An automated detection device and method for adapting to multiple sizes of solar cells

Abstract

The present invention provides an automated testing device and method adapted to solar cells of various sizes, comprising a frame, a conveying platform, a size identification unit, an adjustable testing component, a drive unit, and a control unit. The control unit receives feedback information from the size identification unit and, by searching in a pre-stored full-size-parameter mapping database or by using an interpolation algorithm, calculates and generates a control signal for testing parameters adapted to the solar cell module under test. This control signal then controls the drive unit to move the adjustable testing component to the target position and automatically adjusts the testing parameters of the adjustable testing component. Simultaneously, the adjustable testing component is activated to acquire EL images of the solar cell module under test. A defect analysis module analyzes and processes the acquired EL images and generates a defect detection report. Through a closed-loop design of "automatic size and material identification - adaptive parameter adjustment - precise detection - defect analysis," efficient and accurate testing of multi-size perovskite modules is achieved.

Description

Technical Field

[0001] This invention relates to the field of solar cell testing technology, and in particular to an automated testing device and method adapted to solar cells of various sizes. Background Technology

[0002] Perovskite solar cells have become one of the core directions of photovoltaic industrialization due to their advantages such as high photoelectric conversion efficiency, simple fabrication process, and low cost. In the process of large-scale production of perovskite modules, EL (electroluminescence) testing is a key means of assessing internal defects of the modules (such as dark spots, cracks, poor contact, and poor soldering), which directly determines the product's quality and service life.

[0003] Currently, perovskite production lines cover various specifications, including small-sized samples in the laboratory, medium-sized components in pilot lines, and large-sized components in large-scale production lines. The significant differences in component size across different production lines place extremely high demands on the flexibility and adaptability of EL detection equipment. Research has identified at least the following core problems with existing perovskite EL detection equipment and related technologies:

[0004] 1. Poor adaptability: Most equipment is designed with a fixed size and can only match a single specification of components. When changing to a production line of different sizes, it is necessary to disassemble and adjust the mechanical structure and recalibrate the testing parameters. The operation is cumbersome and the downtime is long, which cannot meet the needs of flexible production.

[0005] 2. Low level of automation: It lacks automatic size recognition and parameter adaptive adjustment mechanism, and relies on manual measurement of component size and adjustment of camera position / focal length / exposure parameters. This is not only inefficient, but also prone to positioning deviation and image blurring due to human operation error, which affects the accuracy of defect recognition.

[0006] 3. Limited detection accuracy: The filter bands of existing equipment are fixed and cannot be switched to adapt to the light emission characteristics of components of different sizes. Stray light interference leads to missed detection or misjudgment of small defects.

[0007] 4. Insufficient compatibility: Although some photovoltaic module EL testing equipment claims to be compatible with multiple sizes, it is not optimized for the photoelectric characteristics of perovskite materials. The testing parameters are not compatible with perovskite modules, making it impossible to accurately identify their unique defects.

[0008] 5. Rigid and flexible components cannot be compatible at the same time: The testing process is cumbersome and cannot simultaneously test both flexible and rigid components.

[0009] 6. Insufficient excitation power supply adaptability: For perovskite modules of different sizes, the module as the unit under test cannot simultaneously meet the voltage difference regulation requirements for both small and large areas. Summary of the Invention

[0010] Based on this, the present invention provides an automated testing device and method adapted to multi-size solar cells. Through a closed-loop design of "automatic size and material identification - adaptive parameter adjustment - accurate detection - defect analysis", combined with modular mechanical structure, intelligent control logic and customized optical system, it achieves efficient and accurate detection of multi-size perovskite modules.

[0011] In a first aspect, this application provides an automated testing device adapted to solar cells of various sizes, comprising:

[0012] frame;

[0013] A conveying platform, mounted on the frame, is used to carry and convey the battery components to be tested;

[0014] A size recognition unit is fixedly installed on the entrance side and above the conveying platform to collect the size and attribute information of the battery assembly under test.

