A photovoltaic cleaning method, device, equipment and medium based on a unmanned aerial vehicle

Abstract

The application relates to the technical field of photovoltaic cleaning, in particular to a photovoltaic cleaning method and device based on a UAV, equipment and a medium. The method comprises the following steps: periodically acquiring first image data of a photovoltaic panel; determining a to-be-cleaned area and a dirt type on the surface of the photovoltaic panel according to the first image data; determining a cleaning mode and a first cleaning route according to the to-be-cleaned area and the dirt type, and controlling the UAV to clean the to-be-cleaned area according to the first cleaning route by using the cleaning mode; after the cleaning is completed, acquiring second image data of the photovoltaic panel, determining whether there is a not-cleaned area according to the second image data, and if there is, controlling the UAV to clean the not-cleaned area again. The application can improve the cleaning quality of the photovoltaic panel, ensure the cleanliness of the photovoltaic panel, and reduce the danger of manual cleaning and protect the safety of personnel by using the UAV to clean the photovoltaic panel.

Description

Technical Field

[0001] This application relates to the field of photovoltaic cleaning technology, and in particular to a photovoltaic cleaning method, apparatus, equipment and medium. Background Technology

[0002] If dust, dirt, bird droppings, or other debris accumulates on the surface of photovoltaic solar panels, it will block sunlight, reducing the amount of light reaching the panels and thus decreasing the efficiency of the solar cells and the amount of electricity generated. Cleaning the photovoltaic panels ensures that solar energy is captured to the maximum extent and improves energy conversion efficiency.

[0003] In related technologies, photovoltaic systems are usually cleaned by manual water washing. However, photovoltaic systems are installed on building roofs, which typically lack protective measures. It is dangerous for personnel to go up and clean photovoltaic systems, and prolonged unshaded work can easily cause fainting, thus endangering their lives. Summary of the Invention

[0004] To address the high risks associated with manual photovoltaic cleaning in existing technologies, this application provides a method, apparatus, equipment, and medium for photovoltaic cleaning based on unmanned aerial vehicles (UAVs).

[0005] Firstly, this application provides a photovoltaic cleaning method based on unmanned aerial vehicles (UAVs), employing the following technical solution:

[0006] A photovoltaic cleaning method based on unmanned aerial vehicles (UAVs) includes:

[0007] Regularly acquire initial image data of photovoltaic systems;

[0008] The area to be cleaned and the type of dirt on the photovoltaic surface are determined based on the first image data;

[0009] The cleaning method and the first cleaning route are determined according to the area to be cleaned and the type of dirt, and the drone is controlled to clean the area to be cleaned according to the cleaning method and the first cleaning route.

[0010] After cleaning is completed, the second image data of the photovoltaic is acquired. Based on the second image data, it is determined whether there are any uncleaned areas. If so, the drone is controlled to perform a second cleaning of the uncleaned areas.

[0011] By adopting the above technical solution, the first image data of the photovoltaic panel is acquired periodically, and the areas to be cleaned and the dirty areas on the photovoltaic surface are determined based on the first image data. The cleaning method and the first cleaning route are determined according to the areas to be cleaned and the type of dirt, enabling the drone to clean the photovoltaic panel more accurately and efficiently. After cleaning, the second image data of the photovoltaic panel is acquired. If there are still unclean areas, the drone is controlled to perform a second cleaning of the unclean areas, which can improve the cleaning quality of the photovoltaic panel and ensure its cleanliness. At the same time, using drones for photovoltaic cleaning can reduce the dangers of manual cleaning and ensure the safety of personnel.

[0012] In a preferred embodiment, this application can be further configured to: determine the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data, including:

[0013] The first image data is preprocessed and features are extracted. The multiple feature regions corresponding to the extracted features are compared with a preset standard image to determine the amount of difference corresponding to each feature region.

[0014] Determine whether the difference amount corresponding to the target feature region exceeds a preset difference amount threshold. If it does, determine the type of dirt corresponding to the target feature region and determine the target feature region as the target sub-region to be cleaned. The target feature region is any one of the multiple feature regions.

[0015] Several sub-regions to be cleaned, identified from the multiple feature regions, are determined as the areas to be cleaned on the photovoltaic surface.

[0016] By adopting the above technical solution, multiple feature regions extracted from the first image data are compared with preset standard images respectively, which can accurately determine the difference of each feature region. When the difference of the target feature region exceeds the preset difference threshold, the target feature region is determined as the target sub-region to be cleaned, thereby improving the efficiency and accuracy of determining the target sub-region to be cleaned and the type of dirt.

