A cleaning control method and system of a photovoltaic cleaning robot

Abstract

A cleaning control method and system of a photovoltaic cleaning robot, relating to the field of photovoltaic device maintenance. The method is applied to a photovoltaic cleaning robot control system, and the method comprises: performing optical testing on a target photovoltaic surface and extracting an optical distribution map by shooting, the optical distribution map containing the transmittance and refractive index of the target photovoltaic surface; determining the ice layer thickness distribution of the surface of the target photovoltaic surface according to the optical distribution map; determining the ice melting strategy of the photovoltaic cleaning robot based on the ice layer thickness distribution, the ice melting strategy including the ice melting temperature and the ice melting path; and controlling the photovoltaic cleaning robot to clean the target photovoltaic surface according to the ice melting strategy. The technical solution provided in the application solves the problem that the fragile material of the photovoltaic module may be damaged during the cleaning process.

Description

Technical Field

[0001] This application relates to the technical field of photovoltaic equipment maintenance, specifically to a cleaning control method and system for a photovoltaic cleaning robot. Background Technology

[0002] In the current operation and maintenance of photovoltaic power plants, photovoltaic cleaning robots play a vital role. They can efficiently and accurately clean photovoltaic modules on a daily basis, which not only ensures that the photovoltaic modules maintain good power generation performance, but also greatly reduces labor costs.

[0003] Currently, in extremely cold weather conditions, ice easily forms on the surface of photovoltaic modules. If stains, dust, bird droppings, or other contaminants already exist on the module surface before the ice forms, these contaminants will solidify under the ice. Typically, hot water is used to remove this ice layer containing contaminants. However, photovoltaic modules are relatively fragile, and the hot water will cause a thermal shock effect on the photovoltaic surface during cleaning. This stimulation could potentially cause the photovoltaic surface to crack, resulting in serious damage to the photovoltaic module. Summary of the Invention

[0004] To address the issue that photovoltaic modules may be damaged during cleaning due to their fragile materials, this application provides a cleaning control method and system for a photovoltaic cleaning robot.

[0005] In a first aspect, this application provides a cleaning control method for a photovoltaic cleaning robot, applied to a photovoltaic cleaning robot control system, the method comprising:

[0006] Optical tests are performed on the target photovoltaic surface, and an optical distribution map is captured and extracted. The optical distribution map includes the transmittance and refractive index of the target photovoltaic surface.

[0007] Based on the optical distribution map, determine the ice layer thickness distribution on the target photovoltaic surface;

[0008] Based on the ice thickness distribution, the ice melting strategy of the photovoltaic cleaning robot is determined, and the ice melting strategy includes ice melting temperature and ice melting path.

[0009] According to the ice-melting strategy, the photovoltaic cleaning machine is controlled to clean the target photovoltaic surface.

[0010] Optionally, determining the ice thickness distribution on the target photovoltaic surface based on the optical distribution map specifically includes:

[0011] The optical distribution map is converted into an optical image matrix, which is composed of multiple pixels, and each pixel stores its corresponding refractive index and transmittance.

[0012] Calculate the refractive index ice layer thickness and the transmittance ice layer thickness of the optical image matrix;

[0013] Calculate the difference in ice thickness between the refractive index ice layer thickness and the light transmittance ice layer thickness;

[0014] Determine whether the ice thickness difference is less than or equal to the ice thickness difference threshold;

[0015] If the ice thickness difference is less than or equal to the ice thickness difference threshold, then either the refractive index ice thickness or the transmittance ice thickness is selected as the ice thickness distribution of the target photovoltaic surface.

[0016] Optionally, determining whether the ice thickness difference is less than or equal to the ice thickness difference threshold further includes:

[0017] If the ice layer thickness difference is greater than the ice layer thickness difference threshold, the optical image matrix is ​​converted into a gradient matrix, which consists of multiple gradient points, wherein one gradient point corresponds to one pixel.