[0015] An adjustable detection assembly includes at least one EL camera, a zoom lens module, a filter assembly, and a movable mounting base;

[0016] A drive unit, connected to the movable mounting base, is used to drive the adjustable detection component to move in three-dimensional space;

[0017] The control unit is electrically connected to the size recognition unit, the drive unit, the adjustable detection component, and the conveying platform, respectively. It receives feedback information from the size recognition unit and searches in a pre-stored full-size-parameter mapping database or calculates and generates detection parameter control signals adapted to the battery assembly under test using an interpolation algorithm. This controls the drive unit to move the adjustable detection component to the target position and automatically adjusts the detection parameters of the adjustable detection component. Simultaneously, the adjustable detection component is activated to acquire EL images of the battery assembly under test. The detection parameters include focal length, exposure time, and filter band.

[0018] The defect analysis module, connected to the control unit, is used to analyze and process the acquired EL images and generate a defect detection report.

[0019] Optionally, the conveying platform is provided with automatic limiting mechanisms on both sides, and the automatic limiting mechanisms include side stops, adjustment servos, and guide rails;

[0020] The adjustment servo is used to drive the side stop bar to move along the guide rail to a position that matches the width of the battery assembly under test, according to the control signal from the control unit.

[0021] Optionally, the size recognition unit includes:

[0022] Laser rangefinders are symmetrically installed on both sides of the entrance of the conveying platform to scan the edge of the battery assembly under test in order to obtain the length and width of the battery assembly under test.

[0023] The visual positioning module includes an industrial camera and an image analysis module. The industrial camera is used to capture surface images of the battery assembly under test. The image analysis module is used to identify the corner positions and thickness information of the battery assembly under test through an edge detection algorithm, and to identify the rigidity and / or flexibility properties of the battery assembly under test based on image features.

[0024] Optionally, the movable mounting base is fixedly connected to the drive unit;

[0025] The filter assembly includes at least three filters in different wavelength bands, which can be quickly switched by an electric rotary wheel or slider mechanism to adapt to the light emission characteristics of different battery components under test.

[0026] Optionally, the drive unit includes an X-axis linear module, a Y-axis linear module, and a Z-axis lifting module, used to drive the adjustable detection component to move in three-dimensional directions.

[0027] Optionally, the control unit includes:

[0028] An industrial computer with a built-in full-size-parameter mapping database; the database pre-stores detection parameters corresponding to solar cell modules of various specifications and supports users to add new specifications of modules and their corresponding optimal detection parameters.

[0029] The PLC controller is used to receive signals from the industrial computer and control the conveying platform and the adjustable detection component to work together.

[0030] Optionally, the control unit is further configured to:

[0031] When the size and attribute information of the battery component under test do not have a complete match in the full-size-parameter mapping database, the optimal adaptation parameters are calculated and generated based on the parameters of adjacent specifications using an interpolation algorithm.

[0032] Optionally, in the automatic EL image acquisition step, for large-sized components that exceed the field of view of a single camera, multiple images are acquired using a dual-camera collaborative or single-camera partitioned movement method, and a complete component EL image is synthesized using an image stitching algorithm.

[0033] Based on the same inventive concept, this application also provides an automated testing method adapted to solar cells of various sizes, executed by the automated testing method adapted to solar cells of various sizes provided by the proposer, including the following steps:

[0034] The control conveyor platform moves the battery component under test and transports it to the preset detection area until the position sensor is triggered and then pauses.

[0035] The control size recognition unit collects the size information and attribute information of the battery component under test; the size information includes at least length, width and thickness, and the attribute information includes rigid components or flexible components;

[0036] Based on the size and attribute information fed back by the size recognition unit, a detection parameter control signal adapted to the battery component under test is generated from the pre-stored full-size-parameter mapping database or by interpolation algorithm.

[0037] The control drive unit moves the adjustable detection component to the target position and automatically adjusts the detection parameters of the adjustable detection component, thereby controlling the EL camera to acquire EL images of the battery component under test.

[0038] The control defect analysis module analyzes and processes the acquired EL images and generates a defect detection report.

[0039] Optionally, before controlling the EL camera to perform image acquisition, the method further includes:

[0040] The size of the battery assembly under test is compared with a preset threshold.

