Distributed photovoltaic power station operation and maintenance method and equipment

Abstract

The invention is suitable for the technical field of photovoltaic operation and maintenance, and particularly relates to a distributed photovoltaic power station operation and maintenance method and equipment, and the method comprises the steps: obtaining image data and operation data of a distributed photovoltaic power station, and providing a detailed data basis for subsequent analysis; according to the image data of the distributed photovoltaic power station, the first operation and maintenance parameter of the distributed photovoltaic power station is determined, and the physical anomaly of the photovoltaic module is identified by evaluating the abnormal condition in the image data; according to the operation data of the distributed photovoltaic power station, second operation and maintenance parameters of the distributed photovoltaic power station are determined, and the problem of electrical performance or thermal performance is detected by evaluating an abnormal mode in the operation data; based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, operation and maintenance management is carried out on the distributed photovoltaic power station, the operation and maintenance management level of the photovoltaic power station is improved, the operation and maintenance efficiency and accuracy are enhanced, and potential problems can be found and solved in time.

Description

Technical Field

[0001] The present application belongs to the field of photovoltaic operation and maintenance technology, and in particular, relates to a distributed photovoltaic power station operation and maintenance method and equipment. Background Art

[0002] Photovoltaic operation and maintenance refers to the regular inspection, maintenance and management of photovoltaic power generation systems to ensure their efficient, safe and reliable operation. Good photovoltaic operation and maintenance can not only extend the service life of the system, but also improve power generation efficiency, reduce operating costs, and ensure the safety and compliance of the power station.

[0003] Existing photovoltaic operation and maintenance methods are relatively backward. Many photovoltaic power stations still rely on regular manual on-site inspections, which is time-consuming and labor-intensive, and it is difficult to achieve high-frequency and full-coverage inspections. The insufficient application of automated and intelligent operation and maintenance tools leads to slow fault detection and response, and the inability to handle emergencies in a timely manner. Summary of the invention

[0004] The embodiments of the present application provide a distributed photovoltaic power station operation and maintenance method and equipment, which can solve the technical problems in the photovoltaic operation and maintenance process, such as insufficient application of automated and intelligent operation and maintenance tools, slow fault detection and response speed, and inability to handle sudden problems in a timely manner.

[0005] In a first aspect, an embodiment of the present application provides a distributed photovoltaic power station operation and maintenance method, including: Obtain image data and operation data of distributed photovoltaic power stations; Determine a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station; wherein the first operation and maintenance parameter is used to reflect the physical abnormality of the distributed photovoltaic power station; Determining a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station; wherein the second operation and maintenance parameter is used to reflect the abnormal operation of the distributed photovoltaic power station; Based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, operation and maintenance management of the distributed photovoltaic power station is performed.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The distributed photovoltaic power station operation and maintenance method provided in the embodiment of the present application provides a detailed data basis for subsequent analysis by acquiring image data and operation data of the distributed photovoltaic power station. According to the image data of the distributed photovoltaic power station, the first operation and maintenance parameter of the distributed photovoltaic power station is determined, so that the physical anomalies of the photovoltaic components can be identified by evaluating the abnormal conditions in the image data. According to the operation data of the distributed photovoltaic power station, the second operation and maintenance parameter of the distributed photovoltaic power station is determined, so that problems in electrical performance or thermal performance can be detected by evaluating the abnormal patterns in the operation data. Based on the first operation and maintenance parameters of the distributed photovoltaic power station and the second operation and maintenance parameters, the distributed photovoltaic power station is operated and managed, which improves the operation and maintenance management level of the photovoltaic power station, enhances the operation and maintenance efficiency and accuracy, and helps to timely discover and solve potential problems.

[0007] In a second aspect, an embodiment of the present application provides a distributed photovoltaic power station operation and maintenance system, including: An acquisition unit, used for acquiring image data and operation data of a distributed photovoltaic power station; An image unit, used to determine a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station; wherein the first operation and maintenance parameter is used to reflect the physical abnormality of the distributed photovoltaic power station; An operation unit, configured to determine a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station; wherein the second operation and maintenance parameter is used to reflect an abnormal operation of the distributed photovoltaic power station; A management unit is used to perform operation and maintenance management on the distributed photovoltaic power station based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station.

[0008] In a third aspect, an embodiment of the present application provides a distributed photovoltaic power station operation and maintenance device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method as described in any one of the above aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a distributed photovoltaic power station operation and maintenance device, the distributed photovoltaic power station operation and maintenance device executes the method of any one of the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a flow chart of a distributed photovoltaic power station operation and maintenance method provided by an embodiment of the present application; Figure 2 It is an operation schematic diagram of a distributed photovoltaic power station operation and maintenance method provided by an embodiment of the present application; Figure 3 It is a flowchart of step S230 in the distributed photovoltaic power station operation and maintenance method provided in one embodiment of the present application; Figure 4 It is a structural schematic diagram of a distributed photovoltaic power station operation and maintenance system provided by an embodiment of the present application; Figure 5 It is a structural schematic diagram of a distributed photovoltaic power station operation and maintenance equipment provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] Existing photovoltaic operation and maintenance methods are relatively backward. Many photovoltaic power stations still rely on regular manual on-site inspections, which is time-consuming and labor-intensive, and it is difficult to achieve high-frequency and full-coverage inspections. The insufficient application of automated and intelligent operation and maintenance tools leads to slow fault detection and response, and the inability to handle emergencies in a timely manner.

[0020] To solve the above problems, the embodiments of the present application provide a distributed photovoltaic power station operation and maintenance method and equipment. In this method, by acquiring the image data and operation data of the distributed photovoltaic power station, a detailed data basis is provided for subsequent analysis. According to the image data of the distributed photovoltaic power station, the first operation and maintenance parameter of the distributed photovoltaic power station is determined, so that the physical anomaly of the photovoltaic module can be identified by evaluating the abnormal situation in the image data. According to the operation data of the distributed photovoltaic power station, the second operation and maintenance parameter of the distributed photovoltaic power station is determined, so that the electrical performance or thermal performance problems can be detected by evaluating the abnormal mode in the operation data. Based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, the distributed photovoltaic power station is operated and managed, which improves the operation and maintenance management level of the photovoltaic power station, enhances the operation and maintenance efficiency and accuracy, and helps to timely discover and solve potential problems.

[0021] The distributed photovoltaic power station operation and maintenance method provided in the embodiment of the present application can be applied to distributed photovoltaic power station operation and maintenance equipment. At this time, the distributed photovoltaic power station operation and maintenance equipment is the executor of the distributed photovoltaic power station operation and maintenance method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of distributed photovoltaic power station operation and maintenance equipment.