[0018] Calculate the transmittance gradient vector and refractive index gradient vector at multiple gradient points;

[0019] Based on the transmittance gradient vector and refractive index gradient vector of the multiple gradient points, calculate the transmittance-refractive index correlation coefficient corresponding to each of the multiple gradient points;

[0020] A weighting function is used to calculate the ice layer thickness weighting coefficients for multiple gradient points;

[0021] Based on the transmittance-refractive index correlation coefficients and ice thickness weighting coefficients corresponding to multiple gradient points, the ice thickness distribution on the target photovoltaic surface is calculated, and the ice thickness distribution includes the ice thickness corresponding to each of the multiple gradient points.

[0022] Optionally, the weighting function is specifically:

[0023]

[0024] in, Let be the weighting coefficient of the gradient point in the x-th row and y-th column of the gradient matrix, and r(x, y) be the correlation coefficient between the refractive index gradient vector and the transmittance gradient vector within the gradient point in the x-th row and y-th column of the gradient matrix. , , and These represent the lateral gradient value of refractive index, the longitudinal gradient value of refractive index, the lateral gradient value of transmittance, and the longitudinal gradient value of transmittance at the gradient point in the x-th row and y-th column of the gradient matrix, respectively.

[0025] Optionally, determining the ice-melting strategy for the photovoltaic cleaning robot based on the ice layer thickness distribution specifically includes:

[0026] The path weights of the gradient points are determined based on their physical height relative to the ground.

[0027] Multiply the path weights of multiple gradient points by their respective ice thicknesses to determine the melting priority of the multiple gradient points.

[0028] The ice-melting path is determined based on the melting priority of multiple gradient points.

[0029] Optionally, determining the ice-melting strategy for the photovoltaic cleaning robot based on the ice thickness distribution further includes:

[0030] Calculate the required melting heat at the multiple gradient points based on the ice thickness at the multiple gradient points;

[0031] Based on the ambient temperature and the required melting heat at multiple gradient points, the melting temperature at multiple gradient points is calculated using the heat balance conduction formula.

[0032] Optionally, after controlling the photovoltaic cleaning machine to clean the target photovoltaic surface, the method further includes:

[0033] Once the ice layer on the target photovoltaic surface melts, the composition table of the atomized water vapor is updated, and the composition table contains cleaning ingredients for stubborn stains.

[0034] Secondly, this application provides a cleaning control system for a photovoltaic cleaning robot. The system is a photovoltaic cleaning robot control system, comprising a testing module, a processing module, and a control module, wherein:

[0035] The testing module is used to perform optical tests on the target photovoltaic surface and capture and extract an optical distribution map, which includes the transmittance and refractive index of the target photovoltaic surface.

[0036] The processing module is used to determine the ice thickness distribution on the target photovoltaic surface according to the optical distribution map; and to determine the ice melting strategy of the photovoltaic cleaning robot based on the ice thickness distribution, wherein the ice melting strategy includes ice melting temperature and ice melting path.

[0037] The control module is used to control the photovoltaic cleaning machine to clean the target photovoltaic surface according to the ice melting strategy.

[0038] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0039] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0040] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0041] 1. This application obtains an optical distribution map including transmittance and refractive index by conducting comprehensive optical testing on the target photovoltaic surface, thereby accurately determining the ice layer thickness. Then, based on the ice layer thickness, the appropriate atomized water vapor melting temperature and melting path are calculated. At this point, the photovoltaic cleaning robot is controlled to adjust the atomized water vapor temperature and spray it onto the target photovoltaic surface according to the melting path. This ensures efficient ice melting while avoiding the risk of thermal shock, effectively preventing damage to the photovoltaic modules. Finally, combined with subsequent rinsing and cleaning, the ice layer and the dirt beneath it are thoroughly removed, restoring the optimal light transmittance of the photovoltaic modules, maintaining good power generation efficiency, reducing corrosion and damage caused by dirt accumulation, and extending their service life. In addition, the entire process is highly automated, greatly reducing manual operation, lowering labor costs and safety risks, while avoiding module damage caused by improper cleaning, reducing maintenance and replacement costs, and comprehensively improving the economic benefits and operation and maintenance efficiency of photovoltaic power plants.