[0041] For sizes smaller than the preset threshold, use a single-camera single-shot mode; for sizes greater than or equal to the preset threshold, use a dual-camera shooting mode or a single-camera multi-shot stitching mode.

[0042] Optionally, after generating the defect detection report, the following may also be included:

[0043] Based on the analysis results of the defect detection report, the conveying platform is controlled to divert the detected components to the qualified area or the rework area, and the adjustable detection component is reset or its state is adjusted to adapt to the next component to be detected, so as to achieve continuous automated detection.

[0044] The automated testing equipment adapted to solar cells of various sizes provided in this invention includes a control unit that receives feedback information from a size identification unit and searches in a pre-stored full-size-parameter mapping database or calculates and generates a control signal for testing parameters adapted to the solar cell module under test using an interpolation algorithm. This control unit moves an adjustable testing component to a target position and automatically adjusts the testing parameters of the adjustable testing component. Simultaneously, the adjustable testing component is activated to acquire EL images of the solar cell module under test. The defect analysis module analyzes and processes the acquired EL images and generates a defect detection report. Through a closed-loop design of "automatic size and material identification - adaptive parameter adjustment - accurate detection - defect analysis," combined with modular mechanical structure, intelligent control logic, and customized optical system, efficient and accurate detection of perovskite modules of various sizes is achieved.

[0045] Compared with existing technologies, the automated testing scheme adapted to solar cells of various sizes provided by this invention has the following advantages:

[0046] 1. No manual intervention is required; it automatically identifies perovskite components of different sizes and whether they are rigid or flexible, and quickly adapts to the testing parameters.

[0047] 2. Covers all specifications of testing needs from small-sized samples to large-sized components, and is compatible with testing and production lines.

[0048] 3. Improve detection accuracy and efficiency, reduce labor costs, and meet the continuous detection needs of large-scale production.

[0049] 4. Optimize optical design based on the characteristics of perovskite materials to accurately identify their unique defects. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the structure of an automated testing device adapted to multi-size solar cells provided by the present invention;

[0051] Figure 2 yes Figure 1 A schematic diagram of the core components of an automated testing equipment that adapts to multiple sizes of solar cells;

[0052] Figure 3 This is a flowchart illustrating an automated testing method for adapting to multi-size solar cells provided by the present invention.

[0053] Explanation of reference numerals in the attached figures:

[0054] 101. Frame; 102. Conveying platform; 103. Automatic limit mechanism;

[0055] 20. Adjustable detection component; 201. EL camera; 202. Zoom lens module; 203. Filter assembly; 204. Movable mounting base; 30. Size recognition unit; 302. Linear movement module; 50. Control unit. Detailed Implementation

[0056] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the present application and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present application are shown in the drawings, not the entire structure. Various modifications and variations can be made to the present application without departing from its spirit or scope, which will be apparent to those skilled in the art. Therefore, the present application is intended to cover modifications and variations of the present application that fall within the scope of the technical solutions claimed in the corresponding claims and their equivalents. It should be noted that the implementation methods provided in the embodiments of the present application can be combined with each other without contradiction.

[0057] Figure 1 This is a schematic diagram of the structure of an automated testing device adapted to multi-size solar cells provided in an embodiment of this application. Figure 1 As shown, the automated inspection equipment provided in this application embodiment mainly includes a frame 101, a conveying platform 102, an adjustable inspection component 20, a size recognition unit 30, a drive unit, a control unit 50, and a defect analysis module.

[0058] The frame 101 is the supporting foundation for the entire equipment. For example, it can be constructed using aluminum alloy profiles to ensure rigidity while reducing the weight of the equipment. Its dimensions are approximately 2.8m long × 2.0m wide × 2.2m high.

