A photovoltaic module defect detection method and a detection device thereof

Abstract

The application provides a photovoltaic module defect detection method and a detection device thereof. The detection device comprises a support assembly, a photovoltaic module mounting groove is mounted on the support assembly, a longitudinal electric sliding rail is fixed to the top of the support assembly, longitudinal electric sliding blocks are arranged on the longitudinal electric sliding rail, a transverse electric sliding rail is fixed between the longitudinal electric sliding blocks, transverse electric sliding seats are arranged on the transverse electric sliding rail, and an image detection equipment and a superconducting quantum interference sensing assembly are fixed to the transverse electric sliding seats. A magnetic field generator is arranged on the outer side of the support assembly. When the image detection equipment does not analyze that the photovoltaic module has defects, the photovoltaic module is rechecked by the superconducting quantum interference sensing assembly. Since visual detection cannot sometimes find subtle defects, the photovoltaic module defects cannot be found in time. The application can find the defects existing in the photovoltaic module in time, thereby prolonging the service life of the photovoltaic module.

Description

TECHNICAL FIELD

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

[0002] A photovoltaic module is the core part of a solar photovoltaic power generation system, and its main function is to convert solar energy into electrical energy. A photovoltaic module is usually composed of multiple solar cell pieces combined in series and parallel, and is packaged in a protective frame and a glass or plastic cover. Its working principle is based on the photoelectric effect of semiconductors. When sunlight shines on the cell piece, photons excite electrons in the semiconductor material, generating current and voltage.

[0003] Photovoltaic modules have various types and specifications to adapt to different application scenarios and needs, such as monocrystalline silicon photovoltaic modules, which have relatively high efficiency but relatively high cost; polycrystalline silicon photovoltaic modules, which have relatively low cost and relatively considerable efficiency; thin-film photovoltaic modules, which have flexibility and good weak light performance. Regardless of the type of photovoltaic module, defect detection is required before shipment and actual use for the following reasons:

[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 can cause current transmission to be blocked, and hot spots can cause local energy loss. Timely detection and repair of defects can ensure that the module always maintains high power generation efficiency, thereby improving the output power of the entire photovoltaic system.

[0006] 2. Extend the service life

[0007] Various defects, if not promptly addressed, will gradually worsen over time, accelerating the aging and damage of the module. Regular detection can help identify and address problems early, effectively extending the service life of the photovoltaic module and reducing replacement costs.

[0008] 3. Improve system reliability

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

[0010] 4. Reduce safety risks

[0011] Some serious defects, such as short circuits, can cause fires and other safety incidents. Regular defect detection can help identify and eliminate these potential safety hazards in advance, ensuring the safety of personnel and equipment.

[0012] For the above reasons, the detection of defects in photovoltaic modules has become increasingly important. In the prior art, photovoltaic modules that have not yet been shipped are usually detected visually, but visual detection sometimes cannot find subtle defects. In addition, when the visual detection device detects a problem, the original visual system is usually used for rechecking. If the visual system has a problem, the problem cannot be promptly identified. Therefore, there is room for improvement. For photovoltaic modules in use, the prior art usually involves periodic manual inspection. In order to save labor, some places use unmanned aerial vehicles for inspection. For example, in the patent document with application number 202310746748.5, a method and system for unmanned aerial vehicle photovoltaic module inspection and cleaning are disclosed. When applied to a monitoring terminal, the method includes: in response to a first input operation of a user, obtaining basic navigation parameters; planning a flight path based on the basic navigation parameters; generating a set of field images, inputting the set of field images into a first identification model based on training, and obtaining photovoltaic module identification results and a set of binary images; inputting the set of binary images into a second identification model based on training; in response to a second input operation of the user, controlling the unmanned aerial vehicle inspection and cleaning module to clean the photovoltaic module; the invention obtains basic navigation parameters through the input operation of the user, generates a corresponding flight path and sends it to the unmanned aerial vehicle inspection and cleaning module, so that the unmanned aerial vehicle flies along the corresponding flight path; the monitoring terminal monitors the unmanned aerial vehicle inspection and cleaning module, obtains field image data, identifies photovoltaic modules and defects existing on the photovoltaic modules, and cleans the photovoltaic modules with stains through the cleaning mechanism carried on the unmanned aerial vehicle. In the above patent document, the field image data is obtained through the inspection of the unmanned aerial vehicle, and the photovoltaic modules and the defects existing on the photovoltaic modules are identified. The same as the photovoltaic modules that have not yet been shipped, the detection is performed visually. Similarly, visual detection sometimes cannot find subtle defects. In addition, the inspection of the unmanned aerial vehicle requires pre-planning of the path, and the program is relatively complicated, so the photovoltaic module cannot be detected in a timely manner.

[0013] Therefore, there is a need to provide a new technical solution to solve the above technical problems. SUMMARY

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

[0015] S1: placing or installing the photovoltaic module into the photovoltaic module installation slot;

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

[0017] S3: Image detection device two collects images of the position of the defects, transmits the detected images to the control end, the control end processes the images collected by image detection device two, judges whether the photovoltaic module has defects, yes, sends to the user end, prompts the staff that the photovoltaic module has problems; otherwise, jump to S5;

[0018] S4: Start the magnetic field generator, collect data of the photovoltaic module through the superconducting quantum interference sensing assembly one, and transmit the collected data to the control end, the control end judges whether the photovoltaic module has defects, yes, jump to S6, otherwise, end the detection and remove the photovoltaic module, or end the detection and wait for the next detection period;

[0019] S5: Start the magnetic field generator, collect data of the photovoltaic module through the superconducting quantum interference sensing assembly one, and transmit the collected data to the control end, the control end judges whether the photovoltaic module has defects, yes, send to the user end, prompt the staff that the photovoltaic module has problems, otherwise, end the detection and remove the photovoltaic module, or end the detection and wait for the next detection period;

[0020] S6: The superconducting quantum interference sensing assembly two collects data of the photovoltaic module, and transmits the collected data to the control end, the control end judges whether the photovoltaic module has defects, yes, sends to the user end, prompts the staff that the photovoltaic module has problems; otherwise, end the detection and wait for the next detection period.

[0021] As a preferred solution, in S6, after three detection periods, the data collected by the superconducting quantum interference sensing assembly two and the control end all judge that the photovoltaic module has no defects, and the data collected by the superconducting quantum interference sensing assembly one and the control end all judge that the photovoltaic module has defects, then the control end prompts the staff to detect the superconducting quantum interference sensing assembly one and the superconducting quantum interference sensing assembly two, and judge whether the superconducting quantum interference sensing assembly one and / or the superconducting quantum interference sensing assembly two has problems.

[0022] As a preferred solution, in S2, first start the lifting support leg, adjust the angle of the photovoltaic module, adjust to the appropriate angle, then image detection device one takes a picture of the photovoltaic module, transmits the photographed image to the control end, the control end processes the images collected by image detection device one, judges whether the photovoltaic module has defects, yes, jump to S3, otherwise, jump to S4.

[0023] As a preferred solution, in the 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 that of the photovoltaic module, the magnetic field generator is started, the data of a pair of photovoltaic modules collected by the superconducting quantum interference sensor assembly are acquired, and the acquired data are transmitted to the control end, the control end judges whether the photovoltaic module has a defect, and if yes, the control end sends the information to the user end to prompt the staff that the photovoltaic module has a problem; otherwise, the process jumps to the S5.

[0024] As a preferred solution, the image detection device one includes 3n camera ones, n≥1, and n is an integer; the S2 specifically includes: the camera ones shoot the photovoltaic module, the camera ones transmit the shot images to the control end, the control end processes the images collected by each camera one, judges whether the photovoltaic module has a defect, and if not, the process jumps to the S4; if yes, the process re-measures the defect position, re-measurement is performed by using a camera one that does not detect the defect position in the collected images, the camera one that does not detect the defect position shoots the defect position, and the collected image of the defect position is analyzed by the control end to determine whether the photovoltaic module has a defect, if yes, the process jumps to the S3; if not, re-measurement is performed by using a second camera one that does not detect the defect position, the second camera one that does not detect the defect position shoots the defect position, and the collected image of the defect position is analyzed by the control end to determine whether the photovoltaic module has a defect, if yes, the process jumps to the S3; if not, the process jumps to the S4.

