Method and device for cleaning photovoltaic panel based on unmanned aerial vehicle

Abstract

The invention discloses a method and device for cleaning a photovoltaic panel based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the position of a coverage area and the shape of the coverage area, the coverage area is internally provided with a plurality of sub-areas, and each sub-area comprises at least one photovoltaic panel. And determining the take-off position of the unmanned aerial vehicle according to the position and the shape of the coverage area. The cleanliness of each sub-area and the distance between each sub-area and the take-off position of the unmanned aerial vehicle are obtained. And according to the cleanliness of each sub-region and the distance between each sub-region and the take-off position of the unmanned aerial vehicle, selecting the unmanned aerial vehicle with the matched cruising ability. According to the method and the device, the pollution weight of each sub-region is calculated according to the cleaning degree of each sub-region and the distance from each sub-region to the take-off position of the unmanned aerial vehicle, and the corresponding unmanned aerial vehicle is selected, so that the unmanned aerial vehicle is ensured to have enough cruising ability to complete cleaning operation, and the cleaning efficiency of the unmanned aerial vehicle is improved; and the photovoltaic panel can be cleaned more thoroughly when the unmanned aerial vehicle is used for cleaning the photovoltaic panel.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic power stations, and in particular to a method and device for cleaning photovoltaic panels based on drones. Background Art

[0002] Cleaning photovoltaic panels is an important part of ensuring their efficient operation and extending their service life. Using drones to clean photovoltaic panels can effectively improve cleaning efficiency. However, drones currently have insufficient battery life when cleaning large areas or operating in complex environments, which not only affects the progress of the task, but also leads to incomplete cleaning. Summary of the invention

[0003] The present application provides a method and device for cleaning photovoltaic panels based on a drone, so as to solve the problem of insufficient endurance of the drone when cleaning a large area or operating in a complex environment.

[0004] In a first aspect, the present application provides a method for cleaning photovoltaic panels based on a drone, comprising:

[0005] Acquire the position and shape of the coverage area, wherein a plurality of sub-areas are arranged in the coverage area, and each sub-area includes at least one photovoltaic panel;

[0006] Determine the take-off position of the drone based on the location and shape of the coverage area;

[0007] Get the cleanliness level of each sub-area;

[0008] Get the distance between each sub-area and the take-off position of the drone;

[0009] Select a drone with matching endurance based on the cleanliness of each sub-area and the distance between each sub-area and the take-off position of the drone.

[0010] In a feasible implementation, obtaining the position and shape of the coverage area includes:

[0011] Acquire an image of the coverage area, where the image of the coverage area includes each sub-area within the coverage area;

[0012] Boundary information of the coverage area is extracted according to the image, and the shape of the coverage area is determined according to the boundary information.

[0013] In a feasible implementation, determining the take-off position of the drone according to the position and shape of the coverage area includes:

[0014] Calculate the center position of the coverage area according to the boundary information of the coverage area and the shape of the coverage area;

[0015] Determine the take-off position of the drone based on the center position.

[0016] In a feasible implementation, the multiple sub-areas in the coverage area are respectively provided with different marks. Before obtaining the distance between each sub-area and the take-off position of the drone, the marks of each sub-area in the coverage area are identified according to the image to determine the boundaries of each sub-area.

[0017] In a feasible implementation, obtaining the distance between each sub-area and the take-off position of the drone includes:

[0018] Determine the farthest point of each sub-area from the take-off position of the UAV according to the boundary of each sub-area;

[0019] Calculate the distance from the take-off position of the drone to the farthest point in each sub-area.

[0020] In a feasible implementation, calculating the distance from the take-off position of the drone to the farthest point of each sub-area includes:

[0021] Identify the relative height distance and relative horizontal distance between the take-off position and the farthest point in each sub-area;

[0022] Identify obstacles between the take-off position and the farthest point of each sub-area;

[0023] Plan the path of the UAV from the take-off position to the farthest point in each sub-area based on the relative height distance, relative horizontal distance and obstacles.

