Laser bird repellent system and method, medium and electronic equipment applied to offshore photovoltaics

Abstract

The present disclosure provides a laser bird-repelling system applied to offshore photovoltaics, a laser bird-repelling method applied to offshore photovoltaics, a computer-readable storage medium and an electronic device, and relates to the field of bird-repelling technology. The laser bird-repelling system includes a plurality of support piles and a laser bird-repelling device configured at one end of each support pile, and the laser bird-repelling device includes: a rotating table; a laser, mounted on the rotating table; a laser driving unit; a data processing unit, which is used to obtain a scene image, identify the scene image, and when a bird object is identified, use the scene image to perform depth estimation to determine the distance of the bird object relative to the laser bird-repelling device, and when the distance is less than or equal to a distance threshold, send a rotation instruction to the rotating table to control the laser emitting end of the laser to rotate to match the direction of the bird object, and control the laser driving unit to drive the laser to work. The present disclosure can realize automatic bird-repelling control in offshore photovoltaic scenes.

Description

Background Technology

[0002] The photovoltaic modules of offshore solar power are installed in an outdoor environment at sea. Birds can easily perch on the photovoltaic modules and build nests on them. Their droppings can also pollute the photovoltaic modules, affecting their normal operation and potentially causing damage to them in severe cases.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a laser bird deterrence system, a laser bird deterrence method, a computer-readable storage medium, and an electronic device for use in marine photovoltaic systems, thereby at least to some extent overcoming the adverse effects of bird activity on marine photovoltaic systems.

[0005] According to a first aspect of this disclosure, a laser bird deterrent system for offshore photovoltaic applications is provided, comprising multiple support piles and a laser bird deterrent device configured at one end of each support pile. The support piles are distributed at different locations around the offshore photovoltaic site based on the shape and area of ​​the area and the effective working range of the laser in the laser bird deterrent device. The other end of each support pile is fixed to the seabed using a pile driving process. The laser bird deterrent device includes: a rotating platform for rotating in response to a rotation command; a laser mounted on the rotating platform; a laser driving unit for driving the laser; and a data processing unit for acquiring scene images, recognizing the scene images, performing depth estimation using the scene images when a bird is identified, determining the distance of the bird relative to the laser bird deterrent device, and sending a rotation command to the rotating platform when the distance is less than or equal to a distance threshold to control the laser emitting end of the laser to rotate to match the direction of the bird, and controlling the laser driving unit to drive the laser.

[0006] Optionally, at the offshore photovoltaic site, the laser bird deterrent device corresponds to one or more monitoring cameras, each monitoring camera being configured on the photovoltaic support; wherein, the data processing unit acquires scene images including: the data processing unit acquires scene images collected by the monitoring cameras.

[0007] Optionally, the process by which the data processing unit uses the scene image to perform depth estimation to determine the distance of the bird object relative to the laser bird deterrence device includes: the data processing unit uses the scene image to perform depth estimation to determine the distance of the bird object relative to the monitoring camera, obtains the distance of the laser bird deterrence device relative to the monitoring camera, and determines the distance of the bird object relative to the laser bird deterrence device based on the distance of the bird object relative to the monitoring camera and the distance of the laser bird deterrence device relative to the monitoring camera.

[0008] Optionally, the process of the data processing unit performing depth estimation using the scene image includes: the data processing unit determining the size of the scene image; if the size of the scene image is larger than a first preset size, downsampling the scene image to obtain a first intermediate image; inputting the scene image into a trained first neural network to obtain a first depth image; and inputting the first intermediate image into the first neural network to obtain a second depth image; the first neural network is trained using training samples with a size less than or equal to the first preset size; and inputting the first depth image and the second depth image into a second neural network to perform depth image fusion to obtain the depth estimation result of the scene image.

[0009] Optionally, the size of the training samples of the first neural network is between a first preset size and a second preset size, wherein the first preset size is larger than the second preset size, and the size of the first intermediate image is the midpoint between the first preset size and the second preset size.

