Distributed photovoltaic project tracked robot inspection and cleaning method and system

Abstract

The invention discloses a distributed photovoltaic project tracked robot inspection and cleaning method and system, and relates to the technical field of photovoltaic panel cleaning, and the method comprises the steps that a tracked cleaning robot moves and is positioned to a specified photovoltaic panel area after receiving a cleaning task sent by an operation and maintenance management platform; the stain type and the stain thickness are recognized, whether the photovoltaic panel has defects or not is recognized, a sensor detects environment parameters, and the rail cleaning robot conducts initial cleaning testing; the water pressure and the nozzle aperture are adjusted, micro-bubble water flow is released, and the photovoltaic panel is cleaned; and after cleaning is completed, the visual detection assembly scans the defect part of the photovoltaic panel again and feeds back defect information to the operation and maintenance management platform, the visual detection assembly can conduct accurate detection and defect recognition on the surface of the photovoltaic panel, and a better cleaning effect is achieved by adjusting the diameter of the microbubbles. When the photovoltaic panel has defects, the diameter and the water pressure of the microbubbles are adjusted to cope with the defects, and further damage is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic panel cleaning, and in particular to a distributed photovoltaic project tracked robot inspection and cleaning method and system. Background Art

[0002] The inspection and cleaning of photovoltaic panels have always been a pain point in the field of photovoltaic power generation. Dirty photovoltaic panels will cause a decrease in power generation efficiency, damage to photovoltaic components, and loss of energy output. Due to the installation form of photovoltaic panels, the inspection and cleaning of photovoltaic panels are difficult and are urgent problems to be solved. Tracked robot inspection and cleaning is a new power station management system that integrates digital information, robots, artificial intelligence and other advanced technologies. It can reduce manual intervention, improve power generation efficiency, and ensure production safety.

[0003] With the rapid development of renewable energy, distributed photovoltaic projects have been widely used around the world as a clean and efficient way to generate electricity. However, photovoltaic panels are exposed to the outdoor environment for a long time, and will inevitably be affected by dust, stains and other environmental factors, resulting in reduced power generation efficiency. In order to maintain the efficient operation of the photovoltaic system, regular inspection and cleaning have become crucial maintenance work. Traditional manual inspection and cleaning methods are not only time-consuming and labor-intensive, but also have safety hazards and uneven cleaning problems. With the advancement of artificial intelligence and robotics technology, automated inspection and cleaning systems have gradually become a trend in industry development.

[0004] However, there are still many deficiencies in the automated inspection and cleaning systems currently on the market. First, most systems lack the ability to accurately identify and classify stains, and are unable to adjust cleaning parameters according to different types of stains and the condition of the photovoltaic panels, resulting in poor cleaning results or potential damage to the photovoltaic panels. Second, the accuracy and comprehensiveness of existing systems in defect detection need to be improved, making it difficult to promptly detect and evaluate minor damage or performance degradation of photovoltaic panels. Summary of the invention

[0005] In view of the problems that the existing inspection and cleaning technologies for distributed photovoltaic projects cannot accurately identify and classify stains, have no defect detection, and cannot intelligently adjust cleaning parameters, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: by combining advanced visual inspection technology, deep learning algorithm, distinguishing whether there are defects and then adaptively adjusting cleaning parameters, the inspection efficiency and cleaning quality of photovoltaic projects can be improved.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a rail robot inspection and cleaning method for distributed photovoltaic projects, comprising: the rail cleaning robot moves and locates to the designated photovoltaic panel area after receiving the cleaning task sent by the operation and maintenance management platform; the visual detection component scans the surface of the photovoltaic panel, identifies the type and thickness of the stains, identifies whether the photovoltaic panel has defects, the rail cleaning robot collects environmental parameters, and performs an initial cleaning test; after the initial cleaning test is completed, the rail cleaning robot aligns the cleaning component with the cleaning area, adjusts the water pressure and nozzle aperture, releases micro-bubble water flow, adjusts the cleaning force and rotation speed of the cleaning brush, and cleans the photovoltaic panel; after the cleaning is completed, the visual detection component rescans the defective part of the photovoltaic panel, and feeds back the defect information to the operation and maintenance management platform, and the operation and maintenance management platform generates a defect report based on the defect information and issues an early warning.

[0008] As a preferred solution of the distributed photovoltaic project rail robot inspection and cleaning method of the present invention, wherein: the visual detection component scans the surface of the photovoltaic panel, identifies the type and thickness of the stain, and identifies whether the photovoltaic panel has defects, including that the visual detection component installed on the rail cleaning robot performs a comprehensive scan of the photovoltaic panel to generate high-precision point cloud data on the surface of the photovoltaic panel; the visual detection component uses the YOLOv8 model to identify the type of stain, the area covered by the stain, the type of photovoltaic panel defects, and the size of the photovoltaic panel defects; uses a depth map to measure the thickness of the stain and the depth of the photovoltaic panel defects; and classifies and evaluates the severity of the identified photovoltaic panel defects based on the identification and measurement results.

[0009] As a preferred solution of the distributed photovoltaic project rail robot inspection and cleaning method of the present invention, the initial cleaning test includes a cleaning force test of the cleaning brush, a cleaning brush rotation speed test, a water pressure test and a nozzle aperture adjustment test.