[0024] In a feasible implementation, the drones with matching endurance are selected based on the cleanliness of each sub-area and the distance between each sub-area and the take-off position of the drone, including:

[0025] Determine a first proportion corresponding to the cleanliness level of the sub-area;

[0026] Determine a second ratio corresponding to the distance between the sub-area and the take-off position of the UAV;

[0027] Calculate the pollution weight of the sub-area according to the first proportion and the second proportion;

[0028] A drone with matching endurance is selected based on the pollution weight of the sub-area.

[0029] In a feasible implementation, the drones include a first drone, a second drone, and a third drone, the first drone has a smaller endurance than the second drone, and the second drone has a smaller endurance than the third drone; selecting drones with matching endurances according to weights includes:

[0030] If the pollution weight of the sub-area is greater than zero and less than or equal to the first threshold, the first drone is selected for cleaning;

[0031] If the pollution weight of the sub-area is greater than the first threshold and less than or equal to the second threshold, the second drone is selected for cleaning;

[0032] If the pollution weight of the sub-area is greater than the second threshold and less than or equal to the third threshold, the third drone is selected for cleaning.

[0033] In a feasible implementation, a backup battery is provided in the drone, and the backup battery supports the drone to fly from the farthest point in the coverage area to the take-off position.

[0034] In a second aspect, the present application further provides a data processing device, comprising a processor and a memory, wherein the memory is used to store computer-readable instructions, and the processor is used to call the instructions stored in the memory to execute the method of the first aspect.

[0035] The method and data processing device provided in the present application determine the take-off position of the drone according to the position and shape of the coverage area to ensure that the path from the drone to each sub-area of ​​the coverage area is optimal, calculate the pollution weight of each sub-area according to the cleanliness of each sub-area and the distance from each sub-area to the take-off position of the drone, and select the corresponding drone according to different weights to ensure that the drone has sufficient endurance to complete the cleaning operation, thereby improving the cleaning efficiency of the drone and enabling the drone to clean photovoltaic panels more thoroughly. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0037] Figure 1 This is a schematic diagram of the coverage area involved in this application;

[0038] Figure 2 An interactive schematic diagram of a data processing device involved in this application;

[0039] Figure 3 A flow chart of a method for cleaning photovoltaic panels based on drones involved in this application;

[0040] Figure 4 This is a structural diagram of the data processing device involved in this application.

[0041] Notes on the attached drawings:

[0042] 1-covered area, 2-photovoltaic panels, 3-first drone, 4-second drone, 5-third drone, 6-server, 7-drone transporter;

[0043] 401 - processor, 402 - communication interface, 403 - memory, 404 - bus, 405 - computer readable instructions. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings.

[0045] See also Figure 1 , Figure 1 A schematic diagram of a coverage area architecture provided in an embodiment of the present application. Figure 1 As shown, it includes a coverage area 1, a photovoltaic panel 2, a first drone 3, a second drone 4, a third drone 5, and a drone transporter 6. There are multiple sub-areas set in the coverage area 1. The sub-area division standard is determined by the environmental factors of the coverage area 1. For example, in this application, it is determined according to the wind and sand factors in the environment where the coverage area 1 is located. When the wind and sand pass through the photovoltaic panel 2, due to the different wind force, wind speed and wind direction, the degree of its deposition in the coverage area 1 is also different. According to the degree of wind and sand deposition, the coverage area 1 is divided into multiple sub-areas, and each sub-area is set with different marks to distinguish, such as Figure 1 The different colored areas represent different sub-areas. The cleanliness levels of adjacent sub-areas are similar or not much different, which reduces the occurrence of extra detours or detours during the drone cleaning operation. The flight path of the drone is optimized in advance before the cleaning operation, which improves the cleaning efficiency of the drone and saves energy. The endurance required for the drone to clean each sub-area is calculated based on the cleanliness level of each sub-area and the distance from each sub-area to the drone's take-off position, and the corresponding drone is selected for cleaning based on the calculated endurance. For example, in this application, the endurance power required for the drone to clean the sub-area is calculated based on the cleanliness level of each sub-area and the distance from each sub-area to the drone's take-off position, and the corresponding drone is selected based on the calculated endurance power to ensure that the drone can complete the round trip from the take-off position to the sub-area and complete the cleaning operation of the sub-area, effectively improving the cleaning efficiency of the drone during cleaning.

[0046] The coverage area 1 also includes a server, which is used to control the operation of electronic equipment such as the first drone 3, the second drone 4 and the third drone 5. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The server can also be implemented by a server cluster consisting of multiple servers.

