Solar panel edge enhancement system

Abstract

The invention relates to the technical field of an outdoor environment of a photovoltaic power station, in particular to a solar cell panel edge enhancement system, which comprises a control device and an equipment device, and is characterized in that an image acquisition module acquires surface image data of a cell panel edge area through an industrial camera; the edge ash deposition analysis module generates an ash deposition grade map according to the image gray value distribution and the real-time meteorological data; the cooperative control module calculates a control parameter combination of the actuating frequency, the spraying angle and the scanning path according to the atlas; the equipment device comprises a piezoelectric actuation dust removal array, a flow guide type air curtain generator and a low-power laser cleaner which are used for executing vibration dust removal, airflow purging and laser cleaning operation respectively. And the effect evaluation module calculates the cleaning efficiency by comparing image data before and after cleaning and performs feedback optimization. Dust accumulation on the edge of the cell panel is effectively inhibited through intelligent sensing and cooperative control, and the stability of photoelectric conversion efficiency is maintained.

Description

Technical Field

[0001] This invention relates to the field of outdoor environmental technology for photovoltaic power plants, and more particularly to edge enhancement systems for solar panels. Background Technology

[0002] In outdoor applications of large-scale ground-mounted photovoltaic power plants, solar panels directly convert sunlight into direct current (DC) electricity based on the photovoltaic effect of semiconductor materials. Photovoltaic modules are often made of silicon-based materials such as monocrystalline or polycrystalline silicon. When photons are incident on the PN junction inside the panel, they excite electron-hole pairs and form a built-in electric field, thereby generating a potential difference and current output.

[0003] Existing solar panel technology suffers from several key challenges. Specifically, in the outdoor environment of large-scale ground-mounted photovoltaic power plants, the surface of solar panels is directly exposed to the atmosphere, making them prone to dust particle accumulation. Since solar panel modules are typically installed at a fixed angle, dust tends to accumulate at the edges and bottom, especially at lower angles, forming dust accumulation zones. Dust cover reduces the light transmittance of the solar panels, leading to increased heating of the module components and impacting photoelectric conversion efficiency. For example, in areas with frequent sandstorms, dust accumulation accelerates, routine maintenance becomes difficult, the dust accumulation area expands continuously, light absorption is hindered, the panel temperature rises abnormally, and power generation efficiency decreases significantly. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a solar panel edge enhancement system, which solves the technical problem of reduced power generation efficiency caused by dust accumulation at the edges of the solar panel.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: The solar panel edge enhancement system provided by the present invention includes an equipment device and a control device, wherein the control device establishes a communication connection with the equipment device; The control device includes: The image acquisition module is configured to acquire surface image data of the edge area of ​​the solar panel using an industrial camera deployed on the photovoltaic array support. The edge dust accumulation analysis module is configured to receive the surface image data and generate a dust accumulation level map characterizing the dust accumulation thickness and coverage density based on the gray value distribution of the surface image data and real-time meteorological data. The collaborative control module is configured to receive the ash accumulation level map and calculate and generate a combination of control parameters based on it. The combination of control parameters includes actuation frequency, spray angle and scanning path. The equipment includes: A piezoelectric actuated dust removal array, embedded in the frame of a solar panel, is configured to receive the actuation frequency and generate high-frequency mechanical vibration to break the adhesion between dust and the panel surface. A flow-directing air curtain generator, installed above the edge of the solar panel, is configured to receive the jet angle and generate directional laminar flow to blow away and remove loose dust particles. A low-power laser cleaner, integrated into the bracket, is configured to receive the scanning path and emit a laser beam to remove residual contaminants; An effect evaluation module, configured in the control device, is used to trigger the image acquisition module to acquire images again after the device performs an operation, and calculate the cleaning efficiency by comparing the image data before and after cleaning, and feed the evaluation data back to the edge dust accumulation analysis module. The system, through the coordinated action of the control device and the equipment, is used to suppress dust accumulation at the edges of the solar panel and maintain the stability of photoelectric conversion efficiency.