[0025] As a preferred solution, the image detection device two includes 2n camera twos, n≥1, and n is an integer; the S3 specifically includes: the camera twos collect images of the defect position in the S2, transmit the collected images to the control end, the control end processes the images collected by the camera twos, judges whether the photovoltaic module has a defect, and if yes, the control end sends the information to the user end to prompt the staff that the photovoltaic module has a problem; if not, the process re-measures the defect position by using a camera two that does not collect images of the defect position, the camera two that does not collect images of the defect position shoots the defect position, and the collected image of the defect position is analyzed by the control end to determine whether the photovoltaic module has a defect, if yes, the process sends the information to the user end to prompt the staff that the photovoltaic module has a problem; if not, the process jumps to the S5.

[0026] As a preferred solution, the superconducting quantum interference sensing component one comprises at least 2n superconducting quantum interference sensors, n≥1, and n is an integer; the S4 is specifically: starting the magnetic field generator, collecting the magnetic field data of the photovoltaic component and the electrical performance data of the photovoltaic component through the superconducting quantum interference sensor one, and transmitting the collected data to the control end; the control end judges whether the photovoltaic component has defects, otherwise the detection is ended and the photovoltaic component is removed, or the detection is ended and the next detection period is waited; if yes, the defect position is re-measured, the superconducting quantum interference sensor one which does not detect the defect position is used for re-measurement, the superconducting quantum interference sensor one which does not detect the defect position collects the data of the position of the defect, and the image of the collected defect position is analyzed by the control end to analyze whether the photovoltaic component has defects, yes to S6, otherwise the detection is ended and the photovoltaic component is removed, or the detection is ended and the next detection period is waited.

[0027] As a preferred solution, the superconducting quantum interference sensing component two comprises a plurality of superconducting quantum interference sensors two, and the superconducting quantum interference sensors two collect the magnetic field data of the photovoltaic component and the electrical performance data of the photovoltaic component.

[0028] A photovoltaic component defect detection device, comprising a support assembly, a photovoltaic component mounting groove is mounted on the support assembly, the photovoltaic component mounting groove is used for placing or mounting a photovoltaic component, the top of the support assembly on both sides is fixed with longitudinal electric sliding rails one and two, the longitudinal electric sliding rails one and two are arranged on the upper part of the photovoltaic component mounting groove, longitudinal electric sliding blocks one and two are arranged on the longitudinal electric sliding rails one and two respectively, the number of longitudinal electric sliding blocks one and two is two respectively, transverse electric sliding rails one and two are fixed between the longitudinal electric sliding blocks one and two respectively, transverse electric sliding seats one and two are arranged on the transverse electric sliding rail one, image detection equipment one is fixed on the transverse electric sliding seat one, and superconducting quantum interference sensing component one is fixed on the transverse electric sliding seat two; transverse electric sliding seats three and four are arranged on the transverse electric sliding rail two, image detection equipment two is fixed on the transverse electric sliding seat three, and the superconducting quantum interference sensing component two is fixed on the transverse electric sliding seat four; a magnetic field generator is arranged outside the support assembly.

[0029] As a preferred solution, a fixed support leg is arranged on one side of the bottom of the photovoltaic component mounting groove, a lifting support leg is arranged on the other side of the bottom of the photovoltaic component mounting groove, the fixed support leg is rotationally connected to the photovoltaic component mounting groove through a rotating shaft, a pushing block is mounted on the top of the lifting support leg, the pushing block comprises a horizontal plate and a vertical plate, the pushing block is matched with the bottom of the photovoltaic component mounting groove, and limit blocks are mounted on both sides of the bottom of the photovoltaic component mounting groove.

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

[0031] As a preferred solution, one side of the bottom of the fixing support is rotationally connected with a support supporting leg, and the other side of the bottom of the fixing support is connected with a support lifting supporting leg, the support lifting supporting leg is provided with a support pushing block, the support pushing block is provided with a support pushing groove, the support pushing groove is matched with the fixing support, and the lower part of the fixing support is provided with a support limiting block on both sides of the support pushing groove.

[0032] The application can detect photovoltaic modules that have not been factory-installed or photovoltaic modules that have been factory-installed. First, an image detection device one collects images of the photovoltaic modules, a control end analyzes the images collected by the image detection device one, when the images taken by the image detection device one analyze that the photovoltaic modules have defects, the position of the defects is re-measured by an image detection device two, and when the images taken by the image detection device one and the image detection device two both show that the photovoltaic modules have defects, the staff is prompted to handle in time. When the images taken by the image detection device one and the image detection device two do not analyze that the photovoltaic modules have defects, the photovoltaic modules are further rechecked by a superconducting quantum interference sensing assembly. Since visual detection cannot sometimes find subtle defects, the photovoltaic modules may not be able to be found in time. The application can find defects in the photovoltaic modules in time, prompt the staff to handle in time, and thus improve the service life of the photovoltaic modules. In addition, the angle of the photovoltaic panel in the application is adjustable. The photovoltaic panel can be adjusted to the most appropriate shooting angle, and the angle of the photovoltaic panel can be adjusted according to the light in the actual use process, so that the photovoltaic panel can always maintain the best included angle with the sunlight, thereby maximizing the absorption of solar energy and significantly improving the power generation efficiency. BRIEF DESCRIPTION OF DRAWINGS

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

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

[0035] Figure 3 is a structure diagram of angle one of the photovoltaic module defect detection device of the application;

[0036] Figure 4 is a structure diagram of angle two of the photovoltaic module defect detection device of the application;

[0037] Figure 5 is a structure diagram of angle one of the photovoltaic module defect detection device of the application without a magnetic field generator;

[0038] Figure 6 is a structural diagram of angle two of the photovoltaic module defect detection device of the present application without a magnetic field generator;

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

[0040] Figure 8 is a structural diagram of angle one of the photovoltaic module placement slot and support leg of the present application;

[0041] Figure 9 is a structural diagram of angle two of the photovoltaic module placement slot and support leg of the present application;

[0042] Figure 10 is a structural diagram of angle one of the photovoltaic module placement slot and longitudinal electric sliding rail parallel;

[0043] Figure 11 is a structural diagram of angle two of the photovoltaic module placement slot and longitudinal electric sliding rail parallel;

[0044] Figure 12 is a structural diagram of the lifting support leg matched with the photovoltaic module placement slot;

[0045] Figure 13 is a structural diagram of angle one of the longitudinal sliding rail and transverse sliding rail;

[0046] Figure 14 is a structural diagram of angle two of the longitudinal sliding rail and transverse sliding rail;

[0047] Figure 15 is a structural diagram of angle three of the longitudinal sliding rail and transverse sliding rail;

[0048] Figure 16 is a structural diagram of angle one of the fixed support;

[0049] Figure 17 is a structural diagram of angle two of the fixed support;

[0050] Figure 18 is a structural diagram of the fixed support and longitudinal electric sliding rail parallel;

[0051] Figure 19 is a structural diagram of the support lifting support leg matched with the fixed support;

[0052] Reference signs:

[0053] 1, photovoltaic module installation slot; 2, camera one; 3, camera two; 4, superconducting quantum interference sensor one; 5, magnetic field generator; 6, superconducting quantum interference sensor two; 7, lifting support leg; 8, support assembly; 9, support plate; 10, line installation slot; 11, longitudinal electric sliding rail one; 12, longitudinal electric sliding rail two; 13, longitudinal electric sliding block one; 14, longitudinal electric sliding block two; 15, transverse electric sliding rail one; 16, transverse electric sliding rail two; 17, transverse electric sliding seat one; 18, transverse electric sliding seat two; 19, transverse electric sliding seat three; 20, transverse electric sliding seat four; 21, fixed support leg; 23, rotating shaft; 24, horizontal plate; 25, vertical plate; 26, limiting block; 27, fixed support; 28, connecting rod one; 29, connecting rod two; 30, connecting rod three; 31, connecting rod four; 32, support fixed support leg; 33, support lifting support leg; 34, support pushing block; 35, support pushing groove; 36, support limiting block. DETAILED DESCRIPTION

[0054] The specific embodiments of the present application are described in detail below. It should be noted that the specific embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. Figure 1 Figure 19 The specific embodiments of the present application are described in detail below. It should be noted that the specific embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0055] Example one:

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

[0057] S1: Place or install the photovoltaic module into the photovoltaic module installation slot 1; the size of the photovoltaic module installation slot 1 is matched with the size of the photovoltaic module; the number of the photovoltaic module installation slot 1 is not specifically limited, and is set according to the specific circumstances; specifically, when the photovoltaic module is not out of the factory, it can be directly placed into the photovoltaic module installation slot 1 by a mechanical arm or the like for detection, and after the detection is completed, it is removed by a mechanical arm or the like; when the photovoltaic module is used after it is out of the factory, it is fixed in the photovoltaic installation slot 1 by screws, mounting structures, etc., and works;

[0058] ​S2: the image detection device one collects images of the photovoltaic module, transmits the collected images to the control end, the control end is a PLC controller or the like of the peripheral device, the control end processes the images collected by the image detection device one, judges whether the photovoltaic module has defects, yes, jumps to S3, otherwise jumps to S4, and further detects by the superconducting quantum interference sensing assembly; the image detection device one detects each group of photovoltaic modules respectively, the image detection device one includes 3n camera 2, n≥1, and n is an integer; each photovoltaic module needs to be detected by 3n camera 2, if multiple groups of photovoltaic modules need to be collected at the same time, the number of camera 2 needs to be set according to the actual situation, for example, if two groups of photovoltaic modules need to be detected at the same time, 3 camera 2 are needed for each group of photovoltaic modules, and 6 camera 2 are needed to be set at the same time; it should be noted that the camera 2 in the present application can be universal, for example, the first, second and third camera 2 are not only used to collect the images of the first group of photovoltaic modules, but also the first, third and fifth camera 2 can be used to collect the images of the second group of photovoltaic modules, so that 3n camera 2 can collect images of each group of photovoltaic modules;

[0059] The camera 2 collects images of the photovoltaic module; specifically: the S2 specifically photographs the photovoltaic module by the camera 2, the camera 2 transmits the photographed images to the control end, the control end processes the images collected by each camera 1, judges whether the photovoltaic module has defects, and jumps to S4 when there is no defect; when the photovoltaic module has a defect position, the defect position is re-measured by using one of the cameras 2 that does not detect the defect position to re-measure, the camera 2 that does not detect the defect position photographs the defect position, and the collected image of the defect position is analyzed by the control end to analyze whether the photovoltaic module has defects, yes, jumps to S3, otherwise, the second camera 2 that does not detect the defect position is used to re-measure, the second camera 2 that does not detect the defect position photographs the defect position, and the collected image of the defect position is analyzed by the control end to analyze whether the photovoltaic module has defects, yes, jumps to S3, otherwise, jumps to S4;

[0060] In this step, one defect position may be detected, or multiple defect positions may be detected, for example, three camera 2, one defect position, two defect positions are detected respectively, and are described as follows:

[0061] When a defect position is detected: three cameras 2 shoot the photovoltaic module, the three cameras 2 transmit the 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, and if not, jumps to S4; when the photovoltaic module has a defect position, the image of the defect position is collected by the first camera 2, and the other two cameras 2 do not collect the image of the position, the defect position is retested, the second camera 2 is used to shoot the defect position, and the image of the defect position collected is analyzed by the control end to determine whether the photovoltaic module has defects, yes, jump to S3, otherwise, the third camera 2 is used to shoot the defect position, and the image of the defect position collected is analyzed by the control end to determine whether the photovoltaic module has defects, yes, jump to S3, otherwise, jump to S4;

[0062] Two defect positions: when the photovoltaic module has two defect positions, the images of the defect position one and the defect position two are collected by the first camera 2 and the second camera 2 respectively, and the third camera 2 does not collect the images of the defect position one and the defect position two, the retest of the defect position one can be retested by the second camera 2 and the third camera 2, and the retest of the defect position two can be retested by the first camera 2 and the third camera 2, which will not be described in detail here; in addition, the same defect position can also be collected by two cameras 2 or three cameras 2 at the same time, in this case, the result of the second camera 2 retest is that the photovoltaic module has a problem, and directly jumps to S3;

[0063] S3: the image detection device two collects images of the position with defects, transmits the detected images to the control end, the control end processes the images collected by the image detection device two, judges whether the photovoltaic module has defects, yes, sends to the user end, prompts the staff that the photovoltaic module has problems, and then makes the staff repair, replace and the like the photovoltaic module with problems in time, guarantees the power generation efficiency, improves the stability of the system and the like; otherwise, jumps to S5, and further detects through the superconducting quantum interference sensing assembly; the image detection device two includes 2n camera twos 3, n≥1, the camera two 3 collects images of the photovoltaic module; S3 is specifically as follows: the camera two 3 collects images of the position with defects in S2, transmits the detected images to the control end, the control end processes the images collected by the camera two 3, judges whether the photovoltaic module has defects, yes, sends to the user end, prompts the staff that the photovoltaic module has problems; otherwise, the camera two 3 not collecting images of the position with defects is used to retest the position with defects, the camera two 3 not collecting images of the position with defects is used to take pictures of the position with defects, the images of the position with defects collected are analyzed by the control end to determine whether the photovoltaic module has defects, yes, sends to the user end, prompts the staff that the photovoltaic module has problems, otherwise, jumps to S5.

[0064] The control end processes the images collected by the image detection device one and the image detection device two, and the specific steps of judging whether the photovoltaic module has defects are as follows:

[0065] The images collected by the image detection device one and the image detection device two are preprocessed, including grayscale processing and filter denoising, and the specific steps are as follows: the collected images are converted into grayscale images, because the grayscale information can provide sufficient features for the detection of the defects of the photovoltaic module, and the calculation amount of the grayscale image is smaller than that of the color image, and the speed is faster; filter denoising is mainly used to remove various noise interference that the photovoltaic module image may suffer in the collection process, such as salt and pepper noise and Gaussian noise, and the filter technology can effectively remove these noises and improve the image quality, in the embodiment, mean filter or median filter and the like can be used for processing, wherein the mean filter replaces the value of the center pixel with the average value of the pixels in the neighborhood, and has a certain effect on removing Gaussian noise; the median filter sorts the grayscale values of the pixels in the neighborhood, and takes the middle value as the value of the center pixel, and has a good effect on removing salt and pepper noise.

[0066] Feature extraction is performed on the preprocessed image, including edge detection, texture feature extraction, shape feature extraction. Specifically, many defects of the photovoltaic module, such as cracks, breakage, etc. will be shown as changes in the edges on the image. Common edge detection operators include Sobel operator, Canny operator, etc. Sobel operator detects edges by calculating the gray level change gradient in the horizontal and vertical directions of the image. It respectively performs convolution operation on the horizontal and vertical directions of the image to obtain the gradient values in the horizontal and vertical directions, and then calculates the edge strength and direction by formula. The formula is a conventional prior art, and the applicant does not make specific elaboration here. Canny operator can obtain more accurate and continuous edge information through multiple steps such as Gaussian filtering, gradient amplitude and direction calculation, non-maximum suppression, double threshold detection, etc. The texture on the surface of the photovoltaic module will be different under the condition of having defects and not having defects. The embodiment can adopt methods such as gray level co-occurrence matrix (GLCM) to extract texture features. GLCM obtains multiple texture feature parameters such as contrast, correlation, energy, entropy by statistics the frequency of different gray value pixel pairs in the photovoltaic image in a certain direction and distance, which can be used to describe the texture state of the surface of the photovoltaic module and help to judge whether there is a defect. For some defects with obvious shape features, such as missing corners of the cell sheet, black spots, etc., the shape features can be extracted for judgment. For example, by calculating the area, perimeter, circularity (4π×area / perimeter2), rectangularity (the ratio of the area of the defect region to the area of the minimum circumscribed rectangle) and other shape parameters of the defect region, and comparing with the shape parameter range under normal conditions, whether there is a defect can be determined.

