Photovoltaic module defect detection method and detection device thereof

Abstract

The invention provides a photovoltaic module defect detection method and a detection device.The detection device comprises a supporting assembly, a photovoltaic module mounting groove is formed in the supporting assembly, a longitudinal electric sliding rail is fixed to the top of the supporting assembly, and a longitudinal electric sliding block is arranged on the longitudinal electric sliding rail; a transverse electric sliding rail is fixed between the longitudinal electric sliding blocks, a transverse electric sliding seat is arranged on the transverse electric sliding rail, and image detection equipment and a superconducting quantum interference sensing assembly are fixed on the transverse electric sliding seat; a magnetic field generator is arranged on the outer side of the supporting assembly. When the photovoltaic module is not analyzed to have defects in the image shot by the image detection equipment, the photovoltaic module is rechecked through the superconducting quantum interference sensing module, and the defects of the photovoltaic module can not be found in time due to the fact that fine defects cannot be found sometimes through visual detection. According to the invention, defects existing in the photovoltaic module can be found in time, so that the service life of the photovoltaic module is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic module detection, and in particular to a photovoltaic module defect detection method and a detection device thereof. Background Art

[0002] Photovoltaic modules are the core part of solar photovoltaic power generation systems. Their main function is to convert solar energy into electrical energy. Photovoltaic modules are usually composed of multiple solar cells connected in series and parallel, and are encapsulated in a protective frame and a glass or plastic cover. Their working principle is based on the photoelectric effect of semiconductors. When sunlight shines on the cells, photons excite electrons in the semiconductor material to generate current and voltage.

[0003] Photovoltaic modules have various types and specifications to suit different application scenarios and needs, such as: monocrystalline silicon photovoltaic modules: high efficiency, but relatively high cost; polycrystalline silicon photovoltaic modules: relatively low cost, and relatively high efficiency; thin-film photovoltaic modules: flexible, good weak light performance, etc. Regardless of the type of photovoltaic module, defect detection is required when it leaves the factory and is actually used. The specific reasons are as follows:

[0004] 1. Ensure power generation efficiency

[0005] Defects can reduce the efficiency of photovoltaic modules in converting light energy into electrical energy. For example, hidden cracks may cause current transmission obstruction, and hot spots may cause local energy loss. Timely detection and repair of defects can ensure that the modules always maintain a high power generation efficiency, thereby improving the output power of the entire photovoltaic system.

[0006] 2. Extend service life

[0007] If various defects are not dealt with in a timely manner, they will gradually worsen over time, accelerating the aging and damage of components. Through regular inspections, early detection and resolution of problems can effectively extend the service life of photovoltaic components and reduce replacement costs.

[0008] 3. Improve system reliability

[0009] A photovoltaic power station is usually composed of a large number of components. The failure of a single component may affect the stability and reliability of the entire system. Timely detection of defective components and replacement or repair can reduce the probability of system failure and ensure the continuity of power supply.

[0010] 4. Reduce security risks

[0011] Certain serious defects, such as short circuits, may cause safety accidents such as fires. Regular defect detection helps to detect and eliminate these potential safety hazards in advance and ensure the safety of personnel and equipment.

[0012] Based on the above reasons, the detection of defects in photovoltaic modules has become increasingly important. In the prior art, photovoltaic modules that have not yet left the factory are usually inspected visually, but visual inspection sometimes cannot detect subtle defects. In addition, when the visual inspection device detects a problem, it is usually rechecked through the original visual system. If there is a problem with the visual system, the problem cannot be detected in time; therefore, there is a need for improvement; for photovoltaic modules in use, the prior art usually performs regular manual inspections. In order to save labor, some places use drones for inspections. For example, the patent document with application number 202310746748.5 discloses a drone photovoltaic module inspection and cleaning method and system. When the method is applied to a monitoring terminal, it includes: responding to a user's first input operation to obtain basic navigation parameters; planning a track based on the basic navigation parameters; generating a set of on-site images, and inputting the set of on-site images into a first recognition model based on training to obtain Take the photovoltaic component recognition result and the binary image set and input the binary image set into the second recognition model based on the training; respond to the user's second input operation, control the drone patrol cleaning module to clean the photovoltaic component; the invention obtains basic navigation parameters through the user's input operation, generates a corresponding track and sends it to the drone patrol cleaning module, so that the drone flies along the corresponding track; the monitoring terminal monitors the drone patrol cleaning module, obtains on-site image data, identifies photovoltaics and defects on photovoltaics, and cleans photovoltaics with stains by controlling the cleaning mechanism carried by the drone; in the above patent document, on-site image data is obtained through drone inspections, photovoltaics and defects on photovoltaics are identified, which is the same as photovoltaic components that have not left the factory. It is detected through vision. Similarly, visual inspection sometimes cannot find subtle defects. In addition, drone inspections require pre-planned routes, etc. The procedure is relatively cumbersome and photovoltaic components cannot be inspected in a timely manner.

[0013] Therefore, it is necessary to provide a new technical solution to solve the above technical problems. Summary of the invention

[0014] The present application provides a photovoltaic module defect detection method, comprising the following steps:

[0015] S1: Place or install the photovoltaic module into the photovoltaic module installation slot;

[0016] S2: The image detection device 1 collects images of the photovoltaic module and transmits the collected images to the control end. The control end processes the images collected by the image detection device 1 to determine whether the photovoltaic module has defects. If yes, jump to S3, otherwise jump to S4;

[0017] S3: The second image detection device collects images of the defective position and transmits the detected images to the control end. The control end processes the images collected by the second image detection device to determine whether the photovoltaic module has defects. If so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, jump to S5;

[0018] S4: Start the magnetic field generator, collect data from the photovoltaic module through the superconducting quantum interference sensor module, and transmit the collected data to the control end. The control end determines whether the photovoltaic module has defects. If yes, it jumps to S6. Otherwise, it ends the detection and removes the photovoltaic module, or ends the detection and waits for the next detection cycle.

[0019] S5: Start the magnetic field generator, collect data from the photovoltaic module through the superconducting quantum interference sensor module, and transmit the collected data to the control end. The control end determines whether the photovoltaic module has defects. If yes, it will be sent to the user end to remind the staff that there is a problem with the photovoltaic module. Otherwise, the detection is terminated and the photovoltaic module is removed, or the detection is terminated and waits for the next detection cycle.

[0020] S6: The second superconducting quantum interference sensor component collects data from the photovoltaic component and transmits the collected data to the control end. The control end determines whether the photovoltaic component has defects. If so, it sends the data to the user end to prompt the staff that there is a problem with the photovoltaic component; otherwise, the detection ends and waits for the next detection cycle.

[0021] As a preferred solution, in S6, after three detection cycles, when the control end determines that the photovoltaic module has no defects based on the data collected by the superconducting quantum interference sensor component 2, and when the control end determines that the photovoltaic module has defects based on the data collected by the superconducting quantum interference sensor component 1, the control end issues a prompt to prompt the staff to detect the superconducting quantum interference sensor component 1 and the superconducting quantum interference sensor component 2 to determine whether there are problems with the superconducting quantum interference sensor component 1 and / or the superconducting quantum interference sensor component 2.

[0022] As a preferred solution, in S2, the lifting support legs are first started to adjust the angle of the photovoltaic component. After adjusting to a suitable angle, the image detection device takes a picture of the photovoltaic component and transmits the photographed image to the control end. The control end processes the image collected by the image detection device to determine whether the photovoltaic component has defects. If so, jump to S3, otherwise jump to S4.

[0023] As a preferred solution, in S4, the angles of all photovoltaic modules are first adjusted to the same angle, and then the angle of the magnetic field generator is adjusted to be the same as the angle of the photovoltaic module, the magnetic field generator is started, and the data of the photovoltaic module is collected through the superconducting quantum interference sensor component. The collected data is transmitted to the control end, and the control end determines whether there is a defect in the photovoltaic module. If so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, jump to S5.

[0024] As a preferred solution, the image detection device includes 3n cameras, n≥1, and n is an integer; S2 is specifically: camera one shoots the photovoltaic component, camera one transmits the shot image to the control end, the control end processes the images captured by each camera one to determine whether the photovoltaic component has defects, otherwise jumps to S4, if yes, the defect position is re-measured, the re-measurement is performed using one of the captured images of the camera one that did not detect the defect position, the camera one that did not detect the defect position shoots the defect position, and the image of the defect position is analyzed by the control end to determine whether the photovoltaic component has defects, if yes, jumps to S3, otherwise the second camera one whose captured image did not detect the defect position is used for re-measurement, the second camera one that did not detect the defect position shoots the defect position, and the image of the defect position is analyzed by the control end to determine whether the photovoltaic component has defects, if yes, jumps to S3, otherwise.

[0025] As a preferred solution, the image detection device 2 includes 2n cameras 2, n≥1, and n is an integer; S3 is specifically: camera 2 collects images of the defective position in S2, and transmits the detected image to the control end, the control end processes the image collected by camera 2 to determine whether the photovoltaic component has defects, and if so, sends it to the user end to prompt the staff that there is a problem with the photovoltaic component; otherwise, the defective position is re-measured by camera 2 that has not collected the image of the defective position, and camera 2 that has not collected the image of the defective position shoots the defective position, and the collected image of the defective position is analyzed by the control end to determine whether the photovoltaic component has defects, and if so, sends it to the user end to prompt the staff that there is a problem with the photovoltaic component, otherwise jump to S5.