[0047] Optionally, the electronic device may be a personal computer (PC), a laptop or a smart phone, or an all-in-one machine, a handheld computer, a tablet computer (pad), a smart TV player terminal, a vehicle-mounted terminal or a portable device. The operating system of the electronic device on the PC side, such as an all-in-one machine, may include but is not limited to Linux system, Unix system, Windows series system (such as Windows XP, Windows 7, etc.), Mac OS X system (Apple computer operating system) and other operating systems. The operating system of the electronic device on the mobile side, such as a smart phone, may include but is not limited to Android system, IOS (Apple mobile phone operating system), Window system and other operating systems.

[0048] The electronic device can install and run applications, and the server can be a server corresponding to the application installed in the electronic device, providing application services for the application. Among them, the application can be a separately integrated application software, or a small program embedded in other applications, or a system on a web page, etc., which is not limited here. In the embodiment of the present application, the application installed in the first drone 3, the second drone 4 or the third drone 5 is used to clean the photovoltaic panel 2, and control the drone to fly in the coverage area, etc.

[0049] The functions performed by the electronic device or server can be implemented by the processor inside it. The processor may include a control board or a control chip. Exemplarily, the processor may include a battery management system (BMS), a micro control unit (MCU), a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a pulse width modulator (PWM) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0050] It should be noted that Figure 1The structure and number of the devices or apparatuses shown, and the area and shape of the areas shown are only examples, and the embodiments of the present application are not limited thereto. The figures may also include devices or components not shown. For example, a cleaning device may also be included to clean the photovoltaic panels 2 within the coverage area 1. For the sake of simplicity, they are not described one by one in the drawings.

[0051] For another example, the device or component that may not be shown in the figure may also include a camera for collecting images, for example, the collected image is an image of the coverage area 1, etc. The present application does not limit the position of the camera, and the camera may be located on the fourth drone, or the camera may be located on a device within the coverage area 1.

[0052] See also Figure 2 , Figure 2 The interactive schematic diagram of the data processing method provided in the embodiment of the present application. The method can be executed by a data processing device, which can be a server, or can be a device or component within the coverage area 1, or a computer program (including program code, program instructions), or a device or component within the fourth drone.

[0053] See also Figure 2 and Figure 3 The method includes but is not limited to the following steps S201-S205:

[0054] S201 , obtaining a position of a coverage area 1 and a shape of the coverage area 1 , wherein a plurality of sub-areas are arranged in the coverage area 1 , and each sub-area includes at least one photovoltaic panel 2 .

[0055] The position of the coverage area 1 is received by the data processing device and transmitted to the drone transport vehicle 6. The position of the coverage area 1 can be the longitude and latitude of the coverage area 1 and the terrain of the location of the coverage area 1. After receiving the position of the coverage area 1, the drone transport vehicle 6 transports other cleaning auxiliary devices required for the drone to complete the cleaning operation, such as a water tank, to the vicinity of the location of the coverage area 1. The data processing device can be a device in the coverage area 1, such as a positioning system or a real-time monitoring system; the data processing device can also be a device on the fourth drone, and the fourth drone detects and determines the position of the coverage area 1 and sends the position to the drone transport vehicle 6. After receiving the image of the coverage area 1, the data processing device determines the shape of the coverage area 1. The image of the coverage area 1 can be obtained by a camera in the coverage area 1 or on the fourth drone, and then transmitted to the data processing device through a mobile network or a wireless network. The data processing device analyzes and processes the received image of the coverage area 1, extracts the boundary information of the coverage area 1, and determines the shape of the coverage area 1. The division standard of the sub-areas of the coverage area 1 is determined by the environmental factors of the coverage area 1. For example, in the present application, it is determined based on the wind and sand factors in the environment in which the coverage area 1 is located. When the wind and sand pass through the photovoltaic panel 2, due to the differences in wind force, wind speed and wind direction, the degree of its deposition in the coverage area 1 is also different. The coverage area 1 is divided into multiple sub-areas according to the degree of wind and sand deposition, and each sub-area is provided with different marks to distinguish them. After the drone obtains the image of the coverage area 1, it identifies the different marks and determines the boundaries of the corresponding sub-areas. The cleanliness levels of adjacent sub-areas are similar or not much different, which reduces the occurrence of extra detours or detours during the drone cleaning operation. The flight path of the drone is optimized in advance before the cleaning operation, which improves the cleaning efficiency of the drone and saves energy.