[0017] In a preferred embodiment, this application can be further configured to: determine the cleaning method based on the area to be cleaned and the type of dirt, including:

[0018] Based on the type of dirt corresponding to the target sub-area to be cleaned, the cleaning method and cleaning intensity corresponding to the target sub-area to be cleaned are determined, and the cleaning method includes airflow cleaning and water flow cleaning.

[0019] By adopting the above technical solutions, different cleaning methods and cleaning intensities can be selected according to different types of dirt, which can remove dirt more effectively and improve the cleaning effect.

[0020] In a preferred embodiment, this application can be further configured to: determine a first cleaning route based on the area to be cleaned and the type of dirt, including:

[0021] Determine the flight start point of the UAV and the area point of the target sub-region to be cleaned;

[0022] Based on the type of dirt in the target sub-area to be cleaned, determine the flight speed of the drone corresponding to the target sub-area to be cleaned;

[0023] A first cleaning route is generated based on the flight starting point, the corresponding area points of the several sub-areas to be cleaned, and the flight speed of the UAV.

[0024] By adopting the above technical solution, the flight speed of the drone can be determined according to the type of dirt in the target sub-area to be cleaned, which can improve the accuracy of determining the first cleaning route of the drone. The first cleaning route can be generated based on the flight starting point, the corresponding area points of several sub-areas to be cleaned, and the flight speed of the drone. This can optimize the flight path, reduce unnecessary flights and waste, thereby reducing energy consumption, while ensuring the cleaning effect and quality.

[0025] In a preferred embodiment, this application can be further configured to: determine the flight start point of the UAV and the area points of the target sub-region to be cleaned, including:

[0026] Determine the flight start point of the UAV and the center point of the target sub-area to be cleaned;

[0027] An initial cleaning route is generated based on the flight starting point and the center points of the various sub-regions to be cleaned.

[0028] Determine the spray width of the drone;

[0029] Based on the initial cleaning route and spray width, determine the spray coverage area corresponding to the target sub-area to be cleaned;

[0030] Determine whether the spray coverage area corresponding to the target sub-area to be cleaned is larger than the area of ​​the target sub-area to be cleaned;

[0031] If it is not greater than, then the region point corresponding to the target sub-region to be cleaned is determined, and the number of the region points is multiple.

[0032] By adopting the above technical solution, and by determining the flight start point of the UAV and the center point of the target sub-area to be cleaned, and generating an initial cleaning route, the cleaning route and cleaning method can be formulated more accurately and efficiently, thereby improving cleaning efficiency. Based on the initial cleaning route and spray width, it can be determined whether the spray coverage area corresponding to the target sub-area to be cleaned reaches the area of ​​the target sub-area to be cleaned. If it does not reach the area, the area point of the target sub-area to be cleaned is re-determined, which can optimize the flight path of the UAV and improve the cleaning uniformity and cleaning quality of the target sub-area to be cleaned.

[0033] In a preferred embodiment, this application can be further configured as follows: after controlling a drone to clean the area to be cleaned according to the first cleaning route using the cleaning method, the method further includes:

[0034] A second cleaning route is generated based on the first cleaning route, and the second cleaning route is in the opposite direction to the first cleaning route.

[0035] The drone is controlled to use the airflow cleaning method to clean the area to be cleaned according to the second cleaning route, so as to remove water stains and residues on the photovoltaic panel.

[0036] By adopting the above technical solution, the drone is controlled to clean the area to be cleaned by using airflow cleaning method and following the second cleaning route, which can effectively remove water stains and residues on the photovoltaic surface and enhance the cleaning effect.

[0037] In a preferred embodiment, this application can be further configured such that the method also includes:

[0038] Obtain historical weather data for the area where the photovoltaic power station is located;

[0039] The cleaning cycle is determined based on the historical weather data.

[0040] The system acquires weather data within a preset time period, adjusts the cleaning cycle based on the weather data, and controls the drone to clean the photovoltaic system according to the adjusted cleaning cycle.

[0041] By adopting the above technical solution, the cleaning cycle is adjusted according to weather data within a preset time period, and the drone is controlled to clean the photovoltaic system according to the adjusted cleaning cycle, which can reduce the energy consumption of the drone cleaning process and improve the cleaning efficiency.

[0042] Secondly, this application provides a photovoltaic cleaning device based on a drone, which adopts the following technical solution:

[0043] A drone-based photovoltaic cleaning device includes:

[0044] The acquisition module is used to periodically acquire the first image data of the photovoltaic system.

[0045] The determination module is used to determine the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data;

[0046] The control module is used to determine the cleaning method and the first cleaning route according to the area to be cleaned and the type of dirt, and to control the drone to clean the area to be cleaned according to the cleaning method and the first cleaning route.

[0047] The cleaning module is used to acquire second image data of the photovoltaic after cleaning is completed, determine whether there are unclean areas based on the second image data, and if so, control the drone to perform secondary cleaning on the unclean areas.