[0026] As a preferred solution, the superconducting quantum interference sensing component 1 includes at least 2n superconducting quantum interference sensors 1, n≥1, and n is an integer; the S4 is specifically: starting the magnetic field generator, collecting the magnetic field data and electrical performance data of the photovoltaic component through the superconducting quantum interference sensor 1, and transmitting the collected data to the control end, the control end determines whether the photovoltaic component has defects, otherwise the detection is terminated and the photovoltaic component is removed, or the detection is terminated and waiting for the next detection cycle; if yes, the defect position is retested, and the superconducting quantum interference sensor 1 that does not detect the defect position is retested using the collected data, and the superconducting quantum interference sensor 1 that does not detect the defect position collects data for the defect position, and the image of the collected defect position is analyzed by the control end to determine whether the photovoltaic component has defects, and if yes, jump to S6, otherwise the detection is terminated and the photovoltaic component is removed, or the detection is terminated and waiting for the next detection cycle.

[0027] As a preferred solution, the second superconducting quantum interference sensing component includes a plurality of second superconducting quantum interference sensors, and the second superconducting quantum interference sensor collects magnetic field data and electrical performance data of the photovoltaic component.

[0028] A photovoltaic module defect detection device comprises a support assembly, wherein a photovoltaic module mounting groove is installed on the support assembly, and the photovoltaic module mounting groove is used to place or install the photovoltaic module, and a longitudinal electric slide rail 1 and a longitudinal electric slide rail 2 are fixed on the top of the support assembly on both sides, and the longitudinal electric slide rail 1 and the longitudinal electric slide rail 2 are arranged on the upper part of the photovoltaic module mounting groove, and a longitudinal electric slider 1 and a longitudinal electric slider 2 are respectively arranged on the longitudinal electric slide rail 1 and the longitudinal electric slide rail 2, and the number of the longitudinal electric slider 1 and the longitudinal electric slider 2 is two respectively, and the longitudinal electric slider 1 and the longitudinal electric slider 2 are two. A transverse electric slide rail 1 and a transverse electric slide rail 2 are fixed between blocks 2 respectively, and a transverse electric slide rail 1 and a transverse electric slide rail 2 are arranged on the transverse electric slide rail 1, an image detection device 1 is fixed on the transverse electric slide rail 1, and a superconducting quantum interference sensor component 1 is fixed on the transverse electric slide rail 2; a transverse electric slide rail 3 and a transverse electric slide rail 4 are arranged on the transverse electric slide rail 2, an image detection device 2 is fixed on the transverse electric slide rail 3, and the superconducting quantum interference sensor component 2 is fixed on the transverse electric slide rail 4; a magnetic field generator is arranged on the outer side of the support component.

[0029] As a preferred solution, a fixed support leg is provided on one side of the bottom of the photovoltaic component mounting groove, and a lifting support leg is provided on the other side of the bottom of the photovoltaic component mounting groove. The fixed support leg is rotatably connected to the photovoltaic component mounting groove through a rotating shaft, and a pushing block is installed on the top of the lifting support leg. The pushing block includes a horizontal plate and a vertical plate. The pushing block cooperates with the bottom of the photovoltaic component mounting groove, and limit blocks are installed on both sides of the pushing block at the bottom of the photovoltaic component mounting groove.

[0030] As a preferred solution, a fixed bracket is provided on the outer ring of the support plate, and a magnetic field generator is fixed on the fixed bracket.

[0031] As a preferred solution, one side of the bottom of the fixed bracket is rotatably connected to the fixed support leg of the bracket, and the other side of the bottom of the fixed bracket is connected to the bracket lifting support leg. A bracket pushing block is provided on the bracket lifting support leg, and a bracket pushing groove is opened on the bracket pushing block. The bracket pushing groove cooperates with the fixed bracket, and a bracket limit block is installed on both sides of the bracket push groove at the lower part of the fixed bracket.

[0032] The present application can detect both photovoltaic modules that have not been shipped out of the factory and photovoltaic modules that have been installed in the factory. First, the image of the photovoltaic module is collected by the image detection device 1, and the control end analyzes the image collected by the image detection device 1. When the captured image analyzes that the photovoltaic module is defective, the position of the defect is re-measured by the image detection device 2. If the images captured by the two devices both show that the photovoltaic module is defective, the staff is prompted to deal with it in time. When one or both of the images captured by the image detection device 1 and the image detection device 2 analyze that the photovoltaic module is defective, the photovoltaic module is further re-checked by the superconducting quantum interference sensor component, because visual inspection sometimes cannot find subtle defects and may not be able to find defects in the photovoltaic module in time. The present application can find defects in the photovoltaic module in time, prompt the staff to deal with it in time, thereby improving the service life of the photovoltaic module, etc. In addition, the angle of the photovoltaic panel in the present application is adjustable, which is not only convenient for adjusting to the most suitable shooting angle, but also convenient for adjusting the angle of the photovoltaic module according to the light during actual use, so that the photovoltaic panel can always maintain the best angle with the sunlight, thereby maximizing the absorption of solar energy and significantly improving the power generation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a logic diagram of the photovoltaic module defect detection method of the present application;

[0034] Figure 2 is a logic diagram of a specific photovoltaic module defect detection method of the present application;

[0035] Figure 3 This is a schematic structural diagram of the photovoltaic module defect detection device of the present application from an angle 1;

[0036] Figure 4 This is a schematic structural diagram of the photovoltaic module defect detection device of the present application from an angle 2;

[0037] Figure 5 is a schematic structural diagram of angle 1 of the photovoltaic module defect detection device of the present application without a magnetic field generator;

[0038] Figure 6 This is a schematic structural diagram of angle 2 of the photovoltaic module defect detection device of the present application when a magnetic field generator is not provided;

[0039] Figure 7 is a schematic structural diagram of angle 3 of the photovoltaic module defect detection device of the present application without a magnetic field generator;

[0040] Figure 8 It is a schematic diagram of the structure of the photovoltaic module placement slot and the support leg at angle 1 of the present application;

[0041] Fig. 9 It is a schematic diagram of the structure of the photovoltaic module placement slot and the angle 2 of the support leg of the present application;

[0042] Fig.10 It is a structural schematic diagram of the angle 1 in which the photovoltaic module placement slot is parallel to the longitudinal electric slide rail;

[0043] Fig.11 It is a structural schematic diagram of the angle 2 in which the photovoltaic module placement groove is parallel to the longitudinal electric slide rail;

[0044] Fig.12 It is a schematic diagram of the structure of the lifting support legs that cooperate with the photovoltaic module placement slot;

[0045] Fig.13 It is a schematic diagram of the structure at an angle of one with the longitudinal sliding rail and the transverse sliding rail;

[0046] Fig.14 It is a structural schematic diagram of the angle 2 with the longitudinal sliding rail and the transverse sliding rail;

[0047] Fig.15 It is a schematic diagram of the structure of the angle three with the longitudinal sliding rail and the transverse sliding rail;

[0048] Fig.16 is a schematic diagram of the structure of the fixed bracket at angle 1;

[0049] Fig.17 is a structural schematic diagram of angle 2 of the fixed bracket;

[0050] Fig.18 It is a schematic diagram of a structure in which the fixed bracket is parallel to the longitudinal electric slide rail;

[0051] Fig.19 It is a schematic diagram of the structure of the bracket lifting support legs cooperating with the fixed bracket;

[0052] Reference numerals:

[0053] 1. Photovoltaic module installation slot; 2. Camera 1; 3. Camera 2; 4. Superconducting quantum interference sensor 1; 5. Magnetic field generator; 6. Superconducting quantum interference sensor 2; 7. Lifting support legs; 8. Support assembly; 9. Support plate; 10. Line installation slot; 11. Longitudinal electric slide rail 1; 12. Longitudinal electric slide rail 2; 13. Longitudinal electric slider 1; 14. Longitudinal electric slider 2; 15. Horizontal electric slide rail 1; 16. Horizontal electric slide rail 2; 17. Horizontal electric Movable slide one; 18, horizontal electric slide two; 19, horizontal electric slide three; 20, horizontal electric slide four; 21, fixed support leg; 23, rotating shaft; 24, horizontal plate; 25, vertical plate; 26, limit block; 27, fixed bracket; 28, connecting rod one; 29, connecting rod two; 30, connecting rod three; 31, connecting rod four; 32, bracket fixed support leg; 33, bracket lifting support leg; 34, bracket push block; 35, bracket push slot; 36, bracket limit block. DETAILED DESCRIPTION

[0054] The following combination Figure 1 — Fig.19 The specific implementation of the present invention is described in detail. It should be noted that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0055] Embodiment 1:

[0056] This embodiment provides a photovoltaic module defect detection method, comprising the following steps:

[0057] S1: placing or installing the photovoltaic module in the photovoltaic module installation slot 1; the size of the photovoltaic module installation slot 1 is adapted to the size of the photovoltaic module; the number of photovoltaic module installation slots 1 is not specifically limited and is set according to specific circumstances; specifically, before the photovoltaic module leaves the factory, it can be directly placed in the photovoltaic module installation slot 1 for inspection by a mechanical arm, etc., and after the inspection is completed, it is removed by the cooperation of a mechanical arm, etc.; when the photovoltaic module leaves the factory and is used, the photovoltaic module is fixed in the photovoltaic installation slot 1 by screws, installation structures, etc., and the work is carried out;

[0058] S2: The image detection device 1 collects images of the photovoltaic components and transmits the collected images to the control end, which is a peripheral PLC controller, etc. The control end processes the images collected by the image detection device 1 to determine whether the photovoltaic components have defects. If yes, it jumps to S3, otherwise it jumps to S4 and performs further detection through the superconducting quantum interference sensor component; the image detection device 1 detects each group of photovoltaic components respectively, and the image detection device 1 includes 3n cameras 2, n ≥ 1, and n is an integer; each photovoltaic component needs to be guaranteed to be detected by 3n cameras 2, and if it is necessary to detect at the same time To collect images of multiple groups of photovoltaic components, it is necessary to set the camera 2 according to the actual situation. For example, if two groups of photovoltaic components are detected at the same time, each group of photovoltaic components requires 3 cameras 2, and 6 cameras 2 need to be set at the same time; it should be noted that the camera 2 in this application can be universal, such as not only fixing the first, second, and third cameras 2 for collecting images of the first group of photovoltaic components, but also using the first, third, and fifth cameras 2 for collecting images of the second group of photovoltaic components, so as to ensure that each group of photovoltaic components has 3n cameras 2 for collecting images;

[0059] Camera 1-2 collects images of photovoltaic components; specifically: S2 is specifically camera 1-2 photographing photovoltaic components, camera 1-2 transmits the photographed images to the control end, the control end processes the images collected by each camera 1 to determine whether the photovoltaic component has defects, and jumps to S4 when there is no defect; when there is a defective position in the photovoltaic component, the defective position is re-measured, and the re-measurement is performed using one of the cameras 1 whose images have not detected the defective position. The camera 1-2 that has not detected the defective position photographs the defective position, and the image of the defective position collected is analyzed by the control end to determine whether the photovoltaic component has defects. If so, jump to S3, otherwise, the second camera 1-2 whose images have not detected the defective position is used for re-measurement, and the second camera 1-2 that has not detected the defective position photographs the defective position. The image of the defective position collected is analyzed by the control end to determine whether the photovoltaic component has defects. If so, jump to S3, otherwise, the second camera 1-2 that has not detected the defective position is used for re-measurement. The second camera 1-2 that has not detected the defective position photographs the defective position, and the image of the defective position collected is analyzed by the control end to determine whether the photovoltaic component has defects. If so, jump to S3, otherwise jump to S4;

[0060] In this step, one defect position may be detected, or multiple defect positions may be detected. Take three cameras 1-2, respectively detecting one defect position and two defect positions as an example for explanation:

[0061] When a defective position is detected: three cameras 2 shoot the photovoltaic module, and the three cameras 2 transmit the captured images to the control end. The control end processes the images collected by the three cameras 2 to determine whether the photovoltaic module has defects. If there is no defect, jump to S4; when there is a defective position in the photovoltaic module, the image of the defective position is collected by the first camera 2, and the other two cameras 2 do not collect the image of the position, the defective position is re-measured, and the second camera 2 is used to shoot the defective position. The captured image of the defective position is analyzed by the control end to determine whether the photovoltaic module has defects. If so, jump to S3, otherwise the third camera 2 is used to shoot the defective position, and the captured image of the defective position is analyzed by the control end to determine whether the photovoltaic module has defects. If so, jump to S3, otherwise jump to S4;

[0062] Two defect positions: When there are two defect positions in a photovoltaic module, the images of defect position one and defect position two are collected by the first camera one 2 and the second camera one 2 respectively, and the third camera one 2 does not collect images of defect position one and defect position two. The retest of defect position one can be carried out by the second camera one 2 and the third camera one 2, and the retest of defect position two can be carried out by the first camera one 2 and the third camera one 2, which will not be described in detail here; in addition, the same defect position can also be collected by two cameras one 2 or three cameras one 2 at the same time. In this case, the result of the retest of the second camera two is equivalent to that there is a problem with the photovoltaic module, and it can jump directly to S3;

[0063] S3: The image detection device 2 collects images of the defective position and transmits the detected images to the control end. The control end processes the images collected by the image detection device 2 to determine whether the photovoltaic module has defects. If so, it is sent to the user end to remind the staff that there is a problem with the photovoltaic module, so that the staff can repair or replace the problematic photovoltaic module in time to ensure power generation efficiency and improve system stability. Otherwise, jump to S5 to perform further detection through the superconducting quantum interference sensor component; the image detection device 2 includes 2n cameras 2 3, n ≥ 1, and the cameras 2 3 collect images of the photovoltaic module; S3 has The structure is as follows: the camera 23 collects images of the defective position in S2, and transmits the detected images to the control end. The control end processes the images collected by the camera 23 to determine whether the photovoltaic module has defects. If so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, the defective position is re-measured by the camera 23 that has not collected the image of the defective position, and the camera 23 that has not collected the image of the defective position takes a picture of the defective position. The collected image of the defective position is analyzed by the control end to determine whether the photovoltaic module has defects. If so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module, otherwise jump to S5.

[0064] The control end processes the images collected by the image detection device 1 and the image detection device 2 to determine whether the photovoltaic module has defects. The specific steps are as follows:

[0065] The images collected by the image detection device 1 and the image detection device 2 are preprocessed, including grayscale processing and filtering denoising, specifically: the collected images are converted into grayscale images, because the grayscale information can provide sufficient features for the detection of photovoltaic module defects, and the processing of grayscale images is less computationally intensive and faster than that of color images; filtering denoising is mainly used to remove various noise interferences that may occur to the photovoltaic module images during the acquisition process, such as salt and pepper noise, Gaussian noise, etc. The use of filtering technology can effectively remove these noises and improve image quality. In this embodiment, mean filtering or median filtering can be used for processing, wherein the mean filtering uses the average value of the pixels in the neighborhood to replace the value of the center pixel, which has a certain effect on removing Gaussian noise; the median filtering sorts the grayscale values ​​of the pixels in the neighborhood and takes the middle value as the value of the center pixel, which has a better effect on removing salt and pepper noise.

[0066] The preprocessed image is subjected to feature extraction, including edge detection, texture feature extraction, and shape feature extraction. Specifically, many defects of photovoltaic modules, such as cracks and breakages, will appear as edge changes in the image. Commonly used edge detection operators include the Sobel operator and the Canny operator. The Sobel operator detects edges by calculating the grayscale change gradient of the image in the horizontal and vertical directions. It performs convolution operations on the horizontal and vertical directions of the image, respectively, to obtain the gradient values ​​in the horizontal and vertical directions, and then calculates the edge strength and direction through the formula. The formula is a conventional prior art, and the applicant will not elaborate on it in detail here. The Canny operator can obtain more accurate and continuous edge information through multiple steps such as Gaussian filtering, calculation of gradient amplitude and direction, non-maximum suppression, and dual threshold detection. The texture of the surface of the photovoltaic module is There will be differences between defective and non-defective cases. In this embodiment, methods such as gray-level co-occurrence matrix (GLCM) can be used to extract texture features. GLCM obtains multiple texture feature parameters such as contrast, correlation, energy, entropy, etc. by counting the frequency of pixel pairs with different gray values ​​in a photovoltaic image in a certain direction and distance. These parameters can be used to describe the texture state of the surface of the photovoltaic module and help determine whether there are defects. For some defects with obvious shape features, such as missing corners and black spots of the battery cell, their shape features can be extracted for judgment. For example, by calculating shape parameters such as the area, perimeter, circularity (4π×area / perimeter2), and rectangularity (the ratio of the area of ​​the defective area to the area of ​​the minimum circumscribed rectangle) of the defective area, and comparing them with the shape parameter range under normal conditions, it is determined whether there is a defect.