[0059] The conveyor platform 102 is mounted on the frame 101 and is used to carry and transport the battery module M to be tested. The battery module to be tested can be the perovskite solar cell provided in this application, or other types of solar cells; this application embodiment does not limit the types. In a preferred embodiment, the conveyor platform 102 adopts a roller and belt conveyor structure, with the belt surface covered with an anti-slip rubber layer to prevent the battery module M to be tested from sliding or being scratched during transport. For example, the roller spacing is 5cm, and the maximum load capacity is 50kg. Automatic limit mechanisms 103 are also provided on both sides of the conveyor platform 102. These automatic limit mechanisms 103 include side stops, adjustment servos, and guide rails. Figure 1 and 2 (Not explicitly shown in the text). The side stop bar is slidably connected to the guide rail. The servo adjusts the side stop bar along the guide rail according to the control signal from the control unit 50. Figure 1 Move the servo drive side stop bar (in the X direction, as indicated by the middle arrow) to a position that matches the width of the battery assembly M under test. For example, adjust the servo drive side stop bar along the width direction of the battery assembly M under test (e.g., ...). Figure 2 The device moves in the W direction and is automatically limited according to the width W of the battery component M under test, thereby positioning and fixing the battery component M under test and preventing displacement during the testing process. The W direction is parallel to the X direction.

[0060] The size recognition unit 30 is fixedly installed on the entrance side and above the conveyor platform 102, and is used to collect detailed size and attribute information of the battery assembly M under test. In a preferred embodiment, such as Figure 2 As shown, the size recognition unit 30 includes a laser rangefinder (not shown) and a vision positioning module 301. The laser rangefinder is symmetrically installed on both sides of the entrance of the conveyor platform 102. By scanning the edge of the battery module M under test, it obtains the length L and width W of the battery module M with high precision. For example, when the battery module M under test enters the detection area, the laser rangefinder emits a laser beam to scan the edge of the module. The laser rangefinder collects the length L and width W parameters of the perovskite solar cell in real time, such as a measurement range of 0-2m and a measurement accuracy of ±0.1mm. The vision positioning module (industrial camera) acquires images with 5 megapixels.

[0061] The visual positioning module 301 includes an industrial camera and an image analysis module. The industrial camera is used to capture surface images of the battery component M under test; the image analysis module identifies the corner positions and thickness information H of the component using an edge detection algorithm (such as the Canny algorithm), and identifies the rigidity and / or flexibility properties of the battery component M under test based on image features (such as image texture). For example, the visual positioning module is an industrial camera, mounted above the conveyor platform 102, used to capture surface images of the battery component M under test. The image analysis module identifies the corner positions and thickness information of the component using an edge detection algorithm (such as the Canny algorithm), with a positioning accuracy ≤0.05mm, providing precise coordinates for the positioning of the battery component M under test, and providing a basis for the precise setting of subsequent testing parameters.

[0062] The adjustable detection component 20 is the core component for performing EL detection, including at least one EL camera 201, a zoom lens module 202, a filter assembly 203, and a movable mounting base 204. The EL camera 201 can be a high-resolution CCD camera, such as one with ≥16 megapixels and a frame rate ≥15fps, supporting high-speed shooting and adapting to continuous detection requirements. The zoom lens module 202 can automatically adjust the focal length according to control commands to ensure clear imaging of components of different sizes and distances. For example, the focal length range of a single EL camera 201 is 12-35mm, with an aperture of F1.8. The focal length range of a dual EL camera 201 is 10-100mm. The filter assembly 203 includes at least three filters of different wavelengths, which are quickly switched via an electric rotary wheel or slider mechanism to adapt to the luminous characteristics of different battery components M under test. For example, cutoff filters of 450nm, 550nm and 650nm can be configured to match the light emission characteristics of perovskite and other solar cells. These filters can be detachably connected to the zoom lens module 202 via the threaded carrier of the EL camera 201. The appropriate wavelength can be switched according to the size of the component and the light emission intensity to match the light emission characteristics of different battery components M under test, effectively eliminating stray light interference.

[0063] The entire adjustable detection assembly 20 is connected to the drive unit via the movable mounting base 204. Figure 1 and Figure 2 (Not shown) A fixed connection is made, which drives the adjustable detection component 20 to move in three dimensions, covering the detection range of components of different sizes. The size recognition unit 30 can also be equipped with a linear motion module 302 to achieve at least one-dimensional motion of the size detection camera.