[0010] As a preferred solution of the distributed photovoltaic project rail robot inspection and cleaning method of the present invention, the water pressure and nozzle aperture are adjusted, and the release of microbubble water flow includes two conditions; the first condition is that there is no defect in the photovoltaic panel, and the second condition is that there is a defect in the photovoltaic panel.

[0011] As a preferred solution of the distributed photovoltaic project rail robot inspection and cleaning method of the present invention, when there are no defects in the photovoltaic panel, the microbubble diameter B1 is adjusted according to the stain thickness and environmental parameters, and the calculation formula for adjusting the microbubble diameter B1 is as follows:

[0012]

[0013] Wherein, B1 represents the microbubble diameter when there is no defect, D represents the stain thickness, E represents the ambient temperature, H represents the ambient humidity, α and β are both empirical parameters, and x represents the stain thickness coordinate; the water pressure P1 is adjusted according to the determined microbubble diameter and stain surface area, and the calculation formula for adjusting the water pressure P1 is as follows:

[0014]

[0015] Wherein, P1 represents the water pressure when there is no defect, S represents the stain area, i represents the stain index, γ and δ are both empirical parameters, n represents the number of stains, i represents the stain number, and B1 represents the microbubble diameter when there is no defect; the nozzle aperture N1 is adjusted according to the determined microbubble diameter and water pressure, and the adjustment formula of the nozzle aperture N1 is as follows:

[0016]

[0017] Among them, N1 represents the nozzle aperture when there is no defect, which combines the influence of water pressure and microbubble diameter, P1 is the water pressure when there is no defect, B1 represents the microbubble diameter when there is no defect, η is an empirical parameter, which represents the scaling factor of water pressure and is used to adjust the influence of water pressure on the nozzle aperture, θ is an empirical parameter, which represents the exponent of the nonlinear influence of water pressure on the nozzle aperture, ζ is an empirical parameter, which is used to adjust the influence of microbubble diameter on the nozzle aperture, and λ is an empirical parameter, which represents the water pressure attenuation factor and is used to adjust the exponential attenuation effect of water pressure on the nozzle aperture.

[0018] As a preferred solution of the distributed photovoltaic project rail robot inspection and cleaning method of the present invention, when there are defects in the photovoltaic panel, the microbubble diameter B2 is adjusted according to the stain thickness, environmental parameters and defect parameters. The formula for adjusting the microbubble diameter B2 is as follows:

[0019]

[0020] Wherein, B2 represents the microbubble diameter when there is a defect, D represents the stain thickness, E represents the ambient temperature, H represents the ambient humidity, α is an empirical parameter representing the rate at which the microbubble diameter decays with the stain thickness, β is an empirical parameter used to adjust the influence of environmental parameters on the microbubble diameter, x is an integral variable representing the thickness or coordinate within the defect, F represents the defect depth, ξ is an empirical parameter used to adjust the influence of defect parameters on the microbubble diameter, and L represents the defect area; the water pressure P2 is adjusted according to the microbubble diameter, stain surface area and defect parameters, and the formula for adjusting the water pressure P2 is as follows:

[0021]

[0022] Among them, P2 represents the water pressure when there is a defect, S represents the stain area, i represents the stain index, γ is an empirical parameter, which represents the weight of the influence of the microbubble diameter on the water pressure adjustment, n represents the number of stains, B2 represents the microbubble diameter when there is a defect, F represents the defect depth, and κ is an empirical parameter used to adjust the benchmark value of the water pressure on the defect parameter. represents Laplace transform, L represents defect area, and λ represents the empirical parameter used for Laplace transform. The nozzle aperture is adjusted in combination with water pressure, microbubble diameter and defect parameters. The calculation formula of the nozzle aperture is as follows:

[0023]

[0024] Among them, N2 represents the nozzle aperture when there is a defect, which combines the effects of water pressure, microbubble diameter and defect, P2 represents the water pressure when there is a defect, F represents the defect depth, μ is an empirical parameter used to adjust the effect of water pressure on the nozzle aperture, L represents the defect area, v is an empirical parameter used to adjust the exponential response of water pressure to defects, σ ​​is an empirical parameter used to adjust the exponential attenuation of water pressure to defect parameters, η is an empirical parameter representing the scale factor of water pressure, which is used to adjust the effect of water pressure on the nozzle aperture, B2 represents the microbubble diameter when there is a defect, ζ is an empirical parameter used to adjust the baseline value of the microbubble diameter when there is a defect, λ is an empirical parameter used to adjust the exponential attenuation of the effect of water pressure on defects, and θ is an empirical parameter used to adjust the nonlinear relationship between water pressure and nozzle aperture.

[0025] As a preferred solution of the distributed photovoltaic project rail robot inspection and cleaning method of the present invention, the operation and maintenance management platform generates a defect report and issues an early warning based on the defect information, receives the defect information, and classifies the defect information by type; evaluates the severity of each defect and records the location of the defect according to preset standards; writes a report containing all defects; generates specific repair or replacement suggestions based on the defect type and severity; compares the severity with the preset early warning threshold to determine the early warning level; and sends the early warning information to the designated recipient based on the early warning level.

