Photovoltaic cleaning robot, adaptive control method thereof, and readable medium

Abstract

The present invention provides a photovoltaic cleaning robot, an adaptive control method thereof, and a readable medium. The adaptive control method includes: determining the photovoltaic module to be cleaned based on the power generation of the photovoltaic module; moving the photovoltaic cleaning robot to the coordinate position of the module to be cleaned and collecting a surface image of the photovoltaic module to be cleaned; extracting feature information from the surface image and determining the dust area based on the feature information; calculating dust spatial distribution data based on the dust area, and planning a cleaning path based on the dust spatial distribution data. The present invention can selectively clean certain photovoltaic modules and plan an optimal path based on the image information, which not only improves cleaning efficiency but also reduces resource waste.

Description

Technical Field

[0001] The present invention mainly relates to the field of new energy technology, and in particular to a photovoltaic cleaning robot and its adaptive control method and readable medium. Background Art

[0002] With the rapid development of renewable energy, photovoltaic power generation has gained widespread application as an important clean energy source. However, over the long term, photovoltaic modules are susceptible to environmental factors such as dust and bird droppings. These impurities accumulate on the module surface, reducing power generation efficiency. Therefore, regular cleaning and inspection of photovoltaic modules is essential to maintain efficient operation and stable performance.

[0003] Traditionally, cleaning photovoltaic panels is performed manually. However, for large-scale photovoltaic installations, manual cleaning is inefficient, costly, and poses safety risks. The advent of photovoltaic cleaning robots has replaced manual inspection and surface cleaning of photovoltaic panels to some extent. However, existing cleaning robots typically employ fixed, one-way or two-way cleaning paths, lacking proper monitoring of the cleaning environment and panel status. The degree of contamination on the surfaces of different panels may vary, and fixed cleaning schedules may result in untimely cleaning of some panels, reducing power generation efficiency. Furthermore, excessive cleaning can lead to wasted water resources. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a photovoltaic cleaning robot and its adaptive control method and readable medium, so as to solve the problems of unsatisfactory cleaning effect and waste of resources in existing cleaning methods.

[0005] In order to solve the above technical problems, the present invention provides an adaptive control method for a photovoltaic cleaning robot, comprising: determining the photovoltaic component to be cleaned according to the power generation of the photovoltaic component; the photovoltaic cleaning robot moves to the coordinate position of the component to be cleaned, and collects the surface image of the photovoltaic component to be cleaned; extracts feature information of the surface image, and determines the dust area based on the feature information; calculates the dust spatial distribution data according to the dust area, and plans the cleaning path according to the dust spatial distribution data.

[0006] Optionally, it also includes: calculating a cleaning satisfaction function based on the dust cleaning degree factor and power generation growth rate of the photovoltaic component surface; judging whether the cleaning satisfaction function is greater than or equal to a first preset threshold value, and if not, replanning the cleaning path or adding a cleaning mode.

[0007] Optionally, the cleaning satisfaction function is calculated using the following formula:

[0008]

[0009] in, is the cleaning satisfaction function, is the dust cleaning degree factor, is the power generation growth rate, and is the weight coefficient.

[0010] Optionally, the dust cleaning degree factor is calculated using the following formula:

[0011]

[0012] in, is the dust cleaning degree factor, Indicates the amount of dust before cleaning. Indicates the amount of dust after cleaning.

[0013] Optionally, planning a cleaning path according to the dust spatial distribution data includes:

[0014] Determining dust distribution on photovoltaic modules based on the dust spatial distribution data;

[0015] When the dust in the middle or edge of a PV panel is greater than that in other parts, the planned cleaning path is for the PV cleaning robot to move forward to clean the middle of the PV panel, move one robot body length, rotate 90° counterclockwise to continue cleaning, move one robot body length, and rotate 90° counterclockwise again, and so on, until it reaches the edge of the PV panel.

[0016] When the dust on the surface of the photovoltaic module is evenly distributed, the planned cleaning path is: when the photovoltaic cleaning robot moves to the position to be cleaned, it first rotates 90° clockwise and moves towards the edge of the photovoltaic module. After reaching the edge of the photovoltaic module, it rotates 90° counterclockwise, moves one body position, and then rotates 90° counterclockwise again. At this time, it continues to move towards the edge of the photovoltaic module, and so on. When it cleans the corner of the photovoltaic module, it returns to the initial cleaning position along the edge of the module.