[0010] Optionally, if the size of the scene image is between a first preset size and a second preset size, the data processing unit inputs the scene image into a first neural network and determines the obtained first depth image as the depth estimation result of the scene image.

[0011] Optionally, if the size of the scene image is smaller than the second preset size, the data processing unit upsamples the scene image to obtain a second intermediate image; wherein the size of the second intermediate image is between the first preset size and the second preset size; the second intermediate image is input into the first neural network to obtain a third depth image; the third depth image is downsampled to obtain the depth estimation result of the scene image.

[0012] According to a second aspect of this disclosure, a laser bird deterrence method for offshore photovoltaic (PV) applications is provided. The laser bird deterrence system for offshore PV includes multiple support piles and a laser bird deterrence device configured at one end of each support pile. The support piles are distributed at different locations around the offshore PV site based on the shape and area of ​​the PV area and the effective working range of the laser in the laser bird deterrence device. The other end of each support pile is fixed to the seabed using a pile driving process. The laser bird deterrence device includes a rotating platform, a laser, and a laser driving unit. The rotating platform rotates in response to a rotation command. The laser is mounted on the rotating platform. The laser driving unit drives the laser. The laser bird deterrence method includes: acquiring a scene image and identifying the scene image; if a bird is identified, performing depth estimation using the scene image to determine the distance between the bird and the laser bird deterrence device; if the distance is less than or equal to a distance threshold, sending a rotation command to the rotating platform to control the laser emitter of the laser to rotate to match the direction of the bird, and controlling the laser driving unit to drive the laser.

[0013] According to a third aspect of this disclosure, a laser bird deterrent device for offshore photovoltaic (PV) applications is provided. The laser bird deterrent system for offshore PV includes multiple support piles and a laser bird deterrent device configured at one end of each support pile. The support piles are distributed at different locations around the offshore PV site based on the shape and area of ​​the PV area and the effective working range of the laser in the laser bird deterrent device. The other end of each support pile is fixed to the seabed using a pile driving process. The laser bird deterrent device includes a rotating platform, a laser, and a laser driving unit. The rotating platform rotates in response to a rotation command. The laser is mounted on the rotating platform. The laser driving unit drives the laser. The laser bird deterrent device includes: an image recognition module for acquiring and recognizing scene images; a distance determination module for performing depth estimation using the scene image when a bird is identified, to determine the distance between the bird and the laser bird deterrent device; and a bird deterrent activation module for sending a rotation command to the rotating platform when the distance is less than or equal to a distance threshold, controlling the laser emitter of the laser to rotate to match the direction of the bird, and controlling the laser driving unit to drive the laser.

[0014] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the aforementioned laser bird deterrence method applied to marine photovoltaic systems.

[0015] According to a fifth aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; the processor is configured to implement the above-described laser bird deterrence method applied to marine photovoltaics by executing the executable instructions.

[0016] In some embodiments of the present disclosure, the technical solutions provided include, on the one hand, a laser bird deterrent system is deployed for special scenarios such as offshore photovoltaic power generation, and an automatic laser bird deterrent strategy is achieved by combining image recognition. The entire bird deterrent process is completed automatically without human control, ensuring the timeliness of bird deterrence, protecting the offshore photovoltaic site from interference by bird activities, and ensuring the normal operation of the photovoltaic modules. On the other hand, the present disclosure uses images to perform depth estimation and combines the estimated depth to determine the distance of the birds. When the distance is less than a threshold, the laser is driven to work. This linkage analysis and processing method at least achieves intelligent control of the laser, avoiding resource waste and the impact of continuous laser emission on the photovoltaic modules.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0019] Figure 1 A block diagram illustrating an exemplary embodiment of the present disclosure of a laser bird deterrent system applied to marine photovoltaic systems is shown.

[0020] Figure 2 A flowchart illustrating the depth estimation process of an embodiment of this disclosure is shown schematically.

[0021] Figure 3 A flowchart illustrating an exemplary embodiment of the present disclosure of a laser bird deterrence method applied to marine photovoltaics is shown.