[0026] Another object of the present invention is to provide a distributed photovoltaic project rail robot inspection and cleaning system, including: a task sending module, which is used for the rail cleaning robot to move and position to the designated photovoltaic panel area after receiving the cleaning task sent by the operation and maintenance management platform; an identification module, which is used for the visual detection component to scan the surface of the photovoltaic panel, identify the type and thickness of the stain, and identify whether the photovoltaic panel has defects. The rail cleaning robot collects environmental parameters and performs an initial cleaning test; a cleaning module, which is used for the rail cleaning robot to align the cleaning component with the cleaning area after the initial cleaning test is completed, adjust the water pressure and nozzle aperture, release micro-bubble water flow, adjust the cleaning force and rotation speed of the cleaning brush, and clean the photovoltaic panel; a feedback module, which is used for the visual detection component to re-scan the defective part of the photovoltaic panel after the cleaning is completed, and feedback the defect information to the operation and maintenance management platform, and the operation and maintenance management platform generates a defect report based on the defect information and issues an early warning.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned distributed photovoltaic project rail robot inspection and cleaning method are implemented.

[0028] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned distributed photovoltaic project rail robot inspection and cleaning method are implemented.

[0029] Beneficial effects of the present invention: The visual inspection component generates high-precision point cloud data on the surface of the photovoltaic panel, and loads the YOLOv8 model for real-time analysis, identifies the type and thickness of stains, as well as defects such as cracks, bubbles and delamination of the photovoltaic panel, and can accurately detect and identify defects on the surface of the photovoltaic panel, providing a basis for subsequent cleaning work. When there are no defects in the photovoltaic panel, the microbubble diameter and water pressure are adjusted according to the thickness of the stain and environmental parameters, and the appropriate water pressure and nozzle aperture are calculated by formula; by adjusting the size of the microbubble diameter to cope with different types of stains and stain thicknesses, a better cleaning effect is achieved. When there are defects in the photovoltaic panel, the microbubble diameter and water pressure are adjusted to cope with the defects, and the cleaning parameters are dynamically adjusted according to the actual situation to ensure the cleaning effect and protection of the photovoltaic panel, avoid further damage, and extend the service life of the photovoltaic panel. Through the feedback of defect information, rapid early warning can be achieved, so that the staff can find problems in time and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0031] Figure 1 It is a flow chart of the present invention;

[0032] Figure 2 This is a result diagram of photovoltaic panel stain recognition according to the present invention;

[0033] Figure 3 A schematic diagram of the depth of the photovoltaic panel of the present invention;

[0034] Figure 4 This is a flow chart for calculating the thickness of stains on photovoltaic panels according to the present invention;

[0035] Figure 5 This is a block diagram of the direct force / position hybrid control of the cleaning brush of the present invention;

[0036] Figure 6 This is a block diagram of the force / position hybrid control between the cleaning brushes of the present invention;

[0037] Figure 7 This is a hot spot recognition result diagram in the infrared image of the present invention;

[0038] Figure 8 This is a diagram showing the result of identifying hidden cracks in the short-wave infrared image of the present invention;

[0039] Fig. 9 Schematic diagram of the overhead track and robotic arm. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Example 1

[0043] Reference Figures 1 to 9 , which is an embodiment of the present invention, provides a rail robot inspection and cleaning method for a distributed photovoltaic project.

[0044] First, the distributed photovoltaic project rail robot inspection and cleaning method provided by the present application, the terminal may include one or more processors and a memory for storing data, wherein the processor may include but is not limited to a processing system such as a microprocessor MCU or a programmable logic device FPGA. The above-mentioned terminal may also include a transmission device and an input and output device for communication functions. It can be understood by those skilled in the art that it does not limit the structure of the above-mentioned terminal.

[0045] The memory can be used to store computer programs, such as the computer program corresponding to the distributed photovoltaic project rail robot inspection and cleaning method in this embodiment. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implementing the above method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0046] The transmission device is used to receive or send data via a network. The above network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0047] like Figures 1 to 9 As shown, an embodiment of the present invention provides a distributed photovoltaic project rail robot inspection and cleaning method, comprising the following steps:

[0048] S1: After receiving the cleaning task sent by the operation and maintenance management platform, the track cleaning robot moves and locates to the designated photovoltaic panel area.

[0049] Specifically, the operation and maintenance management platform generates cleaning tasks based on preset cleaning plans or real-time monitoring data, and then prioritizes the cleaning tasks based on factors such as the degree of contamination of the photovoltaic panels and the importance of their locations.

[0050] Furthermore, the operation and maintenance management platform packages the cleaning information into cleaning tasks and sends the cleaning tasks to the designated rail cleaning robots through the wireless communication network. The cleaning information includes the specific location coordinates of the target photovoltaic panel, the estimated degree of contamination, and the priority of the cleaning task.

[0051] Furthermore, the rail cleaning robot uses an overhead track and can efficiently move in a large photovoltaic power station. The rail robot is mounted on a robotic arm and moves along a track installed above the photovoltaic panel array via the robotic arm.

[0052] Furthermore, the track cleaning robot considers the distance from the current position to the target photovoltaic panel area, plans the optimal path, and moves to the designated photovoltaic panel area along the optimal path, continuously performing obstacle detection and obstacle avoidance along the way.

[0053] Furthermore, after the track cleaning robot reaches the target area, it uses a variety of sensors for precise positioning, which may include GPS, lidar, vision systems, etc., to ensure that the relative position of the cleaning robot and the target photovoltaic panel is accurate.