[0022] For example, the distributed photovoltaic power station operation and maintenance equipment can be a mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, desktop computer, smart large screen, smart TV, handheld device with wireless communication function, computing device or other processing device connected to a wireless modem, vehicle-mounted equipment, Internet of Vehicles terminal, computer, laptop computer, communication equipment, computing equipment, etc.

[0023] In order to better understand the distributed photovoltaic power station operation and maintenance method provided in the embodiment of the present application, the specific implementation process of the distributed photovoltaic power station operation and maintenance method provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of a distributed photovoltaic power station operation and maintenance method provided in an embodiment of the present application is shown. Figure 2 The operation flow chart of the distributed photovoltaic power station operation and maintenance method provided in the embodiment of the present application is shown. The distributed photovoltaic power station operation and maintenance method includes: S100, acquiring image data and operation data of a distributed photovoltaic power station.

[0025] It can be understood that the image data and operation data of distributed photovoltaic power stations are two different types of input data. Image data can come from cameras installed at different locations in the power station, which collect photos or videos of photovoltaic panels, photovoltaic brackets and other equipment. Image data can reflect the physical condition of the power station, such as damage, stains, obstructions on the surface of photovoltaic panels or deformation of photovoltaic brackets. Operation data can include the real-time power generation, battery storage status, system load, temperature and other electrical parameters of the power station. Image data and operation data can be obtained through sensors and monitoring equipment, or by manual upload. Distributed photovoltaic power station image data and operation data can be analyzed by performing separate analyses to more comprehensively evaluate the health of the power station and formulate effective operation and maintenance strategies.

[0026] S200, determining a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station; wherein the first operation and maintenance parameter is used to reflect the physical abnormality of the distributed photovoltaic power station.

[0027] It can be understood that the first operation and maintenance parameter reflects the abnormality of the physical entity of the photovoltaic power station, such as damage to the photovoltaic panel surface and photovoltaic bracket. The image data can be processed to convert the color space of the photovoltaic panel surface, so that the RGB space is converted into a color space more suitable for analysis (such as HSV or Lab), highlighting the details and damage of the photovoltaic panel surface. The converted color space image is subjected to color segmentation processing to determine the color distribution of the color space image and identify abnormal areas of the photovoltaic panel surface, such as discoloration or stains. By detecting the threshold of the color distribution, the area with abnormal color characteristics can be extracted, and then the abnormal degree of the photovoltaic panel surface is calculated to form a quantitative abnormal coefficient, which is used to reflect the physical abnormal degree of the photovoltaic panel surface, that is, the first abnormal coefficient. For abnormal detection of photovoltaic brackets, the geometric features of the photovoltaic bracket can be extracted by image processing of the photovoltaic bracket area. By performing spatial analysis on the photovoltaic bracket image, possible structural problems such as deformation or corrosion can be identified. Structural problems can be manifested as morphological changes of the photovoltaic bracket, such as bending or deformation areas. By calculating the degree of change in the surface area of ​​the photovoltaic bracket, an abnormal coefficient reflecting the degree of physical abnormality of the photovoltaic bracket can be obtained, namely the second abnormal coefficient. By combining the first abnormal coefficient and the second abnormal coefficient, a parameter reflecting the overall physical abnormality of the distributed photovoltaic power station can be obtained, namely the first operation and maintenance parameter, so as to make an accurate assessment of the physical health of the power station and timely identify potential equipment failures or damages.

[0028] In a possible implementation, S200, determining a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station, includes: S210, performing image recognition based on the image data of the distributed photovoltaic power station to obtain a first area image set and a second area image set of the distributed photovoltaic power station; wherein the first area image set is used to reflect the physical condition of the photovoltaic panel surface of the distributed photovoltaic power station; and the second area image set is used to reflect the physical condition of the photovoltaic bracket of the distributed photovoltaic power station.

[0029] It can be understood that in order to accurately perform specific detection on the photovoltaic panel surface and photovoltaic bracket, the image data of the photovoltaic power station must capture different areas of the photovoltaic panel surface and photovoltaic bracket respectively. Therefore, the photovoltaic power station will be photographed from multiple angles during image acquisition, and the photovoltaic panel surface and photovoltaic bracket area will be photographed separately. Because the photovoltaic panel surface and photovoltaic bracket have different positions in physical space, their shapes, sizes and detection methods are also different. The image of the photovoltaic panel area mainly focuses on the surface state of the battery panel, while the photovoltaic bracket area focuses on the structural characteristics and support state of the photovoltaic bracket. Edge detection algorithms (such as the Canny algorithm) can be used to extract edge information in the image, and morphological operations such as expansion, corrosion, opening operation, closing operation, etc. can be used to further process the edge image, and the geometric shape features of the photovoltaic panel surface and photovoltaic bracket are analyzed, so that the photovoltaic panel surface and photovoltaic bracket images can be effectively distinguished. After all images are distinguished, all photovoltaic panel area images are taken as the first area image set, and all photovoltaic bracket area images are taken as the second area image set, and they are stored separately.

[0030] S220, based on the first regional image set of the distributed photovoltaic power station, determine a first abnormality coefficient corresponding to the first regional image set; wherein the first abnormality coefficient is used to reflect the abnormal situation of the photovoltaic panel surface of the distributed photovoltaic power station.

[0031] It can be understood that the first area image set can be converted into a color space, and a suitable color model can be selected for analysis, so as to accurately determine the abnormality of the photovoltaic panel surface by capturing the color changes in the image. Through the color space conversion, the color features of the images in the first area image set are extracted to form a color distribution data set that can be analyzed, and the color segmentation method is used to distinguish the color areas in the image, and the threshold detection technology is used to identify the areas where abnormal colors appear in the image. The area of ​​abnormal color may indicate stains, pollution or damage on the photovoltaic panel surface. By analyzing the area and distribution of the abnormal color area, an abnormal color distribution data reflecting the degree of abnormality can be calculated, and based on the abnormal color distribution data, the first abnormal coefficient of the first area image set is obtained to reflect the abnormality of the photovoltaic panel surface.

[0032] Optionally, S220, based on a first regional image set of a distributed photovoltaic power station, determining a first abnormality coefficient corresponding to the first regional image set includes: S221, performing color space conversion on images in a first region image set of a distributed photovoltaic power station to obtain a color space image set of the distributed photovoltaic power station.

[0033] It can be understood that color space conversion is to convert an image from a standard RGB color space to a color space that is more suitable for image analysis and processing. The RGB color space is mainly based on three channels: red, green, and blue, while color spaces such as HSV (hue, saturation, brightness) or Lab (brightness, green-red channel, blue-yellow channel) can better express the visual perception characteristics of an image. For example, the hue information in the HSV color space can effectively distinguish different colors, while saturation and brightness help analyze the intensity and detail changes of colors. The images in the first region image set can be converted into a color space to obtain a set of images in the first region image set in a color space, making it easier to extract color feature information from the image, such as the color difference of stains or damaged areas on the photovoltaic panel surface, providing clearer image data for subsequent image segmentation and anomaly detection.