[0006] Furthermore, in the solar panel edge enhancement system of the present invention, the preprocessing operation of the surface image data by the image acquisition module includes: using a Gaussian filtering algorithm for noise filtering and using a histogram equalization algorithm for contrast enhancement, so as to improve the recognition accuracy of the edge dust accumulation analysis module for dust accumulation features.

[0007] Furthermore, in the solar panel edge enhancement system of the present invention, the specific steps of the edge dust accumulation analysis module in generating the dust accumulation level map include: dividing the surface image data into multiple analysis grids; for each analysis grid, extracting its gray value variance as a first indicator of dust accumulation distribution uniformity, and extracting its average gray value as a second indicator of dust accumulation thickness; using wind speed and humidity in the real-time meteorological data as weighting factors, weighting and fusing the first indicator and the second indicator, and outputting the dust accumulation level of each analysis grid.

[0008] Furthermore, in the solar panel edge enhancement system of the present invention, when the collaborative control module calculates the combination of control parameters, it performs the following mapping: the actuation frequency is positively correlated with the adhesion strength reflected by the dust accumulation level; the spray angle is the combined angle of the photovoltaic panel tilt angle and the current wind direction vector; the scanning path is arranged in descending order according to the dust accumulation level of the analysis grid to determine the priority order.

[0009] Furthermore, in the solar panel edge enhancement system of the present invention, the laser beam emitted by the low-power laser cleaner has a wavelength of 1064nm. This wavelength has a selective absorption effect on organic sticky pollutants, causing the molecular bonds of the pollutants to break through photothermal action.

[0010] Furthermore, in the solar panel edge enhancement system of the present invention, the startup sequence of the airflow-guided air curtain generator and the low-power laser cleaner is coordinated by the collaborative control module: when the dust accumulation level spectrum indicates the presence of the organic sticky pollutants, the collaborative control module controls the low-power laser cleaner to start before the airflow-guided air curtain generator to pre-treat the designated area.

[0011] Furthermore, in the solar panel edge enhancement system of the present invention, the vibration phase difference of multiple piezoelectric ceramic elements in the piezoelectric actuated dust removal array is adjustable to generate traveling wave propagating mechanical vibration on the surface of the solar panel frame, thereby driving dust in a preset direction.

[0012] Furthermore, in the solar panel edge enhancement system of the present invention, the formula for calculating the cleaning efficiency by the effect evaluation module is: Cleaning efficiency = (average gray value before cleaning - average gray value after cleaning) / average gray value before cleaning × 100%; the effect evaluation module is also configured to send a cleaning instruction to the collaborative control module when the cleaning efficiency is lower than a preset threshold.

[0013] Furthermore, in the solar panel edge enhancement system of the present invention, after receiving the evaluation data, the edge dust accumulation analysis module dynamically adjusts the coefficients of the weighting factors using the recursive least squares method to adapt the dust accumulation prediction model under different environments.

[0014] Furthermore, in the solar panel edge enhancement system of the present invention, the preset direction is directed towards the main coverage area of ​​the directional laminar flow of the airflow-guided wind curtain generator, and the propagation speed of the traveling wave-like mechanical vibration is matched with the flow velocity of the directional laminar flow to optimize the efficiency of dust particle stripping and removal.