[0048] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0049] One or more processors;

[0050] Memory;

[0051] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the drone-based photovoltaic cleaning method as described in any of the first aspects.

[0052] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0053] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the unmanned aerial vehicle-based photovoltaic cleaning method as described in any of the first aspects.

[0054] In summary, this application includes the following beneficial technical effects:

[0055] This application acquires first image data of the photovoltaic panel periodically, determines the areas to be cleaned and the dirty areas on the photovoltaic surface based on the first image data, and determines the cleaning method and first cleaning route according to the areas to be cleaned and the type of dirt, enabling drones to clean the photovoltaic panels more accurately and efficiently. After cleaning, second image data of the photovoltaic panel is acquired. If there are still unclean areas, the drone is controlled to perform a second cleaning of the unclean areas, which can improve the cleaning quality of the photovoltaic panel and ensure its cleanliness. At the same time, using drones for photovoltaic cleaning can reduce the dangers of manual cleaning and ensure the safety of personnel. Attached Figure Description

[0056] Figure 1 This is a schematic flowchart of a photovoltaic cleaning method based on a drone provided in an embodiment of this application;

[0057] Figure 2 This is a schematic diagram of the spray coverage area provided in the embodiments of this application;

[0058] Figure 3 This is a schematic diagram of the structure of a photovoltaic cleaning device based on a drone provided in an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0060] The following is in conjunction with the appendix Figure 1 - Appendix Figure 4 This application will be described in further detail.

[0061] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0064] This application provides a photovoltaic cleaning method based on unmanned aerial vehicles (UAVs), such as... Figure 1As shown, the method provided in this application embodiment is executed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application embodiment does not impose any limitations on this connection. The method includes steps S101-S104, wherein:

[0065] S101. Periodically acquire the first image data of the photovoltaic system.

[0066] In this embodiment, a cleaning cycle can be set, and the drone can be controlled to clean according to the cycle. The drone can be equipped with an image acquisition device to capture images of the photovoltaic system, obtaining first image data. Simultaneously, during the acquisition of the first image data, the drone can also carry a cleaning agent, which is sprayed onto the photovoltaic system while the first image data is being acquired. By the time the drone cleans the photovoltaic system, the cleaning agent has softened the dirt on the photovoltaic panel, making it easier to clean.

[0067] S102. Determine the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data.

[0068] In this embodiment, the area to be cleaned is an area containing dirt, and the types of dirt include stains, leaves, snow, and dust. First, the first image data can be preprocessed to improve the accuracy of subsequent processing. The preprocessing steps may include image denoising, image brightness adjustment, and contrast adjustment. Then, a target detection algorithm is used to detect the first image data to obtain the area to be cleaned and the type of dirt contained in the first image data.

[0069] The object detection algorithm can be pre-trained by collecting an image dataset containing different types of dirt on the photovoltaic surface (such as stains, leaves, snow, dust, etc.) and labeling these images to mark the boundaries and categories of each dirt. Using the collected and labeled dataset, an object detection model is trained. The object detection algorithm can be YOLO, Faster R-CNN, or SSD. The trained object detection model is then applied to the area to be cleaned and the detection of dirt types on the photovoltaic surface. The object detection algorithm can output each segmented dirt area and its corresponding dirt type.

[0070] S103. Determine the cleaning method and first cleaning route based on the area to be cleaned and the type of dirt, and control the drone to clean the area to be cleaned according to the first cleaning route using the cleaning method.

[0071] In this embodiment, the corresponding cleaning method can be determined according to the type of dirt in each dirty area in the area to be cleaned. The cleaning methods include airflow cleaning and water flow cleaning. A first cleaning route can be generated according to the location of each dirty area in the area to be cleaned. The first cleaning route passes through the flight start point of the drone and each dirty area.

[0072] S104. After cleaning is completed, acquire the second image data of the photovoltaic system, determine whether there are any uncleaned areas based on the second image data, and if so, control the drone to perform a second cleaning of the uncleaned areas.

[0073] After cleaning is completed, the image acquisition equipment on the drone can be used to acquire image data of the photovoltaic surface again to obtain the second image data of the photovoltaic. If it is determined from the second image data that there are no uncleaned areas on the photovoltaic surface, the drone is controlled to return to its home location.

[0074] This application embodiment acquires first image data of the photovoltaic panel periodically, determines the areas to be cleaned and the dirty areas on the photovoltaic surface based on the first image data, and determines the cleaning method and first cleaning route based on the areas to be cleaned and the type of dirt, enabling the drone to clean the photovoltaic panel more accurately and efficiently. After cleaning is completed, second image data of the photovoltaic panel is acquired. If there are unclean areas, the drone is controlled to perform a second cleaning of the unclean areas, which can improve the cleaning quality of the photovoltaic panel and ensure its cleanliness. At the same time, using drones for photovoltaic cleaning can reduce the dangers of manual cleaning and ensure the safety of personnel.