[0054] Furthermore, the rail cleaning robot sends a confirmation message to the operation and maintenance management platform, including the current position, preparation status and other information. Then the rail cleaning robot enters the standby state and prepares to start the next stage of inspection and cleaning work.

[0055] Furthermore, the rail robot integrates cleaning and inspection functions, greatly improving the operation and maintenance efficiency of photovoltaic power stations. While performing cleaning tasks, it can also conduct comprehensive inspections. The high-definition visual inspection system and advanced YOLOv8 model enable it to identify stains, cracks, bubbles and delamination on photovoltaic panels in real time. At the same time, the long-wave infrared camera can detect potential hot spot effects and effectively prevent damage to photovoltaic panels.

[0056] S2: The visual inspection component scans the surface of the photovoltaic panel, identifies the type and thickness of the stain, and identifies whether the photovoltaic panel has defects. The track cleaning robot collects environmental parameters and performs an initial cleaning test.

[0057] Specifically, the visual inspection component conducts a comprehensive scan of the photovoltaic panels, generates high-precision point cloud data of the surface of the photovoltaic panels, and loads the YOLOv8 model, which has been specially trained to identify information such as stain types (such as dust, bird droppings, leaves, etc.), stain thickness, photovoltaic panel cracks, bubbles, and delamination. It then performs real-time analysis of the point cloud data to identify and locate the above features.

[0058] Furthermore, the point cloud CP1 of the unstained photovoltaic panel in the depth map is selected, the photovoltaic panel spatial plane P1 is fitted according to the point cloud CP1, the specific stain point cloud CPn in the depth map is selected, and the distance between the point CPn and the spatial plane P1 is calculated, which is the stain thickness.

[0059] Furthermore, based on the identification results, the stain coverage area is calculated, the identified cracks, bubbles and delaminations are classified and the severity is evaluated, and the specific size of the defects is calculated.

[0060] Furthermore, a temperature sensor is used to measure the current ambient temperature, and a humidity sensor is used to measure the current ambient humidity.

[0061] Preferably, other sensors may be used to collect other relevant environmental parameters including light intensity, wind speed, etc.

[0062] Furthermore, the rail cleaning robot performs an initial cleaning test and records the cleaning test data for subsequent cleaning intensity adjustment. The initial cleaning test includes a cleaning brush cleaning force test, a cleaning brush rotation speed test, a water pressure test, and a nozzle aperture adjustment test.

[0063] Furthermore, the nozzle is a variable aperture electric nozzle.

[0064] Furthermore, increasing water pressure will cause the size of microbubbles to decrease. High pressure will make the water flow more turbulent, and the bubbles produced will be smaller and more uniform. The aperture size of the nozzle will directly affect the formation of microbubbles.

[0065] Furthermore, when the diameter of the microbubbles is adjusted to a smaller size, the small bubbles are able to enter tiny cracks and pores, making the cleaning more thorough. The smaller bubbles can form a more even coverage, ensuring full contact between the cleaning solution and the stained surface, having a gentler impact on the cleaned surface, and being able to reduce potential damage to the surface of the photovoltaic panel.

[0066] Furthermore, when the diameter of the microbubbles is increased, the larger bubbles produce a stronger impact force when they burst, which is more effective in removing stubborn or heavy stains, and is particularly suitable for removing large particle pollutants such as bird droppings, etc. Using large bubbles can reduce the risk of clogging of nozzles and pipes.

[0067] S3: After the initial cleaning test is completed, the track cleaning robot aims the cleaning component at the cleaning area, adjusts the water pressure and nozzle aperture, releases the micro-bubble water flow, adjusts the cleaning force and rotation speed of the cleaning brush, and cleans the photovoltaic panel.

[0068] Specifically, when there are no defects in the photovoltaic panel, the microbubble diameter B1 is adjusted according to the stain thickness and environmental parameters, and the calculation formula for adjusting the microbubble diameter B1 is as follows:

[0069]

[0070] Among them, B1 represents the diameter of microbubbles when there is no defect, D represents the thickness of the stain, E represents the ambient temperature, H represents the ambient humidity, α is an empirical parameter used to adjust the rate at which the diameter of microbubbles changes with the thickness of the stain, β is an empirical parameter used to adjust the degree to which microbubbles are affected by the environment, and x represents the coordinate of the stain thickness.

[0071] Preferably, an integral is used to reflect the overall influence of the stain thickness, an exponential term is used to reflect the nonlinear change of the microbubble diameter with the stain thickness, the influence of environmental factors is considered, and temperature and humidity parameters are introduced to make the calculation results more accurate.

[0072] Further, the water pressure P1 is adjusted according to the determined microbubble diameter and the stain surface area, and the calculation formula for adjusting the water pressure P1 is as follows:

[0073]

[0074] Among them, P1 represents the water pressure when there is no defect, S represents the stain area, i represents the stain index, γ is an empirical parameter, which represents the influence weight of microbubble diameter on water pressure adjustment, δ is an empirical parameter, which represents the offset used to avoid zero or negative values ​​in the logarithmic function when calculating the influence of microbubble diameter on water pressure, n represents the number of stains, i represents the stain number, and B1 represents the microbubble diameter when there is no defect.

[0075] Preferably, the combined influence of multiple stain points is considered, a logarithmic function is used to reflect the nonlinear relationship between water pressure and microbubble diameter, and the stain area is introduced as a normalization factor to make the result more universal.