[0015] Beneficial effects of this invention; This invention achieves accurate perception and quantitative assessment of the dust accumulation status at the edges of solar panels through the collaborative work of an image acquisition module and an edge dust accumulation analysis module; it forms a hierarchical processing capability for different dust accumulation characteristics by intelligently scheduling multiple types of cleaning devices through a collaborative control module; and it enables the system to have adaptive characteristics for continuous optimization through a closed-loop feedback mechanism constructed by an effect evaluation module. Ultimately, it significantly reduces energy consumption while improving cleaning efficiency and effectively maintaining the light transmittance of the solar panel surface, thereby ensuring the long-term stable operation of the photovoltaic power generation system. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the system architecture of the present invention. Detailed Implementation

[0018] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0019] Please see Figure 1 The solar panel edge enhancement system provided by the present invention includes an equipment device and a control device, wherein the control device establishes a communication connection with the equipment device; The control device includes: The image acquisition module is configured to acquire surface image data of the edge area of ​​the solar panel using an industrial camera deployed on the photovoltaic array support. The edge dust accumulation analysis module is configured to receive the surface image data and generate a dust accumulation level map characterizing the dust accumulation thickness and coverage density based on the gray value distribution of the surface image data and real-time meteorological data. The collaborative control module is configured to receive the ash accumulation level map and calculate and generate a combination of control parameters based on it. The combination of control parameters includes actuation frequency, spray angle and scanning path. The equipment includes: A piezoelectric actuated dust removal array, embedded in the frame of a solar panel, is configured to receive the actuation frequency and generate high-frequency mechanical vibration to break the adhesion between dust and the panel surface. A flow-directing air curtain generator, installed above the edge of the solar panel, is configured to receive the jet angle and generate directional laminar flow to blow away and remove loose dust particles. A low-power laser cleaner, integrated into the bracket, is configured to receive the scanning path and emit a laser beam to remove residual contaminants; An effect evaluation module, configured in the control device, is used to trigger the image acquisition module to acquire images again after the device performs an operation, and calculate the cleaning efficiency by comparing the image data before and after cleaning, and feed the evaluation data back to the edge dust accumulation analysis module. The system, through the coordinated action of the control device and the equipment, is used to suppress dust accumulation at the edges of the solar panel and maintain the stability of photoelectric conversion efficiency.

[0020] In the implementation of the solar panel edge enhancement system, the control device initiates the cleaning process via an image acquisition module. Industrial cameras deployed on the photovoltaic array support acquire images of the panel edge area at preset intervals. The acquired surface image data is transmitted to the control center via a wireless network. During transmission, the image acquisition module calls its built-in preprocessing program to perform Gaussian filtering on the raw images to suppress environmental noise interference and uses histogram equalization technology to enhance the contrast between dusty and clean areas, providing a high-quality image foundation for subsequent analysis.

[0021] After receiving the preprocessed surface image data, the edge dust accumulation analysis module divides the image into several standard-sized analysis grids. The variance of the grayscale values ​​of each analysis grid is extracted as an index of dust accumulation distribution uniformity, while the average grayscale value serves as a reference value for dust accumulation thickness. This module simultaneously accesses real-time wind speed and humidity data transmitted from a weather station, using meteorological parameters as dynamic weighting factors. A weighted fusion algorithm is then used to convert image feature values ​​into quantified dust accumulation levels. Finally, a dust accumulation level map is generated in grid units, where each grid cell is labeled with its corresponding dust accumulation severity level.

[0022] The collaborative control module performs multi-parameter collaborative calculations based on the dust accumulation level map. For the piezoelectric actuation dust removal array embedded in the solar panel frame, its actuation frequency is adaptively adjusted based on the dust adhesion intensity displayed by the grid cells. The adhesion intensity data is derived from the correlation analysis of historical humidity records and a dust composition database. The jet angle of the airflow-guided air curtain generator is synthesized by vector calculation with the photovoltaic panel tilt angle and real-time wind direction data to ensure that the directional laminar flow can cover the maximum effective area. The scanning path planning of the low-power laser cleaner adopts a greedy algorithm principle, prioritizing the processing of continuous areas with the highest dust accumulation level.