[0017] Optionally, determining the photovoltaic component to be cleaned based on the power generation of the photovoltaic component includes: obtaining the power generation of the photovoltaic component per unit time; calculating the difference between the power generation of the photovoltaic component per unit time and the rated power generation of the photovoltaic component, and when the difference is greater than a second preset threshold, setting the photovoltaic component as the photovoltaic component to be cleaned.

[0018] Optionally, the rated power generation of the photovoltaic module is determined by the following formula:

[0019]

[0020] in, is the rated daily power generation of the photovoltaic module, is the equivalent solar radiation, A is the installation area of ​​the photovoltaic module, is the power generation efficiency of the photovoltaic module.

[0021] Optionally, extracting feature information of the surface image includes: extracting shape features of the surface image using morphological operations; extracting texture features of the surface image using Gabor filters; extracting color features of the surface image using HSV color space; and extracting local features of the surface image using feature descriptors.

[0022] Optionally, determining the dust area based on the feature information includes: inputting the shape feature, the texture feature, the color feature, and the local feature into a dust recognition model, and the dust recognition model outputting the dust area.

[0023] Optionally, the dust spatial distribution data includes dust distribution density, and calculating the dust distribution density includes: dividing the pixels of the dust area by the pixels of the photovoltaic module to obtain the dust distribution density.

[0024] Optionally, the method further includes: establishing a robot movement coordinate system according to the arrangement order of the photovoltaic components; and determining the coordinate position of the component to be cleaned based on the robot movement coordinate system.

[0025] In order to solve the above technical problems, the present invention provides a photovoltaic cleaning robot, which is controlled by the adaptive control method described above.

[0026] To solve the above technical problem, the present invention provides a computer-readable medium storing computer program code, which implements the above-mentioned adaptive control method when executed by a processor.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] The adaptive control method of the photovoltaic cleaning robot of the present invention can selectively clean part of the photovoltaic modules by combining the power generation of the photovoltaic modules and the image information of the photovoltaic modules, and can plan the optimal path according to the image information. This can not only improve the cleaning efficiency, but also reduce resource waste, extend the service life of the photovoltaic modules, and improve the power generation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are included to provide a further understanding of the present application. They are incorporated into and constitute a part of this application. The accompanying drawings illustrate embodiments of the present application and, together with this specification, serve to explain the principles of the present application. In the accompanying drawings:

[0030] Figure 1 4 is a flow chart of an adaptive control method for a photovoltaic cleaning robot according to an embodiment of the present invention.

[0031] Figure 2 FIG. 4 is a schematic diagram of a robot movement coordinate system according to an embodiment of the present invention.

[0032] Figure 3 FIG. 4 is a flowchart of extracting feature information of a surface image according to an embodiment of the present invention.

[0033] Figure 4 and Figure 5 Schematic diagram of two cleaning paths according to an embodiment of the present invention.

[0034] Figure 6 4 is a system block diagram of a photovoltaic cleaning robot according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] To more clearly illustrate the technical solutions of the embodiments of this application, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0036] Flowcharts are used in this application to illustrate the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0037] The main purpose of the present invention is to solve the problem that the single-mode cleaning method of the existing cleaning robot cannot combine photovoltaic components and cleaning environment information, thereby resulting in unsatisfactory cleaning effects and waste of resources.

[0038] By combining image processing, machine learning, path planning, and motion control technologies, this invention can effectively analyze the distribution of dust and dirt on the surface of photovoltaic panels and set the cleaning direction and motion trajectory for the cleaning robot. This not only improves cleaning efficiency, but also reduces energy waste, extends the service life of photovoltaic panels, and improves power generation efficiency.

[0039] Figure 1 FIG. 1 is a flow chart of an adaptive control method of a photovoltaic cleaning robot according to an embodiment of the present invention. Figure 1 As shown, the adaptive control method 100 of the photovoltaic cleaning robot includes the following steps:

[0040] Step S1: Determine the photovoltaic modules to be cleaned according to the power generation of the photovoltaic modules.