[0034] S222, performing color segmentation on images in the color space image set of the distributed photovoltaic power station to obtain image color distribution data in the color space image set.

[0035] It can be understood that the purpose of color segmentation is to separate the regions of different colors in the image and analyze the distribution of different colors in the image. After conversion to color space, the color regions in the image can be distinguished using segmentation algorithms (such as threshold-based segmentation, K-means clustering, or Gaussian mixture models, etc.). The segmentation result is a marker of the color region, representing the different color regions in the image, such as the color difference between the surface of a photovoltaic panel and the background or other structures. The color distribution data in the image, i.e., the image color distribution data, can be obtained. The image color distribution data describes the spatial distribution of each color region in the image and provides a basis for subsequent abnormal color detection.

[0036] S223, performing threshold detection on the image color distribution data of the color space image set to obtain abnormal color distribution data of the first region image set.

[0037] It can be understood that threshold detection is to determine which color areas are beyond the normal range after image segmentation, representing possible abnormal situations. By presetting a threshold range corresponding to the color value of the normal photovoltaic panel surface, abnormal color distribution in the surface area of ​​the photovoltaic panel can be detected. For example, when the photovoltaic panel is stained, damaged or corroded, it will cause the color to change and present a different hue from the normal state. Through threshold detection, it is determined which areas have color deviations exceeding the set threshold range, and these areas are marked as abnormal areas, which helps to improve the accuracy of detection and reduce misjudgments caused by changes in lighting or other external factors.

[0038] S224, determining a first abnormality coefficient corresponding to the first region image set based on the abnormal color distribution data of the first region image set.

[0039] It can be understood that the first anomaly coefficient is a quantitative indicator used to indicate the abnormal situation in the first area image set, reflecting the degree of physical abnormality of the photovoltaic panel surface. Through further analysis of the abnormal color distribution data, a digital abnormality score, namely the first anomaly coefficient, can be obtained. The first anomaly coefficient reflects the severity of the abnormal color distribution in the photovoltaic panel area. After threshold detection, a binary image can be obtained to represent the abnormal area (usually 1) and the normal area (usually 0), and the proportion of the abnormal area in the entire photovoltaic panel area is calculated. The proportion of abnormal areas can be calculated by the following formula: Abnormal area proportion = number of pixels in the abnormal area / total number of pixels = ∑ i,j image(i,j) / (H×W) where image(i,j) is the value of the pixel in the i-th row and j-th column. If it is an abnormal area, it is 1, otherwise it is 0. H and W are the height and width of the image, respectively.

[0040] The degree of abnormality can be further determined by calculating the color difference in each abnormal area, and by calculating the color deviation of each pixel, for example, in HSV or Lab color space, calculating the Euclidean distance of the color value. Set the color center of each abnormal area to the average color of all pixels in the area, and then compare it with the color center of the normal area. The calculation of color deviation Z can be expressed as Euclidean distance: , where H1, S1, V1 are the color values ​​of the pixels in the abnormal area, and H2, S2, V2 are the average color values ​​of the pixels in the normal area. By calculating the color deviation of the pixels in all abnormal areas, the color deviation value of each area can be obtained.

[0041] The first abnormality coefficient can be obtained by weighting the proportion of the abnormal area and the color deviation to form the final abnormality coefficient. Assuming that a weighted formula is used to combine the proportion and color deviation, the first abnormality coefficient obtained is: first abnormality coefficient = α × abnormal area proportion + β × average color deviation, where α and β are weight coefficients used to control the contribution of the abnormal area proportion and color deviation to the abnormality coefficient. The values ​​of α and β can be determined through experience or experiments. The average color deviation is the average value of the color deviation of all pixels in the abnormal area. By combining the proportion of the abnormal area and the color deviation, the first abnormality coefficient obtained can effectively quantify the degree of abnormality of the photovoltaic panel. The larger the value of the first abnormality coefficient, the higher the degree of abnormality of the photovoltaic panel surface, and there may be more serious damage or failure.

[0042] S230, based on the second regional image set of the distributed photovoltaic power station, determine a second abnormality coefficient corresponding to the second regional image set; wherein the second abnormality coefficient is used to reflect the abnormal situation of the photovoltaic support of the distributed photovoltaic power station.

[0043] It can be understood that the geometric features can be extracted from the second region image set, see Figure 3 , select a suitable geometric analysis method for processing in order to accurately capture the deformation or damage of the photovoltaic bracket. Through geometric feature extraction, the shape, size and structural information of the photovoltaic bracket image in the second area image set are extracted to form a geometric data set for analysis. The morphological analysis method can be used to distinguish the photovoltaic bracket area in the image, and the structural analysis technology can be used to identify abnormal conditions such as deformation, bending or corrosion on the photovoltaic bracket. The existence of abnormal areas may indicate damage to the photovoltaic bracket or potential safety hazards. By analyzing the morphology, size and distribution of the abnormal area, an abnormal geometric feature distribution data reflecting the degree of abnormality can be calculated, that is, combined with the structural weakening index. At the same time, the mechanical model is used for stress analysis to calculate the stress response index of the photovoltaic bracket, which reflects the stress state of the photovoltaic bracket under the external load, that is, the stress response index. The second abnormal coefficient of the second area image set is obtained by combining the structural weakening index and the stress response index to reflect the abnormality of the photovoltaic bracket.

[0044] Optionally, S230, determining a second abnormality coefficient corresponding to the second regional image set based on the second regional image set of the distributed photovoltaic power station, includes: S231, based on the second regional image set of the distributed photovoltaic power station, obtaining a spatial lattice corresponding to the second regional image set.

[0045] It can be understood that the geometric feature area of ​​the photovoltaic bracket can be identified by extracting features from the photovoltaic bracket image in the second area image set. The two-dimensional pixel points on the image are converted into dot matrix data in three-dimensional space through geometric mapping and spatial coordinate transformation. With the help of laser radar (LiDAR) data or stereo vision technology, the depth value of the pixel can be calculated to establish a dot matrix in three-dimensional space to form a spatial dot matrix representation of the photovoltaic bracket.

[0046] S232: construct a three-dimensional feature set corresponding to the photovoltaic brackets of the distributed photovoltaic power station based on the space lattice corresponding to the second regional image set.