[0075] One possible implementation of this application embodiment involves determining the area to be cleaned and the type of dirt on the photovoltaic surface based on first image data, including:

[0076] The first image data is preprocessed and features are extracted. The extracted feature regions are compared with the preset standard image to determine the amount of difference corresponding to each feature region.

[0077] Determine whether the difference amount corresponding to the target feature region exceeds the preset difference amount threshold. If it does, determine the type of dirt corresponding to the target feature region and determine the target feature region as the target sub-region to be cleaned. The target feature can be any one of multiple features.

[0078] Several sub-regions to be cleaned, identified from multiple feature regions, are determined as the areas to be cleaned on the photovoltaic surface.

[0079] In this embodiment, preprocessing may include image denoising and adjusting brightness and contrast. Specifically, image denoising algorithms, such as Gaussian filtering or median filtering, can be used to reduce noise in the first image data. Histogram equalization or contrast enhancement algorithms can be used to adjust the brightness and contrast of the first image data. Furthermore, edge detection algorithms (such as Canny edge detection) can be used to extract edge information from the first image data, thereby obtaining multiple feature regions. Color features of different feature regions are extracted using color space conversion and color statistics methods. Texture features of feature regions are extracted using texture descriptors (such as local binary mode, gray-level co-occurrence matrix, etc.), thereby obtaining feature information for each feature region. The preset standard image is a photovoltaic image without dirt, which can be pre-stored in a database in the electronic device. The feature regions are compared with the preset standard image using difference measurement methods (such as mean square error, structural similarity index, etc.) to determine the difference amount corresponding to each feature region among the multiple feature regions. The preset difference threshold can be set according to actual needs; this embodiment does not impose specific limitations.

[0080] If the difference in the target feature area does not exceed the preset difference threshold, it indicates that the target feature area is not a dirty area; if the difference in the target feature area exceeds the preset difference threshold, it indicates that the target feature area is a dirty area. The database of the electronic device stores feature information corresponding to each type of dirt. By comparing the feature information of the target feature area with the feature information corresponding to each type of dirt stored in the database, the type of dirt in the target feature area can be determined.

[0081] This application embodiment compares multiple feature regions extracted from the first image data with a preset standard image to accurately determine the difference in each feature region. When the difference in the target feature region exceeds a preset difference threshold, the target feature region is identified as the target sub-region to be cleaned, thereby improving the efficiency and accuracy of determining the target sub-region to be cleaned and the type of dirt.

[0082] One possible implementation of this application embodiment determines the cleaning method based on the area to be cleaned and the type of dirt, including:

[0083] Based on the type of dirt corresponding to the target sub-area to be cleaned, determine the corresponding cleaning method and cleaning intensity for the target sub-area to be cleaned. The cleaning methods include airflow cleaning and water flow cleaning.

[0084] In this embodiment, the types of dirt include stains, leaves, snow, and dust. For dirt types such as stains, snow, and dust that adhere to the photovoltaic panel, the cleaning method can be set to water flow cleaning with a relatively high cleaning intensity. For dirt types such as leaves that do not adhere to the photovoltaic panel, the cleaning method can be set to first use airflow cleaning to wash the dirt off the photovoltaic panel, and then use water flow cleaning to remove any remaining dust on the surface with a relatively high cleaning intensity. The cleaning intensity corresponding to each type of dirt can be set according to actual needs and stored in a database. This embodiment does not impose specific limitations on this.

[0085] Specifically, in one possible scenario, the drone could carry a water tank (including a water pump) and an air compressor. The water tank stores water, and the water pump draws water from the tank through pipes, then sprays the water onto the photovoltaic panels through nozzles or spray guns to complete water flow cleaning. The air compressor draws in air, pressurizes it, and then sprays the airflow onto the photovoltaic panels through nozzles or spray guns to complete airflow cleaning. In another possible scenario, the drone could carry a water pipe and an air compressor, with the water tank positioned on the ground. The water pipe would then spray water from the tank onto the photovoltaic panels to complete water flow cleaning.

[0086] The embodiments of this application select different cleaning methods and cleaning intensities according to different types of dirt, which can remove dirt more effectively and improve the cleaning effect.

[0087] One possible implementation of this application embodiment involves determining a first cleaning route based on the area to be cleaned and the type of dirt, including:

[0088] Determine the drone's flight start point and the area points of the target sub-region to be cleaned;

[0089] Determine the flight speed of the drone corresponding to the target sub-area to be cleaned based on the type of dirt in the target sub-area to be cleaned;

[0090] The first cleaning route is generated based on the flight starting point, the corresponding area points of several sub-areas to be cleaned, and the flight speed of the UAV.