[0042] 2. When calculating the ice layer thickness on the target photovoltaic surface, the ice layer is not pure and often contains impurities from the environment (salt, dust). When the impurity content is high, the ice layer thickness calculated by the Beer-Lambert law will be inaccurate. Therefore, this application calculates the refractive index ice layer thickness and the transmittance ice layer thickness separately, and then compares the difference between the two. If the difference is small, it indicates that the ice layer purity is high, and the calculated ice layer thickness is more accurate. If the difference is large, gradient analysis is performed on the optical distribution map of the ice layer. By calculating the correlation between the refractive index and transmittance at the gradient point, the trend of the change of the two at that gradient point is determined. If the change areas of the two are highly consistent, it indicates that the ice layer thickness calculated at that gradient point has high reliability, and therefore a high weighting coefficient is assigned. Conversely, a low weighting coefficient is assigned. Finally, based on the weighting coefficients of each gradient point, the ice layer thickness distribution of the entire ice layer is calculated, thereby simulating the effect of filtering out impurities in the ice layer, making the final calculated ice layer thickness at each gradient point more accurate. Attached Figure Description

[0043] Figure 1 This is a schematic flowchart of a cleaning control method for a photovoltaic cleaning robot provided in an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of the cleaning control system of a photovoltaic cleaning robot provided in an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0046] Explanation of reference numerals in the attached diagram: 1. Test module; 2. Processing module; 3. Control module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0048] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0049] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0050] In today's large-scale photovoltaic power plant operation and maintenance system, photovoltaic cleaning robots have become a crucial link in ensuring the efficient and stable operation of the power plant. Leveraging advanced sensing technology and precise mechanical construction, they can efficiently and accurately perform daily cleaning of photovoltaic modules. This automated cleaning process not only effectively ensures that photovoltaic modules maintain good power generation performance, significantly improving power generation efficiency and creating a stable power output for the power plant, but also greatly reduces labor costs. This frees up manpower from the heavy and high-risk work of cleaning photovoltaic modules, allowing them to focus on more technically demanding and innovative operation and maintenance tasks.

[0051] However, in extremely cold weather conditions, ice can easily form on the surface of photovoltaic modules. If there are already stains, dust, bird droppings, or other dirt on the module surface before the ice forms, these dirt will be frozen under the ice, making cleaning more difficult. To deal with this situation, hot water is used for cleaning. Although this cleaning method is relatively simple and effective, the photovoltaic module material is relatively fragile. The hot water will generate a thermal shock effect on the cleaning surface during the cleaning process. This stimulation may cause the photovoltaic surface to break, which will cause serious damage to the photovoltaic module.

[0052] To address the aforementioned problems, this application provides a cleaning control method for a photovoltaic cleaning robot. This method is applied to the photovoltaic robot control system, such as... Figure 1 As shown, the method includes steps S101 to S104, which are as follows:

[0053] S101. Perform optical testing on the target photovoltaic surface and capture and extract the optical distribution map, which includes the transmittance and refractive index of the target photovoltaic surface.

[0054] In the above steps, professional optical testing equipment (spectrometer and refractive index meter) mounted on the photovoltaic cleaning robot is used to measure the pre-positioned target photovoltaic surface. Specifically, the spectrophotometer is adjusted to a suitable wavelength range so that the emitted light is uniformly projected onto the target photovoltaic surface. By measuring the intensity of light transmitted through the photovoltaic surface, the transmittance data of each area is calculated. Simultaneously, the refractive index meter is used to measure the light refraction at different positions on the target photovoltaic surface point by point, thereby obtaining the corresponding refractive index data. During the measurement process, the photovoltaic surface is scanned at uniform and sufficiently fine intervals to ensure coverage of the entire target photovoltaic area. After the measurement is completed, the collected discrete data of transmittance and refractive index are processed. An interpolation algorithm is used to transform the discrete data into continuous distribution information. Image processing technology is then used to visually present the distribution of transmittance and refractive index in different colors or grayscale values, ultimately generating an optical distribution map containing the transmittance and refractive index information of the target photovoltaic surface.