[0022] Figure 4 A block diagram illustrating an exemplary embodiment of the present disclosure of a laser bird deterrent device applied to marine photovoltaics is shown.

[0023] Figure 5 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically. Detailed Implementation

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0025] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances. Furthermore, all terms such as "first," "second," and "third" used below are for distinction purposes only and should not be construed as limiting the scope of this disclosure.

[0027] Figure 1 A block diagram illustrating an exemplary embodiment of a laser bird deterrent system applied to marine photovoltaic systems is shown. (Reference) Figure 1 The laser bird deterrent system 1 of this disclosure may include a support pile 11 and a laser bird deterrent device 12.

[0028] It should be understood that the laser bird deterrent device 12 can be configured at one end of the support pile 11, and the other end of the support pile 11 can be fixed to the seabed through a pile driving process. In offshore photovoltaic scenarios, there are usually multiple laser bird deterrent devices 12 and support piles 11. Specifically, the location and number of support piles 11 can be determined according to the shape and area of ​​the offshore photovoltaic area and the effective working range (i.e., the effective bird deterrent range) of the laser in the laser bird deterrent device 12. In the exemplary solution of this disclosure, multiple support piles 11 can be distributed at different locations on the periphery of the offshore photovoltaic site, rather than inside the offshore photovoltaic site, thereby minimizing the impact of the laser on the photovoltaic modules.

[0029] like Figure 1 As shown, the laser bird deterrent device 12 of this embodiment may include a rotating table 121, a laser 122, a laser driving unit 123, and a data processing unit 124.

[0030] The rotary table 121 can rotate in response to a rotation command.

[0031] The laser 122 can be mounted on a rotating stage. Specifically, a 520nm green laser can be used, for example. In conjunction with the rotation of the rotating stage 121, the laser 122 of this disclosure can achieve continuous rotation angles including horizontal rotation from 0° to 360° and vertical rotation from -85° to 20°.

[0032] The laser driving unit 123 can be used to drive the laser 122 to work.

[0033] The data processing unit 124 is used to perform data analysis and processing operations to control the bird-repelling operation of the laser bird-repelling device 12.

[0034] In addition, the laser bird deterrent device 12 may also include a power storage unit, a power management unit, and a housing. The power storage unit stores the electrical energy required for the operation of the laser bird deterrent device 12. This electrical energy can originate from a marine photovoltaic power generation system or a power supply specifically configured for the laser bird deterrent device 12. The power management unit controls the power supply mode of the laser bird deterrent device 12. The housing has a structure that encloses the other unit modules of the laser bird deterrent device 12. To meet the requirements of marine photovoltaic exposure, waterproofing, and corrosion resistance, the selection of the housing material must at least consider these factors. For example, an aluminum alloy housing can be used; this disclosure does not limit this selection.

[0035] The data processing process of data processing unit 124 is described below.

[0036] First, a scene image can be acquired and then the scene image can be recognized.

[0037] According to some embodiments of this disclosure, the laser bird deterrent device 12 may also include a camera, and the scene image acquired by the data processing unit 124 is the image captured by the camera.

[0038] According to other embodiments of this disclosure, in a marine photovoltaic scenario, the laser bird deterrent device 12 can correspond to one or more monitoring cameras, each monitoring camera being configured on a photovoltaic support. Specifically, the monitoring cameras are distributed on the same or different photovoltaic supports. It is understood that the monitoring cameras are pre-installed cameras used to monitor the on-site environment in a marine photovoltaic scenario. Furthermore, monitoring cameras within a predetermined distance from the laser bird deterrent device 12 can be identified as the corresponding monitoring cameras; this disclosure does not limit the specific value of the predetermined distance.

[0039] In this case, the scene image can be an image captured by the aforementioned surveillance camera.

[0040] Recognizing scene images includes determining whether a scene image contains bird images. This process can be implemented using a classification model based on a convolutional neural network or through template comparison, and this disclosure does not impose any limitations on this.