[0041] Optionally, determining the photovoltaic components to be cleaned according to the power generation of the photovoltaic components includes:

[0042] Step S11: Obtaining the power generation per unit time of the photovoltaic module.

[0043] Alternatively, the current power generation of the photovoltaic module can be calculated by using an electric power formula in combination with measuring the current and voltage of the photovoltaic module.

[0044] P=I×V×coefficient

[0045] Where P is power (watts, W); I is current (amperes, A); and V is current (volts, V).

[0046] Calculating power generation per unit time: Power generation is typically the integral (or accumulation) of power per unit time. If you have average power or instantaneous power data for a certain period of time, you can calculate total power generation by multiplying power by time. Assuming the time period is Δt (in hours or seconds), the power generation per unit time, E (in watt-hours, Wh), can be calculated using the following formula:

[0047]

[0048] is the instantaneous power at the i-th time point, Δt is the time interval, and n is the number of time points.

[0049] Step S12: Calculate the difference between the power generation per unit time of the photovoltaic module and the rated power generation of the photovoltaic module. When the difference is greater than a second preset threshold, set the photovoltaic module as a photovoltaic module to be cleaned.

[0050] Optionally, the unit time refers to one day. The rated power generation of a photovoltaic module refers to the rated daily power generation of the photovoltaic module. The rated power generation of a photovoltaic module is determined by the following formula:

[0051]

[0052] in, is the rated daily power generation of the photovoltaic module, is the equivalent solar radiation, A is the installation area of ​​the photovoltaic module, The power generation efficiency of photovoltaic modules is usually between 0.15 and 0.22, depending on the technology of the photovoltaic modules.

[0053] Equivalent solar radiation refers to the "converted value" of the total solar radiation received by photovoltaic panels in a day, usually expressed as "equivalent solar hours", that is, the total energy of solar radiation received by photovoltaic panels in a day, assuming it is calculated under standard conditions (usually 1000 W / m²). Equivalent solar radiation is usually expressed in kWh / m²·day (kilowatt-hours per square meter per day). Equivalent solar radiation value Typically provided by meteorological data, this value can be estimated using local irradiance data and historical records. Equivalent solar radiation varies significantly across regions, with higher values ​​typically found in tropical and subtropical regions and lower values ​​in high latitudes. You can obtain the equivalent solar radiation for your area by consulting relevant meteorological data or using specialized solar radiation databases (such as PVGIS, NASA, or meteorological websites).

[0054] Optionally, system losses are also considered when calculating the rated daily power generation of the PV modules. The rated daily power generation of the PV modules is calculated using the following formula:

[0055]

[0056] If there are certain system losses in the photovoltaic system (such as inverter efficiency, connection loss, shading, etc.), the loss coefficient needs to be Multiply by, usually the loss factor Between 0.75 and 0.85.

[0057] Dust and dirt on the surface of photovoltaic modules can reduce power generation. This invention uses the difference between actual power generation and rated daily power generation to determine which modules are most dusty and dirty, and then selects them as the modules to be cleaned. In other words, this invention does not clean all photovoltaic modules, thus avoiding waste of resources.

[0058] Step S2: The photovoltaic cleaning robot moves to the coordinate position of the component to be cleaned and collects the surface image of the photovoltaic component to be cleaned.

[0059] Optionally, before step S2, the method further includes: establishing a robot movement coordinate system according to the arrangement order of the photovoltaic components, and determining the coordinate position of the component to be cleaned based on the robot movement coordinate system.

[0060] Figure 2 FIG is a schematic diagram of a robot moving coordinate system according to an embodiment of the present invention. Figure 2As shown, the PV modules are arranged in sequence and fixed to the PV mount, with the same gap between adjacent modules. Based on the module arrangement sequence, a robot coordinate system is established, with the center of the side of the first PV module as the origin, the PV module arrangement sequence as the x-axis, and the PV module side line as the y-axis. The PV modules are sorted using the positive x-axis as the initial direction. For example, the first PV module is designated X1, the second X2, and so on, until the last module is designated Xn. PV module dimensions may vary between manufacturers and models. Here, L is used to represent the short side dimension of the PV module, and a is the gap between adjacent PV modules.