[0056] S202, determining a take-off position of the UAV according to the position of the coverage area 1 and the shape of the coverage area 1.

[0057] After receiving the position of the coverage area 1, the drone transporter 6 transports the drone and other cleaning accessories required to complete the cleaning operation, such as a water tank, to the vicinity of the coverage area 1. The data processing device can be a device in the coverage area 1, such as a positioning system or a real-time monitoring system; the data processing device can also be a device on the fourth drone, which detects and determines the position of the coverage area 1 and sends the position to the drone transporter 6. The data processing device receives the image of the coverage area 1, pre-processes the image, converts the color image into a black and white image, detects the boundary features of the coverage area 1 to extract the contour of the coverage area 1, and calculates the aspect ratio and circularity of the contour. The circularity calculation formula is as follows:

[0058]

[0059] Where: Y is the circularity, S is the area of ​​coverage area 1, and C is the perimeter of the outline of coverage area 1. The area of ​​coverage area 1 is determined according to the number of pixels in the image. For example, if the circularity of coverage area 1 is close to 1 and the relationship between area and perimeter conforms to the formula of a circle, coverage area 1 is a circle; if the aspect ratio of coverage area 1 is close to a certain value, such as 1:1, and the angles of the four corners are close to 90 degrees, coverage area 1 is a rectangle; if the outline of coverage area 1 consists of multiple vertices, it is classified by the number of vertices. After determining the image of coverage area 1, the center position of coverage area 1 is calculated, and the center position is used as the take-off position of the drone. For example, coordinates are established with the position of the drone transport vehicle 6 as the origin, the horizontal direction is the X-axis, and the longitudinal direction is the Y-axis. If coverage area 1 is a circle with a radius of R, the coordinates of the center of the circle are (X0, YO), then the coordinates of the center position are (X0, Y0); if coverage area 1 is a rectangle, the coordinates of the lower left corner of the rectangle are (X1, Y1), and the coordinates of the upper right corner are (X2, Y2), then the coordinates of the center position are If the coverage area 1 is a polygon, the coordinates of the center position of the polygon are determined by using the positions of multiple vertices of the polygon.

[0060] S203, obtaining the cleanliness level of each sub-region.

[0061] The data processing device receives the image of the coverage area 1, extracts the image of each sub-area, pre-processes the image, converts the image of the sub-area into a grayscale image, identifies the dirt area in the image, calculates the dirt area ratio, unit dirt density and dirt type, and obtains a comprehensive cleanliness score to determine the cleanliness of each sub-area.

[0062] S204, obtaining the distance between each sub-area and the take-off position of the UAV.

[0063] Extract the outline of the sub-area and the feature points on the outline from the image of the sub-area, calculate the distance from each feature point to the take-off position of the drone, select the vertex with the largest distance as the farthest point, and use the maximum distance as the distance from the corresponding sub-area to the take-off position of the drone. For example, if the sub-area is a circle, the farthest point is located on the extension line from the center of the circle to the take-off position of the drone, and the distance is the radius of the circle plus the distance from the center of the circle to the given point; if the sub-area is a rectangular area, the vertex farthest from the fixed point among the four vertices of the rectangle is the farthest point, and the maximum distance is the distance from the take-off position of the drone to the farthest point; if the sub-area is a polygonal area, the farthest point is the point farthest from the take-off position of the drone among multiple vertices, and the maximum distance is the distance from the take-off position of the drone to the vertex. Determine the maximum distance from the take-off position of the drone to the sub-area, and use the maximum distance as a reference factor for calculating the endurance of the drone to ensure that when the corresponding drone is selected, the cleaning area of ​​the drone can cover the entire corresponding sub-area, so that the drone can clean more thoroughly.

[0064] S205, selecting a drone with a matching endurance according to the cleanliness level of each sub-area and the distance between each sub-area and the take-off position of the drone.