[0023] When control parameters are sent to the equipment, the piezoelectric ceramic elements in the piezoelectric actuated dust removal array generate high-frequency mechanical vibrations according to the specified frequency. By adjusting the vibration phase difference between adjacent elements within the array, a traveling wave propagation effect can be formed on the surface of the solar panel frame, causing dust particles to move in a preset aggregation direction. After the flow-guiding air curtain generator is activated synchronously, the directional laminar flow it generates coordinates with the direction of traveling wave propagation, peeling loosened dust particles from the panel surface and blowing them to the detachment area. For stubborn pollutants, a low-power laser cleaner emits a laser beam of a specific wavelength, decomposing the molecular structure of organic sticky substances through a photothermal effect.

[0024] The performance evaluation module triggers secondary image acquisition after the device completes its operation, calculating cleaning efficiency by comparing the changes in grayscale values ​​of corresponding grids before and after cleaning. When the cleaning efficiency in a local area is detected to be lower than a preset threshold, the evaluation module automatically generates a re-cleaning instruction and sends it to the collaborative control module. Simultaneously, cleaning efficiency data and corresponding environmental parameters are fed back to the edge dust accumulation analysis module for dynamically adjusting the weighting factor coefficients, enabling the dust accumulation prediction model to continuously optimize. The system achieves precise suppression of edge dust accumulation through a closed-loop control mechanism, ultimately achieving the technical goal of maintaining stable photoelectric conversion efficiency.

[0025] In the preprocessing of surface image data by the image acquisition module, the Gaussian filtering algorithm uses a 3×3 convolution kernel for spatial domain filtering, effectively suppressing random interference caused by illumination fluctuations or camera noise. Histogram equalization processing, targeting images with concentrated gray-level distributions, enhances the contrast difference between the gray-accumulated areas and the glass substrate by expanding the dynamic range of gray levels. This preprocessing mechanism is particularly crucial in backlit conditions in the early morning or in hazy weather, as it can eliminate the influence of environmental factors on image quality and provide a well-defined data foundation for subsequent analysis.

[0026] The edge dust accumulation analysis module divides surface image data into a 5cm × 5cm analysis grid. The variance of the grayscale value of each grid reflects the dispersion of dust distribution, and the average grayscale value is negatively correlated with the dust accumulation thickness. The module's built-in wind speed weight factor decreases as speed increases, while the humidity weight factor has a non-linear growth curve set according to the condensation risk. The weighted fusion algorithm uses the least squares method to fit historical data to establish a regression model, ultimately outputting a physically meaningful dust accumulation level value, achieving an accurate mapping from image features to the physical quantity of dust accumulation.

[0027] The parameter mapping relationship of the collaborative control module is established based on a large amount of field test data. The positive correlation curve between actuation frequency and adhesion strength is calibrated through vibration transmission efficiency experiments to ensure effective dust removal under different humidity conditions. The vector synthesis calculation of the spray angle incorporates a wind direction fluctuation compensation coefficient to prevent sudden gusts from causing airflow coverage deviation. The descending order arrangement strategy of the scanning path, combined with grid neighborhood relationships, optimizes the path, reduces the idle travel time of the laser cleaner, and improves the overall cleaning efficiency.

[0028] The low-power laser cleaner uses a 1064nm wavelength, which is near the absorption peak of water and exhibits selective absorption characteristics for organic pollutants containing moisture. The laser beam is transmitted to the scanning mirror assembly via optical fiber, and the photothermal effect causes the moisture inside the pollutants to vaporize instantaneously, creating a micro-explosion phenomenon. This mechanism is particularly effective for sticky substances such as bird droppings and pollen, achieving molecular-level removal without damaging the glass antireflective coating.

[0029] When the dust accumulation level map indicates the presence of high-humidity organic pollutants in a certain area, the collaborative control module initiates a timing optimization program. A low-power laser cleaner first irradiates the target area, reducing the viscosity of the pollutants through photothermal decomposition. A flow-guiding air curtain generator activates after a 0.5-second delay, utilizing laminar flow to peel off the embrittled pollutants as a whole. This step-by-step treatment method prevents the diffusion of wet pollutants under airflow, thus avoiding secondary pollution.