[0055] S102. Determine the ice layer thickness distribution on the target photovoltaic surface based on the optical distribution map.

[0056] In the above steps, the optical distribution map is first denoised to remove obvious outliers. Then, the average transmittance of the optical distribution map is calculated. Finally, the Beer-Lambert law is used to calculate the ice layer thickness on the target photovoltaic surface. The Beer-Lambert law formula is as follows:

[0057]

[0058] Where T is the transmittance. d is the absorption coefficient of the ice layer to light, and d is the thickness of the ice layer;

[0059] However, in the formula for calculating the Beer-Lambert law... This refers to the absorption coefficient of light by a pure ice layer. In reality, ice layers often contain impurities (salt, dust particles, etc.), which may lead to inaccurate calculation results for ice layers. Therefore, this application first converts the optical distribution map into an optical image matrix, which consists of multiple pixels, each storing its corresponding refractive index and transmittance. Then, the refractive index ice layer thickness of the current optical distribution map is calculated using the Beer-Lambert law formula, and the transmittance ice layer thickness of the current optical distribution map is calculated using the Snell's law formula. It should be explained that if the ice layer is pure with extremely low impurity content, the calculated refractive index ice layer thickness and transmittance ice layer thickness will be very similar. However, when the impurity content is high, the calculated refractive index ice layer thickness and transmittance ice layer thickness will show a large difference due to the different degrees of influence of impurities on the refractive index and transmittance of the ice layer. Based on this characteristic, this application uses the difference in ice layer thickness between the refractive index ice layer thickness and the transmittance ice layer thickness to determine the impurity content of the current ice layer. If the difference in ice layer thickness is less than or equal to the ice layer thickness difference threshold, it indicates that the impurity content of the current ice layer is low, and either the refractive index ice layer thickness or the transmittance ice layer thickness at the pixel can be selected as the ice layer thickness of that pixel.

[0060] If the difference in ice thickness exceeds a threshold, gradient analysis of the optical image matrix is ​​required to filter out pure ice regions, thereby improving the accuracy of the calculation results. Specifically:

[0061] First, the optical image matrix is ​​converted into a gradient matrix. Then, the refractive index gradient vector and transmittance gradient vector at each gradient point are calculated. The correlation coefficient between the refractive index gradient vector and transmittance gradient vector at each gradient point is calculated using the Pearson correlation coefficient formula. If the refractive index gradient vector and transmittance gradient vector at a gradient point are highly correlated, it indicates that the trends of refractive index and transmittance at that gradient point are highly consistent, impurities have a small impact on refractive index and transmittance, and thus the impurity content at that gradient point is low. Conversely, if the refractive index gradient vector and transmittance gradient vector at a gradient point are low, it indicates that impurities have a large impact on refractive index and transmittance at that gradient point, and the impurity content is high. Based on this characteristic, the correlation coefficient between the refractive index gradient vector and transmittance gradient vector is introduced into the weighting function to determine the weight coefficient of each gradient point. Then, the weight coefficient of each gradient point is normalized so that the sum of all gradient points is 1. Finally, the ice layer thickness at each gradient point is calculated based on the weight coefficient of each gradient point. The specific weighting function is as follows:

[0062]

[0063] in, Let be the weighting coefficient of the gradient point in the x-th row and y-th column of the gradient matrix, and r(x, y) be the correlation coefficient between the refractive index gradient vector and the transmittance gradient vector within the gradient point in the x-th row and y-th column of the gradient matrix. , , and These represent the lateral gradient value of refractive index, the longitudinal gradient value of refractive index, the lateral gradient value of transmittance, and the longitudinal gradient value of transmittance at the gradient point in the x-th row and y-th column of the gradient matrix, respectively.

[0064] In the above formula, This is used to represent the degree of variation of optical parameters in the horizontal and vertical directions. If the calculated value is large, it indicates that the optical parameters change drastically around that pixel, and the optical properties of the ice layer are unstable, meaning that the final calculated weight coefficient is small. Then, the expression form of the correlation coefficient is introduced. If the relevance is high in this expression form, then The correlation coefficient will be smaller, resulting in a larger final weighting coefficient. In addition, the correlation coefficient can further amplify the degree of change of optical parameters in the horizontal and vertical directions, thereby further increasing the weighting value of gradient points with higher impurity content, thus reducing the impact of impurities on the calculation results in subsequent calculations.