[0061] The coordinate position of the component to be cleaned can be calculated using the following formula:

[0062]

[0063] S is the coordinate position of the component to be cleaned (unit: cm);

[0064] L is the side dimension of the photovoltaic module (unit: cm);

[0065] a is the distance between two adjacent PV panels (unit: cm);

[0066] n is the serial number of the photovoltaic module to be cleaned.

[0067] Start and release the photovoltaic cleaning robot at the starting position, control the photovoltaic cleaning robot to move along the central axis of the photovoltaic module (the middle line of the photovoltaic module), and stop when it moves to the coordinate position of the module to be cleaned.

[0068] Optionally, the surface image data of the photovoltaic components is collected by an image acquisition module of the photovoltaic cleaning robot.

[0069] Optionally, before dust recognition, some preprocessing of the image is required to enhance the characteristics of dust and reduce the impact of noise on recognition. Preprocessing includes but is not limited to:

[0070] Denoising: Use filters (such as Gaussian filtering and median filtering) to reduce noise in the image, especially small spots similar to dust and messy background noise.

[0071] Contrast enhancement: Through methods such as histogram equalization and local contrast enhancement, the contrast between the dust area and the background in the image is improved, making the dust more obvious.

[0072] Edge detection: Use algorithms such as Canny edge detection to extract edge information from the image, which helps to identify the outline of dust areas.

[0073] Image Normalization: Adjusts the brightness and color balance of the image to make dust areas easier to distinguish.

[0074] Step S3: extracting feature information of the surface image and determining the dust area based on the feature information.

[0075] Figure 3 FIG1 is a flow chart of extracting feature information of a surface image according to an embodiment of the present invention. Extracting feature information from an image is the core step of dust recognition. Because dust usually has some specific visual features, such as small and irregular shapes, low texture complexity, etc. Figure 3 As shown, features can be extracted in the following ways:

[0076] Step S31: Morphological operations. Dust often appears as small spots or tiny objects. Morphological operations (such as dilation, erosion, opening, and closing) can help highlight these small areas and extract shape features. Erosion helps remove noise, while dilation helps highlight the presence of dust.

[0077] Step S32: Extracting texture features. Dust often has texture features different from the background. The texture features of dust can be extracted using methods such as Gabor filter and LBP (local binary pattern).

[0078] Step S33: Extract color features. The color of dust is usually slightly different from the background surface. Especially on certain backgrounds, dust can be identified by color distribution. The HSV color space can be used for color feature extraction to help separate the dust area from the background.

[0079] Step S34: Extract local features, such as feature descriptors such as SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), which can help identify small objects in the image.

[0080] After feature extraction, the shape features, texture features, color features, and local features are input into a dust recognition model, and the dust recognition model outputs a dust region. Optionally, the dust recognition model is obtained based on machine learning or deep learning training.

[0081] Optionally, after identifying the dust area, some post-processing operations are also included to improve the accuracy and robustness of the recognition. Post-processing operations include but are not limited to:

[0082] Region merging and filtering: Merge small detected regions into larger regions to avoid detecting multiple tiny noise points or misidentified regions.

[0083] Dust shape and size judgment: Based on the shape, size, and distribution of dust, areas that do not meet the dust characteristics are further filtered out.

[0084] Connected region analysis: By analyzing the connectivity of dust regions, isolated small regions are removed and the actual dust regions are retained.

[0085] Step S4: Calculate the dust spatial distribution data according to the dust area, and plan the cleaning path according to the dust spatial distribution data.

[0086] Dust spatial distribution data includes, but is not limited to, dust density and dust intensity mapping. Dust density refers to the ratio of pixels in each dust region to the total number of pixels in the PV module. Dust intensity mapping involves generating a heat map of dust intensity across the entire surface.

[0087] After understanding the dust distribution, a path planning algorithm can be used to generate an optimal cleaning route for the cleaning robot. Using a greedy algorithm and dynamically adjusted path planning, the robot starts cleaning dusty areas based on dust density and gradually moves to less dusty areas. Furthermore, if certain areas have already been cleaned during the cleaning process, the robot's direction and path can be dynamically adjusted based on real-time feedback to ensure that no areas are left untouched.