[0065] The data processing device selects a drone with matching endurance according to the pollution weight of each sub-area, wherein the drones are divided into a first drone 3, a second drone 4 and a third drone 5, the endurance of the first drone 3 is greater than the endurance of the second drone 4, and the endurance of the second drone 4 is greater than the endurance of the third drone 5. The endurance of a drone involves multiple aspects such as flight time, flight distance, mission execution capability, environmental factors, battery technology, flight mode and management, maintenance and upkeep, etc. For example, in the present application, the endurance of a drone is determined by the cleaning efficiency of the drone and the power storage capacity of the drone, then the cleaning efficiency of the first drone 3 is less than that of the second drone 4, the cleaning efficiency of the second drone 4 is equal to that of the third drone 5, but the power storage capacity of the third drone 5 is greater than that of the second drone 4.

[0066] In this application, the pollution weight of a sub-area is defined as a function of the cleanliness of the sub-area and the distance between the sub-area and the take-off position of the drone. The calculation formula is as follows:

[0067] Q=αJ+βX k

[0068] Where: Q is the pollution weight of the sub-area. J is the cleanliness of the sub-area, which is between 0 and 1. α is the weight coefficient corresponding to the cleanliness of the sub-area, that is, the first proportion corresponding to the cleanliness of the sub-area; X is the distance between the sub-area and the take-off position of the drone. β is the weight coefficient corresponding to the distance between the sub-area and the take-off position of the drone, that is, the second proportion corresponding to the distance between the sub-area and the take-off position of the drone. k is the distance index between the sub-area and the take-off position of the drone. The first proportion α, the second proportion β and the distance index k can be adjusted according to the specific application scenario. For example, when the cleanliness of the sub-area is more important than the distance, increase the value of the first proportion; when the influence of the distance is greater, the distance index can be adjusted to the square of the distance or other exponential forms. In this application, the first proportion α and the second proportion β are set to correspond to the cleanliness score. When the cleanliness score of the sub-area is greater than or equal to 0 and less than or equal to 3, the sub-area is easier to clean, the first proportion is 0.3, and the second proportion is 0.7; when the cleanliness score of the sub-area is greater than 3 and less than or equal to 6, the sub-area is moderately difficult to clean, the first proportion is 0.5, and the second proportion is 0.5; when the cleanliness score of the sub-area is greater than 6 and less than or equal to 1, the first proportion is 0.7 and the second proportion is 0.3. For example, there is a sub-area with a cleanliness level of 6, a distance of 10 units from the take-off position of the drone, the first proportion is 0.5, the second proportion is 0.5, and the distance index is 1, then the weight calculation result is:

[0069] Q = 6 × 0.5 + 0.5 × 10 = 8

[0070] If the distance index is adjusted to 2, the weight calculation result is:

[0071] Q = 6 × 0.7 + 0.5 × 10 2 =54.2

[0072] After calculating the pollution weight of the sub-area, a drone with matching endurance is selected accordingly. If the pollution weight of the sub-area is greater than zero and less than or equal to the first threshold, the first drone 3 is selected for cleaning; if the pollution weight of the sub-area is greater than the first threshold and less than or equal to the second threshold, the second drone 4 is selected for cleaning; if the pollution weight of the sub-area is greater than the second threshold and less than or equal to the third threshold, the third drone 5 is selected for cleaning. For example, when the first proportion is 0.7, the second proportion is 0.3, the distance index is 1, and the distance range between the sub-area and the take-off position of the drone is between 0 and 10 units and includes 0 units and 10 units, the first threshold is set to 3, the second threshold is set to 7, and the third threshold is set to 10. If the pollution weight of the sub-area is greater than 0 and less than or equal to 3, the first drone 3 is selected for cleaning; if the pollution weight of the sub-area is greater than 3 and less than or equal to 7, the second drone 4 is selected for cleaning; if the pollution weight of the sub-area is greater than 7 and less than or equal to 10, the third drone 5 is selected for cleaning.