[0065] The formula for calculating ice thickness is:

[0066]

[0067] Where d is the thickness of the ice layer. Let x be the refractive index of the gradient point in the x-th row and y-th column of the gradient matrix, and let y be the ice thickness. Let x be the light transmittance and ice thickness at the gradient point in the x-th row and y-th column of the gradient matrix. The weight coefficient of the gradient point in the x-th row and y-th column of the gradient matrix, where n is the total number of gradient points in the gradient image.

[0068] In the above formula, and Both are calculated using the current ice layer as a pure ice layer, which are the refractive index ice layer thickness and the light transmittance ice layer thickness. However, due to the influence of impurities, both are multiplied by their corresponding weighting coefficients to reduce the contribution of the ice layer thickness at gradient points with higher impurity content, thereby obtaining the accurate ice layer thickness at that gradient point.

[0069] S103. Based on the ice layer thickness distribution, determine the ice melting strategy for the photovoltaic cleaning robot, wherein the ice melting strategy includes ice melting temperature and ice melting path.

[0070] In the above steps, it should be noted that compared to directly using hot water to melt the ice, the atomized water vapor has a significantly increased specific surface area, allowing for full contact with the ice layer and even coverage of the ice surface. This prevents localized overheating or uneven heating, avoiding thermal stress damage to the photovoltaic modules, while ensuring complete melting of the ice layer. Furthermore, the temperature and flow rate of the atomized water vapor are easily adjustable, allowing for flexible adjustments based on ice thickness and environmental conditions, achieving high energy efficiency.

[0071] During the de-icing process, freshly melted ice water flows down the photovoltaic surface towards the ground. However, due to the low ambient temperature, the areas along the path of this melted ice water will refreeze, requiring further de-icing and impacting overall de-icing efficiency. To address this issue, this application determines the path weights of multiple gradient points on the target photovoltaic surface based on their physical distances from the ground. Generally, the farther a gradient point is from the ground, the longer its flow path on the photovoltaic surface, making it more likely to require de-icing and thus giving it a higher path weight. These path weights are then multiplied by their corresponding ice thicknesses to determine the de-icing priority of each gradient point. It's important to note that areas with thinner ice layers have less melted ice water and therefore a smaller impact, resulting in a lower de-icing priority even if they are far from the ground. Conversely, areas with thicker ice layers have more melted ice water and therefore a larger impact, resulting in a higher de-icing priority even if they are close to the ground. Finally, the de-icing path is determined based on the de-icing priority of these gradient points, thereby improving overall de-icing efficiency.

[0072] When using atomized water vapor to melt ice, it's understood that if the melting temperature is too low, the melting efficiency will be low; if the melting temperature is too high, it will cause thermal shock to the photovoltaic surface. Therefore, controlling the melting temperature is crucial to ensure efficient melting while avoiding damage to the photovoltaic surface. Thus, in calculating the melting temperature of the atomized water vapor, this application first calculates the required melting heat at multiple gradient points based on the ice thickness. Then, based on the ambient temperature and the required melting heat at multiple gradient points, the melting temperature at each gradient point is calculated using the heat balance conduction formula. Specifically, the following formula can be used:

[0073]

[0074] in, This refers to the melting temperature. Let r be the ambient temperature, v be the radius of the water vapor particle, v be the velocity of the water vapor particle, and d be the thickness of the ice layer. Let be the water vapor flow rate, and k be the melting ice heat balance constant.