[0030] The phase difference control of the piezoelectric actuated dust removal array employs sinusoidal phase-by-phase delay technology, enabling the mechanical vibration wave to propagate directionally along the frame at a speed of 0.5 m / s. The Coriolis force generated by the traveling wave drives the dust particles to migrate in a directional manner, making it particularly suitable for cleaning dust accumulation in the frame grooves. By adjusting the phase angle of the piezoelectric element's drive signal, the propagation direction of the traveling wave can be flexibly changed to adapt to photovoltaic panels with different installation tilt angles.

[0031] The cleaning efficiency calculation in the effectiveness evaluation module uses an analysis grid as the basic unit, performing differential calculations on the grayscale values ​​of the same coordinate area before and after cleaning. When the cleaning efficiency of a certain grid is below 85%, the module automatically marks that area as a residual focus area and sends a re-cleaning instruction, including coordinate information, to the collaborative control module. This grid-based precise evaluation mechanism avoids over-cleaning of slightly contaminated areas and reduces equipment energy consumption.

[0032] The edge dust accumulation analysis module uses recursive least squares to dynamically update the weight factor coefficients. After receiving performance evaluation data each time, the algorithm adjusts the wind speed-humidity weight matrix with a learning rate of 0.1. This adaptive mechanism enables the dust accumulation prediction model to track seasonal climate changes, such as automatically increasing the humidity weight coefficient during the rainy season and increasing the wind speed influence weight during the dry season, maintaining the stability of prediction accuracy under different climatic conditions.

[0033] The traveling wave propagation direction is set to point towards the laminar core region of the airflow-guided air curtain generator, with the propagation velocity maintaining a fixed ratio of 1:1.2 to the laminar flow velocity. This velocity matching design ensures that dust particles detached from the plate surface precisely enter the region of maximum airflow kinetic energy, achieving optimal carrying effect. The system offers three velocity ratio configurations for dust particles of different sizes, allowing users to select the optimal parameter combination based on the predominant dust type in their local area.

[0034] The solar panel edge enhancement system is primarily designed for outdoor applications in large-scale ground-mounted photovoltaic power plants. After system startup, the control unit initiates the cleaning process via the image acquisition module. Industrial cameras deployed on the photovoltaic array support periodically acquire images of the panel edge areas. The acquired surface image data is transmitted to the control center via a wireless network. During transmission, the image acquisition module uses a built-in preprocessing program to perform Gaussian filtering on the raw images to suppress environmental noise interference and employs histogram equalization technology to enhance the contrast between dusty and clean areas.

[0035] The edge dust accumulation analysis module receives preprocessed surface image data and divides the image into standard-sized analysis grids. The variance of grayscale values ​​in each grid is extracted as an index of dust accumulation distribution uniformity, while the average grayscale value serves as a reference value for dust accumulation thickness. This module simultaneously accesses real-time wind speed and humidity data transmitted from a weather station, using meteorological parameters as dynamic weighting factors. A weighted fusion algorithm transforms image feature values ​​into quantified dust accumulation levels. Finally, a dust accumulation level map is generated, organized by grid, with each grid cell labeled with its corresponding dust accumulation severity level.

[0036] The collaborative control module performs multi-parameter collaborative calculations based on the dust accumulation level map. For the piezoelectric actuation dust removal array embedded in the solar panel frame, its actuation frequency is adaptively adjusted based on the dust adhesion intensity displayed by the grid cells. The adhesion intensity data is derived from the correlation analysis of historical humidity records and a dust composition database. The jet angle of the airflow-guided air curtain generator is synthesized through vector calculations using the photovoltaic panel tilt angle and real-time wind direction data, enabling directional laminar flow to cover the maximum effective area. The scanning path planning of the low-power laser cleaner prioritizes the processing of continuous areas with the highest dust accumulation level.