[0073] It can be seen from the calculation formula of the pollution weight of the sub-area that the shorter the distance from the sub-area to the take-off position of the drone, the smaller the pollution weight of the corresponding sub-area, the less energy the corresponding drone consumes, and the more sub-areas the corresponding drone can clean under the same energy consumption. Therefore, before the drone performs cleaning operations, the present application optimizes the flight path of the drone. When calculating the distance from the take-off position of the drone to the farthest point of the sub-area, the relative height distance and relative horizontal distance between the take-off position and the farthest point of the corresponding sub-area are first identified to determine the take-off angle, take-off direction, ascent height and displacement distance of the drone. The camera identifies obstacles between the take-off position of the drone and the farthest point of the sub-area. The camera can be set on a device in the coverage area 1 or in the fourth drone. The obstacle avoidance path is planned for the drone based on the obstacle information identified by the camera, which reduces the flight distance and power consumption of the drone and improves the cleaning efficiency of the drone. Before the drone arrives at the corresponding sub-area and starts cleaning operations, it can identify the arrangement of the photovoltaic panels 2 in the sub-area based on the image of the sub-area. For example, if the sub-area contains a large area of ​​regularly arranged photovoltaic panels 2, the drone uses a serpentine path for cleaning; if the sub-area contains circular or annular photovoltaic panels 2, the drone can use a circular path. Different cleaning paths are used for photovoltaic panels 2 with different arrangements, thereby reducing energy consumption and cleaning time.

[0074] Taking into account that some sub-areas to be cleaned are more difficult to clean or have a larger cleaning area, the required cleaning time exceeds the cleaning capacity of a single drone; or the photovoltaic panels in some sub-areas to be cleaned need to be put into use after a shorter cleaning time, the data processing device can analyze and calculate the total cleaning time of all sub-areas to be cleaned in the coverage area based on the acquired image. At the same time, the data processing device stores the endurance of each drone. At this time, the data processing device selects one or more drones to clean the sub-areas to be cleaned according to the total cleaning time and the endurance of each drone. It is worth noting that the multiple drones can be fully charged drones or partially charged drones. In order to simplify the flight path of the drones, in the multi-drone cleaning mode, the fully charged drones and the partially charged drones are divided into different batches to clean the photovoltaic panels.

[0075] The drone is equipped with a backup battery, which supports the drone to fly from the farthest point of coverage area 1 to the take-off position. When an emergency occurs during the drone cleaning operation, such as thunderstorms or low temperatures, the drone's own power is insufficient to support it to return from the sub-area to the drone's take-off position. At this time, the drone's backup battery is activated to ensure the drone's safe return. If the drone encounters an emergency during flight, such as a bird being involved, the drone can choose to empty the water in the water tank it carries and activate the backup battery to return to the take-off position. Or choose to make an emergency landing and activate the backup battery to keep the positioning system turned on.

[0076] See also Figure 4 , Figure 4 A schematic diagram of the structure of a data processing device provided in the present application. The data processing device includes a processor and a memory, wherein the memory is used to store computer-readable instructions, and the processor is used to call the instructions stored in the memory to execute the above-mentioned method for cleaning photovoltaic panels based on drones.

[0077] The data processing device may be a server, or may be a device or component within the coverage area 1, or may be executed by a computer program or the like. Figure 4 As shown, the data processing device includes a processor 401, a communication interface 402 and a memory 403. The processor 401, the communication interface 402 and the memory 403 may be connected to each other via a bus 404, or may be connected in other ways.

[0078] The processor 401 includes one or more processors, for example, one or more central processing units. When the processor 401 is a central processing unit (CPU), the CPU may be a single-core CPU or a multi-core CPU. In the embodiment of the present application, the processor 401 is used to control the data processing device to implement Figure 2 The embodiment shown.

[0079] The memory 403 includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), and the memory 403 is used to store relevant instructions and data.

[0080] The communication interface 402 is used to implement communication with other devices. In the embodiment of the present application, the memory 403 stores computer-readable instructions 405, and the processor 401 is used to call the instructions stored in the memory 403. When the data processing device is a server, the above instructions are used to perform the following steps:

[0081] (1) obtaining a position and a shape of a coverage area, wherein a plurality of sub-areas are arranged in the coverage area, and each sub-area includes at least one photovoltaic panel;

[0082] (2) Determine the take-off position of the UAV based on the location and shape of the coverage area;

[0083] (3) Obtain the cleanliness level of each sub-area and the distance between each sub-area and the take-off position of the UAV;

[0084] (4) Select a drone with a matching endurance based on the cleanliness of each sub-area and the distance between each sub-area and the take-off position of the drone.