[0067] The image after feature extraction is segmented. According to the gray value distribution characteristics of the image, one or more threshold values are set to divide the image into different regions. For a photovoltaic module image, if the gray value of the defect region is significantly lower or higher than that of the normal region, a suitable threshold value can be set to determine the region with a gray value lower than the threshold value as a possible defect region and the region with a gray value higher than the threshold value as a normal region. The threshold value is set in real time according to the differences in materials, surface state, light intensity, shooting angle, image processing purpose, etc. For example, assuming that the image gray value range is 0-255, the threshold value for monocrystalline silicon photovoltaic panels is initially set between 120 and 150 when distinguishing between normal regions and possible defect regions such as stains and minor scratches, because the gray value of the normal region is relatively high and concentrated, while the gray value of the defect region is slightly lower. In actual application, the threshold value can be further adjusted according to the specific image conditions. For polycrystalline silicon photovoltaic panels, the gray value distribution is more dispersed, so a more detailed threshold value needs to be tried, such as selecting a threshold value in the range of 80-120 for segmentation, observing the segmentation effect, and then fine-tuning according to the actual situation. When a new photovoltaic panel is simply segmented into a normal region and an edge region (such as an edge structure with a mounting frame), if the gray value of the edge region is relatively low, the threshold value can be set to about 180-200 to segment the image into a central normal region and an edge region. For photovoltaic panels with more dust coverage, the gray value of the dust-covered region is usually low, so the threshold value can be set between 100 and 130 to separate the dust-covered region from the relatively clean normal region, but subsequent optimization is still needed according to the actual segmentation effect. If the image is collected in a strong light environment, in order to segment the possible shadow region (such as the shaded part) and the normally illuminated region, the threshold value may need to be set to a higher value, such as 200-220, because the gray value of the shadow region is significantly lower than that of the normally illuminated region. For images collected in a weak light environment, if different reflection characteristics need to be distinguished (such as the cell region and the connecting line region), the threshold value may need to be set to a lower value, such as between 60 and 80, and then adjusted according to the actual segmentation effect. To distinguish between the cell and the busbar, the gray value of the cell region may be relatively high, and the gray value of the busbar may be relatively low. The threshold value can be initially set between 130 and 160 to segment the image into different regions, and then the actual component shape, position, and other characteristics can be used to further confirm whether the target component is accurately segmented. For region segmentation, the commonly used segmentation method in the prior art, such as region growing, is used. It starts from one or more seed points in the image and continuously merges adjacent pixels into the same region according to certain similarity criteria (such as gray similarity, texture similarity, etc.), until the stop condition is met. By this method, the photovoltaic module image can be segmented into different regions, which is convenient for subsequent identification and analysis of defect regions.

[0068] After the image segmentation is completed, classification and recognition are performed, mainly using a method based on machine learning or a method based on deep learning to classify and recognize the image after the segmentation is completed, to confirm whether the detection area of the photovoltaic module has defects; the method based on machine learning uses a support vector machine (SVM) or an artificial neural network (ANN), the principle of the support vector machine (SVM) is to take the extracted image features as an input vector, train the SVM model so that it can distinguish between normal photovoltaic module images and images with different types of defects, and the SVM can separate different categories of data as much as possible by finding an optimal hyperplane, and has good performance when processing small samples and high-dimensional data; the artificial neural network (ANN) includes a multi-layer perceptron and the like, by constructing a neural network with an input layer, a hidden layer and an output layer, the image features are input into the network, and through the training process of forward propagation and back propagation, the weights of the network are constantly adjusted, so that the network can accurately classify the photovoltaic module image and judge whether there is a defect and the type of the defect; the method based on deep learning uses a convolutional neural network (CNN), the CNN is the most widely used deep learning model in the field of image detection, the CNN has structures such as a convolutional layer, a pooling layer and a fully connected layer, the convolutional layer performs convolution operation 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 comprehensively judges the extracted features and outputs the final classification result; for example, a commonly used CNN model such as ResNet, VogNet and the like can be trained on a large number of photovoltaic module image data sets, so as to accurately identify various defect types of the photovoltaic module; through the above series of image processing techniques, the control end can effectively process the photovoltaic module images collected by the image detection device one and the image detection device two, so as to accurately judge whether the photovoltaic module has defects and what kind of defects exist.

[0069] The embodiment provides a specific application example.

[0070] The image detection device one and the image detection device two are used to collect images of a standard size (1.65 m x 0.99 m) polycrystalline silicon photovoltaic module, and the collection resolution is set to correspond to 2 pixel points per millimeter, so that the pixel size of the entire photovoltaic module image is about (1.65 x 1000 x 2) x (0.99 x 1000 x 2) = 3300 x 1980 pixels.

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

[0072] The preprocessed image is feature extracted: the Sobel operator is used for edge detection, and first, the horizontal direction gradient G x and the vertical direction gradient G y are calculated. Taking the pixel point with coordinates (100, 100) in the image as an example (here, only the calculation process is illustrated, and the entire image will be traversed in practice): for the horizontal direction gradient G x , a common 3*3 horizontal template is used for convolution operation with the image, and it is assumed that the gray values of the pixels around the pixel point are as follows: the gray value of the pixel at the coordinates (99, 99) is 50; the gray value of the pixel at the coordinates (100, 99) is 55; the gray value of the pixel at the coordinates (101, 99) is 60; the gray value of the pixel at the coordinates (99, 100) is 52; the gray value of the pixel at the coordinates (100, 100) is 58; the gray value of the pixel at the coordinates (101, 100) is 62; the gray value of the pixel at the coordinates (99, 101) is 54; the gray value of the pixel at the coordinates (100, 101) is 60; and the gray value of the pixel at the coordinates (101, 101) is 65. According to the horizontal direction 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, the horizontal direction gradient value of the pixel point (100, 100) is 41, which reflects the gray scale change degree of the pixel point in the horizontal direction. By performing the above calculation on each pixel point in the image, the gray scale change gradient of the entire image in the horizontal direction can be obtained, and then the information is used for edge detection and subsequent image processing operations.

[0073] Similarly, for the vertical direction gradient G y , a vertical template is used for convolution operation, and the vertical direction gradient calculation formula is G y(100, 100) = 50 * (-1) + 55 * (-2) + 60 * (-1) + 52 * 0 + 58 * 0 + 62 * 0 + 54 * 1 + 60 * 2 + 65 * 1 = 19, the vertical gradient value of the pixel point (100, 100) is 19, which reflects the degree of gray scale change of the pixel point in the vertical direction. By calculating each pixel point in the image in the above manner, the gray scale change gradient of the entire image in the vertical direction can be obtained, and then the information is used for edge detection and subsequent image processing operations.

[0074] Then according to the edge intensity formula, The edge intensity of the pixel point is calculated According to the results, the edge intensity of the pixel point with coordinates (100, 100) is about 45.19. The edge direction θ(x, y) of the pixel point (100, 100) is calculated according to the edge direction formula. The pixel point (100, 100) is brought into the formula, The edge intensity and direction calculated above are the edge detection results of the pixel point. By traversing the entire image, the edge information of all pixel points can be obtained, and then the edge situation of the photovoltaic module image, such as the edge of the cell piece and the edge of the module frame, can be detected.