[0047] It can be understood that the three-dimensional feature set of the photovoltaic bracket can be constructed by performing spatial analysis on the spatial lattice extracted from the second area image set. According to the distribution information of the spatial lattice, the three-dimensional shape of the photovoltaic bracket can be obtained by using three-dimensional reconstruction technology, such as stereo matching or structured light scanning method. According to the constructed three-dimensional model, the geometric features of the photovoltaic bracket are extracted, such as the length, width, height, angle, degree of curvature, etc. of the photovoltaic bracket. The extracted geometric features constitute the three-dimensional feature set of the photovoltaic bracket, which provides a basis for subsequent structural analysis and defect detection.

[0048] S233, performing geometric feature analysis on the three-dimensional feature set corresponding to the photovoltaic bracket of the distributed photovoltaic power station to obtain a structural weakening index corresponding to the photovoltaic bracket of the distributed photovoltaic power station.

[0049] It can be understood that by geometrically analyzing the three-dimensional feature set of the photovoltaic bracket, the weakened areas that may affect the structural safety of the photovoltaic bracket can be identified. By analyzing the geometric features of the photovoltaic bracket, structural problems such as shape changes, bending, cracks and corrosion of the photovoltaic bracket can be identified. Geometric morphological methods, such as curvature analysis or deformation analysis, can be used to identify areas with obvious structural weakening. Based on these geometric analysis results, the structural weakening index of the photovoltaic bracket is calculated as an indicator of possible structural damage or weakening of the photovoltaic bracket.

[0050] Exemplarily, S233, performing geometric feature analysis on a three-dimensional feature set corresponding to a photovoltaic bracket of a distributed photovoltaic power station to obtain a structural weakening index corresponding to the photovoltaic bracket of the distributed photovoltaic power station, including: S2331, for a three-dimensional feature set corresponding to a photovoltaic support of a distributed photovoltaic power station, determine a discontinuous interval in the three-dimensional feature set; wherein the discontinuous interval is used to reflect an area where a linear interruption occurs in the three-dimensional feature set.

[0051] It can be understood that discontinuous intervals in a three-dimensional feature set can be identified through geometric morphological analysis methods, such as surface smoothing or boundary detection. Discontinuous intervals represent damaged, cracked or detached areas of the photovoltaic support structure and are key indicators for determining structural weakening. By calculating the change in the normal vector or tangent vector of the surface features of the photovoltaic support, the parts with sudden changes or interruptions in the geometric features can be found. These change areas and interruption areas are discontinuous intervals.

[0052] S2332, analyzing the discontinuous intervals in the three-dimensional feature set to determine defect data corresponding to the photovoltaic bracket of the distributed photovoltaic power station; wherein the defect data is used to reflect the geometric defect distribution of the photovoltaic bracket.

[0053] It can be understood that the discontinuous interval represents the fracture, deformation or damage of the photovoltaic bracket in geometric form, which is common in corrosion, cracks, bending and other problems. The three-dimensional point cloud data (spatial dot matrix) of the photovoltaic bracket can be processed to extract the surface discontinuous area. Use geometric algorithms (such as surface reconstruction and surface fitting) to accurately locate the discontinuous interval and further quantify the size, shape, location and other characteristics of the defect. The length, width, depth and other parameters of the crack or deformation can be calculated through geometric feature extraction technology to form a defect data set, that is, the defect data corresponding to the photovoltaic bracket. The defect data corresponding to the photovoltaic bracket can accurately reflect the distribution of geometric defects on the photovoltaic bracket and provide basic data for subsequent structural analysis, maintenance and prevention.

[0054] Exemplarily, S2332, analyzing the discontinuous intervals in the three-dimensional feature set to determine defect data corresponding to the photovoltaic bracket of the distributed photovoltaic power station includes: S23321, perform geometric linear identification on discontinuous intervals in the three-dimensional feature set to determine geometric defect feature data in the three-dimensional feature set.

[0055] It can be understood that geometric linear recognition is to identify those areas showing obvious geometric defect characteristics by analyzing the spatial lattice data in the three-dimensional data. Based on the spatial distribution of points in the surface data of the photovoltaic bracket, areas with obvious geometric discontinuities can be found. For example, when there are cracks or corrosion, the point cloud data may show irregular distribution or obvious geometric fractures in a certain direction. By performing linear or nonlinear fitting on these point clouds, surface geometric features (such as cracks, depressions, deformations, etc.) can be identified and their morphology, length, width, depth and other parameters can be further quantified, thus laying the foundation for subsequent defect data analysis.

[0056] S23322, based on the geometric defect feature data in the three-dimensional feature set, obtain the geometric defect type in the three-dimensional feature set.

[0057] It can be understood that defects can be classified based on the geometric defect feature data in the acquired three-dimensional feature set to determine the type of defect. This can be done by further quantifying the defect features, such as determining the geometric morphology, development trend and matching with known defect types. Various types of geometric defects can be identified and classified by matching defect data with known defect types through image processing, data clustering analysis, etc. Geometric defect types can include different types of structural problems such as cracks, corrosion, deformation, and bending. According to the manifestation characteristics of the defects, combined with the spatial distribution and geometric parameters of the defects, the defects can be divided into different types to facilitate subsequent structural analysis and evaluation.

[0058] S23323, performing distribution data analysis based on the geometric defect types in the three-dimensional feature set to determine defect data corresponding to the photovoltaic brackets of the distributed photovoltaic power station.

[0059] It is understandable that the purpose of distribution data analysis is to quantify the distribution of defects (such as the number, location, density, etc.) to more comprehensively understand the scope of defects on photovoltaic brackets. For example, there may be multiple small cracks in one area, while another area may have a single but large corrosion. By statistically analyzing the spatial distribution, type and number of defects in photovoltaic brackets, it can provide strong data support for subsequent structural analysis.

[0060] S2333, obtaining a structural weakening index corresponding to the photovoltaic bracket of the distributed photovoltaic power station according to defect data corresponding to the photovoltaic bracket of the distributed photovoltaic power station.

[0061] It can be understood that the defect data of photovoltaic brackets can be quantified by introducing an index similar to entropy to reflect the health status of photovoltaic brackets. Entropy is an indicator used to measure the uncertainty and diversity of the system, which can quantify the diversity and distribution of defects. In the application of photovoltaic brackets, defects are classified and the probability distribution of each defect is calculated. Then, the degree of uncertainty of the defect is calculated using the entropy formula, and a structural weakening index is finally obtained through normalization. The range of the structural weakening index indicates that the higher the value, the more serious the structural weakening of the photovoltaic bracket, and the lower the value, the better the health status of the photovoltaic bracket. It can accurately evaluate the degree of structural weakening of the photovoltaic bracket and provide a basis for maintenance and repair decisions.