[0076] Furthermore, the nozzle aperture N1 is adjusted according to the determined microbubble diameter and water pressure. The adjustment formula of the nozzle aperture N1 is as follows:

[0077]

[0078] Among them, N1 represents the nozzle aperture when there is no defect, which combines the influence of water pressure and microbubble diameter, P1 is the water pressure when there is no defect, B1 represents the microbubble diameter when there is no defect, η is an empirical parameter, which represents the scaling factor of water pressure and is used to adjust the influence of water pressure on the nozzle aperture, θ is an empirical parameter, which represents the exponent of the nonlinear influence of water pressure on the nozzle aperture, ζ is an empirical parameter, which is used to adjust the influence of microbubble diameter on the nozzle aperture, and λ is an empirical parameter, which represents the water pressure attenuation factor and is used to adjust the exponential attenuation effect of water pressure on the nozzle aperture.

[0079] Preferably, the effects of water pressure and microbubble diameter are combined, an exponential term is used to reflect the nonlinear effect of water pressure on pore size, and multiple empirical parameters are introduced to increase the flexibility of the model and balance the effects of water pressure and microbubble diameter on pore size.

[0080] Furthermore, when there are defects on the surface of the photovoltaic panel, the microbubble diameter B2 is adjusted according to the stain thickness, environmental parameters and defect parameters. The formula for adjusting the microbubble diameter B2 is as follows:

[0081]

[0082] Among them, B2 represents the diameter of microbubbles when there is a defect, D represents the thickness of the stain, E represents the ambient temperature, H represents the ambient humidity, α is an empirical parameter, which represents the rate at which the diameter of microbubbles decays with the thickness of the stain, β is an empirical parameter, which is used to adjust the influence of environmental parameters on the diameter of microbubbles, x is an integral variable, which represents the thickness or the coordinate within the defect, F represents the defect depth, ξ is an empirical parameter, which is used to adjust the influence of defect parameters on the diameter of microbubbles, and L represents the defect area.

[0083] Preferably, defect parameters are introduced on the basis of the defect-free formula, and an exponential function is used to reflect the influence of defect depth and area on the microbubble diameter, while maintaining consideration of stain thickness and environmental parameters.

[0084] Furthermore, the water pressure needs to be adjusted according to the microbubble diameter, the stain surface area and the defect parameters. The water pressure P2 is adjusted according to the microbubble diameter, the stain surface area and the defect parameters. The formula for adjusting the water pressure P2 is as follows:

[0085]

[0086] Among them, P2 represents the water pressure when there is a defect, S represents the stain area, i represents the stain index, γ is an empirical parameter, which represents the weight of the influence of the microbubble diameter on the water pressure adjustment, n represents the number of stains, B2 represents the microbubble diameter when there is a defect, F represents the defect depth, and κ is an empirical parameter used to adjust the benchmark value of the water pressure on the defect parameter. represents Laplace transform, L represents the defect area, and λ represents the empirical parameter used for Laplace transform.

[0087] Preferably, the influence of defect depth and area on water pressure is considered, a hyperbolic sine function is used to reflect the nonlinear influence of defect parameters, Laplace transform is introduced, and comprehensive consideration of multiple stain points is retained.

[0088] Furthermore, the nozzle aperture is adjusted in combination with the water pressure, microbubble diameter and defect parameters. The calculation formula of the nozzle aperture is as follows:

[0089]

[0090] Among them, N2 represents the nozzle aperture when there is a defect, which combines the effects of water pressure, microbubble diameter and defect, P2 represents the water pressure when there is a defect, F represents the defect depth, μ is an empirical parameter used to adjust the effect of water pressure on the nozzle aperture, L represents the defect area, v is an empirical parameter used to adjust the exponential response of water pressure to defects, σ ​​is an empirical parameter used to adjust the exponential attenuation of water pressure to defect parameters, η is an empirical parameter representing the scale factor of water pressure, which is used to adjust the effect of water pressure on the nozzle aperture, B2 represents the microbubble diameter when there is a defect, ζ is an empirical parameter used to adjust the baseline value of the microbubble diameter when there is a defect, λ is an empirical parameter used to adjust the exponential attenuation of the effect of water pressure on defects, and θ is an empirical parameter used to adjust the nonlinear relationship between water pressure and nozzle aperture.

[0091] Preferably, water pressure, microbubble diameter and defect parameters are comprehensively considered, multiple exponential terms are used to reflect the nonlinear effects of various factors, and multiple empirical parameters are introduced to make the formula highly flexible and adaptable, balancing the effects of various factors on the nozzle aperture.

[0092] In summary, in most cases, photovoltaic panels are defect-free. Defect-free photovoltaic panels can withstand higher water pressure and smaller microbubbles, and can effectively remove stains without causing additional wear on the surface, maximizing cleaning efficiency and reducing cleaning time and cost. Defective photovoltaic panels are more vulnerable to damage during the cleaning process, especially high-pressure water flow and small microbubbles may aggravate defects. By adjusting the microbubble diameter and water pressure, further damage to existing defects can be avoided. By adjusting the parameters more finely, it is ensured that the cleaning effect of photovoltaic panels is still good even in the case of defects, thereby maintaining its power generation efficiency and service life. By calculating the cleaning parameters by distinguishing between defects and non-defects, the structural integrity of photovoltaic panels can be protected while ensuring the cleaning effect, extending their service life, and optimizing resource use. This method can adapt to the actual situation of photovoltaic panels and provide a smarter and more efficient cleaning solution. By optimizing the size and water pressure of microbubbles, stains can be effectively removed, especially for stains of different types and thicknesses, providing a precise cleaning solution. The adjustment of the microbubble diameter makes cleaning more gentle, reduces potential damage to the surface of photovoltaic panels, and thus extends the service life of photovoltaic panels. During the cleaning process, various parameter adjustments are carried out automatically without human intervention, which improves cleaning efficiency and intelligence.