[0041] If a scene image is found to not contain bird objects, it can be discarded.

[0042] If a scene image is identified as containing a bird object, the scene image can be used to perform depth estimation to determine the distance of the bird object relative to the laser bird deterrent device 12.

[0043] Specifically, in embodiments where the scene image originates from the aforementioned surveillance camera, on one hand, depth estimation can be performed using the scene image to determine the distance of the bird object relative to the surveillance camera. On the other hand, given that the installation positions of the laser bird deterrent device 12 and the surveillance camera are fixed, the distance of the laser bird deterrent device 12 relative to the surveillance camera can be obtained. Therefore, the distance of the bird object relative to the laser bird deterrent device 12 can be determined based on both the distance of the bird object relative to the surveillance camera and the distance of the laser bird deterrent device 12 relative to the surveillance camera.

[0044] If the determined distance is less than or equal to a distance threshold, the data processing unit 124 can send a rotation command to the rotating stage 121 to control the laser emitting end of the laser 122 to rotate to match the direction of the bird object, i.e., the laser emitting end is aligned with the bird object. Next, the data processing unit 124 can control the laser driving unit 123 to drive the laser 122 to work and emit laser light. This disclosure does not limit the specific value of the distance threshold.

[0045] If the distance is determined to be greater than the distance threshold, the camera is controlled to track the bird until the bird exceeds the predetermined range or the distance is less than or equal to the distance threshold.

[0046] Depth estimation can be implemented using neural networks. However, a discrepancy may arise between the image size used for neural network training and the image size of the current scene image, especially in scenarios where the surveillance camera and the laser bird deterrent device 12 are independently configured, making perfect matching difficult. This can lead to inaccurate depth estimation. To at least improve the accuracy of depth estimation, this disclosure also provides a depth estimation scheme.

[0047] First, the data processing unit 124 determines the size of the scene image. If the size of the scene image is larger than a first preset size, the scene image is downsampled to obtain a first intermediate image. The scene image is then input into a trained first neural network to obtain a first depth image. The first intermediate image is then input into the first neural network to obtain a second depth image. The first neural network is trained using training samples with a size less than or equal to the first preset size. Next, the first depth image and the second depth image can be input into the second neural network for depth image fusion to obtain the depth estimation result of the scene image. Both the first neural network and the second neural network can be based on a convolutional neural network structure; this disclosure does not limit the specific model structure and training process of either.

[0048] According to some embodiments of this disclosure, the size of the training samples of the first neural network is between a first preset size and a second preset size, wherein the first preset size is larger than the second preset size. For example, the first preset size is 512×512, and the second preset size is 256×256. The size of the aforementioned first intermediate image is the midpoint between the first preset size and the second preset size.

[0049] If the size of the scene image is between the first preset size and the second preset size, the data processing unit 124 can input the scene image into the first neural network and determine the obtained first depth image as the depth estimation result of the scene image.

[0050] If the size of the scene image is smaller than the second preset size, the data processing unit 124 can upsample the scene image to obtain a second intermediate image; wherein the size of the second intermediate image is between the first preset size and the second preset size. Next, the second intermediate image can be input into the first neural network to obtain a third depth image. Then, the third depth image is downsampled to obtain the depth estimation result of the scene image.

[0051] refer to Figure 2 The process of depth estimation described above is illustrated by example.

[0052] In step S202, a scene image can be acquired.

[0053] In step S204, the size of the scene image can be determined.

[0054] In step S206, the relationship between the size of the scene image and the preset size can be determined. The preset size can be understood as the aforementioned first preset size, or a size range formed by the first preset size and the second preset size. When the preset size is understood as a size range formed by the first preset size and the second preset size, "less than the preset size" means less than the second preset size, "greater than the preset size" means greater than the first preset size, and "equal to the preset size" means between the first preset size and the second preset size.

[0055] If the scene image size is larger than the preset size, proceed to step S208; if the scene image size is equal to the preset size, proceed to step S212; if the scene image size is smaller than the preset size, proceed to step S214.