[0037] When control parameters are sent to the equipment, the piezoelectric ceramic elements in the piezoelectric actuated dust removal array generate high-frequency mechanical vibrations according to the specified frequency. By adjusting the vibration phase difference between adjacent elements within the array, a traveling wave propagation effect is formed on the surface of the solar panel frame, causing dust particles to move in a preset aggregation direction. After the flow-guiding air curtain generator is activated synchronously, the directional laminar flow it generates coordinates with the direction of the traveling wave propagation, peeling loosened dust particles from the panel surface and blowing them to the detachment area. For stubborn pollutants, a low-power laser cleaner emits a laser beam of a specific wavelength, which decomposes the molecular structure of organic sticky substances through a photothermal effect.

[0038] The performance evaluation module triggers secondary image acquisition after the device completes its operation, calculating cleaning efficiency by comparing the changes in grayscale values ​​of corresponding grids before and after cleaning. When the cleaning efficiency in a local area is detected to be lower than a preset threshold, the evaluation module automatically generates a re-cleaning instruction and sends it to the collaborative control module. Simultaneously, cleaning efficiency data and corresponding environmental parameters are fed back to the edge dust accumulation analysis module for dynamically adjusting the weighting factor coefficients, enabling the dust accumulation prediction model to continuously optimize. The system achieves precise suppression of edge dust accumulation through a closed-loop control mechanism, ultimately achieving the technical goal of maintaining stable photoelectric conversion efficiency.

[0039] When implemented in areas with frequent sandstorms, the system addresses rapid dust accumulation by increasing image acquisition frequency. For morning dew conditions, the edge dust accumulation analysis module automatically increases the weighting of humidity parameters to accurately identify highly adhesive, wet dust. When meteorological data indicates impending rainfall, the collaborative control module delays cleaning operations to avoid water waste. This adaptive mechanism allows the system to adapt to different regional and seasonal climatic characteristics, maintaining stable cleaning performance.

[0040] The system's communication connection adopts an industrial wireless network protocol to ensure reliable data transmission between the control device and the equipment. For large-scale photovoltaic arrays, multiple edge enhancement systems can form a distributed clean energy system through the network, with clean energy resources centrally managed by the control platform. The power supply for the equipment is directly derived from the electricity generated by the photovoltaic panels, achieving an energy self-sufficiency operation mode. The system adopts a modular design, facilitating the retrofitting and upgrading of existing photovoltaic power plants and the integrated installation of new power plants.

[0041] This invention addresses the issue of reduced power generation efficiency caused by dust accumulation at the edges of solar panels by constructing a closed-loop controlled intelligent cleaning system. The system first periodically acquires surface image data of the solar panel edge area using an image acquisition module, visually reflecting the dust accumulation status using grayscale distribution characteristics. An edge dust accumulation analysis module divides the image data into an analysis grid, combining wind speed and humidity parameters from real-time meteorological data to generate a quantified dust accumulation level map using a weighted fusion algorithm, accurately characterizing dust thickness and coverage density. A collaborative control module dynamically calculates optimized control parameter combinations based on the dust accumulation level map, including an actuation frequency targeting dust adhesion strength, a spray angle adapting to the solar panel tilt angle and wind direction, and a scanning path based on dust distribution priority. In the equipment, a piezoelectric actuated dust removal array generates high-frequency mechanical vibration to break down dust adhesion, a flow-guiding air curtain generator simultaneously generates directional laminar flow to sweep away loose particles, and a low-power laser cleaner selectively removes residual contaminants. An effectiveness evaluation module calculates cleaning efficiency by comparing image data before and after cleaning and feeds the evaluation data back to the analysis module, enabling adaptive adjustment of the prediction model. The system continuously suppresses edge dust accumulation and maintains the light transmittance of the solar panel surface through a closed-loop process of data acquisition, analysis and decision-making, precise execution and effect verification, thereby ensuring the stability of photoelectric conversion efficiency.