[0093] Furthermore, the cleaning brush actuator is equipped with a tactile element capable of measuring the contact force, which includes a pressure sensor, a torque sensor, a displacement sensor, etc., and can monitor the magnitude, direction and point of action of the force exerted on the object in real time. The cleaning brush controls the joint and converts the control command issued by the controller into a downward pressure command, thereby adjusting the cleaning force.

[0094] Furthermore, when cracks, bubbles, delamination, internal connection failure, etc. occur in the photovoltaic panel, it will cause local overheating of the photovoltaic panel, which may cause local burning of the photovoltaic panel, aging of the packaging material, and other damages, which is called the hot spot effect. Use a long-wave infrared camera to capture the infrared thermal image of the photovoltaic panel and monitor the surface temperature distribution of the photovoltaic panel. When the temperature of a certain area on the photovoltaic panel is 6.5℃ higher than the surrounding area, it is considered a hot spot, and the hot spot alarm information and hot spot location information are uploaded.

[0095] S4: After cleaning, the visual inspection component rescans the defective parts of the photovoltaic panel and feeds back the defect information to the operation and maintenance management platform. The operation and maintenance management platform generates a defect report based on the defect information and issues an early warning.

[0096] Specifically, after the rail cleaning robot completes the scheduled cleaning task, it sends a cleaning completion signal to the operation and maintenance management platform. The visual inspection component conducts a comprehensive scan of the cleaned surface of the photovoltaic panel to generate new precision point cloud data, and the YOLOv8 model is applied again, focusing on residual stains, previously identified defects, and new damage that may be caused by the cleaning process.

[0097] Further, the stain coverage area and thickness before and after cleaning are compared to calculate the cleaning efficiency, such as the stain removal percentage.

[0098] Furthermore, a defect review is conducted to re-evaluate previously discovered defects, confirm whether the cleaning process has affected the defects, and identify any possible new defects.

[0099] Furthermore, the operation and maintenance management platform receives defect information and classifies the defect information by type; evaluates the severity of each defect and records the location of the defect based on preset standards; evaluates the potential impact of each defect on the performance of the photovoltaic panel; writes a report containing all defects; generates specific repair or replacement recommendations based on the type and severity of the defect; compares the defect information with the preset warning threshold to determine the warning level; and sends the warning information to the designated recipient based on the warning level.

[0100] Furthermore, the report includes defect type, defect location, defect severity, potential impact and recommended treatment methods.

[0101] Example 2

[0102] Reference Figures 1 to 9 , which is an embodiment of the present invention, provides a distributed photovoltaic project rail robot inspection and cleaning system, including:

[0103] The task sending module is used for the rail cleaning robot to move and locate to the designated photovoltaic panel area after receiving the cleaning task sent by the operation and maintenance management platform;

[0104] The recognition module is used for the visual inspection component to scan the surface of the photovoltaic panel, identify the type and thickness of the stain, and identify whether the photovoltaic panel has defects. The track cleaning robot collects environmental parameters and performs an initial cleaning test;

[0105] Cleaning module, used for cleaning. After the initial cleaning test is completed, the rail cleaning robot aims the cleaning component at the cleaning area, adjusts the water pressure and nozzle aperture, releases the micro-bubble water flow, adjusts the cleaning force and speed of the cleaning brush, and cleans the photovoltaic panel;

[0106] The feedback module is used to rescan the defective parts of the photovoltaic panel after cleaning. The visual inspection component feeds back the defect information to the operation and maintenance management platform. The operation and maintenance management platform generates a defect report and issues an early warning based on the defect information.

[0107] For the specific definition of the rail robot inspection and cleaning system for distributed photovoltaic projects, please refer to the definition of the rail robot inspection and cleaning method for distributed photovoltaic projects above, which will not be repeated here. Each module in the above-mentioned distributed photovoltaic project rail robot inspection and cleaning system can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0108] Example 3

[0109] Based on the first two embodiments, an embodiment of the present invention provides a computer device, which may be a server. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium.

[0110] The database of the computer device is used to store action detection data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in any of the above-mentioned sparse tensor operation acceleration method embodiments are implemented.

[0111] In one embodiment, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in any of the above-mentioned sparse tensor operation acceleration method embodiments are implemented.

[0112] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0113] Example 4

[0114] As an embodiment of the present invention, a rail robot inspection and cleaning method for a distributed photovoltaic project is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0115] Specifically, using a photovoltaic panel cleaning robot for cleaning operations can greatly reduce labor input and save labor outsourcing costs. Taking a photovoltaic station with an installed capacity of 10,000 kilowatts as an example, according to the market average cleaning price of 1 yuan / square meter, the cost of a single cleaning is 47,000 yuan. Calculated based on an average of 4 cleanings per year, the annual cleaning cost exceeds 188,000 yuan. At the same time, the robot's visual heating imaging function is used to conduct equipment inspections, effectively reducing the cost of personnel inspection and maintenance, and improving the economic benefits of the photovoltaic power generation system.