[0056] In step S208, the scene image can be input into the first neural network to obtain a first depth image; and the scene image can be downsampled and then input into the first neural network to obtain a second depth image.

[0057] In step S210, the first depth image and the second depth image can be input into the second neural network to obtain a depth image corresponding to the scene image.

[0058] In step S212, the scene image can be input into the first neural network to obtain a depth image corresponding to the scene image.

[0059] In step S214, the scene image can be upsampled and then input into the first neural network, and the output of the model can be downsampled to obtain a depth image corresponding to the scene image.

[0060] In addition to the depth estimation methods described above, this disclosure may also employ other ranging schemes that combine deep learning, and this disclosure does not impose any restrictions on such schemes.

[0061] Furthermore, this disclosure also provides a laser bird deterrence method for marine photovoltaic applications. This method is applied to a laser bird deterrence system for marine photovoltaic systems. The system includes multiple support piles and a laser bird deterrence device disposed at one end of each support pile. The support piles are distributed at different locations around the marine photovoltaic site based on the shape and area of ​​the marine photovoltaic area and the effective working range of the laser in the laser bird deterrence device. The other end of each support pile is fixed to the seabed using a pile driving process. The laser bird deterrence device may include a rotating platform, a laser, and a laser driving unit. The rotating platform rotates in response to a rotation command. The laser is mounted on the rotating platform. The laser driving unit drives the laser to operate.

[0062] Figure 3 A flowchart illustrating an exemplary embodiment of this disclosure of a laser bird-repelling method applied to marine photovoltaic systems is shown. (Reference) Figure 3 The laser bird deterrence method may include the following steps:

[0063] S32. Acquire scene images and perform scene image recognition.

[0064] S34. If a bird object is identified, depth estimation is performed using the scene image to determine the distance of the bird object relative to the laser bird deterrent device.

[0065] S36. When the distance is less than or equal to the distance threshold, send a rotation command to the turntable to control the laser emitter of the laser to rotate to match the direction of the bird object, and control the laser drive unit to drive the laser to work.

[0066] According to an exemplary embodiment of this disclosure, at a marine photovoltaic site, the laser bird deterrent device corresponds to one or more monitoring cameras, each monitoring camera being configured on a photovoltaic support; wherein, acquiring scene images includes: acquiring scene images captured by the monitoring cameras.

[0067] According to an exemplary embodiment of the present disclosure, the process of using scene images to perform depth estimation to determine the distance of a bird object relative to a laser bird deterrent device includes: using scene images to perform depth estimation to determine the distance of the bird object relative to a monitoring camera, obtaining the distance of the laser bird deterrent device relative to the monitoring camera, and determining the distance of the bird object relative to the laser bird deterrent device based on the distance of the bird object relative to the monitoring camera and the distance of the laser bird deterrent device relative to the monitoring camera.

[0068] According to an exemplary embodiment of the present disclosure, the process of depth estimation using a scene image includes: determining the size of the scene image; if the size of the scene image is larger than a first preset size, downsampling the scene image to obtain a first intermediate image; inputting the scene image into a trained first neural network to obtain a first depth image; and inputting the first intermediate image into the first neural network to obtain a second depth image; the first neural network is trained from training samples with a size less than or equal to the first preset size; and inputting the first depth image and the second depth image into a second neural network to perform depth image fusion to obtain a depth estimation result of the scene image.

[0069] According to an exemplary embodiment of the present disclosure, the size of the training samples of the first neural network is between a first preset size and a second preset size, wherein the first preset size is larger than the second preset size, and the size of the first intermediate image is the midpoint between the first preset size and the second preset size.

[0070] According to an exemplary embodiment of the present disclosure, if the size of the scene image is between a first preset size and a second preset size, the scene image is input into a first neural network, and the resulting first depth image is determined as the depth estimation result of the scene image.

