Method for repelling birds in transformer substation based on intelligent sensing

Abstract

The invention relates to a method for repelling birds in a transformer substation based on intelligent sensing, and the method is characterized in that the method comprises the following steps: collecting image data and environment data in the transformer substation, and carrying out the preprocessing; constructing a bird recognition model, inputting the preprocessed image data into the bird recognition model, outputting the types and specific coordinates of birds in the image by the bird recognition model, and transmitting the specific coordinates to the unmanned aerial vehicle; constructing a bird repelling device decision model, inputting the bird type and the preprocessed environment data into the bird repelling device decision model, and outputting an optimal bird repelling device by the bird repelling device decision model and transmitting the optimal bird repelling device to the unmanned aerial vehicle; the unmanned aerial vehicle flies to the optimal bird repelling device placement point, is connected with the optimal bird repelling device and flies to the specific coordinates of the bird to execute the bird repelling task through the bird repelling device.

Description

Technical Field

[0001] This invention relates to a bird deterrence method for substations based on intelligent sensing, belonging to the field of intelligent operation and maintenance technology for power systems. Background Technology

[0002] With the continuous improvement of the automation and intelligence level of power systems, substations play a crucial role in power transmission and voltage transformation in power grid operation. However, in the outdoor equipment areas of substations, there are often phenomena such as birds nesting, resting, and excrement pollution. These behaviors can not only lead to a decline in the insulation performance of equipment, electric shock and short circuits, and relay protection malfunctions, but may also cause line tripping or even large-scale power outages.

[0003] Due to the variability of the actual environment and the varied and unpredictable size and behavior of different birds, the effectiveness of a single or fixed bird deterrent device can be greatly affected. For example, strobe lights can strongly stimulate birds' vision at night, but are almost ineffective in bright daylight. Bird repellents are effective on clear, windless days, but in rainy or windy weather, they are quickly washed away by rainwater or the active ingredients are dispersed by the wind, resulting in insufficient concentration to make them unbearable for birds. Laser bird deterrents are more effective in good weather, but their effectiveness is greatly reduced in foggy or visibility-impaired conditions. Bionic bird deterrents usually require a certain distance to be effective; if the birds to be deterred are large or aggressive, there is a risk of the device crashing due to bird attacks.

[0004] Furthermore, traditional bird control systems are mostly passive response modes, lacking intelligent scheduling and coordination mechanisms, and unable to achieve adaptive decision-making for multiple areas and bird types. While existing drone-based bird control technology has improved flexibility, it typically relies on manual remote control or simple scripted tasks, lacking closed-loop linkage with perception and decision-making models. As a result, the bird control process suffers from low automation, high energy consumption, rigid strategies, and weak learning capabilities. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a bird control method for substations based on intelligent sensing.

[0006] The technical solution of the present invention is as follows: On the one hand, the present invention provides a method for bird control in substations based on intelligent sensing, comprising the following steps: Collect image data and environmental data within the substation and perform preprocessing; A bird recognition model is constructed. The preprocessed image data is input into the bird recognition model, and the bird recognition model outputs the type of bird in the image and its specific coordinates. At the same time, the specific coordinates are transmitted to the drone. A decision model for bird deterrence devices is constructed. The bird type and preprocessed environmental data are input into the decision model. The decision model outputs the optimal bird deterrence device and transmits it to the drone. The drone flies to the optimal bird deterrent device placement point, connects to the optimal bird deterrent device, and flies to the specific coordinates of the bird to perform the bird deterrent task through the bird deterrent device. After the bird deterrence task is completed, the image data of the substation is input into the bird recognition model to identify whether there are still birds. If there are, the bird deterrence task is re-executed through the above steps; otherwise, the bird deterrence task is completed, and the drone flies to the currently connected bird deterrence device placement point to separate and place the bird deterrence device.

[0007] Preferably, the image data within the substation is extracted from surveillance video data and consists of frame images from the surveillance video.

[0008] Preferably, the environmental data includes temperature data, rainfall data, snowfall data, wind speed data, and light intensity data.

[0009] Preferably, the bird recognition model is built based on the YOLO model.

[0010] Preferably, the decision model of the bird deterrent device is based on a neural network.

[0011] Preferably, the bird repelling device includes a laser bird repelling device, an ultrasonic bird repelling device, a spray bird repelling device, a flash bird repelling device, and a bionic bird repelling device.

[0012] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the present invention.

[0013] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the present invention.