[0088] When the photovoltaic cleaning robot arrives at the designated location, it generates two optional cleaning path plans based on the distribution of dust and dirt on the surface of the photovoltaic modules. Figure 4 and Figure 5 Schematic diagram of two cleaning paths according to an embodiment of the present invention.

[0089] The first cleaning path, such as Figure 4 As shown in the figure, when dust is concentrated in the center or edges of a PV panel, the PV cleaning robot first cleans that area. The robot then moves forward to clean the center of the PV panel, moves one robot unit length, and then rotates 90° counterclockwise to continue cleaning. After moving one unit length, it rotates 90° counterclockwise again, and so on. In this cleaning scheme, the PV cleaning robot moves in a U-shaped trajectory until it reaches the edge of the PV panel.

[0090] The second cleaning path, such as Figure 5 As shown in the figure, when dust is evenly distributed on the surface of a PV panel, the PV cleaning robot moves to the cleaning position and first rotates 90° clockwise toward the edge of the PV panel. After reaching the edge, it rotates 90° counterclockwise, moves one body length, and then rotates 90° counterclockwise again, continuing to move toward the edge of the PV panel. This cleaning scheme uses a "J"-shaped trajectory. When it reaches the corner of a PV panel, it returns to its initial cleaning position along the edge of the panel.

[0091] Optionally, the adaptive control method of the photovoltaic cleaning robot further includes:

[0092] Step S5: Evaluate the cleaning results, calculate the cleaning satisfaction function based on the dust cleaning degree factor on the surface of the photovoltaic module and the power generation growth rate of the photovoltaic module after cleaning, and determine whether the cleaning satisfaction function is greater than or equal to the first preset threshold. If not, re-plan the cleaning path or add a cleaning mode.

[0093] Satisfaction is defined as a comprehensive factor that includes both the degree of dust cleaning and the increase in power generation. Assume that the cleaning satisfaction function S can be expressed as:

[0094]

[0095] in, and are weight coefficients, representing the influence of dust cleaning degree and power generation increment on the final satisfaction, and , which can be adjusted according to specific needs.

[0096] The dust cleaning degree factor can be expressed as follows:

[0097]

[0098] in, is the dust cleaning degree factor, Indicates the amount of dust before cleaning. Indicates the amount of dust after cleaning. The amount of dust can be expressed by measuring the dust coverage or pollution index on the surface of the photovoltaic module. The larger the value, the more dust there is.

[0099] It indicates the growth rate of power generation. The higher it is, the more obvious the improvement of power generation will be after cleaning. is the power generation before cleaning, is the increase in power generation after cleaning.

[0100] If the S value is high, it means that the cleaning effect is good, the dust is fully removed, and the power generation is significantly improved. If the S value is low, it may mean that the dust cleaning effect is poor, or the power generation is not significantly improved after cleaning.

[0101] This invention monitors cleaning progress through image processing and sensor feedback. Based on the cleaning results, the system can assess which areas have been cleaned and which areas need to be cleaned again. This allows the robot's motion trajectory to be dynamically adjusted, or additional cleaning modes to further improve cleaning efficiency.

[0102] In an embodiment of the present invention, robot motion control and execution control the robot's movement across the surface of the photovoltaic panel according to a planned cleaning route. Positioning is performed using technologies such as LiDAR and visual SLAM, ensuring the robot accurately tracks the path and performs the cleaning task. Appropriate cleaning motions, such as longitudinal, transverse, or spiral sweeps, are set to maximize cleaning effectiveness. The robot should have real-time detection capabilities during the cleaning process, able to sense the current dirt removal effect. If dirt in certain areas is not completely cleaned, the robot can automatically adjust its motion trajectory and cleaning force.

[0103] The adaptive control method of the photovoltaic cleaning robot of the present invention can selectively clean part of the photovoltaic modules by combining the power generation of the photovoltaic modules and the image information of the photovoltaic modules, and can plan the optimal path according to the image information. This can not only improve the cleaning efficiency, but also reduce resource waste, extend the service life of the photovoltaic modules, and improve the power generation efficiency.