[0075] In the above formula, the most efficient ice-melting process without causing thermal shock to the photovoltaic surface is when the heat provided by the atomized water vapor is exactly equal to the heat absorbed by the ice layer to melt. Given the thickness of the ice layer, the total heat required for the ice layer to melt can be calculated using the ice layer melting heat formula. Then, based on the ice-melting time specified in the actual situation, the required ice-melting efficiency can be obtained. Finally, based on the heat balance transfer formula of ambient temperature-atomized water vapor-ice layer and the basic parameters of the current atomization equipment, the optimal airflow, airflow velocity, and water vapor particles are adjusted. Among them, the ice-melting heat balance constant k integrates relatively fixed physical property parameters such as the density of water, the density of pure ice layer, the melting heat of pure ice layer, and the specific heat capacity of water vapor. The value of k remains constant, making the calculation more focused on the influence of variables on the ice-melting temperature. Finally, based on these adjusted parameters, the most efficient ice-melting temperature can be calculated.

[0076] S104. According to the ice-melting strategy, control the photovoltaic cleaning machine to clean the target photovoltaic surface.

[0077] In the above steps, the photovoltaic cleaning robot control system adjusts the heating device according to the ice-melting strategy to heat or cool the water used to generate atomized water vapor, so that the atomized water vapor reaches the set temperature requirement. Then, the atomization device is activated to convert the water that meets the temperature standard into fine water vapor particles. Finally, the atomized water vapor is sprayed onto the target photovoltaic surface in a uniform and stable manner through the nozzle assembly. It should be noted that during the spraying process, the photovoltaic cleaning robot will follow the ice-melting path to ensure that the atomized water vapor fully covers every part of the target photovoltaic surface, so as to reduce the difficulty of subsequent rinsing and cleaning of the target photovoltaic surface.

[0078] In one possible implementation, after the ice layer melts, in order to further remove some stubborn dirt, it is necessary to spray atomized water vapor containing decontamination ingredients onto the surface of the target photovoltaic module to reduce the viscosity of the stubborn dirt.

[0079] Reference Figure 2 This application also provides a cleaning control system for a photovoltaic cleaning robot, which is a photovoltaic cleaning robot control system, including a testing module 1, a processing module 2, and a control module 3, wherein:

[0080] Test module 1 is used to perform optical tests on the target photovoltaic surface and capture and extract an optical distribution map, which includes the transmittance and refractive index of the target photovoltaic surface.

[0081] Processing module 2 is used to determine the ice thickness distribution on the target photovoltaic surface based on the optical distribution map; and to determine the ice melting strategy of the photovoltaic cleaning robot based on the ice thickness distribution, the ice melting strategy including the ice melting temperature and the ice melting path.

[0082] Control module 3 is used to control the photovoltaic cleaning machine to clean the target photovoltaic surface according to the de-icing strategy.

[0083] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0084] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0085] The communication bus 302 is used to enable communication between these components.

[0086] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0087] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0088] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0089] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a cleaning control method of a photovoltaic cleaning robot.

[0090] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a cleaning control method of a photovoltaic cleaning robot. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0091] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0092] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0096] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0097] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A cleaning control method for a photovoltaic cleaning robot, characterized in that, The method, applied to a photovoltaic cleaning robot control system, includes: Optical tests are performed on the target photovoltaic surface, and an optical distribution map is captured and extracted. The optical distribution map includes the transmittance and refractive index of the target photovoltaic surface. Based on the optical distribution map, the ice layer thickness distribution on the target photovoltaic surface is determined, specifically including: The optical distribution map is converted into an optical image matrix, which is composed of multiple pixels, and each pixel stores its corresponding refractive index and transmittance. Calculate the refractive index ice layer thickness and the transmittance ice layer thickness of the optical image matrix; Calculate the difference in ice thickness between the refractive index ice layer thickness and the light transmittance ice layer thickness; Determine whether the ice thickness difference is less than or equal to the ice thickness difference threshold; If the ice layer thickness difference is less than or equal to the ice layer thickness difference threshold, then either the refractive index ice layer thickness or the light transmittance ice layer thickness is selected as the ice layer thickness distribution of the target photovoltaic surface. Based on the ice thickness distribution, the ice melting strategy of the photovoltaic cleaning robot is determined, and the ice melting strategy includes ice melting temperature and ice melting path. According to the ice-melting strategy, the photovoltaic cleaning machine is controlled to clean the target photovoltaic surface.