[0091] In this embodiment, the drone's flight starting point can be determined and input by the staff. The target area can have one or more area points, which are the points the first cleaning route needs to pass through. The database can pre-store the correspondence between dirt types and drone flight speeds. Specifically, for easier-to-clean dirt types, such as dust and leaves, a faster flight speed can be set; for harder-to-clean dirt types, such as snow and stains, a slower flight speed can be set to achieve better cleaning results. The correspondence between dirt types and drone flight speeds can be flexibly set based on practical experience, and this embodiment does not impose specific limitations. Using path planning algorithms such as Dijkstra's algorithm or A* algorithm, a first cleaning path can be generated based on the drone's flight starting point, the area points corresponding to several sub-areas to be cleaned, and the drone's flight speed.

[0092] This application embodiment determines the drone's flight speed based on the type of dirt in the target sub-area to be cleaned, which can improve the accuracy of determining the drone's first cleaning route. The first cleaning route is generated based on the flight starting point, the corresponding area points of several sub-areas to be cleaned, and the drone's flight speed. This can optimize the flight path, reduce unnecessary flights and waste, thereby reducing energy consumption, while ensuring cleaning effect and quality.

[0093] One possible implementation of this application embodiment involves determining the flight start point of the UAV and the area points of the target sub-region to be cleaned, including:

[0094] Determine the starting point of the drone's flight and the center point of the target sub-area to be cleaned;

[0095] An initial cleaning route is generated based on the flight start point and the center points of several sub-regions to be cleaned.

[0096] Determine the spray width of the drone;

[0097] Based on the initial cleaning route and spray width, determine the spray coverage area corresponding to the target sub-area to be cleaned;

[0098] Determine whether the spray coverage area corresponding to the target sub-area to be cleaned is larger than the area of ​​the target sub-area to be cleaned;

[0099] If it is not greater than, then the corresponding region points of the target sub-region to be cleaned are determined, and the number of region points is multiple.

[0100] In this embodiment, the center point of the target area to be cleaned can be determined during the preprocessing and feature extraction steps of the first image data. The center point can be determined by calculating the geometric center point of the target area to be cleaned. Using path planning algorithms such as Dijkstra's algorithm or A* algorithm, an initial cleaning route can be generated based on the UAV's flight starting point and the center points of several sub-areas to be cleaned. The flight altitude can be set according to the UAV's cleaning characteristics; the flight altitude represents the distance between the UAV and the photovoltaic system, and the spray width formed on the photovoltaic system when the UAV flies over it at this flight altitude is determined. Based on the spray width, the spray coverage area formed on the target area to be cleaned when the UAV flies over it can be determined. Figure 2 As shown, the arrows indicate the direction of the drone's flight path, the elliptical area represents the target sub-area to be cleaned, and the shaded area represents the spray coverage area formed on the target sub-area when the drone flies over it. If the width of the target sub-area to be cleaned in the drone's flight direction is greater than the drone's spray width, then the corresponding spray coverage area of ​​the target sub-area to be cleaned is less than the area of ​​the target sub-area to be cleaned. The drone cannot cover the entire area of ​​the target sub-area to be cleaned when it flies over the target sub-area according to the initial flight path.

[0101] If the spray coverage area corresponding to the target sub-area to be cleaned is larger than the area of ​​the target sub-area to be cleaned, then the center point of the target sub-area to be cleaned is taken as the region point of the target sub-area to be cleaned. If the spray coverage area corresponding to the target sub-area to be cleaned is not larger than the area of ​​the target sub-area to be cleaned, then the target sub-area to be cleaned can be divided into multiple sub-areas, and the farthest distance of the boundary of each sub-area is not greater than the spray width of the drone. Then, the center point of each sub-area is determined, and the center point of each sub-area corresponding to the target sub-area to be cleaned is taken as the region point of the target sub-area to be cleaned.

[0102] This application embodiment determines the flight start point of the drone and the center point of the target sub-area to be cleaned, and generates an initial cleaning route. This allows for more accurate and efficient formulation of cleaning routes and methods, thereby improving cleaning efficiency. Based on the initial cleaning route and spray width, it can be determined whether the spray coverage area corresponding to the target sub-area to be cleaned reaches the area of ​​the target sub-area to be cleaned. If not, the area point of the target sub-area to be cleaned is re-determined, which can optimize the drone's flight path and improve the cleaning uniformity and cleaning quality of the target sub-area to be cleaned.