[0071] According to an exemplary embodiment of this disclosure, if the size of the scene image is smaller than a second preset size, the scene image is upsampled to obtain a second intermediate image; wherein the size of the second intermediate image is between the first preset size and the second preset size; the second intermediate image is input into a first neural network to obtain a third depth image; the third depth image is downsampled to obtain a depth estimation result of the scene image.

[0072] Since the steps of the laser bird deterrence method applied to marine photovoltaics in this embodiment are the same as those in the above-described system embodiment, they will not be repeated here.

[0073] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0074] Furthermore, this example embodiment also provides a laser bird deterrent device for offshore photovoltaic (PV) applications. The laser bird deterrent device is applied to a laser bird deterrent system for offshore PV. The system includes multiple support piles and a laser bird deterrent device configured at one end of each support pile. The support piles are distributed at different locations around the offshore PV site based on the shape and area of ​​the PV area and the effective working range of the laser in the laser bird deterrent device. The other end of each support pile is fixed to the seabed using a pile-driving process. The laser bird deterrent device includes a rotating platform, a laser, and a laser driving unit. The rotating platform rotates in response to a rotation command. The laser is mounted on the rotating platform. The laser driving unit drives the laser to operate.

[0075] Figure 4 A block diagram schematically illustrates a laser bird deterrent device applied to marine photovoltaic systems, representing an exemplary embodiment of this disclosure. (Reference) Figure 4 According to an exemplary embodiment of the present disclosure, a laser bird deterrent device 4 for marine photovoltaic applications may include an image recognition module 41, a distance determination module 43, and a bird deterrent activation module 45.

[0076] Specifically, the image recognition module 41 can be used to acquire scene images and recognize them; the distance determination module 43 can be used to perform depth estimation using the scene images when a bird object is identified, so as to determine the distance of the bird object relative to the laser bird deterrence device; the bird deterrence activation module 45 can be used to send a rotation command to the rotating platform to control the laser emitting end of the laser to rotate to match the direction of the bird object when the distance is less than or equal to the distance threshold, and control the laser driving unit to drive the laser to work.

[0077] Since the functional modules of the laser bird deterrent device applied to marine photovoltaics in this embodiment are the same as those in the above-described system embodiment, they will not be described again here.

[0078] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Exemplary Methods" section above.

[0079] The program product for implementing the above-described method according to embodiments of the present disclosure may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0080] The program product may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), an optical disk, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0081] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0082] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0083] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0084] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0085] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0086] The following reference Figure 5 To describe an electronic device 500 according to such an embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this disclosure. The data processing unit described above in this disclosure can be configured in the form of electronic device 500.

[0087] like Figure 5 As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0088] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure. For example, the processing unit 510 can perform various steps of the laser bird deterrence method applied to marine photovoltaic systems according to embodiments of this disclosure.

[0089] Storage unit 520 may include readable media in the form of volatile storage units, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include read-only memory (ROM) 5203.

[0090] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0091] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0092] Electronic device 500 can also communicate with one or more external devices 600 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0093] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0094] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0095] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0096] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0097] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A laser bird-repellent system for offshore photovoltaics, characterized in that: The invention comprises a plurality of supporting piles and a laser bird-repelling device arranged at one end of each supporting pile, wherein each supporting pile is distributed at different positions outside the offshore photovoltaic site according to the shape and area of ​​the offshore photovoltaic area and the effective working range of the laser in the laser bird-repelling device, and the other end of the supporting pile is fixed to the seabed by a pile sinking process; wherein the laser bird-repelling device comprises: A rotating platform, used for rotating in response to a rotation instruction; A laser is mounted on the rotating table; A laser driving unit, used to drive the laser to work; A data processing unit is used to acquire a scene image, identify the scene image, and when a bird object is identified, use the scene image to perform depth estimation to determine the distance of the bird object relative to the laser bird repellent device, and when the distance is less than or equal to a distance threshold, send a rotation instruction to the rotating table to control the laser emitting end of the laser to rotate to match the direction of the bird object, and control the laser driving unit to drive the laser to work; The process of the data processing unit performing depth estimation using the scene image includes: The data processing unit determines the size of the scene image, and if the size of the scene image is larger than a first preset size, downsamples the scene image to obtain a first intermediate image, inputs the scene image into a trained first neural network to obtain a first depth image, and inputs the first intermediate image into the first neural network to obtain a second depth image; the first neural network is trained by training samples whose size is less than or equal to the first preset size; the first depth image and the second depth image are input into a second neural network to perform depth image fusion to obtain a depth estimation result of the scene image.