[0042] In outdoor applications of large-scale ground-mounted photovoltaic power plants, the solar panel edge enhancement system achieves efficient dust suppression through the following specific implementation methods. After system startup, industrial cameras deployed on the photovoltaic array support periodically acquire images of the panel edge area. The acquired surface image data is transmitted to the control center via a wireless network. The image acquisition module has a built-in preprocessing program that performs Gaussian filtering on the raw images to eliminate environmental noise interference and uses histogram equalization technology to enhance the contrast between dusty and clean areas.

[0043] The edge dust accumulation analysis module divides the preprocessed image into a standard-sized analysis grid, extracts the variance of grayscale values ​​for each grid as an index of dust accumulation distribution uniformity, and calculates the average grayscale value as a reference value for dust accumulation thickness. This module synchronously integrates real-time wind speed and humidity data transmitted from weather stations, and uses a weighted fusion algorithm to convert image feature values ​​and meteorological parameters into a quantified dust accumulation level map. For example, under morning dew conditions, the system automatically increases the weighting coefficient of the humidity parameter to accurately identify highly adhesive, moist dust.

[0044] The collaborative control module dynamically calculates control parameter combinations based on the dust accumulation level map. For the piezoelectric actuated dust removal array, its actuation frequency is adaptively adjusted based on the dust adhesion intensity displayed by the grid cells. The adhesion intensity data is derived from the correlation analysis of historical humidity records and a dust composition database. The jet angle of the airflow-guided air curtain generator is synthesized from the photovoltaic panel tilt angle and real-time wind direction data through vector calculation to ensure maximum effective coverage of the directional laminar flow. The scanning path of the low-power laser cleaner adopts a greedy algorithm principle, prioritizing the processing of continuous areas with the highest dust accumulation level.

[0045] When control parameters are sent to the equipment, the piezoelectric ceramic elements in the piezoelectric actuated dust removal array generate high-frequency mechanical vibrations according to the specified frequency. By adjusting the vibration phase difference between adjacent elements within the array, a traveling wave propagation effect is formed on the surface of the solar panel frame, causing dust particles to move in a preset aggregation direction. After the flow-guiding air curtain generator is activated synchronously, the directional laminar flow it generates coordinates with the traveling wave propagation direction, peeling the loosened dust particles off the panel surface. For organic pollutants such as bird droppings, a low-power laser cleaner emits a laser beam of a specific wavelength, decomposing the molecular structure of the pollutants through a photothermal effect.

[0046] The performance evaluation module triggers secondary image acquisition after the cleaning operation is completed, calculating the cleaning efficiency by comparing the changes in grayscale values ​​of the corresponding grids before and after cleaning. When the cleaning efficiency of a local area is detected to be lower than a preset threshold, the evaluation module automatically generates a re-cleaning instruction and sends it to the collaborative control module. Simultaneously, the cleaning efficiency data and corresponding environmental parameters are fed back to the edge dust accumulation analysis module, which uses recursive least squares to dynamically adjust the weighting factor coefficients, enabling the dust accumulation prediction model to continuously optimize. Through this closed-loop control mechanism, the system achieves precise suppression of edge dust accumulation, effectively maintaining the stability of photoelectric conversion efficiency.