[0067] After feature extraction, the image is segmented. According to the grayscale distribution characteristics of the image, one or more thresholds are set to segment the image into different areas. For the photovoltaic module image, if the grayscale value of the defective area is significantly lower or higher than the grayscale value of the normal area, the area with a grayscale value lower than the threshold can be determined as a possible defective area by setting a suitable threshold, and the area with a grayscale value higher than the threshold can be determined as a normal area. Regarding the setting of the threshold, it is adjusted in real time according to the difference in materials, surface conditions, light intensity, shooting angle, image processing purpose, etc. For example, assuming that the grayscale value range of the image is 0-255, the grayscale value of the monocrystalline silicon photovoltaic panel is 0-255. When performing threshold segmentation, the main purpose is to distinguish between normal areas and possible defective areas such as stains and slight scratches. The threshold can be initially set between grayscale values ​​of 120 and 150, because the grayscale value of normal areas is relatively high and concentrated, while the grayscale value of defective areas is often slightly lower. In actual application, it can be further adjusted according to the specific image situation. For polycrystalline silicon photovoltaic panels, since their grayscale value distribution is more dispersed, it is necessary to try different thresholds more carefully. For example, try to select a threshold in the range of 80-120 for segmentation, observe the segmentation effect, and then fine-tune according to the actual situation. For new photovoltaic panels, when performing simple normal area and edge area (installation edge) segmentation, the threshold can be adjusted according to the actual situation. When segmenting images with edge structures such as frames, if the grayscale value of the edge area is relatively low, the threshold can be set at around 180-200 to segment the image into a central normal area and an edge area; for photovoltaic panels covered with a lot of dust, the grayscale value of the dust area is usually low. At this time, you can try to set the threshold between 100-130 to separate the dust-covered area from the relatively clean normal area, but it also needs to be optimized later according to the actual segmentation effect; if the image is collected in a strong light environment, in order to segment the possible shadow area (such as the blocked part) from the normal light area, the threshold may need to be set at a higher value, such as 200 -220, because the grayscale value of the shadow area will be significantly lower than that of the normal light area; for images collected in a weak light environment, if you want to distinguish areas with different reflective characteristics (such as the cell area and the connection line area), the threshold may need to be set at a lower value, such as between 60-80, and then adjusted according to the actual segmentation effect; to distinguish between cells and bus bars, the cell area may have a relatively high grayscale value and the bus bar grayscale value is relatively low. You can first try to set the threshold between 130-160, and after segmenting the image into different areas, further confirm whether the target component is accurately segmented based on the actual component shape, position and other features. For the segmentation of the area, the segmentation method commonly used in the prior art is adopted, such as the region growing method, which starts from one or more seed points in the image and continuously merges adjacent pixels into the same area according to certain similarity criteria (such as grayscale similarity, texture similarity, etc.) until the stop condition is met. This method can segment the photovoltaic module image into different areas, which is convenient for the subsequent identification and analysis of defective areas.

[0068] After the image segmentation is completed, classification and recognition are carried out. The machine learning-based method or the deep learning-based method is mainly used to classify and identify the segmented image to confirm whether there are defects in the detection area of ​​the photovoltaic module; the machine learning-based method uses support vector machine (SVM) or artificial neural network (ANN). The principle of support vector machine (SVM) is to use the extracted image features as input vectors, and train the SVM model so that it can distinguish between normal photovoltaic module images and images with different types of defects. SVM separates different categories of data as much as possible by finding an optimal hyperplane, and shows good performance when processing small samples and high-dimensional data; artificial neural networks (ANN) include multi-layer perceptrons, etc. By constructing a neural network with an input layer, a hidden layer and an output layer, the image features are input into the network. After the forward propagation and back propagation training process, the network weights are continuously adjusted so that the network can accurately classify photovoltaic module images. The deep learning-based method uses a convolutional neural network (CNN), which is currently the most widely used deep learning model in the field of image detection. CNN has structures such as convolutional layers, pooling layers, and fully connected layers. The convolutional layer performs convolution operations by sliding the convolution kernel on the image to automatically extract local features of the image; the pooling layer is used to reduce the data dimension and reduce the amount of calculation while retaining important features; the fully connected layer makes a comprehensive judgment on the extracted features and outputs the final classification result; for example, commonly used CNN models such as ResNet and VogNet can be trained on a large number of photovoltaic module image data sets to accurately identify various defect types of photovoltaic modules; through the above series of image processing technical means, the control end can effectively process the photovoltaic module images collected by the image detection device 1 and the image detection device 2, so as to accurately judge whether there are defects in the photovoltaic modules and what kind of defects exist.

[0069] This embodiment provides a specific application example:

[0070] Image detection device 1 and image detection device 2 are used to capture images of a polycrystalline silicon photovoltaic module of standard size (1.65m×0.99m). The capture resolution is set to 2 pixels per millimeter, so that the pixel size of the entire photovoltaic module image is approximately (1.65×1000×2)×(0.99×1000×2)=3300×1980 pixels.

[0071] The above images collected by the image detection device 1 and the image detection device 2 are preprocessed: the values ​​of the RGB three channels of the collected color image are calculated according to certain weights to obtain gray values, converted into gray images, and grayed by using the weighted average method, the formula is Gray = 0.299 × R + 0.587 × G + 0.114 × B. After the graying process, the image becomes a single-channel gray image, and the gray value range of each pixel is between 0 and 255; when there is a certain degree of Gaussian noise in the collected image, mean filtering is used for denoising, and a 3×3 neighborhood window is selected for mean filtering, that is, for each pixel in the image, the average gray value of the surrounding 3×3 pixels, a total of 9 pixels, is calculated, and the average value is assigned to the current pixel as the gray value after denoising;

[0072] Perform feature extraction on the preprocessed image: Use the Sobel operator for edge detection, first calculate the horizontal gradient G x and the vertical gradient G y , taking the pixel with coordinates (100, 100) in the image as an example (this is just an example to illustrate the calculation process, and the entire image will actually be traversed): For the horizontal gradient G x , use the common 3×3 horizontal template to perform convolution operation with the image, assuming that the grayscale values ​​of some pixels around the pixel are: the grayscale value of the pixel at (99, 99) is 50; the grayscale value of the pixel at (100, 99) is 55; the grayscale value of the pixel at (101, 99) is 60; the grayscale value of the pixel at (99, 100) is 52; the grayscale value of the pixel at (100, 100) is 58; the grayscale value of the pixel at (101, 100) is 62; the grayscale value of the pixel at (99, 101) is 54; the grayscale value of the pixel at (100, 101) is 60; the grayscale value of the pixel at (101, 101) is 65, according to the horizontal gradient calculation formula: G x (100, 100) = 50 × (-1) + 55 × 0 + 60 × 1 + 52 × (-2) + 58 × 0 + 62 × 2 + 54 × (-1) + 60 × 0 + 65 × 1 = 41, and the horizontal gradient value of the pixel point (100, 100) is 41. This value reflects the grayscale change degree of the pixel point in the horizontal direction. By performing the above calculation on each pixel point in the image, the grayscale change gradient of the entire image in the horizontal direction can be obtained, and then this information can be used for subsequent image processing operations such as edge detection;

[0073] Similarly, for the vertical gradient G y , use the vertical template to perform convolution operation, and calculate the vertical gradient according to the formula: G y(100, 100) = 50 × (-1) + 55 × (-2) + 60 × (-1) + 52 × 0 + 58 × 0 + 62 × 0 + 54 × 1 + 60 × 2 + 65 × 1 = 19, and the vertical gradient value of the pixel (100, 100) is 19. This value reflects the grayscale change degree of the pixel in the vertical direction. By performing the above calculation on each pixel in the image, the grayscale change gradient of the entire image in the vertical direction can be obtained, and then this information can be used for subsequent image processing operations such as edge detection;

[0074] Then according to the edge strength formula, Calculate the edge strength of the pixel According to the results, the edge strength of the pixel with coordinates (100, 100) is about 45.19. The edge direction θ(x, y) of the pixel (100, 100) is calculated according to the edge direction formula. The edge direction formula is: Substitute the pixel (100, 100) into the formula, The edge strength and direction calculated above are the edge detection results of the pixel. By traversing the entire image, the edge information of all pixels can be obtained, and then the edge of the photovoltaic module image can be detected, such as the edge of the battery cell, the edge of the module frame, etc.

[0075] Gray-level co-occurrence matrix (GLCM) is used to extract texture features. The distance d = 2 and direction θ = 0° (horizontal direction) are selected to construct GLCM. The grayscale level L of the grayscale processed image is 256, and the constructed GLCM is a 256x256 square matrix. By traversing the image and counting the frequency of occurrence of pixel pairs with different grayscale values ​​at the specified distance and direction, the GLCM matrix is ​​obtained, and then the following texture feature parameters are extracted from the GLCM matrix:

[0076] (1) Contrast: The calculation formula is Contrast = ∑∑(ij)^2*P(i, j). After calculation, the Contrast of this application is 35.6, which means that there are certain changes in the surface texture of the photovoltaic module, such as local unevenness or tiny defects that may cause texture changes.

[0077] In a 256x256 square matrix, i and j are used to refer to the row and column indices in the matrix respectively, and their corresponding actual meanings are the different grayscale values ​​in the image; p(i, j) represents the frequency of occurrence of pixels with a grayscale value of and pixels with a grayscale value of at a specified distance and direction obtained by traversing the image according to the selected distance (d=2) and direction (θ=0°, horizontal direction).