[0014] The present invention has the following beneficial effects: 1. This invention realizes a device that can collect surrounding environmental factors and bird and nesting information based on intelligent sensing devices, make autonomous decisions, generate a set of efficient bird-repelling combination schemes adapted to different environments, and automatically send the generated schemes to the intelligent nest and automatically complete the combination between bird-repelling tools and drones. The whole process does not require human intervention, realizes the optimal combination of bird-repelling tools, and improves bird-repelling efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0018] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0019] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0020] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0021] See Figure 1 A method for bird control in a substation based on intelligent sensing includes the following steps: Collect image data and environmental data within the substation and perform preprocessing; A bird recognition model is constructed. The preprocessed image data is input into the bird recognition model, and the bird recognition model outputs the type of bird in the image and its specific coordinates. At the same time, the specific coordinates are transmitted to the drone. A decision model for bird deterrence devices is constructed. The bird type and preprocessed environmental data are input into the decision model. The decision model outputs the optimal bird deterrence device and transmits it to the drone. The drone flies to the optimal bird deterrent device placement point, connects to the optimal bird deterrent device, and flies to the specific coordinates of the bird to perform the bird deterrent task through the bird deterrent device. After the bird deterrence task is completed, the image data of the substation is input into the bird recognition model to identify whether there are still birds. If there are, the bird deterrence task is re-executed through the above steps; otherwise, the bird deterrence task is completed, and the drone flies to the currently connected bird deterrence device placement point to separate and place the bird deterrence device.

[0022] In some embodiments, the image data within the substation is extracted based on surveillance video data and consists of frame images from the surveillance video.

[0023] In some embodiments, the environmental data includes temperature data, rainfall data, snowfall data, wind speed data, and light intensity data.

[0024] In one specific embodiment, a 110kV substation in a certain area was selected as the test scenario. Five high-definition monitoring cameras and four sets of environmental sensor nodes were deployed (collecting data such as temperature, humidity, wind speed, light intensity, and rainfall, respectively). Three multi-rotor drones and five types of bird deterrent devices (laser, ultrasound, strobe, spray, and bionic sound device) were also provided.

[0025] The monitoring video frame rate is set to 25fps, and the resolution is 1920×1080.

[0026] The environmental sensor has a sampling period of 5 seconds, and the collected environmental data samples are shown in the table below:

[0027] At the same time, the above data is preprocessed by normalization.

[0028] In some embodiments, the bird recognition model is built based on the YOLO model.

[0029] In one specific embodiment, the bird recognition model is built based on the YOLOv8s model, and the training dataset contains approximately 12,000 sample images (including four common bird species: sparrows, pigeons, magpies, and crows). At the experimental site, bird instances were detected, and the output is shown in the table below:

[0030] The confidence level correction formula is as follows:

[0031] in: Indicates the corrected number The confidence level of each test result; , These represent the correction weights, respectively. In this embodiment, , ; In this embodiment, the reference wind speed is represented. ; In this embodiment, the reference illumination value is represented. ; Indicates the first Wind data at the time of image data acquisition corresponding to each detection result; Indicates the first Illumination data at the time of image data acquisition corresponding to each detection result; In this embodiment, the confidence threshold is set to 0.7, so the identification result with number #3 is removed.

[0032] In some embodiments, the bird deterrent device decision model is based on a neural network.

[0033] In one specific embodiment, the bird deterrent device decision model is constructed based on a long short-term memory network, and based on bird detection results and environmental data, it ultimately selects a spray bird deterrent device to deter birds.

[0034] In some embodiments, the bird repelling device includes a laser bird repelling device, an ultrasonic bird repelling device, a spray bird repelling device, a flash bird repelling device, and a bionic bird repelling device.

[0035] In some embodiments, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0036] In some embodiments, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the method as described in any embodiment of the present invention.

[0037] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0038] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0039] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0040] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0041] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for bird control in a substation based on intelligent sensing, characterized in that, Includes the following steps: Collect image data and environmental data within the substation and perform preprocessing; A bird recognition model is constructed. The preprocessed image data is input into the bird recognition model, and the bird recognition model outputs the type of bird in the image and its specific coordinates. At the same time, the specific coordinates are transmitted to the drone. A decision model for bird deterrence devices is constructed. The bird type and preprocessed environmental data are input into the decision model. The decision model outputs the optimal bird deterrence device and transmits it to the drone. The drone flies to the optimal bird deterrent device placement point, connects to the optimal bird deterrent device, and flies to the specific coordinates of the bird to perform the bird deterrent task through the bird deterrent device. After the bird deterrence task is completed, the image data of the substation is input into the bird recognition model to identify whether there are still birds. If there are, the bird deterrence task is re-executed through the above steps; otherwise, the bird deterrence task is completed, and the drone flies to the currently connected bird deterrence device placement point to separate and place the bird deterrence device.

2. The bird control method for substations based on intelligent sensing according to claim 1, characterized in that, The image data within the substation is extracted from surveillance video data and consists of frame images from the surveillance video.

3. The bird control method for substations based on intelligent sensing according to claim 1, characterized in that, The environmental data includes temperature data, rainfall data, snowfall data, wind speed data, and light intensity data.

4. The bird control method for substations based on intelligent sensing according to claim 1, characterized in that, The bird identification model is built based on the YOLO model.

5. A method for bird control in a substation based on intelligent sensing according to claim 1, characterized in that, The decision model of the bird deterrent device is based on a neural network.

6. A method for bird control in a substation based on intelligent sensing according to claim 1, characterized in that, The bird repelling devices include laser bird repelling devices, ultrasonic bird repelling devices, spray bird repelling devices, flash bird repelling devices, and bionic bird repelling devices.

7. An electronic 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 program, it implements the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

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