Claims

1. A solar panel edge enhancement system, characterized in that, It includes equipment and a control device, wherein the control device establishes a communication connection with the equipment. The control device includes: The image acquisition module is configured to acquire surface image data of the edge area of ​​the solar panel using an industrial camera deployed on the photovoltaic array support. The edge dust accumulation analysis module is configured to receive the surface image data and generate a dust accumulation level map characterizing the dust accumulation thickness and coverage density based on the gray value distribution of the surface image data and real-time meteorological data. The collaborative control module is configured to receive the ash accumulation level map and calculate and generate a combination of control parameters based on it. The combination of control parameters includes actuation frequency, spray angle and scanning path. The equipment includes: A piezoelectric actuated dust removal array, embedded in the frame of a solar panel, is configured to receive the actuation frequency and generate high-frequency mechanical vibration to break the adhesion between dust and the panel surface. A flow-directing air curtain generator, installed above the edge of the solar panel, is configured to receive the jet angle and generate directional laminar flow to blow away and remove loose dust particles. A low-power laser cleaner, integrated into the bracket, is configured to receive the scanning path and emit a laser beam to remove residual contaminants; An effectiveness evaluation module, configured in the control device, is used to trigger the image acquisition module to acquire images again after the device performs an operation, calculate the cleaning efficiency by comparing the image data before and after cleaning, and feed the evaluation data back to the edge dust accumulation analysis module.

2. The solar panel edge enhancement system as described in claim 1, characterized in that, The image acquisition module performs the following preprocessing operations on the surface image data: it uses a Gaussian filtering algorithm for noise filtering and a histogram equalization algorithm for contrast enhancement, in order to improve the recognition accuracy of the edge ash accumulation analysis module for ash accumulation features.

3. The solar panel edge enhancement system as described in claim 1, characterized in that, The specific steps of the edge ash accumulation analysis module in generating the ash accumulation level map include: dividing the surface image data into multiple analysis grids; for each analysis grid, extracting its gray value variance as a first indicator of ash accumulation distribution uniformity, and extracting its average gray value as a second indicator of ash accumulation thickness; using wind speed and humidity in the real-time meteorological data as weighting factors, weighting and fusing the first indicator and the second indicator, and outputting the ash accumulation level of each analysis grid.

4. The solar panel edge enhancement system as described in claim 3, characterized in that, When the collaborative control module calculates the combination of control parameters, it performs the following mapping: the actuation frequency is positively correlated with the adhesion strength reflected by the dust accumulation level; the spray angle is the combined angle of the photovoltaic panel tilt angle and the current wind direction vector; the scanning path is arranged in descending order according to the dust accumulation level of the analysis grid to determine the priority order.

5. The solar panel edge enhancement system as described in claim 1, characterized in that, The low-power laser cleaner emits a laser beam with a wavelength of 1064 nm, which has a selective absorption effect on organic sticky pollutants and breaks the molecular bonds of the pollutants through photothermal action.

6. The solar panel edge enhancement system as described in claim 5, characterized in that, The startup sequence of the airflow-guided air curtain generator and the low-power laser cleaner is coordinated by the collaborative control module: when the ash accumulation level spectrum indicates the presence of the organic sticky pollutants, the collaborative control module controls the low-power laser cleaner to start before the airflow-guided air curtain generator to pre-treat the designated area.

7. The solar panel edge enhancement system as described in claim 1, characterized in that, The vibration phase difference of multiple piezoelectric ceramic elements in the piezoelectric actuated dust removal array is adjustable to generate traveling wave propagating mechanical vibration on the surface of the solar panel frame, thereby driving dust in a preset direction.

8. The solar panel edge enhancement system as described in claim 1, characterized in that, The formula for calculating the cleaning efficiency by the effect evaluation module is: Cleaning efficiency = (Average gray value before cleaning - Average gray value after cleaning) / Average gray value before cleaning × 100%; The effect evaluation module is also configured to send a cleaning instruction to the collaborative control module when the cleaning efficiency is lower than a preset threshold.

9. The solar panel edge enhancement system as described in claim 3, characterized in that, After receiving the evaluation data, the edge dust accumulation analysis module dynamically adjusts the coefficients of the weighting factors using the recursive least squares method to adapt the dust accumulation prediction model to different environments.

10. The solar panel edge enhancement system as described in claim 7, characterized in that, The preset direction points to the main coverage area of ​​the directional laminar flow of the air curtain generator, and the propagation speed of the traveling wave-like mechanical vibration matches the flow velocity of the directional laminar flow to optimize the efficiency of dust particle stripping and removal.

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