[0078] (2) Correlation: The calculation formula is Correlation = ∑∑[(i - μ i )(j-μ j )*P(i,j)] / (σ i *σ j ), Correlation = 0.62 is calculated, indicating that there is a certain linear relationship between pixels with different gray values, which can be used to further analyze the uniformity and regularity of the surface texture of photovoltaic modules; where μ i represents the average gray value of all pixels with gray value i; similarly, μ j represents the average gray value of all pixels with gray value j; (i-μ i ) represents the deviation between the gray value of the pixel with gray value i and its average gray value; (j-μ j ) represents the deviation of the gray value of the pixel with gray value j from its average gray value; in the formula for calculating the correlation, all possible combinations of i and j are calculated (i-μ i )(j-μ j )×P(i, j) and accumulate them to measure the degree of linear relationship between pixels of different gray values; if this accumulated value is large, then divide it by (σ i *σ j ) (the standard deviation product of pixels with gray values ​​i and j respectively) will be close to 1, indicating that pixels with different gray values ​​are highly correlated; if the accumulated value is small, the correlation value obtained will be close to -1, indicating a high anti-correlation; if the accumulated value is close to 0, it means there is no correlation; therefore, when analyzing image texture features through the gray level co-occurrence matrix, it is used to measure the deviation product between pixels with different gray values ​​based on their respective average gray values, and then to calculate the correlation to understand the degree of linear relationship between pixels with different gray values ​​in the image.

[0079] (3) Energy: The calculation formula is Energy = ∑∑P(i, j)^2, and Energy = 0.28 is obtained. The energy value reflects the uniformity of the image texture. The photovoltaic module itself has a certain complexity of the image. Its surface may have different structures such as solar cells and bus bars. The texture may also show various changes due to factors such as light and materials. At this time, the normal range of the energy value may be relatively wide. In this embodiment, the energy value between 0.3 and 0.6 is normal. The energy value of 0.28 is at a slightly low level. There may be some factors that affect the texture uniformity, such as defects. It needs to be further analyzed in combination with other texture feature parameters and the actual image appearance.

[0080] (4) Entropy: The calculation formula is Entropy = -∑∑P(i, j)*log(P(i, j)), and Entropy = 1.25. In this embodiment, the entropy value is between 0.8 and 1.5, which is in the normal range. When the entropy value is greater than 1.5, it means that the image texture is more random, indicating that there may be some irregularities, such as local defects that may cause irregular texture. Further analysis is required in combination with other texture feature parameters and actual image appearance.

[0081] The image after feature extraction is segmented. According to the grayscale value distribution characteristics of the image, it is found that the grayscale value of the normal area is relatively high, while the grayscale value of the area where defects may exist (such as local shadows, slight scratches, etc.) is relatively low. After many experiments, the threshold is set to 120 in this embodiment, and the area with a grayscale value lower than 120 is determined as a possible defective area, and the area with a grayscale value higher than 120 is determined as a normal area. In this way, the photovoltaic module image is preliminarily segmented into areas where defects may exist and normal areas.

[0082] After the image segmentation is completed, classification and recognition are performed. In this embodiment, a simple convolutional neural network (CNN) model is constructed, including a convolution layer, a pooling layer and a fully connected layer. The convolution layer uses a 3×3 convolution kernel, and the step size is set to 1. The pooling layer uses a 2×2 maximum pooling method. The image data after the above feature extraction and image segmentation is used as input and input into the CNN model; the CNN model is trained on a large number of photovoltaic module image data sets (including normal images and images with various defects) in advance. The weight of the model is continuously adjusted during the training process so that the model can accurately classify the input image. For example, the trained CNN model classifies the photovoltaic module image currently collected and processed, and the output result shows that the image has a slight scratch defect, and the accuracy rate reaches more than 90%.

[0083] The above embodiments clarify how the control end uses common image processing techniques to process the photovoltaic module image and ultimately determine whether the photovoltaic module has defects and the type of defects.

[0084] S4: Start the magnetic field generator 5, collect data from the photovoltaic module through the superconducting quantum interference sensor module, and transmit the collected data to the control end. The control end determines whether the photovoltaic module has defects. If yes, it jumps to S6, otherwise, it ends the detection and removes the photovoltaic module, or ends the detection and waits for the next detection cycle; specifically, the workpiece before leaving the factory is inspected, and after the inspection, the photovoltaic module is removed for subsequent packaging and other links; and after the inspection of the photovoltaic module in use after leaving the factory is completed, it continues to wait for the next inspection cycle. The inspection cycle is set according to the specific situation, such as an interval of 12 hours, 24 hours, 48 ​​hours, etc., and is set according to specific needs to complete the inspection of the photovoltaic module within a fixed period;

[0085] The superconducting quantum interference sensing component 1 includes 2n superconducting quantum interference sensors 4, n≥1, and the superconducting quantum interference sensor 4 analyzes the magnetic field data and the electrical performance data of the photovoltaic component; the S4 is specifically: starting the magnetic field generator 5, collecting the magnetic field data and the electrical performance data of the photovoltaic component through the superconducting quantum interference sensor 4, and transmitting the collected data to the control end, the control end determines whether the photovoltaic component has defects, and when there are no defects, the detection is terminated to remove the photovoltaic component, or the detection is terminated to wait for the next detection cycle; when there is a defective position, the defective position is retested, and the retest uses the superconducting quantum interference sensor 4 that does not detect the defective position with the collected data to retest, and the superconducting quantum interference sensor 4 that does not detect the defective position collects data on the defective position, and the image of the collected defective position is analyzed by the control end to determine whether the photovoltaic component has defects, and jumps to S6, otherwise the detection is terminated to remove the photovoltaic component, or the detection is terminated to wait for the next detection cycle;

[0086] S5: Start the magnetic field generator 5, collect data on the photovoltaic module through the superconducting quantum interference sensor component 4, and transmit the collected data to the control end, which determines whether the photovoltaic module has defects. If yes, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module. Otherwise, the detection is terminated and the photovoltaic module is removed, or the detection is terminated and waits for the next detection cycle. In this step, the superconducting quantum interference sensor component 4 is used to perform a single detection, and no retest is performed;

[0087] In this embodiment, the magnetic field is applied to the photovoltaic module mainly in two occasions, specifically:

[0088] Metal electrode: Metal electrode is one of the main transmission channels of current in photovoltaic modules. By applying a suitable magnetic field, the metal electrode can produce a detectable electromagnetic response, thereby detecting whether it has defects or connection problems; the magnetic field strength is set between 0.1 Tesla and 0.5 Tesla. A weaker magnetic field strength can usually make the metal electrode generate enough induced current, which is convenient for superconducting quantum interference sensor detection; the direction of the magnetic field is perpendicular to the plane of the metal electrode, so that the magnetic field and the current direction are perpendicular to each other. According to the law of electromagnetic induction, the induced electromotive force and induced current can be generated to the greatest extent, enhancing the strength and stability of the detection signal; the magnetic field frequency is in the range of 10 Hz to 100 Hz. In this frequency band, the inductance and resistance characteristics of the metal electrode have a relatively stable effect on the induced current, and the generated electromagnetic signal is easy to analyze and process. At the same time, it can also effectively avoid the interference of complex factors such as the skin effect that may be caused by the high-frequency magnetic field.

[0089] Semiconductor materials: Semiconductors are the core part of the photovoltaic effect, and their electrical properties are significantly affected by magnetic fields. By configuring a specific magnetic field, changes in carrier movement and concentration distribution in semiconductors can be detected, and then defects in the semiconductor material itself and contact defects with metal electrodes can be detected; the magnetic field strength is set between 0.5 Tesla and 1 Tesla. Because the conductivity of semiconductors is weak, only under the action of a stronger magnetic field can the movement trajectory of carriers be significantly changed, thereby generating a magnetic field change signal that can be detected by the superconducting quantum interference sensor; the magnetic field direction, for common semiconductor materials such as silicon and germanium, applying a magnetic field along the main crystal axis of the crystal can more effectively affect the migration and diffusion of carriers, so that defects caused by defects The changes in electrical properties caused by this process show more obvious differences in the magnetic field. In this embodiment, taking silicon semiconductor materials as an example, when detecting the PN junction area of ​​a silicon-based photovoltaic module, a magnetic field is applied along the normal direction of the PN junction plane, which can highlight the changes in the carrier recombination, diffusion and other processes near the junction area affected by the magnetic field, and is helpful to detect defects in the junction area. The magnetic field frequency is in the range of 100 Hz to 500 Hz. The high-frequency magnetic field can change the motion state of carriers in the semiconductor more frequently, so that the changes in the carrier scattering, recombination and other processes caused by defects are reflected more quickly in the magnetic field, thereby improving the sensitivity and real-time performance of the detection. At the same time, an appropriate high-frequency magnetic field can also reduce the influence of the capacitance effect inside the semiconductor material on the detection signal.

[0090] The magnetic field generated by the magnetic field generator 5 will cause an induced current in the photovoltaic module. If the photovoltaic module has defects, such as open circuit, short circuit or poor contact, the distribution and intensity of the induced current will be abnormal. By detecting the change of the induced current through the superconducting quantum interference sensor component 4, it can be inferred whether the photovoltaic module has defects; for example, if the induced current in a certain area is significantly lower than the normal level, it may indicate that there is a circuit breaker defect in the area; in addition, the defect may cause a change in the internal resistance of the photovoltaic module. The magnetic field generated by the magnetic field generator 5 interacts with the current, which can indirectly reflect the change in resistance. If the resistance increases or decreases abnormally, it suggests that there may be defects; for example, local resistance increases due to corrosion or damage; in addition, a normal photovoltaic module will present a specific magnetic field distribution pattern under the action of the magnetic field. When the module has defects, such as internal structural damage or impurities, it will interfere with the distribution of the magnetic field. By detecting and analyzing the difference in magnetic field distribution, the location and type of the defect can be identified; for example, if the magnetic field distribution in a certain area is obviously distorted or uneven, it may mean that there is a defect in the area.