[0062] For example, the defect data can be converted into a probability distribution. Assuming that n detection points are collected, each detection point has k possible defect types, and the probability of occurrence of each defect type is calculated. For each defect type i, the probability of its occurrence can be calculated as: P(i)=n i / N, where n i is the number of occurrences of defect type i, and N is the total number of detection points. Entropy can be used to measure the uncertainty of the system. In the structural weakening index, the entropy value reflects the diversity and distribution of different defect types. The entropy can be calculated using the following formula: , where k is the total number of defect types and P(i) is the probability of the i-th defect type. The larger the entropy value H, the more uneven the distribution of defect types and the more serious the structural weakening of the photovoltaic bracket; the smaller the entropy value, the more even the distribution of defect types and the healthier the structure of the photovoltaic bracket. In order to make the structural weakening index more intuitive and comparable, the entropy value can be normalized. For example, the entropy value H usually ranges from 0 to log2(k), where k is the number of defect types. Therefore, the entropy value can be normalized in the following way: H z=H / log2(k),H z is the normalized entropy value. After normalization, the structural weakening index ranges from 0 to 1, where 0 means no defects or a completely healthy structure, and 1 means that the defect type distribution is very uneven and the structure is completely weakened.

[0063] S234, based on the space lattice corresponding to the second regional image set, performing stress analysis on the photovoltaic bracket of the distributed photovoltaic power station to obtain a stress response index corresponding to the photovoltaic bracket of the distributed photovoltaic power station.

[0064] It can be understood that the stress response index is used to characterize the stress response degree of the photovoltaic bracket under conditions such as external loads and environmental changes. Based on the spatial lattice data extracted from the second area image set, a three-dimensional model of the photovoltaic bracket can be constructed, and stress analysis can be performed on it using methods such as finite element analysis. The stress response index can be obtained by analyzing the stress distribution of the photovoltaic bracket under different working conditions. The higher the stress response index, the greater the stress level of the photovoltaic bracket under different working environments or loads, and there may be a higher risk of breakage or stress concentration areas. The stress response index provides important data support for the operation and maintenance of photovoltaic power stations, helping to judge the structural safety of photovoltaic brackets.

[0065] Exemplarily, S234, based on the spatial lattice corresponding to the second regional image set, performing stress analysis on the photovoltaic bracket of the distributed photovoltaic power station to obtain the stress response index corresponding to the photovoltaic bracket of the distributed photovoltaic power station includes: S2341, constructing a finite element model of a photovoltaic support of a distributed photovoltaic power station based on a spatial lattice corresponding to the second regional image set.

[0066] It can be understood that the finite element model is the basis for structural analysis. The three-dimensional geometric model of the photovoltaic bracket can be established by using the spatial lattice information in the second area image set. The spatial lattice can be obtained by laser scanning, stereo vision or other image processing technology. According to the spatial lattice data, combined with the actual size, material properties, connection method and other information of the photovoltaic bracket, an accurate finite element model of the photovoltaic bracket can be constructed. The finite element model decomposes the geometric shape of the photovoltaic bracket into multiple small units, each unit represents a part of the photovoltaic bracket, and each unit has physical properties associated with it (such as stiffness, strength, etc.), which provides the necessary geometric and material information for subsequent stress analysis.

[0067] S2342, determining stress and strain data corresponding to the photovoltaic bracket of the distributed photovoltaic power station based on the finite element model of the photovoltaic bracket of the distributed photovoltaic power station and preset stress analysis parameters.

[0068] It can be understood that the finite element model can simulate the mechanical behavior of the photovoltaic bracket under the action of external loads and calculate its stress and strain distribution under different working conditions. The preset stress analysis parameters may include external load conditions (such as wind force, snow load, earthquake, etc.), fixed constraints of the photovoltaic bracket (such as the connection method between the photovoltaic bracket and the ground), and the constitutive model of the material (such as elasticity, plasticity, etc.). The stress analysis parameters affect the distribution of stress and deformation of the photovoltaic bracket during actual use. Based on the preset stress analysis parameters, the photovoltaic bracket can be solved by the finite element analysis method to obtain stress and strain data, that is, the stress and strain data corresponding to the photovoltaic bracket. The stress and strain data are the quantitative results of the mechanical response of the photovoltaic bracket under specific working conditions, covering stress values ​​and strain values ​​in different parts and directions. The stress data reflects the magnitude of the internal force borne by each node or unit of the photovoltaic bracket under the action of external loads, which can usually include various forms of stress such as normal stress, shear stress, and bending moment. The strain data indicates the degree of deformation of the photovoltaic bracket at each position under the action of the load, which is usually divided into linear strain and nonlinear strain. The stress-strain data provides an important basis for subsequent analysis and can help evaluate the stability, deformation resistance and fatigue damage of photovoltaic brackets during long-term operation.

[0069] S2343, obtaining a stress response index corresponding to the photovoltaic bracket of the distributed photovoltaic power station based on stress-strain data corresponding to the photovoltaic bracket of the distributed photovoltaic power station.

[0070] It can be understood that the calculation process of the stress response index aims to quantify the bearing capacity and deformation response of the photovoltaic bracket under the influence of external environmental factors through a comprehensive analysis of the stress-strain data. The stress response index is essentially a weighted calculation of the stress-strain data of each unit or node of the photovoltaic bracket, taking into account the overall stress condition of the photovoltaic bracket. The stress response index can be used as a quantitative indicator to reflect the stress bearing capacity of the photovoltaic bracket under specific working conditions. The larger the value of the stress response index, the stronger the stress response of the photovoltaic bracket under the working condition, and there may be a greater risk of damage; conversely, the smaller the value of the stress response index, the relatively uniform stress distribution of the photovoltaic bracket and the stronger the bearing capacity.

[0071] S235, determining a second abnormality coefficient corresponding to the second regional image set according to a stress response index and a structural weakening index corresponding to the photovoltaic support of the distributed photovoltaic power station.

[0072] It can be understood that the second abnormality coefficient is a comprehensive evaluation indicator used to quantify the degree of abnormality of photovoltaic brackets under different working conditions, especially abnormal conditions related to stress and structural weakening. Similar to the first abnormality coefficient, it can comprehensively reflect the operating status and potential risks of photovoltaic brackets by weighted combination of stress response index and structural weakening index, thereby providing a scientific basis for maintenance, optimization and safety management of power stations.

[0073] S240: Calculate a first operation and maintenance parameter of the distributed photovoltaic power station according to the first abnormal coefficient and the second abnormal coefficient.