[0116] The following experiments were conducted to verify the effectiveness of the inspection and cleaning method of the tracked robot for distributed photovoltaic projects. Five different areas in a photovoltaic power station were selected as test objects, and the photovoltaic panels in each area were different. The experiment was conducted three times, and each time the photovoltaic panels in each area were fully cleaned and inspected. The tracked cleaning robot used in the experiment includes a visual inspection component, a sensor, and a cleaning component. During the experiment, the YOLOv8 model was used for stain and defect recognition, and the depth map measured the stain thickness and the depth of photovoltaic panel defects.

[0117] Table 1 Comparison of inspection and cleaning effects of rail robots in distributed photovoltaic projects

[0118]

[0119]

[0120] Specifically, based on the data in the above table, we can draw the following conclusions:

[0121] All tested photovoltaic panels achieved significant power generation efficiency improvement after cleaning, with efficiency improvement ranging from 12.8% to 21.6%, and an average improvement of 17.7%, proving that the method of the present invention has significant advantages in improving the power generation efficiency of photovoltaic panels.

[0122] Furthermore, the PV panel No. 005, which had both dust and bird droppings, had an efficiency increase of 21.6% after cleaning, which was the highest among all samples. This shows that this method has excellent versatility and adaptability.

[0123] Furthermore, for defective photovoltaic panels, even though these photovoltaic panels have defects such as microcracks, bubbles or edge damage, the power generation efficiency is still significantly improved after cleaning. By timely identifying and reporting photovoltaic panel defects (such as microcracks of 004, bubbles of 005 and edge damage of 007), this method provides important support for preventive maintenance.

[0124] Furthermore, by comparing the cleaning effects of different stain thicknesses and coverages, we can infer that the method can accurately adjust the cleaning parameters. For example, the cleaning effects of 001 (dust thickness 0.5 mm, coverage 80%) and 006 (dust thickness 0.7 mm, coverage 74%) are similar, which shows that the method can optimize the cleaning parameters according to the specific situation.

[0125] In summary, the distributed photovoltaic project rail robot inspection and cleaning method of the present invention shows significant technical advantages and innovation. It can not only effectively improve the power generation efficiency of photovoltaic panels, but also adapt to complex stains and photovoltaic panel defects. The intelligent and precise control of this method provides strong technical support for the long-term and efficient operation of photovoltaic projects, and is expected to produce significant economic and environmental benefits in terms of improving photovoltaic power generation efficiency, reducing maintenance costs and extending equipment life.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed photovoltaic project rail robot inspection and cleaning method, characterized in that: include: After receiving the cleaning task sent by the operation and maintenance management platform, the track cleaning robot moves and locates to the designated photovoltaic panel area; The visual inspection component scans the surface of the photovoltaic panel to identify the type and thickness of the stain and whether the photovoltaic panel has defects. The track cleaning robot collects environmental parameters and performs an initial cleaning test. After the initial cleaning test is completed, the track cleaning robot aims the cleaning component at the cleaning area, adjusts the water pressure and nozzle aperture, releases the micro-bubble water flow, and adjusts the cleaning force and speed of the cleaning brush to clean the photovoltaic panel; After cleaning, the visual inspection component rescans the defective parts of the photovoltaic panel and feeds back the defect information to the operation and maintenance management platform. The operation and maintenance management platform generates a defect report based on the defect information and issues an early warning.

2. The distributed photovoltaic project rail robot inspection and cleaning method according to claim 1, characterized in that: The visual inspection component scans the surface of the photovoltaic panel, identifies the type and thickness of the stain, and identifies whether the photovoltaic panel has defects, including: The visual inspection component installed on the rail cleaning robot conducts a comprehensive scan of the photovoltaic panels and generates high-precision point cloud data on the surface of the photovoltaic panels; The visual inspection component uses the YOLOv8 model to identify the type of stain, the area covered by the stain, the type of photovoltaic panel defects, and the size of photovoltaic panel defects; Use depth mapping to measure stain thickness and PV panel defect depth; Based on the identification and measurement results, the identified PV panel defects are classified and their severity assessed.

3. The distributed photovoltaic project rail robot inspection and cleaning method according to claim 2, characterized in that: The initial cleaning test includes a cleaning force test of the cleaning brush, a cleaning brush rotation speed test, a water pressure test, and a nozzle aperture adjustment test.

4. The distributed photovoltaic project rail robot inspection and cleaning method according to claim 3, characterized in that: The adjustment of water pressure and nozzle aperture to release microbubble water flow includes two conditions; the first condition is that there is no defect in the photovoltaic panel, and the second condition is that there is a defect in the photovoltaic panel.