[0091] S6: The second superconducting quantum interference sensor component collects data from the photovoltaic component and transmits the collected data to the control end. The control end determines whether the photovoltaic component has defects. If so, it sends the data to the user end to prompt the staff that there is a problem with the photovoltaic component; otherwise, the detection is terminated and the photovoltaic component is removed, waiting for the next detection cycle; more preferably, in this step, after three detection cycles, the control end determines that the photovoltaic component has no defects based on the data collected by the superconducting quantum interference sensor component two, and the control end determines that the photovoltaic component has defects based on the data collected by the superconducting quantum interference sensor component one, then the control end issues a prompt to prompt the staff to detect the superconducting quantum interference sensor component one and the superconducting quantum interference sensor component two, determine whether there are problems with the superconducting quantum interference sensor component one and / or the superconducting quantum interference sensor component two, and adjust the hardware equipment in time to better detect the photovoltaic component; more preferably, the superconducting quantum interference sensor component two includes multiple superconducting quantum interference sensors 2 6, which can detect multiple positions of the photovoltaic component at the same time, thereby improving the detection efficiency.

[0092] The specific working principle of superconducting quantum interference sensor component 1 and superconducting quantum interference sensor component 2 (for the convenience of description, the following is simplified as superconducting quantum interference sensor component) for detecting subtle defects of photovoltaic modules is as follows:

[0093] First, based on the Josephson effect: the core of the superconducting quantum interference sensing component is a superconducting coil containing a Josephson junction. The Josephson junction is composed of two superconductors with a thin insulating layer in between. When a DC voltage is applied to both ends of the junction, a high-frequency superconducting sinusoidal current will be generated, and its frequency is proportional to the applied DC voltage, that is, the AC Josephson effect occurs; at the same time, the Cooper pairs can pass through the insulating layer through the tunnel effect to form a superconducting current, and the superconducting current is related to the phase difference, which is modulated by the magnetic field; based on flux quantization: in a small superconducting ring, the current is quantized, and the magnetic flux will also be quantized. Its quantization unit is the flux quantum Wb. When the external magnetic flux passes through the superconducting ring, the circulating current will generate a magnetic flux in the superconducting ring that is opposite to the change of the external magnetic flux to maintain the quantization of the magnetic flux. The change in the quantum number is a symbol of the change in the external magnetic flux and is measured in units of .

[0094] Secondly, the detection principle is mainly based on the generation and change of magnetic field, magnetic flux coupling and detection, signal conversion and detection. The specific principle is: when the photovoltaic module is detected, the magnetic field generator will generate a magnetic field around the photovoltaic module. Since the metal electrodes and semiconductor materials in the photovoltaic module will produce different electromagnetic responses in the magnetic field, the surrounding magnetic field will change. The superconducting coil in the superconducting quantum interference sensor component will interact with the changing magnetic field around the photovoltaic module, and the magnetic flux will be coupled to the superconducting ring through the low-resistance receiving coil, causing the magnetic flux in the superconducting ring to change. Since the superconducting quantum interference device is extremely sensitive to the change of magnetic flux Even extremely small changes in magnetic flux can cause changes in current and voltage in the superconducting ring. The current and voltage changes in the superconducting ring will be further amplified and converted into measurable electrical signals through the action of the Josephson junction. These electrical signals are closely related to the changes in the magnetic field caused by defects in the photovoltaic module. Through the precise measurement and analysis of these electrical signals, the subtle defects in the photovoltaic module can be inferred, such as cracks in metal electrodes, lattice defects in semiconductor materials, poor contact between different materials, etc., because these defects will change the local current distribution and magnetic field distribution, and then be detected by the superconducting quantum interference sensing component.

[0095] It should be noted that when using this method after the photovoltaic modules leave the factory, it is necessary to consider the waterproofing and drying issues of all electrical equipment. This is a commonly used technical means and this application will not go into detail here. The technicians can set it according to the specific situation.

[0096] Embodiment 2:

[0097] In this embodiment, the angle of the photovoltaic module is adjustable, which facilitates image collection and ensures that the photovoltaic modules used in the factory can adjust the angle of the photovoltaic modules according to the light, so that the photovoltaic panels can always maintain the best angle with the sunlight, thereby maximizing the absorption of solar energy and significantly improving power generation efficiency.

[0098] Specifically: in S2, the lifting support leg 7 is first started to adjust the angle of the photovoltaic component. After adjusting to a suitable angle, the image detection device 1 shoots the photovoltaic component and transmits the shot image to the control end. The control end processes the image collected by the image detection device 1 to determine whether the photovoltaic component has defects. If so, jump to S3, otherwise jump to S4.

[0099] More preferably, in order to ensure that the magnetic field is relatively evenly distributed on the photovoltaic module and the effects on various parts of the photovoltaic module are relatively consistent, in S4, the angles of all photovoltaic modules are first adjusted to the same angle, and then the angle of the magnetic field generator 5 is adjusted to be the same as the angle of the photovoltaic module, that is, the magnetic field generator 5 is ensured to be arranged parallel to the edge of the photovoltaic module; the magnetic field generator 5 is started, and the superconducting quantum interference sensor component 4 is used to detect whether the photovoltaic module produces a magnetic field change under the action of the magnetic field, and the collected data is transmitted to the control end, and the control end determines whether the photovoltaic module has defects. If so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, jump to S5.

[0100] Embodiment three:

[0101] The present embodiment provides a photovoltaic module defect detection device, including a support assembly 8, on which a photovoltaic module mounting groove 1 is installed. Specifically, the support assembly 8 includes two support plates 9, the inner sides of the two support plates 9 are connected with support legs, and the tops of the support legs are installed with photovoltaic module mounting grooves 1, and the photovoltaic module mounting grooves 1 are provided with line mounting grooves 10; the connection between adjacent photovoltaic modules is achieved through the line mounting grooves 10; the photovoltaic module mounting grooves 1 are used to place or install photovoltaic modules, and when the photovoltaic modules are not shipped out of the factory, they can be directly placed in the photovoltaic module mounting grooves 1 for detection by a mechanical arm, etc., and after the detection is completed, they can be moved to the photovoltaic module mounting grooves 1 by the cooperation of the mechanical arm, etc. In addition; when the photovoltaic module is used after leaving the factory, the photovoltaic module is fixed in the photovoltaic installation groove 1 by screws, mounting structures, etc. After the detection is completed, wait for the next detection cycle; the tops of the support assemblies 8 on both sides are fixed with longitudinal electric slide rails 11 and 12, and more specifically, the tops of the two support plates 9 are fixed with longitudinal electric slide rails 11 and 12; the longitudinal electric slide rails 11 and 12 are arranged on the upper part of the photovoltaic module installation groove 1, and the longitudinal electric slide rails 11 and 12 are respectively provided with longitudinal electric sliders 13 and 14, and the longitudinal electric sliders 13 and 14 are respectively provided with longitudinal electric sliders 13 and 14. There are two electric sliders 14, and a transverse electric slide rail 15 and a transverse electric slide rail 2 16 are fixed between the longitudinal electric slider 1 13 and the longitudinal electric slider 2 14, respectively. The longitudinal electric slider 1 13 moves along the longitudinal electric slide rail 1 11, and the longitudinal electric slider 2 14 moves along the longitudinal electric slide rail 2 12. The two ends of the transverse electric slide rail 1 15 and the transverse electric slide rail 2 16 are connected to the longitudinal electric slider 1 13 and the longitudinal electric slider 2 14 by screws, bonding, welding and other commonly used connection methods in the prior art; the longitudinal electric slider 1 13 and the longitudinal electric slider 2 14 move synchronously, thereby driving the transverse electric slide rail 1 15 , a transverse electric slide rail 16 moves along the longitudinal electric slide rail 1 11 and the longitudinal electric slide rail 2 12; a transverse electric slide rail 1 15 is provided with a transverse electric slide seat 1 17 and a transverse electric slide seat 2 18, the transverse electric slide seat 1 17 is fixed with an image detection device 1, and the transverse electric slide seat 2 18 is fixed with a superconducting quantum interference sensor component 1; a transverse electric slide rail 2 16 is provided with a transverse electric slide seat 3 19 and a transverse electric slide seat 4 20, the transverse electric slide seat 3 19 is fixed with an image detection device 2, and the transverse electric slide seat 4 20 is fixed with the superconducting quantum interference sensor component 2; a magnetic field generator 5 is provided on the outer side of the support component 8.

[0102] It should be noted that when using this device after the photovoltaic modules leave the factory, it is necessary to consider the waterproofing and drying of all electrical equipment. This is a commonly used technical means and this application will not go into details here. The technicians can set it according to the specific situation.