[0103] One possible implementation of this application embodiment, after controlling the drone to clean the area to be cleaned according to a first cleaning route, the method further includes:

[0104] A second cleaning route is generated based on the first cleaning route, and the second cleaning route is in the opposite direction to the first cleaning route.

[0105] The drone is controlled to use airflow cleaning to clean the area to be cleaned according to the second cleaning route in order to remove water stains and residues on the photovoltaic panels.

[0106] In this embodiment, after cleaning is completed according to the first cleaning route, the drone can be controlled to perform airflow cleaning in the opposite direction of the first cleaning route, i.e., the second cleaning route, which can remove water stains and residues on the photovoltaic surface.

[0107] This application embodiment uses a drone to clean the area to be cleaned by airflow according to a second cleaning route, which can effectively remove water stains and residues on the photovoltaic surface and enhance the cleaning effect.

[0108] One possible implementation of this application embodiment includes:

[0109] Obtain historical weather data for the area where the photovoltaic power plant is located;

[0110] The cleaning cycle is determined based on historical weather data;

[0111] Acquire weather data within a preset time period, adjust the cleaning cycle based on the weather data, and control the drone to clean the photovoltaic system according to the adjusted cleaning cycle.

[0112] In this embodiment, historical weather data can be weather data from multiple years, including wind and rain conditions. For example, during the autumn and winter seasons when wind is more frequent, more dirt accumulates on the photovoltaic panels, so a shorter cleaning interval can be set. Optionally, it can be set to clean once a month. During the summer season when rain is more frequent, the dirt accumulated on the photovoltaic panels can be washed away by rainwater, so a longer cleaning interval can be set. Optionally, it can be set to clean once every two months. The specific cleaning cycle can be set manually according to the actual weather conditions.

[0113] For the nearest next cleaning time, weather data for a preset duration after the next cleaning time can be obtained. The preset duration can be set according to actual needs, and optionally, it can be 5 days. If rain is detected within the preset duration, the next cleaning time can be postponed until after the rain, so that the photovoltaic system can be pre-cleaned by rainwater, reducing the cleaning difficulty at the next cleaning time and improving cleaning efficiency.

[0114] The embodiments of this application adjust the cleaning cycle based on weather data within a preset time period, and control the drone to clean the photovoltaic according to the adjusted cleaning cycle, which can reduce the energy consumption of the drone cleaning process and improve the cleaning efficiency.

[0115] The above embodiments describe a photovoltaic cleaning method based on drones from the perspective of process flow. The following embodiments describe a photovoltaic cleaning device based on drones from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0116] This application provides a photovoltaic cleaning device based on a drone, such as... Figure 3 As shown, the device may include:

[0117] The acquisition module 301 is used to periodically acquire the first image data of the photovoltaic system.

[0118] The determination module 302 is used to determine the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data;

[0119] The control module 303 is used to determine the cleaning method and the first cleaning route according to the area to be cleaned and the type of dirt, and to control the drone to clean the area to be cleaned according to the cleaning method and the first cleaning route.

[0120] The cleaning module 304 is used to acquire the second image data of the photovoltaic after cleaning is completed, determine whether there are unclean areas based on the second image data, and if so, control the drone to perform secondary cleaning on the unclean areas.

[0121] In a preferred embodiment, this application can be further configured such that, when determining the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data, the determining module 302 is specifically used for:

[0122] The first image data is preprocessed and features are extracted. The multiple feature regions corresponding to the extracted features are compared with the preset standard image to determine the amount of difference corresponding to each feature region.

[0123] Determine whether the difference amount corresponding to the target feature area exceeds the preset difference amount threshold. If it does, determine the type of dirt corresponding to the target feature area and determine the target feature area as the target sub-area to be cleaned. The target feature area can be any one of multiple feature areas.

[0124] Several sub-regions to be cleaned, identified from multiple feature regions, are determined as the areas to be cleaned on the photovoltaic surface.

[0125] In a preferred embodiment, this application can be further configured such that, when the control module 303 determines the cleaning method based on the area to be cleaned and the type of dirt, it is specifically used for:

[0126] Based on the type of dirt corresponding to the target sub-area to be cleaned, determine the corresponding cleaning method and cleaning intensity for the target sub-area to be cleaned. The cleaning methods include airflow cleaning and water flow cleaning.

[0127] In a preferred embodiment, this application can be further configured such that, when the control module 303 determines the first cleaning route based on the area to be cleaned and the type of dirt, it is specifically used for:

[0128] Determine the drone's flight start point and the area points of the target sub-region to be cleaned;

[0129] Determine the flight speed of the drone corresponding to the target sub-area to be cleaned based on the type of dirt in the target sub-area to be cleaned;

[0130] The first cleaning route is generated based on the flight starting point, the corresponding area points of several sub-areas to be cleaned, and the flight speed of the UAV.