[0104] The present disclosure also provides a distributed photovoltaic cleaning robot. Figure 6 1 is a system block diagram of a photovoltaic cleaning robot according to an embodiment of the present invention. Figure 6 As shown, the photovoltaic cleaning robot 600 includes:

[0105] The image acquisition module 61 is used to obtain surface image data of the photovoltaic modules in the direction of the robot's advance.

[0106] Image preprocessing module 62 is used to preprocess PV panel surface image data to improve image quality, extract useful information, and prepare for subsequent analysis and model input. This module primarily improves image quality through a series of operations, providing better input for subsequent feature extraction, classification, or other image processing tasks.

[0107] The dust detection and classification module 63 performs dust detection and classification using machine learning or deep learning methods after feature extraction;

[0108] The dust region post-processing module 64 performs some post-processing operations after identifying the dust region to improve the accuracy and robustness of the identification.

[0109] The path planning module 65 is used to calculate the dust spatial distribution data according to the dust area and plan the cleaning path according to the dust spatial distribution data.

[0110] The present application also includes a computer-readable medium storing a computer program code, which, when executed by a processor, implements the aforementioned adaptive control method for the photovoltaic cleaning robot.

[0111] When the adaptive control method of the photovoltaic cleaning robot is implemented as a computer program, it can also be stored in a computer-readable storage medium as a product. For example, a computer-readable storage medium may include, but is not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memories (EPROMs), cards, sticks, key drives). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0112] The basic concepts have been described above. It will be apparent to those skilled in the art that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0113] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0114] Some aspects of this application may be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. These hardware and software components may be referred to as "data blocks," "modules," "engines," "units," "components," or "systems." A processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, various aspects of this application may be embodied as computer products embodied in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes), optical disks (e.g., compact disks, digital versatile disks, DVDs), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0115] A computer-readable medium may include a propagated data signal embodying computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination thereof. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transmit the program for use. The program code on the computer-readable medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above.

[0116] Similarly, it should be noted that, in order to simplify the presentation of this disclosure and thereby facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this application requires more features than those mentioned. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0117] As used herein, unless the context clearly indicates otherwise, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0118] Unless otherwise specified, the relative arrangement of the parts and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. Technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0119] In the description of this application, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "horizontal, vertical, vertical, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of this application; the directional words "inside and outside" refer to the inside and outside relative to the outline of each component itself.

[0120] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used herein to describe the spatial positional relationship of a device or feature to other devices or features as shown in the figures. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figures. For example, if the device in the drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0121] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is solely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. Furthermore, while the terms used in this application are selected from commonly known and commonly used terms, some terms mentioned in this specification may have been selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant sections of this description. Furthermore, this application should be understood not only by the actual terms used, but also by the meaning implied by each term.

[0122] It should be understood that when a component is referred to as being “on another component,” “connected to another component,” “coupled to another component,” or “contacting another component,” it can be directly on, connected to, coupled to, or contacting the other component, or intervening components may be present. In contrast, when a component is referred to as being “directly on another component,” “directly connected to,” “directly coupled to,” or “directly contacting” another component, there are no intervening components. Similarly, when a first component is referred to as being “electrically in contact with” or “electrically coupled to” a second component, an electrical path exists between the first and second components that allows current to flow. This electrical path may include capacitors, coupled inductors, and / or other components that allow current to flow, even without direct contact between the conductive components.

[0123] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are approximate values, which may vary according to the characteristics required by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0124] Although the present application has been described with reference to the current specific embodiments, ordinary technicians in this technical field should recognize that the above embodiments are only used to illustrate the present application, and various equivalent changes or substitutions can be made without departing from the spirit of the present application. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the essential spirit of the present application, they will fall within the scope of the present application.