[0074] It can be understood that the first abnormal coefficient and the second abnormal coefficient can be mapped to the same scale and influence by normalization, and then the first abnormal coefficient and the second abnormal coefficient can be weighted to obtain a comprehensive evaluation parameter, that is, the first operation and maintenance parameter. The first abnormal coefficient and the second abnormal coefficient can be normalized. The purpose of normalization is to eliminate the dimensional differences between the coefficients so that they can be compared and merged in the same interval. Common normalization methods include linear normalization (such as converting data to the [0,1] interval) or Z-score standardization (converting data to zero mean and unit variance). The normalized coefficients can eliminate the calculation deviation caused by different dimensions and make them have the same importance. Different weights can be assigned to each first abnormal coefficient and the second abnormal coefficient according to actual needs. Combine the normalized abnormal coefficients and their respective weights, and obtain the final first operation and maintenance parameter through weighted averaging and other methods. The formula can be expressed as: first operation and maintenance parameter = w1*normalized first abnormal coefficient + w2*normalized second abnormal coefficient, where w1 and w2 are the weights of the first and second abnormal coefficients, respectively, and the normalized first abnormal coefficient and the normalized second abnormal coefficient are the first abnormal coefficient and the second abnormal coefficient after normalization, respectively. The final calculated operation and maintenance parameters will comprehensively reflect the overall health status and operation and maintenance needs of the photovoltaic power station.

[0075] S300, determining a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station; wherein the second operation and maintenance parameter is used to reflect the abnormal operation of the distributed photovoltaic power station.

[0076] It can be understood that the role of the second operation and maintenance parameter is to identify and reflect abnormal conditions during the operation of the photovoltaic power station, such as decreased power generation efficiency, equipment failure or other operation deviations. Operation data can be collected from multiple data sources, and the operation data can include the power generation, power output, environmental monitoring data, equipment status information, etc. of the photovoltaic power station. By analyzing the operation data, such as the change trend of the power generation and power curve, the second operation and maintenance parameter can reveal abnormal fluctuations in the operation of the power station. For example, if the power generation does not reach the expected level, or the photovoltaic power curve deviates from the reference value, the second operation and maintenance parameter will be calculated to identify possible failures or performance degradation. The determination process of the second operation and maintenance parameter may include preprocessing, modeling, and comparative analysis of the operation data, and by comparing with historical data or reference values, an accurate assessment of the health status of the photovoltaic power station is formed. By calculating the second operation and maintenance parameter, timely measures can be taken to repair or adjust to ensure the efficient and stable operation of the photovoltaic power station.

[0077] In a possible implementation, S300, determining a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station, includes: S310, obtaining a power generation data curve in a preset time window according to the operation data of the distributed photovoltaic power station.

[0078] It can be understood that the operation data can include the power generation information of the photovoltaic power station in each time period. By screening the operation data, a power generation curve for a specific time range (such as day, week, month, etc.) can be obtained. A time window can be pre-defined. The time window is a specific time range that can be flexibly set according to actual needs, such as day, week, month, etc. The obtained power generation data curve reflects the overall power generation performance of the photovoltaic power station within the time window, including its power generation fluctuations at different time nodes. The power generation data curve provides basic data for subsequent analysis and is used to evaluate the operation of the power station.

[0079] S320, determining photovoltaic power curve information of the preset time window based on the power generation data curve of the preset time window.

[0080] It can be understood that the power generation data curve reflects the cumulative power generation of the photovoltaic power station in a specific time period, but because power generation and power are two different physical quantities, the power generation curve cannot directly reflect the instantaneous power of the photovoltaic power station at a certain moment. Power refers to the energy output per unit time. Therefore, the power generation data can be converted to obtain the power output level of the photovoltaic power station at each moment. Knowing the power generation in a specific time window, the average power value in the time window can be obtained by dividing it by the duration of the time period. For example, a time window is 1 hour, and the power generation is 500kWh, then the average power in the time window is 500kW. According to the power generation data of different time periods, the photovoltaic power at each time point can be gradually calculated. By performing such a conversion on each time period in the entire preset time window, a photovoltaic power curve, that is, photovoltaic power curve information, is finally obtained. The photovoltaic power curve obtained reflects the power output of the power station at each moment in the time period, and can more accurately describe the power fluctuations and changes of the power station during operation.

[0081] S330, obtaining reference curve information corresponding to the photovoltaic power curve information.

[0082] It can be understood that the benchmark curve information is the photovoltaic power curve information preset based on historical data, ideal state or theoretical model, which represents the power output level of the power station under ideal conditions. The benchmark curve information can be obtained and compared with the photovoltaic power curve of the current operating state to identify deviations in the operation of the photovoltaic power station. The benchmark curve information can be generated by historical operating data or through a calculation model, and can serve as a reference standard for the normal operation of the photovoltaic power station.

[0083] S340, determining a second operation and maintenance parameter of the distributed photovoltaic power station based on matching the photovoltaic power curve information with reference curve information corresponding to the photovoltaic power curve information.

[0084] It can be understood that after obtaining the photovoltaic power curve information and the reference curve information corresponding to the photovoltaic power curve information, matching and comparison can be performed. By analyzing the difference between the actual power and the reference power at each time point, the data deviation between the two can be obtained, that is, the second operation and maintenance parameter. The second operation and maintenance parameter reflects whether there are signs of abnormal operation of the photovoltaic power station in each time period. If the actual power is much lower than the reference power, it may indicate that there are problems with the power station, such as equipment failure, performance degradation, etc.; if the deviation is small, it means that the power station is operating stably.

[0085] Exemplarily, the second operation and maintenance parameter may be calculated by using the photovoltaic power curve information and the reference curve information corresponding to the photovoltaic power curve information as follows: Statistical methods such as root mean square error (RMSE) and mean absolute error (MAE) can be used as the second operation and maintenance parameter to measure the difference between the curves as a whole. The RMSE or MAE measurement can evaluate the overall deviation between the actual PV power curve and the reference curve.

[0086] S400: Perform operation and maintenance management on the distributed photovoltaic power station based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station.

[0087] It can be understood that the abnormal state of the distributed photovoltaic power station can be comprehensively evaluated and the corresponding operation and maintenance strategy can be formulated by comprehensively analyzing the first operation and maintenance parameter and the second operation and maintenance parameter. The first operation and maintenance parameter is mainly used to reflect the health status and structural integrity of the equipment, including the geometric defects of the photovoltaic panel and the photovoltaic bracket, equipment aging, damage, etc.; while the second operation and maintenance parameter focuses on the operation efficiency of the power station, and evaluates the overall working performance of the photovoltaic power station by analyzing the data such as power generation and power curve. The first operation and maintenance parameter combined with the second operation and maintenance parameter can comprehensively monitor the operation of the power station and provide an accurate basis for fault diagnosis, early warning and maintenance. If the first operation and maintenance parameter shows that the equipment is damaged, and the second operation and maintenance parameter indicates that the power generation capacity of the power station has decreased, it may indicate that some equipment has failed or been damaged, and the operation efficiency of the photovoltaic power station is low. Based on the results of the combined analysis of the first operation and maintenance parameter and the second operation and maintenance parameter, maintenance suggestions or alarms can be issued to guide the operation and maintenance personnel to perform corresponding maintenance work, timely discover equipment problems in the power station, dynamically optimize the operation status of the power station according to real-time data, and adjust the operation and maintenance strategy, so as to ensure that the power station maintains an efficient and stable operation state throughout its life cycle.