[0131] In a preferred embodiment, this application can be further configured such that, when the control module 303 determines the flight start point of the UAV and the area point of the target sub-area to be cleaned, it is specifically used for:

[0132] Determine the starting point of the drone's flight and the center point of the target sub-area to be cleaned;

[0133] An initial cleaning route is generated based on the flight start point and the center points of several sub-regions to be cleaned.

[0134] Determine the spray width of the drone;

[0135] Based on the initial cleaning route and spray width, determine the spray coverage area corresponding to the target sub-area to be cleaned;

[0136] Determine whether the spray coverage area corresponding to the target sub-area to be cleaned is larger than the area of ​​the target sub-area to be cleaned;

[0137] If it is not greater than, then the corresponding region points of the target sub-region to be cleaned are determined, and the number of region points is multiple.

[0138] In a preferred embodiment, this application can be further configured such that the device also includes a secondary cleaning module, specifically used for:

[0139] A second cleaning route is generated based on the first cleaning route, and the second cleaning route is in the opposite direction to the first cleaning route.

[0140] The drone is controlled to use airflow cleaning to clean the area to be cleaned according to the second cleaning route in order to remove water stains and residues on the photovoltaic panels.

[0141] In a preferred embodiment, this application can be further configured such that the device also includes an adjustment module, specifically used for:

[0142] Obtain historical weather data for the area where the photovoltaic power plant is located;

[0143] The cleaning cycle is determined based on historical weather data;

[0144] Acquire weather data within a preset time period, adjust the cleaning cycle based on the weather data, and control the drone to clean the photovoltaic system according to the adjusted cleaning cycle.

[0145] The photovoltaic cleaning device based on a drone provided in this application is applicable to the above method embodiments, and will not be described again here.

[0146] This application provides an electronic device, such as... Figure 4 As shown, Figure 4 The illustrated electronic device 400 includes a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, for example, via a bus 402. Optionally, the electronic device 400 may also include a transceiver 404. It should be noted that in practical applications, the transceiver 404 is not limited to one type, and the structure of this electronic device 400 does not constitute a limitation on the embodiments of this application.

[0147] Processor 401 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0148] Bus 402 may include a pathway for transmitting information between the aforementioned components. Bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 402 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0149] The memory 403 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0150] The memory 403 stores application code that executes the solution of this application, and its execution is controlled by the processor 401. The processor 401 executes the application code stored in the memory 403 to implement the content shown in the aforementioned embodiment of the photovoltaic cleaning method based on a drone.

[0151] Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0152] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0153] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0154] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A photovoltaic cleaning method based on unmanned aerial vehicles (UAVs), characterized in that, include: Regularly acquire initial image data of photovoltaic systems; The area to be cleaned and the type of dirt on the photovoltaic surface are determined based on the first image data; The cleaning method and the first cleaning route are determined according to the area to be cleaned and the type of dirt, and the drone is controlled to clean the area to be cleaned according to the cleaning method and the first cleaning route. After cleaning is completed, the second image data of the photovoltaic is acquired, and it is determined whether there are any uncleaned areas based on the second image data. If so, the drone is controlled to perform a second cleaning on the uncleaned areas. The cleaning method is determined based on the area to be cleaned and the type of dirt, including: Based on the type of dirt corresponding to the target sub-area to be cleaned, the cleaning method and cleaning intensity corresponding to the target sub-area to be cleaned are determined. The cleaning method includes airflow cleaning and water flow cleaning. The target sub-area to be cleaned can be any area to be cleaned. Determining the first cleaning route based on the area to be cleaned and the type of dirt includes: Determine the flight start point of the UAV and the area point of the target sub-region to be cleaned, wherein the target sub-region to be cleaned is any one of several sub-regions to be cleaned extracted from the first image data; Based on the type of dirt in the target sub-area to be cleaned, determine the flight speed of the drone corresponding to the target sub-area to be cleaned; A first cleaning route is generated based on the flight starting point, the area points corresponding to the several sub-areas to be cleaned, and the flight speed of the UAV. Determining the flight start point of the UAV and the area point of the target sub-region to be cleaned includes: Determine the flight start point of the UAV and the center point of the target sub-area to be cleaned; An initial cleaning route is generated based on the flight starting point and the center points of the various sub-regions to be cleaned. Determine the spray width of the drone; Based on the initial cleaning route and spray width, determine the spray coverage area corresponding to the target sub-area to be cleaned; Determine whether the spray coverage area corresponding to the target sub-area to be cleaned is larger than the area of ​​the target sub-area to be cleaned; If it is not greater than, then the region point corresponding to the target sub-region to be cleaned is determined, and the number of the region points is multiple; Determining the region points corresponding to the target sub-region to be cleaned includes: The target area to be cleaned is divided into multiple sub-regions, and the farthest distance of the boundary of each sub-region is no greater than the spraying width of the drone; the center point of each sub-region is determined, and the center point of each sub-region in the target area to be cleaned is taken as the region point of the target area to be cleaned.