[0075] The gray level co-occurrence matrix (GLCM) is used to extract texture features. The distance d = 2 and the direction θ = 0° (horizontal direction) are selected to construct the GLCM. After gray scale processing, the image gray level L = 256, then the constructed GLCM is a 256x256 square matrix. By traversing the image, the frequency of different gray value pixel pairs under the specified distance and direction is counted, and then the GLCM matrix is obtained. Then the following texture feature parameters are extracted from the GLCM matrix:

[0076] (1) Contrast: the calculation formula is Contrast = ∑∑(i-j)^2*P(i, j). After calculation, the Contrast of the present application is 35.6; which means that there is a certain change in the texture of the surface of the photovoltaic module, such as local unevenness or small defects leading to texture change.

[0077] In the 256x256 square matrix, i and j are used to represent the row and column indexes in the matrix, which correspond to different gray values in the image; p(i, j) represents the frequency of the pixel pair with gray value i and j under the specified distance (d = 2) and direction (θ = 0°, horizontal direction) in the process of traversing the image.

[0078] (2) Correlation: the calculation formula is Correlation =∑∑[(i-μ i )(j-μ j )*P(i, j)] / (σ i *σ j ), and the calculation result is Correlation = 0.62, which indicates that there is a certain linear relationship between different gray value pixels, and can be used for further analysis of the uniformity and regularity of the surface texture of the photovoltaic module; wherein μ i represents the average gray value of all pixels with the gray value i; similarly, μ j represents the average gray value of all pixels with the gray value j; (i-μ i ) represents the deviation of the gray value of the pixel with the gray value i from the average gray value thereof; (j-μ j ) represents the deviation of the gray value of the pixel with the gray value j from the average gray value thereof; in the formula for calculating the correlation, the degree of linear relationship between different gray value pixels can be measured by calculating (i-μ i )(j-μ j )×P(i, j) and accumulating for all possible i and j combinations; if the accumulation value is large, the correlation value obtained after dividing by (σ i *σ j )(the product of the standard deviations of the pixels with the gray values i and j, respectively) will be close to 1, indicating that the different gray value pixels are highly correlated; if the accumulation value is small, the correlation value obtained will be close to -1, indicating that the different gray value pixels are highly anti-correlated; if the accumulation value is close to 0, there is no correlation; therefore, when analyzing the texture features of an image through a gray co-occurrence matrix, the deviation product of different gray value pixels based on their respective average gray values is an important component for measuring the degree of linear relationship between different gray value pixels in the image.

[0079] (3) Energy: the calculation formula is Energy =∑∑P(i, j)^2, and the result is Energy = 0.28; the energy value reflects the uniformity of the image texture; the photovoltaic module itself has a certain complexity of the image, and the surface thereof can have different structures such as cell pieces and bus bars, and the texture can also present various changes due to factors such as light and materials, and at this time, the normal range of the energy value can be relatively wide; in this embodiment, the energy value is between 0.3 and 0.6, which is a normal case, and the energy value of 0.28 is at a slightly low level, and there can be some factors affecting the uniformity of the texture, such as defects; further analysis is required in combination with other texture feature parameters and actual image appearance.

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

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

[0082] After image segmentation, classification and recognition are performed. In the embodiment, a simple convolutional neural network (CNN) model is constructed, which includes a convolutional layer, a pooling layer and a fully connected layer. The convolutional layer uses a 3x3 convolutional kernel with a step size of 1, and the pooling layer uses a 2x2 maximum pooling method. The image data after the above feature extraction and image segmentation is input into the CNN model as input. The CNN model is trained in advance on a large number of photovoltaic module image data sets (including normal images and images with various defects). During the training process, the weights of the model are constantly adjusted so that the model can accurately classify the input images. For example, the trained CNN model classifies the current collected and processed photovoltaic module image, and the output result shows that the image has a slight scratch defect, and the accuracy is more than 90%.

[0083] The above embodiment clearly shows how the control end uses common image processing techniques to process photovoltaic module images and ultimately determines whether the photovoltaic module has defects and the type of defects.

[0084] S4: start the magnetic field generator 5, collect data of the photovoltaic module through the superconducting quantum interference sensor assembly, and transmit the collected data to the control end. The control end determines whether the photovoltaic module has defects. If yes, jump to S6; otherwise, end the detection, remove the photovoltaic module, or end the detection and wait for the next detection period. Specifically, the workpiece before leaving the factory is detected. After the detection is completed, the photovoltaic module is removed for subsequent packaging and other links. After the photovoltaic module in use after leaving the factory is detected, the next detection period is waited for. The detection period is set according to specific conditions, such as 12 hours, 24 hours, 48 hours, etc. The detection of the photovoltaic module is completed in a fixed period according to specific requirements.

[0085] The superconducting quantum interference sensor assembly one includes 2n superconducting quantum interference sensors one 4, n≥1, which analyze the magnetic field data of the photovoltaic module and the electrical performance data of the photovoltaic module. S4 specifically starts the magnetic field generator 5, collects the magnetic field data of the photovoltaic module and the electrical performance data of the photovoltaic module through the superconducting quantum interference sensor one 4, and transmits the collected data to the control end. The control end determines whether the photovoltaic module has defects. If not, end the detection, remove the photovoltaic module, or end the detection and wait for the next detection period. If there is a defect position, the defect position is re-measured. The re-measurement uses the superconducting quantum interference sensor one 4 that does not detect the defect position to re-measure. The superconducting quantum interference sensor one 4 that does not detect the defect position collects data of the defect position. The collected image of the defect position is analyzed by the control end to determine whether the photovoltaic module has defects. If yes, jump to S6; otherwise, end the detection, remove the photovoltaic module, or end the detection and wait for the next detection period.

[0086] S5: start the magnetic field generator 5, collect data of the photovoltaic module through the superconducting quantum interference sensor assembly one 4, and transmit the collected data to the control end. The control end determines whether the photovoltaic module has defects. If yes, send to the user end to prompt the staff that the photovoltaic module has problems; otherwise, end the detection, remove the photovoltaic module, or end the detection and wait for the next detection period. In this step, the superconducting quantum interference sensor assembly one 4 is detected once without re-measurement.

[0087] In this embodiment, the magnetic field is applied in two situations for the photovoltaic module, specifically:

[0088] Metal electrode: The metal electrode is one of the main transmission channels of current in the photovoltaic module. By applying a suitable magnetic field, the metal electrode can produce a detectable electromagnetic response, thereby detecting whether there is a defect or connection problem; the magnetic field strength is set between 0.1 Tesla and 0.5 Tesla, and a relatively weak magnetic field strength can usually produce sufficient induced current in the metal electrode, facilitating the detection of the superconducting quantum interference sensor; the magnetic field direction 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, under this frequency band, the inductance and resistance characteristics of the metal electrode have a relatively stable effect on the induced current, and the electromagnetic signal generated is easy to analyze and process, while it can also effectively avoid the interference of complex factors such as skin effect that may be caused by high-frequency magnetic field.