[0088] In a possible implementation, S400, based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, performs operation and maintenance management on the distributed photovoltaic power station, including: S410: Generate an operation and maintenance strategy for the distributed photovoltaic power station based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station.

[0089] It can be understood that based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, they can be rated individually. The first operation and maintenance parameter reflects the structural health status of the photovoltaic power station equipment, such as the geometric defects, aging degree and equipment health status of the photovoltaic bracket. According to the value of the first operation and maintenance parameter, a rating can be given to the structural health status of the photovoltaic power station equipment. The higher the rating, the more serious the equipment health problem. The second operation and maintenance parameter reflects the operating performance of the photovoltaic power station, mainly by monitoring the power generation data and power curve changes of the power station to evaluate the working efficiency of the power station. According to the value of the second operation and maintenance parameter, a rating can be given to the operating performance of the photovoltaic power station. The higher the rating, the more serious the performance degradation. After the individual rating, a comparative analysis can be performed based on the ratings between the first operation and maintenance parameter and the second operation and maintenance parameter to find out the relationship between structural health and operating performance, so as to generate an operation and maintenance strategy for the distributed photovoltaic power station.

[0090] Exemplarily, if both the first operation and maintenance parameter and the second operation and maintenance parameter are highly rated, it means that the structural defects of the equipment directly affect the operating efficiency of the power station. At this time, the corresponding operation and maintenance strategy can be mapped through the preset operation and maintenance strategy library, such as the repair or replacement of photovoltaic equipment. On the contrary, if the first operation and maintenance parameter is low-rated and the second operation and maintenance parameter is high-rated, it means that the structure is healthy but the operating efficiency is reduced. The corresponding operation and maintenance strategy is mapped through the preset operation and maintenance strategy library as the adjustment of the external environment or the power station operation mode, such as cleaning photovoltaic components, adjusting the array angle, etc. When both the first operation and maintenance parameter and the second operation and maintenance parameter are in the low rating range, the corresponding operation and maintenance strategy is mapped through the preset operation and maintenance strategy library as regular inspection and preventive maintenance to ensure the long-term stable operation of the power station. Finally, based on the rating and comparative analysis results of the first operation and maintenance parameter and the second operation and maintenance parameter, the operation and maintenance strategy will be formulated in a targeted manner to ensure the healthy operation of the photovoltaic power station.

[0091] S420: Send the operation and maintenance strategy to the management system of the distributed photovoltaic power station to perform operation and maintenance management on the distributed photovoltaic power station.

[0092] It can be understood that after the operation and maintenance strategy is generated, the strategy information can be sent to the management system of the photovoltaic power station through the network interface or communication protocol. After receiving these strategies, the management system can perform corresponding operations according to the instructions of the strategy content, such as arranging equipment inspections, adjusting power generation configurations, and implementing energy efficiency optimization measures, etc., to achieve data- and strategy-based automated operation and maintenance management, timely discover and solve potential problems, improve the operating efficiency of the photovoltaic power station, reduce labor costs, and reduce equipment failures and downtime, thereby ensuring the efficient and stable operation of the power station.

[0093] Corresponding to the distributed photovoltaic power station operation and maintenance method of the above embodiment, the embodiment of the present application also provides a distributed photovoltaic power station operation and maintenance system, and each unit of the system can implement each step of the distributed photovoltaic power station operation and maintenance method. Figure 4 A structural block diagram of a distributed photovoltaic power station operation and maintenance system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0094] Reference Figure 4 , the distributed photovoltaic power station operation and maintenance system includes: An acquisition unit, used for acquiring image data and operation data of a distributed photovoltaic power station; An image unit, used to determine a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station; wherein the first operation and maintenance parameter is used to reflect the physical abnormality of the distributed photovoltaic power station; An operation unit, configured to determine a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station; wherein the second operation and maintenance parameter is used to reflect an abnormal operation of the distributed photovoltaic power station; A management unit is used to perform operation and maintenance management on the distributed photovoltaic power station based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station.

[0095] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0096] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0097] The present application also provides a distributed photovoltaic power station operation and maintenance device, Figure 5This is a schematic diagram of the structure of a distributed photovoltaic power station operation and maintenance device provided in one embodiment of the present application. Figure 5 As shown, the distributed photovoltaic power station operation and maintenance device 6 of this embodiment includes: at least one processor 60 ( Figure 5 Only one is shown), at least one memory 61 ( Figure 5 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the distributed photovoltaic power station operation and maintenance device 6 implements the steps in any of the above-mentioned distributed photovoltaic power station operation and maintenance method embodiments, or the distributed photovoltaic power station operation and maintenance device 6 implements the functions of each unit in the above-mentioned system embodiments.

[0098] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the distributed photovoltaic power station operation and maintenance device 6.

[0099] The distributed photovoltaic power station operation and maintenance equipment 6 can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The distributed photovoltaic power station operation and maintenance equipment can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 5 It is only an example of the distributed photovoltaic power station operation and maintenance equipment 6 and does not constitute a limitation on the distributed photovoltaic power station operation and maintenance equipment 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access equipment, buses, etc.

[0100] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0101] In some embodiments, the memory 61 may be an internal storage unit of the distributed photovoltaic power station operation and maintenance device 6, such as a hard disk or memory of the distributed photovoltaic power station operation and maintenance device 6. In other embodiments, the memory 61 may also be an external storage device of the distributed photovoltaic power station operation and maintenance device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the distributed photovoltaic power station operation and maintenance device 6. Further, the memory 61 may also include both the internal storage unit and the external storage device of the distributed photovoltaic power station operation and maintenance device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0102] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0103] An embodiment of the present application provides a computer program product. When the computer program product is run on a distributed photovoltaic power station operation and maintenance device, the distributed photovoltaic power station operation and maintenance device implements the steps in any of the above-mentioned method embodiments.

[0104] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the distributed photovoltaic power station operation and maintenance equipment, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0105] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed distributed photovoltaic power station operation and maintenance system / distributed photovoltaic power station operation and maintenance equipment and method can be implemented in other ways. For example, the distributed photovoltaic power station operation and maintenance system / distributed photovoltaic power station operation and maintenance equipment embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distributed photovoltaic power station operation and maintenance method, characterized in that: include: Obtain image data and operation data of distributed photovoltaic power stations; Determine a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station; wherein the first operation and maintenance parameter is used to reflect the physical abnormality of the distributed photovoltaic power station; Determining a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station; wherein the second operation and maintenance parameter is used to reflect the abnormal operation of the distributed photovoltaic power station; Based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, operation and maintenance management of the distributed photovoltaic power station is performed.