[0085] The method and data processing device provided by the present application determine the take-off position of the drone according to the position and shape of the coverage area to ensure that the path from the drone to each sub-area of ​​the coverage area is optimal, calculate the pollution weight of each sub-area according to the cleanliness of each sub-area and the distance from each sub-area to the take-off position of the drone, and select the corresponding drone according to the different weights to ensure that the drone has sufficient endurance to complete the cleaning operation, thereby improving the cleaning efficiency of the drone and enabling the drone to clean photovoltaic panels more thoroughly. The cleaning mode of multiple drones improves the cleaning efficiency and avoids insufficient power consumption.

[0086] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0087] In addition, the descriptions of "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0088] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0089] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for cleaning photovoltaic panels based on drones, characterized in that: The method comprises: Acquire the position and shape of the coverage area, wherein a plurality of sub-areas are arranged in the coverage area, and each sub-area includes at least one photovoltaic panel; Determining a take-off position of the UAV according to the position of the coverage area and the shape of the coverage area; Obtaining the cleanliness level of each of the sub-areas; Obtaining the distance between each sub-area and the take-off position of the UAV; A drone with matching endurance is selected according to the cleanliness level of each sub-area and the distance between each sub-area and the take-off position of the drone.

2. The method according to claim 1, characterized in that: The obtaining of the position and shape of the coverage area comprises: Acquire an image of the coverage area, wherein the image of the coverage area includes each of the sub-areas within the coverage area; Boundary information of the coverage area is extracted according to the image, and a shape of the coverage area is determined according to the boundary information.

3. The method according to claim 2, characterized in that Determining the take-off position of the drone according to the position of the coverage area and the shape of the coverage area includes: Calculate the center position of the coverage area according to the boundary information of the coverage area and the shape of the coverage area; The take-off position of the UAV is determined according to the central position.

4. The method according to claim 2, characterized in that: The multiple sub-areas in the coverage area are respectively marked with different labels; Before getting the distance between each sub-area and the take-off position of the drone: Marks of each sub-area of ​​the coverage area are identified according to the image to determine boundaries of each sub-area.

5. The method according to claim 4, characterized in that The obtaining of the distance between each sub-area and the take-off position of the UAV comprises: Determine the farthest point of each sub-area from the take-off position of the drone according to the boundary of each sub-area; The distance from the take-off position of the UAV to the farthest point of each sub-area is calculated.

6. The method according to claim 5, characterized in that The calculating the distance from the take-off position of the drone to the farthest point of each sub-area comprises: Identifying the relative height distance and relative horizontal distance between the take-off position and the farthest point of each of the sub-areas; identifying obstacles between the take-off position and the farthest point of each of the sub-areas; According to the relative height distance, the relative horizontal distance and the obstacle, a path of the UAV from the take-off position to the farthest point of each of the sub-areas is planned.

7. The method according to claim 6, characterized in that The method of selecting a drone with a matching endurance according to the cleanliness level of each sub-area and the distance between each sub-area and the take-off position of the drone includes: Determine a first proportion corresponding to the cleanliness level of the sub-area; Determine a second ratio corresponding to the distance between the sub-area and the take-off position of the UAV; Calculating the pollution weight of the sub-area according to the first proportion and the second proportion; A UAV with matching endurance is selected according to the pollution weight of the sub-area.

8. The method according to claim 7, characterized in that The drones include a first drone, a second drone, and a third drone, the first drone has a smaller endurance than the second drone, and the second drone has a smaller endurance than the third drone; and selecting a drone with matching endurance according to the weights includes: If the pollution weight of the sub-area is greater than zero and less than or equal to a first threshold, the first drone is selected for cleaning; If the pollution weight of the sub-area is greater than the first threshold and less than or equal to the second threshold, the second drone is selected for cleaning; If the pollution weight of the sub-area is greater than the second threshold and less than or equal to the third threshold, the third drone is selected for cleaning.

9. The method according to claim 8, characterized in that The drone is provided with a backup battery, and the backup battery supports the drone to fly from the farthest point of the coverage area to the take-off position.

10. A data processing device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store computer-readable instructions, and the processor is used to call the instructions stored in the memory to execute the method according to any one of claims 1 to 9.

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