[0089] Semiconductor material: The semiconductor is the core part of the photovoltaic effect, and its electrical properties are significantly affected by the magnetic field. By configuring a specific magnetic field, changes in carrier motion and concentration distribution in the semiconductor can be detected, and defects in the semiconductor material itself and contact defects with the metal electrode can be detected; the magnetic field strength is set between 0.5 Tesla and 1 Tesla, because the electrical conductivity of the semiconductor is weak, only under the action of a relatively strong magnetic field, the motion trajectory of the carrier can be changed significantly, thereby generating a magnetic field change signal that can be detected by the superconducting quantum interference sensor; the direction of the magnetic field, for common semiconductor materials such as silicon and germanium, applying a magnetic field along the main crystal axis direction of the crystal can more effectively affect the migration and diffusion of carriers, and the changes in electrical properties caused by defects can exhibit more obvious differences in the magnetic field; this embodiment takes silicon semiconductor material as an example, when detecting the P-N junction region of the silicon-based photovoltaic module, a magnetic field is applied along the normal direction of the P-N junction plane, which can highlight the changes in the processes of carrier recombination and diffusion near the junction region affected by the magnetic field, and is helpful for detecting defects in the junction region; the magnetic field frequency is in the range of 100 Hz to 500 Hz, a high-frequency magnetic field can change the motion state of the carriers in the semiconductor more frequently, so that the changes in the processes of carrier scattering and recombination caused by defects can be more quickly reflected in the magnetic field, thereby improving the sensitivity and real-time performance of the detection, and at the same time, a suitable high-frequency magnetic field can also reduce the influence of the internal capacitance effect of 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 sensing assembly 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 an open circuit defect in that area; in addition, defects may cause changes in the internal resistance of the photovoltaic module, the magnetic field generated by the magnetic field generator 5 interacts with the current, thereby indirectly reflecting the change of the resistance, if the resistance abnormally increases or decreases, it implies that there may be defects; for example, due to corrosion or damage, the local resistance increases; in addition, a normal photovoltaic module will exhibit a specific magnetic field distribution pattern under the action of a magnetic field. When the module has defects, such as internal structure damage or impurities, it will interfere with the distribution of the magnetic field. By detecting and analyzing the difference in the distribution of the magnetic field, the location and type of the defect can be identified; for example, if the magnetic field distribution in a certain area appears significantly distorted or uneven, it may mean that there is a defect in that area.

[0091] S6: The superconducting quantum interference sensing assembly two collects data of the photovoltaic module and transmits the collected data to the control end. The control end judges whether the photovoltaic module has defects. If yes, it is sent to the user end to prompt the staff that the photovoltaic module has problems; otherwise, the detection is ended and the photovoltaic module is removed, waiting for the next detection period; more preferably, in this step, after three detection periods, the data collected by the superconducting quantum interference sensing assembly two and the control end all judge that the photovoltaic module has no defects, and the data collected by the superconducting quantum interference sensing assembly one and the control end all judge that the photovoltaic module has defects, then the control end sends a prompt to prompt the staff to detect the superconducting quantum interference sensing assembly one and the superconducting quantum interference sensing assembly two, judge whether the superconducting quantum interference sensing assembly one and / or the superconducting quantum interference sensing assembly two has problems, adjust the hardware equipment in time, and thus better detect the photovoltaic module; more preferably, the superconducting quantum interference sensing assembly two includes a plurality of superconducting quantum interference sensors two 6, which can simultaneously detect a plurality of positions of the photovoltaic module, thereby improving the detection efficiency.

[0092] The specific working principle of the superconducting quantum interference sensing assembly one and the superconducting quantum interference sensing assembly two (for convenience, the following is simplified as superconducting quantum interference sensing assembly) detecting the subtle defects of the photovoltaic module is as follows:

[0093] Firstly, based on Josephson effect: the core of the superconducting quantum interference sensing component is a superconducting coil containing a Josephson junction, which is composed of two superconductors sandwiching a thin insulating layer. When a direct current voltage is applied across the junction, a high-frequency superconducting sinusoidal current is generated, which is proportional to the applied direct current voltage, i.e. AC Josephson effect occurs. At the same time, Cooper pairs can form a superconducting current through the insulating layer by tunneling effect, and the superconducting current is related to the phase difference, which is modulated by the magnetic field. Based on magnetic flux quantization: in a small superconducting ring, the current is quantized, and the magnetic flux must also be quantized, with a quantum unit of magnetic flux Wb. When external magnetic flux passes through the superconducting ring, a circulating current is generated in the superconducting ring to produce a magnetic flux opposite to the change of the external magnetic flux, so as to maintain the quantization of the magnetic flux. The change of the number of quanta is a symbol of the change of the external magnetic flux, and is in units of.

[0094] Secondly, the detection principle is mainly based on magnetic field generation and change, magnetic flux coupling and detection, signal conversion and detection. The specific principle is as follows: when the photovoltaic module is detected, a magnetic field generator will generate a magnetic field around the photovoltaic module. Due to the different electromagnetic responses of the metal electrodes and semiconductor materials in the photovoltaic module in the magnetic field, the magnetic field around the photovoltaic module changes. The superconducting coil in the superconducting quantum interference sensing component interacts with the changing magnetic field around the photovoltaic module. The magnetic flux is coupled into the superconducting ring through the low-resistance receiving coil, so that the magnetic flux in the superconducting ring changes. Since the superconducting quantum interference device is extremely sensitive to the change of the magnetic flux, even a very small change in the magnetic flux can cause a change in the current and voltage in the superconducting ring. The change of current and voltage 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 magnetic field changes caused by defects in the photovoltaic module. Through accurate measurement and analysis of these electrical signals, the subtle defects in the photovoltaic module, such as cracks in the metal electrodes, lattice defects in the semiconductor materials, and poor contact between different materials, can be inferred. Because these defects will change the local current distribution and magnetic field distribution, which will be detected by the superconducting quantum interference sensing component.

[0095] It should be noted that after the photovoltaic module is shipped, the method needs to consider the waterproof and drying problems of all electrical equipment, which belongs to the commonly used technical means. The present application does not make specific description here, and the skilled person can set it according to the specific situation.

[0096] Example two:

[0097] In this embodiment, the angle of the photovoltaic module can be adjusted, which is convenient for image acquisition and can also ensure that the photovoltaic module shipped for use can adjust the angle of the photovoltaic module according to the light, so that the photovoltaic panel can always maintain the best included angle with the sunlight, thereby maximizing the absorption of solar energy and significantly improving the power generation efficiency.

[0098] Specifically: in the S2, first start the lifting support leg 7, adjust the angle of the photovoltaic module, after adjusting to the appropriate angle, the image detection device one takes a picture of the photovoltaic module, transmits the photographed image to the control end, the control end processes the image collected by the image detection device one, judges whether the photovoltaic module has defects, yes, jump to S3, otherwise jump to S4.

[0099] More preferably, in order to ensure that the distribution of the magnetic field on the photovoltaic module is relatively uniform, the influence on each part of the photovoltaic module is more consistent, in the S4, first adjust the angle of all photovoltaic modules to the same angle, 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 parallel to the edge of the photovoltaic module; start the magnetic field generator 5, detect whether the photovoltaic module produces magnetic field change under the action of the magnetic field through the superconducting quantum interference sensor assembly one 4, and transmit the collected data to the control end, the control end judges whether the photovoltaic module has defects, yes, send to the user end, prompt the staff that the photovoltaic module has problems; otherwise jump to S5.

[0100] Example three:

[0101] The embodiment provides a photovoltaic module defect detection device, which comprises a support assembly 8, a photovoltaic module mounting groove 1 is installed on the support assembly 8, specifically, the support assembly 8 comprises two support plates 9, support legs are connected to the inner sides of the two support plates 9, the photovoltaic module mounting groove 1 is installed on the top of the support legs, and a line mounting groove 10 is arranged on the photovoltaic module mounting groove 1; the connection of adjacent photovoltaic modules is realized through the line mounting groove 10; the photovoltaic module mounting groove 1 is used for placing or mounting a photovoltaic module, when the photovoltaic module has not been put into the market, the photovoltaic module can be directly placed into the photovoltaic module mounting groove 1 through a mechanical arm and the like for detection, and after detection is completed, the photovoltaic module is removed through cooperation of the mechanical arm and the like; when the photovoltaic module is used after being put into the market, the photovoltaic module is fixed in the photovoltaic module mounting groove 1 through screws, mounting structures and the like, and after detection is completed, the next detection period is waited; the top of the support assembly 8 on the two sides is fixed with a longitudinal electric sliding rail one 11 and a longitudinal electric sliding rail two 12, more specifically, the top of the two support plates 9 is fixed with the longitudinal electric sliding rail one 11 and the longitudinal electric sliding rail two 12; the longitudinal electric sliding rail one 11 and the longitudinal electric sliding rail two 12 are arranged on the upper part of the photovoltaic module mounting groove 1, longitudinal electric sliding blocks one 13 and longitudinal electric sliding blocks two 14 are respectively arranged on the longitudinal electric sliding rail one 11 and the longitudinal electric sliding rail two 12, the number of the longitudinal electric sliding blocks one 13 and the longitudinal electric sliding blocks two 14 is two respectively, transverse electric sliding rails one 15 and transverse electric sliding rails two 16 are respectively fixed between the longitudinal electric sliding blocks one 13 and the longitudinal electric sliding blocks two 14, the longitudinal electric sliding blocks one 13 move along the longitudinal electric sliding rail one 11, the longitudinal electric sliding blocks two 14 move along the longitudinal electric sliding rail two 12, the two ends of the transverse electric sliding rails one 15 and the transverse electric sliding rails two 16 are connected with the longitudinal electric sliding blocks one 13 and the longitudinal electric sliding blocks two 14 through a connection mode commonly used in the prior art such as screws, bonding and welding; the longitudinal electric sliding blocks one 13 and the longitudinal electric sliding blocks two 14 move synchronously, thereby driving the transverse electric sliding rails one 15 and the transverse electric sliding rails two 16 to move along the longitudinal electric sliding rail one 11 and the longitudinal electric sliding rail two 12; the transverse electric sliding rails one 15 are provided with transverse electric sliding seats one 17 and transverse electric sliding seats two 18, the image detection equipment one is fixed on the transverse electric sliding seats one 17, and the superconducting quantum interference sensing assembly one is fixed on the transverse electric sliding seats two 18; the transverse electric sliding rails two 16 are provided with transverse electric sliding seats three 19 and transverse electric sliding seats four 20, the image detection equipment two is fixed on the transverse electric sliding seats three 19, and the superconducting quantum interference sensing assembly two is fixed on the transverse electric sliding seats four 20; a magnetic field generator 5 is arranged on the outer side of the support assembly 8.

[0102] It should be noted that when the photovoltaic module is used after being put into the market, the waterproof and drying problems of all electrical equipment need to be considered, which belongs to a common technical means, and the present application does not make specific description here, and the technical personnel can set according to the specific circumstances.

[0103] In this embodiment, first place or install the photovoltaic module into the photovoltaic module installation slot 1, move the horizontal electric sliding rail one 15 along the longitudinal electric sliding rail 11 and the longitudinal electric sliding rail 12, move the image detection device one along the horizontal electric sliding rail one 15, collect the image of the photovoltaic module, transmit the collected image to the control end, analyze the image by the control end, judge whether the photovoltaic module has defects, when the photovoltaic module has defects, move the horizontal electric sliding rail two 16 along the longitudinal electric sliding rail one 11 and the longitudinal electric sliding rail two 12, move the image detection device two along the horizontal electric sliding rail two 16, collect the image of the photovoltaic module, transmit the collected image to the control end, analyze the image by the control end, judge whether the photovoltaic module has defects, when the photovoltaic module has defects, the control end sends a prompt to the user end, reminding the staff to process the photovoltaic module with defects, wherein the user end includes but is not limited to notebook computer, desktop computer, mobile phone, tablet computer, smart bracelet and other devices; when the image collected by the image detection device two is analyzed and determined that the photovoltaic module has no defects, start the magnetic field generator 5, collect the data of the photovoltaic module by the superconducting quantum interference sensing assembly one, and transmit the collected data to the control end, the control end judges whether the photovoltaic module has defects, if yes, send to the user end to prompt the staff that the photovoltaic module has problems, otherwise, end the detection and remove the photovoltaic module, or end the detection and wait for the next detection period.

[0104] When the image collected by the image detection device one is analyzed and determined that the photovoltaic module has no defects, start the magnetic field generator 5, collect the data of the photovoltaic module by the superconducting quantum interference sensing assembly one, and transmit the collected data to the control end, the control end judges whether the photovoltaic module has defects, otherwise, end the detection and remove the photovoltaic module, or end the detection and wait for the next detection period; if yes, collect the data of the photovoltaic module by the superconducting quantum interference sensing assembly two, and transmit the collected data to the control end, the control end judges whether the photovoltaic module has defects, if yes, send to the user end to prompt the staff that the photovoltaic module has problems; otherwise, end the detection and remove the photovoltaic module, and wait for the next detection period.

[0105] Embodiment four:

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

[0107] The support leg comprises 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, and more specifically, each photovoltaic module installation groove 1 comprises two fixed support legs 21 installed on one side of the bottom of the photovoltaic module installation groove 1 and two lifting support legs 7 installed on the other side of the bottom of the photovoltaic module installation groove 1; the fixed support leg 21 is rotationally connected to the photovoltaic module installation groove 1 through a rotating shaft 23, the top of the lifting support leg 7 is provided with a pushing block, the pushing block comprises a horizontal plate 24 and a vertical plate 25, the horizontal plate 24 and the vertical plate 25 are vertically arranged and in an L shape, the pushing block is matched with the bottom of the photovoltaic module installation groove 1, when the photovoltaic module is vertically placed on the support plate 9, the horizontal plate 24 is completely matched with the bottom of the photovoltaic module installation groove 1, and the vertical plate 25 is matched with one side of the photovoltaic module installation groove 1, the bottom of the photovoltaic module installation groove 1 is provided with a limiting block 26 on both sides of the pushing block; the lifting support leg 7 is started to drive the pushing block to move upwards, the angle of the photovoltaic module installation groove 1 is adjusted through the sliding cooperation between the pushing block and the photovoltaic module installation groove 1, and then the angle of the photovoltaic module is adjusted.

[0108] During image acquisition, if strong light makes the photographed image unclear, the angle can be adjusted to select a suitable shooting angle; in addition, the photovoltaic module used after leaving the factory adjusts the angle of the photovoltaic module according to the light, so that the photovoltaic panel can always maintain the best included angle with the sunlight, thereby maximizing the absorption of solar energy and significantly improving the power generation efficiency.

[0109] In the embodiment, when the photovoltaic module is imaged by the image detection device one and the image detection device two, the lifting support leg 7 is first started to adjust the angle of the photovoltaic module, and after the angle is adjusted to a suitable angle, the photovoltaic module is photographed by the image detection device one and the image detection device two, and the photographed image is transmitted to the control end, the control end processes the image collected by the image detection device one to determine whether the photovoltaic module has defects.

[0110] Embodiment five:

[0111] In the embodiment, the magnetic field generator 5 can keep consistent with the angle of the photovoltaic module, thereby ensuring the uniformity of the magnetic field, and specifically:

[0112] The outer side of the support plate 9 is provided with a fixed support 27, which is arranged outside all photovoltaic modules, and a plurality of magnetic field generators 5 are fixed on the fixed support 27; the fixed support 27 comprises a connecting rod one 28, a connecting rod two 29, a connecting rod three 30 and a connecting rod four 31 connected in sequence, which can be round rods, rectangular rods or the like, and the connecting rod one 28, the connecting rod two 29, the connecting rod three 30 and the connecting rod four 31 can be connected by welding or can be integrally formed; one side of the bottom of the fixed support 27 is rotatably connected with a support fixed support leg 32, and the other side of the bottom of the fixed support 27 is connected with a support lifting support leg 33, more specifically, the bottom of the connecting rod one 28 is respectively connected with the support fixed support leg 32 and the support lifting support leg 33, and the bottom of the connecting rod three 30 is respectively connected with the support fixed support leg 32 and the support lifting support leg 33; the support fixed support leg 32 and the support lifting support leg 33 are respectively arranged on one side of the connecting rod two 29 or the connecting rod four 31, and for example, two support fixed support legs 32 are arranged on one side of the connecting rod two 29, and two support lifting support legs 33 are arranged on one side of the connecting rod four 31.