2. The distributed photovoltaic power station operation and maintenance method according to claim 1, characterized in that: Determining a first operation and maintenance parameter of the distributed photovoltaic power station according to the image data of the distributed photovoltaic power station includes: Perform image recognition according to the image data of the distributed photovoltaic power station to obtain a first area image set and a second area image set of the distributed photovoltaic power station; wherein the first area image set is used to reflect the physical condition of the photovoltaic panel surface of the distributed photovoltaic power station; and the second area image set is used to reflect the physical condition of the photovoltaic bracket of the distributed photovoltaic power station; Based on the first regional image set of the distributed photovoltaic power station, determining a first abnormality coefficient corresponding to the first regional image set; wherein the first abnormality coefficient is used to reflect the abnormality of the photovoltaic panel surface of the distributed photovoltaic power station; Based on the second regional image set of the distributed photovoltaic power station, determining a second abnormality coefficient corresponding to the second regional image set; wherein the second abnormality coefficient is used to reflect the abnormality of the photovoltaic support of the distributed photovoltaic power station; A first operation and maintenance parameter of the distributed photovoltaic power station is calculated according to the first abnormality coefficient and the second abnormality coefficient.

3. The distributed photovoltaic power station operation and maintenance method according to claim 2, characterized in that: The determining, based on the first regional image set of the distributed photovoltaic power station, a first abnormality coefficient corresponding to the first regional image set includes: Performing color space conversion on images in the first regional image set of the distributed photovoltaic power station to obtain a color space image set of the distributed photovoltaic power station; Performing color segmentation on images in the color space image set of the distributed photovoltaic power station to obtain image color distribution data of the color space image set; Performing threshold detection on the image color distribution data of the color space image set to obtain abnormal color distribution data of the first region image set; Based on the abnormal color distribution data of the first area image set, a first abnormal coefficient corresponding to the first area image set is determined.

4. The distributed photovoltaic power station operation and maintenance method according to claim 3, characterized in that: The determining, based on the second regional image set of the distributed photovoltaic power station, a second abnormality coefficient corresponding to the second regional image set includes: Based on the second regional image set of the distributed photovoltaic power station, obtaining a spatial lattice corresponding to the second regional image set; Based on the spatial lattice corresponding to the second regional image set, constructing a three-dimensional feature set corresponding to the photovoltaic bracket of the distributed photovoltaic power station; Performing geometric feature analysis on the three-dimensional feature set corresponding to the photovoltaic bracket of the distributed photovoltaic power station to obtain a structural weakening index corresponding to the photovoltaic bracket of the distributed photovoltaic power station; Based on the spatial lattice corresponding to the second regional image set, a stress analysis is performed on the photovoltaic bracket of the distributed photovoltaic power station to obtain a stress response index corresponding to the photovoltaic bracket of the distributed photovoltaic power station; According to the stress response index and the structural weakening index corresponding to the photovoltaic support of the distributed photovoltaic power station, a second abnormality coefficient corresponding to the second regional image set is determined.

5. The distributed photovoltaic power station operation and maintenance method according to claim 4, characterized in that: The performing geometric feature analysis on the three-dimensional feature set corresponding to the photovoltaic bracket of the distributed photovoltaic power station to obtain a structural weakening index corresponding to the photovoltaic bracket of the distributed photovoltaic power station includes: For the three-dimensional feature set corresponding to the photovoltaic bracket of the distributed photovoltaic power station, determine a discontinuous interval in the three-dimensional feature set; wherein the discontinuous interval is used to reflect the area where a linear interruption occurs in the three-dimensional feature set; Analyze the discontinuous interval in the three-dimensional feature set to determine defect data corresponding to the photovoltaic bracket of the distributed photovoltaic power station; wherein the defect data is used to reflect the geometric defect distribution of the photovoltaic bracket; According to the defect data corresponding to the photovoltaic bracket of the distributed photovoltaic power station, a structural weakening index corresponding to the photovoltaic bracket of the distributed photovoltaic power station is obtained.

6. The distributed photovoltaic power station operation and maintenance method according to claim 5, characterized in that: The analyzing the discontinuous interval in the three-dimensional feature set to determine defect data corresponding to the photovoltaic bracket of the distributed photovoltaic power station includes: Performing geometric linear recognition on the discontinuous interval in the three-dimensional feature set to determine geometric defect feature data in the three-dimensional feature set; Based on the geometric defect feature data in the three-dimensional feature set, obtaining the geometric defect type in the three-dimensional feature set; Distribution data analysis is performed according to the geometric defect types in the three-dimensional feature set to determine defect data corresponding to the photovoltaic brackets of the distributed photovoltaic power station.

7. The distributed photovoltaic power station operation and maintenance method according to claim 4, characterized in that: The step of performing stress analysis on the photovoltaic bracket of the distributed photovoltaic power station based on the spatial lattice corresponding to the second regional image set to obtain a stress response index corresponding to the photovoltaic bracket of the distributed photovoltaic power station includes: Constructing a finite element model of a photovoltaic support of the distributed photovoltaic power station based on the spatial lattice corresponding to the second regional image set; Determine stress and strain data corresponding to the photovoltaic bracket of the distributed photovoltaic power station according to the finite element model of the photovoltaic bracket of the distributed photovoltaic power station and preset stress analysis parameters; According to the stress-strain data corresponding to the photovoltaic bracket of the distributed photovoltaic power station, a stress response index corresponding to the photovoltaic bracket of the distributed photovoltaic power station is obtained.

8. The distributed photovoltaic power station operation and maintenance method according to claim 1, characterized in that: Determining a second operation and maintenance parameter of the distributed photovoltaic power station according to the operation data of the distributed photovoltaic power station includes: According to the operating data of the distributed photovoltaic power station, a power generation data curve in a preset time window is obtained; Based on the power generation data curve of the preset time window, determining photovoltaic power curve information of the preset time window; Acquire reference curve information corresponding to the photovoltaic power curve information; The second operation and maintenance parameter of the distributed photovoltaic power station is determined based on matching the photovoltaic power curve information with reference curve information corresponding to the photovoltaic power curve information.

9. The distributed photovoltaic power station operation and maintenance method according to claim 1, characterized in that: The performing operation and maintenance management on the distributed photovoltaic power station based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station includes: Based on the first operation and maintenance parameter and the second operation and maintenance parameter of the distributed photovoltaic power station, generating an operation and maintenance strategy for the distributed photovoltaic power station; The operation and maintenance strategy is sent to the management system of the distributed photovoltaic power station to perform operation and maintenance management on the distributed photovoltaic power station.

10. A distributed photovoltaic power station operation and maintenance device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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