5. The distributed photovoltaic project rail robot inspection and cleaning method according to claim 4, characterized in that: When there are no defects in the photovoltaic panels, The microbubble diameter B1 is adjusted according to the stain thickness and environmental parameters. The calculation formula for adjusting the microbubble diameter B1 is as follows: Among them, B1 represents the microbubble diameter when there is no defect, D represents the stain thickness, E represents the ambient temperature, H represents the ambient humidity, α and β are both empirical parameters, and x represents the stain thickness coordinate; The water pressure P1 is adjusted according to the determined microbubble diameter and the stain surface area. The calculation formula for adjusting the water pressure P1 is as follows: Among them, P1 represents the water pressure when there is no defect, S represents the stain area, i represents the stain index, γ and δ are both empirical parameters, n represents the number of stains, i represents the stain number, and B1 represents the microbubble diameter when there is no defect; The nozzle aperture N1 is adjusted according to the determined microbubble diameter and water pressure. The adjustment formula of the nozzle aperture N1 is as follows: Among them, N1 represents the nozzle aperture when there is no defect, which combines the influence of water pressure and microbubble diameter, P1 is the water pressure when there is no defect, B1 represents the microbubble diameter when there is no defect, η is an empirical parameter, which represents the scaling factor of water pressure and is used to adjust the influence of water pressure on the nozzle aperture, θ is an empirical parameter, which represents the exponent of the nonlinear influence of water pressure on the nozzle aperture, ζ is an empirical parameter, which is used to adjust the influence of microbubble diameter on the nozzle aperture, and λ is an empirical parameter, which represents the water pressure attenuation factor and is used to adjust the exponential attenuation effect of water pressure on the nozzle aperture.

6. The distributed photovoltaic project rail robot inspection and cleaning method according to claim 5, characterized in that: When there are defects in the photovoltaic panels, The microbubble diameter B2 is adjusted according to the stain thickness, environmental parameters and defect parameters. The formula for adjusting the microbubble diameter B2 is as follows: Wherein, B2 represents the microbubble diameter when there is a defect, D represents the stain thickness, E represents the ambient temperature, H represents the ambient humidity, α is an empirical parameter representing the rate at which the microbubble diameter decays with the stain thickness, β is an empirical parameter used to adjust the effect of environmental parameters on the microbubble diameter, x is an integral variable representing the thickness or the coordinate within the defect, F represents the defect depth, ξ is an empirical parameter used to adjust the effect of defect parameters on the microbubble diameter, and L represents the defect area; The water pressure P2 is adjusted according to the microbubble diameter, stain surface area and defect parameters. The formula for adjusting the water pressure P2 is as follows: Among them, P2 represents the water pressure when there is a defect, S represents the stain area, i represents the stain index, γ is an empirical parameter, which represents the weight of the influence of the microbubble diameter on the water pressure adjustment, n represents the number of stains, B2 represents the microbubble diameter when there is a defect, F represents the defect depth, and κ is an empirical parameter used to adjust the benchmark value of the water pressure on the defect parameter. represents Laplace transform, L represents defect area, and λ represents the empirical parameter used for Laplace transform; The nozzle aperture is adjusted based on the water pressure, microbubble diameter and defect parameters. The calculation formula for the nozzle aperture is as follows: Among them, N2 represents the nozzle aperture when there is a defect, which combines the effects of water pressure, microbubble diameter and defect, P2 represents the water pressure when there is a defect, F represents the defect depth, μ is an empirical parameter used to adjust the effect of water pressure on the nozzle aperture, L represents the defect area, v is an empirical parameter used to adjust the exponential response of water pressure to defects, σ ​​is an empirical parameter used to adjust the exponential attenuation of water pressure to defect parameters, η is an empirical parameter representing the scale factor of water pressure, which is used to adjust the effect of water pressure on the nozzle aperture, B2 represents the microbubble diameter when there is a defect, ζ is an empirical parameter used to adjust the baseline value of the microbubble diameter when there is a defect, λ is an empirical parameter used to adjust the exponential attenuation of the effect of water pressure on defects, and θ is an empirical parameter used to adjust the nonlinear relationship between water pressure and nozzle aperture.

7. The distributed photovoltaic project rail robot inspection and cleaning method according to claim 6, characterized in that: The operation and maintenance management platform generates defect reports and issues early warnings based on defect information. The operation and maintenance management platform receives defect information and classifies the defect information by type; Evaluate the severity of each defect and record the location of the defect according to pre-set criteria; Prepare a report containing all defects; Generate specific repair or replacement recommendations based on defect type and severity; Compare the severity with the preset warning threshold to determine the warning level; According to the warning level, the warning information is sent to the designated recipients.

8. A system using the distributed photovoltaic project rail robot inspection and cleaning method as described in any one of claims 1 to 7, characterized in that: include: The task sending module is used for the rail cleaning robot to move and locate to the designated photovoltaic panel area after receiving the cleaning task sent by the operation and maintenance management platform; The recognition module is used for the visual inspection component to scan the surface of the photovoltaic panel, identify the type and thickness of the stain, and identify whether the photovoltaic panel has defects. The track cleaning robot collects environmental parameters and performs an initial cleaning test; Cleaning module, used for cleaning. After the initial cleaning test is completed, the rail cleaning robot aims the cleaning component at the cleaning area, adjusts the water pressure and nozzle aperture, releases the micro-bubble water flow, adjusts the cleaning force and speed of the cleaning brush, and cleans the photovoltaic panel; The feedback module is used to rescan the defective parts of the photovoltaic panel after cleaning. The visual inspection component feeds back the defect information to the operation and maintenance management platform. The operation and maintenance management platform generates a defect report and issues an early warning based on the defect information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed photovoltaic project rail robot inspection and cleaning method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed photovoltaic project rail robot inspection and cleaning method described in any one of claims 1 to 7 are implemented.

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