Claims

1. An adaptive control method for a photovoltaic cleaning robot, characterized in that: include: Determine the photovoltaic modules to be cleaned based on the power generation of the photovoltaic modules; The photovoltaic cleaning robot moves to the coordinate position of the component to be cleaned and collects a surface image of the photovoltaic component to be cleaned; extracting feature information of the surface image, and determining a dust area based on the feature information; Calculating dust spatial distribution data according to the dust area, and planning a cleaning path according to the dust spatial distribution data; The cleaning satisfaction function is calculated based on the dust cleaning degree factor of the photovoltaic module surface and the power generation growth rate after cleaning; Determining whether the cleaning satisfaction function is greater than or equal to a first preset threshold; if not, replanning the cleaning path or adding a cleaning mode; The dust cleaning degree factor is calculated by the following formula: in, is the dust cleaning degree factor, Indicates the amount of dust before cleaning. Indicates the amount of dust after cleaning.

2. The adaptive control method for a photovoltaic cleaning robot according to claim 1, wherein: The cleaning satisfaction function is calculated by the following formula: in, is the cleaning satisfaction function, is the dust cleaning degree factor, is the power generation growth rate, and is the weight coefficient.

3. The adaptive control method for a photovoltaic cleaning robot according to claim 1, wherein: Planning a cleaning path according to the dust spatial distribution data includes: Determining dust distribution on photovoltaic modules based on the dust spatial distribution data; When the dust in the middle or edge of a PV panel is greater than that in other parts, the planned cleaning path is for the PV cleaning robot to move forward to clean the middle of the PV panel, move one robot body length, rotate 90° counterclockwise to continue cleaning, move one robot body length, and rotate 90° counterclockwise again, and so on, until it reaches the edge of the PV panel. When the dust on the surface of the photovoltaic module is evenly distributed, the planned cleaning path is: when the photovoltaic cleaning robot moves to the position to be cleaned, it first rotates 90° clockwise and moves towards the edge of the photovoltaic module. After reaching the edge of the photovoltaic module, it rotates 90° counterclockwise, moves one body position, and then rotates 90° counterclockwise again. At this time, it continues to move towards the edge of the photovoltaic module, and so on. When it cleans the corner of the photovoltaic module, it returns to the initial cleaning position along the edge of the module.

4. The adaptive control method for a photovoltaic cleaning robot according to claim 1, wherein: The PV panels to be cleaned are determined based on their power generation, including: Get the power generation per unit time of photovoltaic modules; The difference between the power generation per unit time of the photovoltaic assembly and the rated power generation of the photovoltaic assembly is calculated, and when the difference is greater than a second preset threshold, the photovoltaic assembly is set as the photovoltaic assembly to be cleaned.

5. The adaptive control method of the cleaning robot according to claim 4, characterized in that: The rated power generation of the photovoltaic module is determined by the following formula: in, is the rated daily power generation of the photovoltaic module, is the equivalent solar radiation, A is the installation area of ​​the photovoltaic module, is the power generation efficiency of the photovoltaic module.

6. The adaptive control method for a photovoltaic cleaning robot according to claim 1, wherein: Extracting feature information of the surface image includes: extracting shape features of the surface image using morphological operations; Extracting texture features of the surface image using a Gabor filter; Extracting color features of the surface image using the HSV color space; Local features of the surface image are extracted using feature descriptors.

7. The adaptive control method for a photovoltaic cleaning robot according to claim 6, wherein: Determining the dust area based on the feature information includes: The shape feature, the texture feature, the color feature, and the local feature are input into a dust recognition model, and the dust recognition model outputs the dust area.

8. The adaptive control method for a photovoltaic cleaning robot according to claim 1, wherein: The dust spatial distribution data includes dust distribution density, and calculating the dust distribution density includes: dividing the pixels of the dust area by the pixels of the photovoltaic module to obtain the dust distribution density.

9. The adaptive control method for a photovoltaic cleaning robot according to claim 1, wherein: Also includes: Establish the robot movement coordinate system according to the arrangement order of photovoltaic modules; The coordinate position of the component to be cleaned is determined based on the robot movement coordinate system.

10. A photovoltaic cleaning robot, characterized in that: It is controlled by the adaptive control method according to any one of claims 1 to 9.

11. A computer-readable medium storing computer program code, characterized in that: When the computer program code is executed by a processor, the computer program code implements the adaptive control method according to claims 1 to 9.

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