[0113] The support fixed support leg 32 and the support lifting support leg 33 can be connected with the support plate 9 by means of adhesion, screw connection, welding or other known fixing methods, and together comprise two support fixed support legs 32 and two support 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; the support lifting support leg 33 is provided with a support pushing block 34, the support pushing block 34 is provided with a support pushing groove 35, the support pushing groove 35 is matched with the fixed support 27, and the lower part of the fixed support 27 is provided with a support limiting block 36 on both sides of the support pushing groove 35; the support lifting support leg 33 is started to drive the support pushing block 34 to move upwards, and through the sliding cooperation of the support pushing groove 35 and the fixed support 27, the angle of the fixed support 27 is adjusted, and then the magnetic field generator 5 is adjusted.

[0114] Before starting the magnetic field generator 5, the angles of all photovoltaic modules are adjusted to the same angle, and then the angle of the magnetic field generator 5 is adjusted to be the same as that of the photovoltaic module, that is, the magnetic field generator 5 is arranged in parallel with the edge of the photovoltaic module; the magnetic field generator 5 is started, the superconducting quantum interference sensing assembly one and the interference sensing assembly two detect the data of the photovoltaic module, and the collected data is transmitted to the control end, and the control end judges whether the photovoltaic module has defects.

[0115] The present application can detect the photovoltaic module before leaving the factory, and also can detect the photovoltaic module after leaving the factory and installing, first, the image detection equipment one is used to collect the image of the photovoltaic module, the control end analyzes the image collected by the image detection equipment one, when the image analysis shows that the photovoltaic module has defects, the image detection equipment two is used to retest the position of the defects, the images taken by the two show that the photovoltaic module has defects, then the staff is prompted to process in time; when the images taken by the image detection equipment one and the image detection equipment two do not analyze that the photovoltaic module has defects, then the superconducting quantum interference sensing assembly is further used to recheck the photovoltaic module, because the visual detection sometimes cannot find the subtle defects, and the defects of the photovoltaic module cannot be found in time, the present application can find the defects in the photovoltaic module in time, prompt the staff to process in time, and then improve the service life of the photovoltaic module and the like; in addition, the angle of the photovoltaic panel in the present application is adjustable, which not only is convenient for adjusting to the most appropriate shooting angle, but also is convenient for adjusting the angle of the photovoltaic module according to the light in the actual use process, so that the photovoltaic panel can always keep the best included angle with the sunlight, thereby the solar energy can be absorbed to the maximum extent, and the power generation efficiency is significantly improved.

[0116] The above discussion of any embodiment is only exemplary, and the technical features in the above embodiments or different embodiments can also be combined under the inventive concept. In order to be brief, they are not provided in details. Therefore, any omission, modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A photovoltaic module defect detection method, characterized in that: The steps include: S1: Before the photovoltaic module leaves the factory, the photovoltaic module is directly placed in the photovoltaic module installation slot (1); after the photovoltaic module leaves the factory, the photovoltaic module is fixed in the photovoltaic module installation slot (1); S2: First, the lifting support leg (7) is started to adjust the angle of the photovoltaic module. The image detection device 1 collects images of the photovoltaic module and transmits the collected images to the control terminal. The control terminal 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 location and transmits the collected images to the control terminal. The control terminal processes the images collected by the second image detection device to determine whether the photovoltaic module has defects. If so, the images are sent to the user terminal to inform the staff that there is a problem with the photovoltaic module. Otherwise, the process jumps to S5. S4: first adjust the angles of all photovoltaic modules to the same angle, then adjust the angle of the magnetic field generator (5) to be the same as the angle of the photovoltaic module, start the magnetic field generator (5), collect data of 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, jumps to S6, otherwise, the detection of the photovoltaic module that has not been shipped out is terminated and the photovoltaic module is removed, or the detection of the photovoltaic module that has been shipped out is terminated 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. The control end determines whether the photovoltaic module has defects. If so, it sends the data 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; S6: The second superconducting quantum interference sensor component collects data from the photovoltaic module and transmits the collected data to the control end. The control end determines whether the photovoltaic module has defects. If so, it sends the data to the user end to remind the staff that there is a problem with the photovoltaic module; 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, if the control end determines that the photovoltaic module has no defects based on the data collected by the superconducting quantum interference sensing component 2, and if the control end determines that the photovoltaic module has defects based on the data collected by the superconducting quantum interference sensing component 1, the control end issues a prompt to prompt the staff to test the superconducting quantum interference sensing component 1 and the superconducting quantum interference sensing component 2 to determine whether there are any problems with the superconducting quantum interference sensing component 1 and / or the superconducting quantum interference sensing component 2.

3. A photovoltaic module defect detection method according to claim 1, characterized in that: The image detection device includes 3n cameras (2), n≥1, and n is an integer; S2 is specifically as follows: camera (2) photographs the photovoltaic module, camera (2) transmits the photographed image to the control end, the control end processes the image collected by each camera (2), and determines whether the photovoltaic module has defects, otherwise jumps to S4, if yes, re-measures the defect position, re-measures using one of the cameras (2) whose images did not detect the defect position, the camera (2) that did 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 module has defects, if yes, jumps to S3, otherwise re-measures using the second camera (2) whose images did not detect the defect position, the second camera (2) that did 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 module has defects, if yes, jumps to S3, otherwise jumps to S4.

4. A photovoltaic module defect detection method according to claim 3, characterized in that: The image detection device 2 includes 2n cameras 2 (3), n≥1, and n is an integer; the specific S3 is: 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, it is sent to the user end to prompt the staff that there is a problem with the photovoltaic module; otherwise, the defect position is re-measured by the camera 2 (3) that has not collected the image of the defect position, and the camera 2 (3) that has not collected the image of the defect position takes a picture of the defect position. The collected image of the defect 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, the process jumps to S5.

5. A photovoltaic module defect detection method according to claim 4, characterized in that: The superconducting quantum interference sensing component 1 includes at least 2n superconducting quantum interference sensors 1 (4), n≥1, and n is an integer; the specific step S4 is: 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 the next detection cycle is waited for; if yes, the defect position is retested, and the retest is performed using the superconducting quantum interference sensor 1 (4) whose collected data does not detect the defect position. 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, jumps to S6, otherwise the detection is terminated and the photovoltaic component is removed, or the detection is terminated and the next detection cycle is waited for.

6. A photovoltaic module defect detection device using a photovoltaic module defect detection method according to any one of claims 1 to 5, 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 photovoltaic assemblies. A longitudinal electric slide rail 1 (11) and a longitudinal electric slide rail 2 (12) are fixed to the top of the support assembly (8) on both sides. The longitudinal electric slide rail 1 (11) and the longitudinal electric slide rail 2 (12) are arranged on the upper part of the photovoltaic assembly installation groove (1). The longitudinal electric slide rail 1 (11) and the longitudinal electric slide rail 2 (12) are provided with a longitudinal electric slider 1 (13) and a longitudinal electric slider 2 (14) respectively. The number of the longitudinal electric slider 1 (13) and the longitudinal electric slider 2 (14) are two respectively. The longitudinal electric slider 1 (13) and the longitudinal electric slider 2 (14) are spaced apart. 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 outside of the support component (8).

7. A photovoltaic module defect detection device according to claim 6, characterized in that: A fixed support leg (21) is installed on one side of the bottom of the photovoltaic module installation groove (1), and a lifting support leg (7) is installed on the other side of the bottom of the photovoltaic module installation groove (1). The fixed support leg (21) and the photovoltaic module installation groove (1) are rotatably connected 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 module installation groove (1). The bottom of the photovoltaic module installation groove (1) is provided with limit blocks (26) on both sides of the push block.

8. The photovoltaic module defect detection device according to claim 6, 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 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), and 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 the lower part of the fixed bracket (27) is provided with bracket limit blocks (36) on both sides of the bracket pushing groove (35).

Images