[0103] In this embodiment, the photovoltaic component is first placed or installed in the photovoltaic component installation groove 1, the horizontal electric slide rail 15 moves along the longitudinal electric slide rail 11 and the longitudinal electric slide rail 12, the image detection device 1 moves along the horizontal electric slide rail 15, and the image of the photovoltaic component is collected. The collected image is transmitted to the control end, and the control end analyzes the image to determine whether the photovoltaic component has defects. When the photovoltaic component has defects, the horizontal electric slide rail 2 16 moves along the longitudinal electric slide rail 11 and the longitudinal electric slide rail 2 12, and the image detection device 2 moves along the horizontal electric slide rail 2 16 to collect the image of the photovoltaic component. The collected image is transmitted to the control end, and the control end analyzes the image to determine whether the photovoltaic component has defects. Whether the component is defective, when the photovoltaic component is defective, the control end sends a prompt to the user end, reminding the staff to deal with the defective photovoltaic component, wherein the user end includes but is not limited to laptop computers, desktop computers, mobile phones, tablets, smart bracelets and other devices; when the image collected by the image detection device 2 is analyzed and determined that the photovoltaic component is not defective, the magnetic field generator 5 is started, and the data of the photovoltaic component is collected through the superconducting quantum interference sensor component 1, and the collected data is transmitted to the control end. The control end determines whether the photovoltaic component is defective, and if so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic component, otherwise the detection is terminated to remove the photovoltaic component, or the detection is terminated and wait for the next detection cycle.

[0104] When the image collected by the image detection device 1 is analyzed and determined that there is no defect in the photovoltaic component, the magnetic field generator 5 is started, and the data of the photovoltaic component is collected through the superconducting quantum interference sensor component 1, and the collected data is transmitted to the control end. The control end determines whether the photovoltaic component has defects. Otherwise, the detection is terminated and the photovoltaic component is removed, or the detection is terminated and the next detection cycle is waited for. If yes, the data of the photovoltaic component is collected through the superconducting quantum interference sensor component 2, and the collected data is transmitted to the control end. The control end determines whether the photovoltaic component has defects. If yes, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic component; otherwise, the detection is terminated and the photovoltaic component is removed, and the next detection cycle is waited for.

[0105] Embodiment 4:

[0106] In this embodiment, the angle of the photovoltaic module is adjustable, which facilitates better image acquisition and can significantly improve power generation efficiency.

[0107] The support legs include a fixed support leg 21 installed on one side of the bottom of the photovoltaic module installation groove 1 and a lifting support leg 7 installed on the other side of the bottom of the photovoltaic module installation groove 1. More specifically, each photovoltaic module installation groove 1 includes two fixed support legs 21 and two lifting support legs 7, the two fixed support legs 21 are installed on one side of the bottom of the photovoltaic module installation groove 1, and the two lifting support legs 7 are installed on the other side of the bottom of the photovoltaic module installation groove 1; the fixed support leg 21 is rotatably connected to the photovoltaic module installation groove 1 through a rotating shaft 23, and a push block is installed on the top of the lifting support leg 7, and the push block includes a horizontal plate 2 4 and the longitudinal plate 25, the transverse plate 24 and the longitudinal plate 25 are arranged vertically in an "L" shape, the pushing block cooperates with the bottom of the photovoltaic module installation groove 1, and when the photovoltaic module and the support plate 9 are placed vertically, the transverse plate 24 is completely fitted with the bottom of the photovoltaic module installation groove 1, and the longitudinal plate 25 is fitted with one side of the photovoltaic module installation groove 1. The bottom of the photovoltaic module installation groove 1 is installed with limited blocks 26 on both sides of the pushing block; the lifting support leg 7 is started, driving the pushing block to move up, and the sliding cooperation between the pushing block and the photovoltaic module installation groove 1 is realized to adjust the angle of the photovoltaic module installation groove 1, thereby realizing the adjustment of the angle of the photovoltaic module;

[0108] When collecting images, if there is strong light that makes the captured image unclear, you can adjust the angle to select a suitable shooting angle. In addition, the photovoltaic modules used in the factory adjust the angle of the photovoltaic modules according to the light, so that the photovoltaic panels can always maintain the best angle with the sunlight, thereby absorbing solar energy to the greatest extent and significantly improving the power generation efficiency.

[0109] In this embodiment, when the image of the photovoltaic component is collected by the image detection device 1 and the image detection device 2, the lifting support leg 7 is first started to adjust the angle of the photovoltaic component. After adjusting to a suitable angle, the image detection device 1 and the image detection device 2 photograph the photovoltaic component, and the photographed image is transmitted to the control end. The control end processes the image collected by the image detection device 1 to determine whether the photovoltaic component has defects.

[0110] Embodiment five:

[0111] In this embodiment, the magnetic field generator 5 can maintain an angle consistent with the photovoltaic module, thereby ensuring the uniformity of the magnetic field. Specifically:

[0112] The outer side of the support plate 9 is ringed with a fixed bracket 27, which is ringed on the outer sides of all photovoltaic modules, and a plurality of magnetic field generators 5 are fixed on the fixed bracket 27; the fixed bracket 27 includes a connecting rod 1 28, a connecting rod 29, a connecting rod 3 30, and a connecting rod 4 31 connected in sequence, and the connecting rod 1 28, the connecting rod 29, the connecting rod 3 30, and the connecting rod 4 31 can be a round rod, a rectangular rod, etc., and the connecting rod 1 28, the connecting rod 29, the connecting rod 3 30, and the connecting rod 4 31 are connected by welding or the like, or they can be formed in an integrated manner; one side of the bottom of the fixed bracket 27 It is rotatably connected to the bracket fixed support leg 32, and the other side of the bottom of the fixed bracket 27 is connected to the bracket lifting support leg 33. More specifically, the bottom of the connecting rod 1 28 is respectively connected to the bracket fixed support leg 32 and the bracket lifting support leg 33, and the bottom of the connecting rod 30 is respectively connected to the bracket fixed support leg 32 and the bracket lifting support leg 33; the bracket fixed support leg 32 and the bracket lifting support leg 33 are respectively jointly arranged on one side of the connecting rod 29 or the connecting rod 4 31. Taking the example of two bracket fixed support legs 32 being arranged on one side of the connecting rod 29, the two bracket lifting support legs 33 are arranged on one side of the connecting rod 4 31.

[0113] The bracket fixed support leg 32 and the bracket lifting support leg 33 can be connected to the support plate 9 by well-known fixing methods such as bonding, screw connection, welding, etc., and include two bracket fixed support legs 32 and two bracket lifting support legs 33, one of which is arranged on the outer side of one support plate 9, and the other is arranged on the outer side of the other support plate 9; a bracket pushing block 34 is arranged on the bracket lifting support leg 33, and a bracket pushing groove 35 is opened on the bracket pushing block 34, and the bracket pushing groove 35 cooperates with the fixed bracket 27, and the lower part of the fixed bracket 27 is equipped with a bracket limit block 36 on both sides of the bracket pushing groove 35; the bracket lifting support leg 33 is started, driving the bracket pushing block 34 to move upward, and the sliding cooperation between the bracket pushing groove 35 and the fixed bracket 27 is realized to adjust the angle of the fixed bracket 27, thereby realizing the adjustment of the magnetic field generator 5.

[0114] Before starting the magnetic field generator 5, first adjust the angles of all photovoltaic modules to the same angle, and then adjust the angle of the magnetic field generator 5 to be the same as the angle of the photovoltaic module, that is, ensure that the magnetic field generator 5 is set parallel to the edge of the photovoltaic module; start the magnetic field generator 5, detect the photovoltaic module through the superconducting quantum interference sensor component 1 and the interference sensor component 2 to collect data of the photovoltaic module, and transmit the collected data to the control end, and the control end determines whether there is a defect in the photovoltaic module.

[0115] The present application can detect both photovoltaic modules that have not been shipped out of the factory and photovoltaic modules that have been installed in the factory. First, the image of the photovoltaic module is collected by the image detection device 1, and the control end analyzes the image collected by the image detection device 1. When the captured image analyzes that the photovoltaic module is defective, the position of the defect is re-measured by the image detection device 2. If the images captured by the two devices both show that the photovoltaic module is defective, the staff is prompted to deal with it in time. When one or both of the images captured by the image detection device 1 and the image detection device 2 analyze that the photovoltaic module is defective, the photovoltaic module is further re-checked by the superconducting quantum interference sensor component, because visual inspection sometimes cannot find subtle defects and may not be able to find defects in the photovoltaic module in time. The present application can find defects in the photovoltaic module in time, prompt the staff to deal with it in time, thereby improving the service life of the photovoltaic module, etc. In addition, the angle of the photovoltaic panel in the present application is adjustable, which is not only convenient for adjusting to the most suitable shooting angle, but also convenient for adjusting the angle of the photovoltaic module according to the light during actual use, so that the photovoltaic panel can always maintain the best angle with the sunlight, thereby maximizing the absorption of solar energy and significantly improving the power generation efficiency.