2. The photovoltaic cleaning method based on unmanned aerial vehicles according to claim 1, characterized in that, Based on the first image data, the areas on the photovoltaic surface to be cleaned and the types of dirt are determined, including: The first image data is preprocessed and features are extracted. The multiple feature regions corresponding to the extracted features are compared with a preset standard image to determine the amount of difference corresponding to each feature region. Determine whether the difference amount corresponding to the target feature region exceeds a preset difference amount threshold. If it does, determine the type of dirt corresponding to the target feature region and determine the target feature region as the target sub-region to be cleaned. The target feature region is any one of the multiple feature regions. Several sub-regions to be cleaned, identified from the multiple feature regions, are determined as the areas to be cleaned on the photovoltaic surface.

3. The photovoltaic cleaning method based on unmanned aerial vehicles according to claim 1, characterized in that, After controlling the drone to clean the area to be cleaned according to the first cleaning route using the cleaning method, the method further includes: A second cleaning route is generated based on the first cleaning route, and the second cleaning route is in the opposite direction to the first cleaning route. The drone is controlled to use the airflow cleaning method to clean the area to be cleaned according to the second cleaning route, so as to remove water stains and residues on the photovoltaic panel.

4. The photovoltaic cleaning method based on unmanned aerial vehicles according to claim 1, characterized in that, The method further includes: Obtain historical weather data for the area where the photovoltaic power station is located; The cleaning cycle is determined based on the historical weather data. The system acquires weather data within a preset time period, adjusts the cleaning cycle based on the weather data, and controls the drone to clean the photovoltaic system according to the adjusted cleaning cycle.

5. A photovoltaic cleaning device based on unmanned aerial vehicles (UAVs), characterized in that, include: The acquisition module is used to periodically acquire the first image data of the photovoltaic system. The determination module is used to determine the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data; The control module is used to determine the cleaning method and the first cleaning route according to the area to be cleaned and the type of dirt, and to control the drone to clean the area to be cleaned according to the cleaning method and the first cleaning route. The cleaning module is used to acquire the second image data of the photovoltaic after cleaning is completed, determine whether there are unclean areas based on the second image data, and if so, control the drone to perform a second cleaning on the unclean areas. The determining module, when performing the task of determining the area to be cleaned and the type of dirt on the photovoltaic surface based on the first image data, is specifically used for: Based on the type of dirt corresponding to the target sub-area to be cleaned, the cleaning method and cleaning intensity corresponding to the target sub-area to be cleaned are determined. The cleaning method includes airflow cleaning and water flow cleaning. The target sub-area to be cleaned can be any area to be cleaned. When the control module performs the step of determining the first cleaning route based on the area to be cleaned and the type of dirt, it is specifically used for: Determine the flight start point of the UAV and the area point of the target sub-region to be cleaned, wherein the target sub-region to be cleaned is any one of several sub-regions to be cleaned extracted from the first image data; Based on the type of dirt in the target sub-area to be cleaned, determine the flight speed of the drone corresponding to the target sub-area to be cleaned; A first cleaning route is generated based on the flight starting point, the area points corresponding to the several sub-areas to be cleaned, and the flight speed of the UAV. The control module, when executing the process of determining the flight start point of the UAV and the area point of the target sub-area to be cleaned, is specifically used for: Determine the flight start point of the UAV and the center point of the target sub-area to be cleaned; An initial cleaning route is generated based on the flight starting point and the center points of the various sub-regions to be cleaned. Determine the spray width of the drone; Based on the initial cleaning route and spray width, determine the spray coverage area corresponding to the target sub-area to be cleaned; Determine whether the spray coverage area corresponding to the target sub-area to be cleaned is larger than the area of ​​the target sub-area to be cleaned; If it is not greater than, then the region point corresponding to the target sub-region to be cleaned is determined, and the number of the region points is multiple; When the control module executes the step of determining the region point corresponding to the target sub-region to be cleaned, it is specifically used for: The target area to be cleaned is divided into multiple sub-regions, and the farthest distance of the boundary of each sub-region is no greater than the spraying width of the drone; the center point of each sub-region is determined, and the center point of each sub-region in the target area to be cleaned is taken as the region point of the target area to be cleaned.

6. An electronic device, characterized in that, include: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the drone-based photovoltaic cleaning method according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, the computer is instructed to perform the photovoltaic cleaning method based on any one of claims 1-4.

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