment 1 of the present invention are implemented.

[0075] The steps involved in the above embodiments 2, 3 and 4 correspond to the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0076] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0077] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A power distribution line bird repelling system based on edge computing of the Internet of Things, characterized in that: include: A bird information collection module is configured to obtain audio and images of birds to be detected; a model recognition module configured to recognize bird audio using a bird sound recognition model and recognize bird images using a bird image recognition model; The controller module is configured to integrate the bird audio recognition result and the bird image recognition result to obtain the bird species; An intelligent decision-making module is configured to generate a bird-repelling strategy according to the type of bird; The bird repelling module is configured to repel birds according to the bird repelling strategy.

2. The power distribution line bird repelling system based on edge computing of the Internet of Things as claimed in claim 1, characterized in that: In the bird information collection module, a microphone is used to obtain the audio of the bird to be detected, and a camera and a lighting are used to obtain the image of the bird to be detected.

3. The power distribution line bird repelling system based on edge computing of the Internet of Things as claimed in claim 1, characterized in that: The bird-repelling module has a built-in voice speaker, a laser generator and an ultrasonic generator, and is used to drive away birds.

4. The power distribution line bird repelling system based on edge computing of the Internet of Things as claimed in claim 1, characterized in that: The bird information collection module and the bird repelling module are integrated in the intelligent sensing terminal. The model building module, the controller module and the intelligent decision-making module are integrated in the edge computing gateway. The edge computing gateway obtains real-time data from the intelligent sensing terminal and the stored data from the bird damage warning system, and uses the obtained data for processing and calculation.

5. The power distribution line bird repelling system based on edge computing of the Internet of Things as claimed in claim 4, characterized in that: In the model recognition module, the bird sound recognition model and the bird image recognition model are composed of the storage algorithm in the bird damage warning system, which is selected and combined according to the information of the smart terminal and then sent to the edge computing gateway.

6. The power distribution line bird repelling system based on edge computing of the Internet of Things as claimed in claim 5, characterized in that: The intelligent sensing terminal is non-electrically and wirelessly connected to the edge computing gateway, and the edge computing gateway is non-electrically and wirelessly connected to the bird damage warning system.

7. The power distribution line bird repelling system based on edge computing of the Internet of Things as claimed in claim 6, characterized in that: The bird damage warning system includes a bird driving away strategy management module, a sound management module, and an algorithm management module, wherein the bird driving away strategy management module is used to store bird driving away strategies, the sound management module is used to store bird audio data, and the algorithm management module is used to store different recognition algorithms.

8. A method for driving away birds from power distribution lines based on edge computing of the Internet of Things, characterized in that: The following steps are involved: Obtain audio and images of birds to be detected; Use the bird sound recognition model to recognize bird audio, and use the bird image recognition model to recognize bird images; Combining the bird audio recognition results and the bird image recognition results, the bird species is obtained; Generate a bird repellent strategy according to the type of bird, and repel the birds according to the bird repellent strategy.

9. A computer-readable storage medium, characterized in that: Multiple instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executed by the distribution line bird-repelling method based on IoT edge computing as described in claim 8.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executed by the distribution line bird-repelling method based on Internet of Things edge computing as described in claim 8.

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