2. The laser bird-repellent system according to claim 1, characterized in that: At the offshore photovoltaic site, the laser bird-repellent device corresponds to one or more monitoring cameras, and each monitoring camera is configured on a photovoltaic bracket; wherein the data processing unit obtains the scene image, including: the data processing unit obtains the scene image collected by the monitoring camera.

3. The laser bird-repellent system according to claim 2, characterized in that: The process of the data processing unit using the scene image to perform depth estimation to determine the distance of the bird object relative to the laser bird repellent device includes: The data processing unit uses the scene image to perform depth estimation to determine the distance of the bird object relative to the surveillance camera, obtains the distance of the laser bird repellent device relative to the surveillance camera, and determines the distance of the bird object relative to the laser bird repellent device based on the distance of the bird object relative to the surveillance camera and the distance of the laser bird repellent device relative to the surveillance camera.

4. The laser bird-repellent system according to claim 1, characterized in that: The size of the training sample of the first neural network is between the first preset size and the second preset size, the first preset size is larger than the second preset size, and the size of the first intermediate image is an intermediate value between the first preset size and the second preset size.

5. The laser bird-repellent system according to claim 4, characterized in that: If the size of the scene image is between the first preset size and the second preset size, the data processing unit inputs the scene image into the first neural network, and determines the obtained first depth image as a depth estimation result of the scene image.

6. The laser bird-repelling system according to claim 4, characterized in that: If the size of the scene image is smaller than the second preset size, the data processing unit upsamples the scene image to obtain a second intermediate image; wherein the size of the second intermediate image is between the first preset size and the second preset size; the second intermediate image is input into the first neural network to obtain a third depth image; and the third depth image is downsampled to obtain a depth estimation result of the scene image.

7. A laser bird-repelling method applied to offshore photovoltaics, characterized in that: The laser bird-repelling method is applied to a laser bird-repelling system for offshore photovoltaics. The laser bird-repelling system comprises a plurality of support piles and a laser bird-repelling device disposed at one end of each of the support piles. Each of the support piles is distributed at different positions outside the offshore photovoltaic site according to the shape and area of ​​the offshore photovoltaic area and the effective working range of the laser in the laser bird-repelling device. The other end of the support pile is fixed to the seabed by a pile sinking process. The laser bird-repelling device comprises a rotating platform, a laser, and a laser driving unit. The rotating platform is used to rotate in response to a rotation instruction. The laser is mounted on the rotating table; The laser driving unit is used to drive the laser to work; wherein the laser bird-repelling method comprises: Acquire a scene image, and identify the scene image; When a bird object is identified, depth estimation is performed using the scene image to determine the distance of the bird object relative to the laser bird repellent device; When the distance is less than or equal to the distance threshold, a rotation instruction is sent to the rotating platform to control the laser emitting end of the laser to rotate to match the direction of the bird object, and control the laser driving unit to drive the laser to work; The process of using the scene image to perform depth estimation includes: Determine the size of the scene image, if the size of the scene image is larger than a first preset size, downsample the scene image to obtain a first intermediate image, input the scene image into a trained first neural network to obtain a first depth image, and input the first intermediate image into the first neural network to obtain a second depth image; the first neural network is trained by training samples whose size is less than or equal to the first preset size; input the first depth image and the second depth image into a second neural network to perform depth image fusion to obtain a depth estimation result of the scene image.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the laser bird-repelling method applied to offshore photovoltaics as described in claim 7 is implemented.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to implement the laser bird-repelling method applied to offshore photovoltaics as described in claim 7 by executing the executable instructions.

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