2. The method according to claim 1, characterized in that, The step of determining whether the ice thickness difference is less than or equal to the ice thickness difference threshold further includes: If the ice layer thickness difference is greater than the ice layer thickness difference threshold, the optical image matrix is ​​converted into a gradient matrix, which consists of multiple gradient points, wherein one gradient point corresponds to one pixel. Calculate the transmittance gradient vector and refractive index gradient vector at multiple gradient points; Based on the transmittance gradient vector and refractive index gradient vector of the multiple gradient points, calculate the transmittance-refractive index correlation coefficient corresponding to each of the multiple gradient points; A weighting function is used to calculate the ice layer thickness weighting coefficients for multiple gradient points; Based on the transmittance-refractive index correlation coefficients and ice thickness weighting coefficients corresponding to multiple gradient points, the ice thickness distribution on the target photovoltaic surface is calculated, and the ice thickness distribution includes the ice thickness corresponding to each of the multiple gradient points.

3. The method according to claim 2, characterized in that, The weighting function is specifically as follows: ； in, Let r(x, y) be the weighting coefficient of the gradient point in the x-th row and y-th column of the gradient matrix, and let r(x, y) be the correlation coefficient between the refractive index gradient vector and the transmittance gradient vector within the gradient point in the x-th row and y-th column of the gradient matrix. , , and These represent the lateral gradient value of refractive index, the longitudinal gradient value of refractive index, the lateral gradient value of transmittance, and the longitudinal gradient value of transmittance at the gradient point in the x-th row and y-th column of the gradient matrix, respectively.

4. The method according to claim 2, characterized in that, The method for determining the ice-melting strategy for the photovoltaic cleaning robot based on the ice thickness distribution specifically includes: The path weights of the gradient points are determined based on their physical height relative to the ground. Multiply the path weights of multiple gradient points by their respective ice thicknesses to determine the melting priority of the multiple gradient points. The ice-melting path is determined based on the melting priority of multiple gradient points.

5. The method according to claim 2, characterized in that, The method for determining the ice-melting strategy for the photovoltaic cleaning robot based on the ice thickness distribution further includes: Calculate the required melting heat at the multiple gradient points based on the ice thickness at the multiple gradient points; Based on the ambient temperature and the required melting heat at multiple gradient points, the melting temperature at multiple gradient points is calculated using the heat balance conduction formula.

6. The method according to claim 1, characterized in that, After controlling the photovoltaic cleaning machine to clean the target photovoltaic surface, the process also includes: Once the ice layer on the target photovoltaic surface melts, the composition table of the atomized water vapor is updated, and the composition table contains cleaning ingredients for stubborn stains.

7. A cleaning control system for a photovoltaic cleaning robot, characterized in that, The system is a photovoltaic cleaning robot control system, including a testing module (1), a processing module (2), and a control module (3), wherein: The test module (1) is used to perform optical tests on the target photovoltaic surface and capture and extract an optical distribution map, which includes the transmittance and refractive index of the target photovoltaic surface. The processing module (2) is used to determine the ice thickness distribution on the target photovoltaic surface according to the optical distribution map. Specifically, it includes: converting the optical distribution map into an optical image matrix, the optical image matrix being composed of multiple pixels, each pixel storing its corresponding refractive index and transmittance; calculating the refractive index ice thickness and transmittance ice thickness of the optical image matrix; calculating the ice thickness difference between the refractive index ice thickness and the transmittance ice thickness; determining whether the ice thickness difference is less than or equal to an ice thickness difference threshold; if the ice thickness difference is less than or equal to the ice thickness difference threshold, selecting either the refractive index ice thickness or the transmittance ice thickness as the ice thickness distribution of the target photovoltaic surface; and determining the ice melting strategy of the photovoltaic cleaning robot based on the ice thickness distribution, the ice melting strategy including the ice melting temperature and the ice melting path. The control module (3) is used to control the photovoltaic cleaning machine to clean the target photovoltaic surface according to the ice melting strategy.

8. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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