[0116] The discussion of any of the above embodiments is only exemplary. Under the concept of the invention, the technical features in the above embodiments or different embodiments may also be combined. For the sake of simplicity, they are not provided in detail. Therefore, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention shall be included in the protection scope of the embodiments of the present invention.

Claims

1. A photovoltaic module defect detection method, characterized in that: The steps include: S1: placing or installing the photovoltaic module into the photovoltaic module installation slot (1); S2: The image detection device 1 collects images of the photovoltaic module and transmits the collected images to the control end. The control end processes the images collected by the image detection device 1 to determine whether the photovoltaic module has defects. If yes, jump to S3, otherwise jump to S4; S3: The second image detection device collects images of the defective position and transmits the collected images to the control end. The control end processes the images collected by the second image detection device to determine whether the photovoltaic module has defects. If so, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, jump to S5; S4: Start the magnetic field generator (5), collect data of the photovoltaic module through the superconducting quantum interference sensor component, and transmit the collected data to the control end, the control end determines whether the photovoltaic module has defects, if yes, jumps to S6, otherwise, ends the detection and removes the photovoltaic module, or ends the detection and waits for the next detection cycle; S5: Start the magnetic field generator (5), collect data of the photovoltaic module through the superconducting quantum interference sensor component, and transmit the collected data to the control end, which determines whether the photovoltaic module has defects. If yes, it will be sent to the user end to prompt the staff that there is a problem with the photovoltaic module. Otherwise, the detection is terminated and the photovoltaic module is removed, or the detection is terminated and waits for the next detection cycle; S6: The second superconducting quantum interference sensor component collects data from the photovoltaic component and transmits the collected data to the control end. The control end determines whether the photovoltaic component has defects. If so, it sends the data to the user end to prompt the staff that there is a problem with the photovoltaic component; otherwise, the detection ends and waits for the next detection cycle.

2. A photovoltaic module defect detection method according to claim 1, characterized in that: In S6, after three detection cycles, when the control end determines that the photovoltaic module has no defects based on the data collected by the superconducting quantum interference sensor component 2, and the control end determines that the photovoltaic module has defects based on the data collected by the superconducting quantum interference sensor component 1, the control end issues a prompt to prompt the staff to detect the superconducting quantum interference sensor component 1 and the superconducting quantum interference sensor component 2 to determine whether there are problems with the superconducting quantum interference sensor component 1 and / or the superconducting quantum interference sensor component 2.

3. A photovoltaic module defect detection method according to claim 1, characterized in that: In S2, the lifting support leg (7) is first started to adjust the angle of the photovoltaic component. After adjusting to a suitable angle, the image detection device 1 takes a picture of the photovoltaic component and transmits the taken image to the control end. The control end processes the image collected by the image detection device 1 to determine whether the photovoltaic component has a defect. If so, the process jumps to S3, otherwise, the process jumps to S4.

4. A photovoltaic module defect detection method according to claim 1, characterized in that: In S4, the angles of all photovoltaic modules are first adjusted to the same angle, and then the angle of the magnetic field generator (5) is adjusted to be the same as the angle of the photovoltaic module, and the magnetic field generator (5) is started to collect data of the photovoltaic module through the superconducting quantum interference sensor component 1, and the collected data is transmitted to the control end, and the control end determines whether there is a defect in the photovoltaic module, and if so, sends it to the user end to prompt the staff that there is a problem with the photovoltaic module; Otherwise jump to S5.

5. A photovoltaic module defect detection method according to claim 1, characterized in that: The image detection device comprises 3n cameras (2), n≥1, and n is an integer; S2 is specifically: camera (2) photographs the photovoltaic component, and camera (2) transmits the photographed image to the control end, and the control end processes the images collected by each camera (2) to determine whether the photovoltaic component has defects, otherwise jump to S4, if yes, re-measure the defect position, and re-measure using a camera (2) whose image does not detect the defect position, and the camera (2) that does not detect the defect position photographs the defect position, and the image of the defect position collected is analyzed by the control end to determine whether the photovoltaic component has defects, if yes, jump to S3, otherwise re-measure using a camera (2) whose image does not detect the defect position, and the second camera (2) that does not detect the defect position photographs the defect position, and the image of the defect position collected is analyzed by the control end to determine whether the photovoltaic component has defects, if yes, jump to S3, otherwise jump to S4.

6. A photovoltaic module defect detection method according to claim 5, characterized in that: The image detection device 2 comprises 2n cameras 2 (3), n≥1, and n is an integer; S3 is specifically: the camera 2 (3) collects images of the position where the defect exists in S2, and transmits the detected images to the control end, the control end processes the images collected by the camera 2 (3), and determines whether the photovoltaic module has defects. If so, the images are sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, the defective position is re-measured by the camera 2 (3) that has not collected the image of the defective position, the camera 2 (3) that has not collected the image of the defective position takes a picture of the defective position, and the collected images of the defective position are analyzed by the control end to determine whether the photovoltaic module has defects. If so, the images are sent to the user end to prompt the staff that there is a problem with the photovoltaic module, otherwise, the process jumps to S5.

7. A photovoltaic module defect detection method according to claim 6, characterized in that: The superconducting quantum interference sensor component 1 includes at least 2n superconducting quantum interference sensors 1 (4), n≥1, and n is an integer; the S4 specifically includes: starting the magnetic field generator (5), collecting magnetic field data and electrical performance data of the photovoltaic component through the superconducting quantum interference sensor 1 (4), and transmitting the collected data to the control end, the control end determines whether the photovoltaic component has defects, otherwise the detection is terminated and the photovoltaic component is removed, or the detection is terminated and waiting for the next detection cycle; if yes, the defect position is retested, and the superconducting quantum interference sensor 1 (4) whose collected data does not detect the defect position is retested, and the superconducting quantum interference sensor 1 (4) that does not detect the defect position collects data on the defect position, and the image of the collected defect position is analyzed by the control end to determine whether the photovoltaic component has defects, and if yes, jump to S6, otherwise, the detection is terminated and the photovoltaic component is removed, or the detection is terminated and waiting for the next detection cycle.

8. A photovoltaic module defect detection device using a photovoltaic module defect detection method according to any one of claims 1 to 7, comprising a support assembly (8), characterized in that: The support assembly (8) is provided with a photovoltaic assembly installation groove (1), and the photovoltaic assembly installation groove (1) is used to place or install the photovoltaic assembly. A longitudinal electric slide rail (11) and a longitudinal electric slide rail (12) are fixed to the top of the support assemblies (8) on both sides. The longitudinal electric slide rail (11) and the longitudinal electric slide rail (12) are arranged on the upper part of the photovoltaic assembly installation groove (1). The longitudinal electric slide rail (11) and the longitudinal electric slide rail (12) are respectively provided with a longitudinal electric slider (13) and a longitudinal electric slider (14). The number of the longitudinal electric slider (13) and the number of the longitudinal electric slider (14) are two, respectively. The longitudinal electric slider (13) and the longitudinal electric slider (14) are spaced apart from each other. A transverse electric slide rail 1 (15) and a transverse electric slide rail 2 (16) are fixed respectively, and a transverse electric slide rail 1 (17) and a transverse electric slide rail 2 (18) are arranged on the transverse electric slide rail 1 (15), an image detection device 1 is fixed on the transverse electric slide rail 1 (17), and a superconducting quantum interference sensor component 1 is fixed on the transverse electric slide rail 2 (18); a transverse electric slide rail 3 (19) and a transverse electric slide rail 4 (20) are arranged on the transverse electric slide rail 2 (16), an image detection device 2 is fixed on the transverse electric slide rail 3 (19), and the superconducting quantum interference sensor component 2 is fixed on the transverse electric slide rail 4 (20); a magnetic field generator (5) is arranged on the outer side of the support component (8).

9. A photovoltaic module defect detection device according to claim 8, characterized in that: A fixed support leg (21) is installed on one side of the bottom of the photovoltaic component installation groove (1), and a lifting support leg (7) is installed on the other side of the bottom of the photovoltaic component installation groove (1). The fixed support leg (21) is rotatably connected to the photovoltaic component installation groove (1) via a rotating shaft (23). A push block is installed on the top of the lifting support leg (7). The push block includes a horizontal plate (24) and a vertical plate (25). The push block cooperates with the bottom of the photovoltaic component installation groove (1). The bottom of the photovoltaic component installation groove (1) is provided with limit blocks (26) on both sides of the push block.

10. A photovoltaic module defect detection device according to claim 8, characterized in that: A fixed bracket (27) is provided on the outer ring of the support assembly (8), and a magnetic field generator (5) is fixed on the fixed bracket (27); one side of the bottom of the fixed bracket (27) is rotatably connected to a bracket fixed support leg (32), and the other side of the bottom of the fixed bracket (27) is connected to a bracket lifting support leg (33); a bracket pushing block (34) is provided on the bracket lifting support leg (33), and a bracket pushing groove (35) is provided on the bracket pushing block (34), and the bracket pushing groove (35) cooperates with the fixed bracket (27), and bracket limit blocks (36) are installed on both sides of the bracket pushing groove (35) at the lower part of the fixed bracket (27).

Images