[0064] refer to Figure 1 The drive unit is connected to the movable mounting base 204 and is used to drive the adjustable detection component 20 to move in three-dimensional space. In a preferred embodiment, the drive unit ( Figure 1 and Figure 2 (Not shown in the diagram) This includes an X-axis linear module, a Y-axis linear module, and a Z-axis lifting module, all driven by servo motors. These modules offer high repeatability (e.g., ±0.02mm), and their travel can be customized based on the maximum detection size. For example, the maximum X-axis travel is 1.5m, the maximum Y-axis travel is 2.0m, and the maximum Z-axis travel is 1.8m. The servo motors have a power of 400W. The drive unit can move its mounting base horizontally along the X-axis (component width direction), horizontally along the Y-axis (component length direction), and vertically along the Z-axis (component height direction), achieving precise positioning of the detection components and covering the detection range of components of different sizes. In other embodiments, the drive unit may also employ a lead screw drive, which will not be described in detail in this application.

[0065] The control unit 50 is the control center of the entire device, and is electrically connected to the size recognition unit 30, the drive unit, the adjustable detection component 20, and the conveyor platform 102. In a preferred embodiment, the control unit 50 includes an industrial computer and a PLC controller. The industrial computer has a built-in full-size-parameter mapping database (e.g., a full-size range from 10cm×10cm to 1.6m×2.4m), which pre-stores detection parameters (e.g., camera 3D position, focal length, exposure time, filter band, limit mechanism width, etc.) corresponding to various specifications of solar cell modules, and supports users to add new specifications of modules and their corresponding optimal detection parameters. The database pre-stores detection parameters such as camera 3D position, focal length, exposure time, filter band, and limit mechanism width for common perovskite components (e.g., 10cm×10cm, 50cm×50cm, 1.2m×1.6m, etc.). The PLC controller receives instructions from the industrial computer and controls the coordinated operation of the conveyor platform 102 and the adjustable detection component 20 to achieve precise control of the coordinated operation of various actuators such as the drive unit, conveyor platform 102, and automatic limit mechanism 103.

[0066] Specifically, the operating logic of the control unit 50 is as follows: It receives the size information (L, W, H) and attribute information fed back by the size recognition unit 30, and immediately searches for a matching item in the pre-stored full-size-parameter mapping database. If a perfectly matching specification is found, the corresponding detection parameters are directly invoked. If no perfectly matching item is found, the optimal detection parameters adapted to the battery component M under test are calculated using an interpolation algorithm based on the parameters of adjacent specifications. Subsequently, corresponding control signals are generated to control the coordinated operation of the conveying platform 102 and the adjustable detection component 20. For example, on the one hand, the drive unit is controlled to precisely move the adjustable detection component 20 to the target position; on the other hand, the automatic limit mechanism 103 adjusts the width, the filter assembly 203 switches the band, the zoom lens module 202 adjusts the focal length, and the EL camera 201 adjusts the exposure time. When the detection parameters meet the size information (L, W, H) and attribute information fed back by the size recognition unit 30, one or two EL cameras 201 are activated to acquire EL images of the battery component M under test.

[0067] Defect Analysis Module ( Figure 1 and Figure 2(Not shown in the image) is connected to the control unit 50. The defect analysis module can be a microprocessor, image processor, or application-specific integrated circuit (ASIC) built into the control unit 50 or set independently. It is used to analyze and process the acquired EL images and generate defect detection reports. In a preferred embodiment, the defect analysis module is based on a deep learning defect recognition model. First, it performs preprocessing on the EL images, such as noise reduction, image distortion and geometric correction, and image enhancement. Then, through image segmentation, synthesis, and feature extraction algorithms, it identifies defects such as dark spots, cracks, poor solder joints, and uneven luminescence, and counts the location, size, dimensions, and quantity of defects, automatically generating a detailed defect detection report. The defect analysis module can also be linked with the production line management system to realize the sorting of qualified components and the marking of unqualified components based on the defect detection report, and synchronously feed back to the PLC controller to achieve fully automated detection.

[0068] Based on the same inventive concept, this application also provides an automated testing method for adapting to multi-size solar cells, which can be executed by the automated testing equipment for adapting to multi-size solar cells provided in the above embodiments. Figure 3 This is a flowchart illustrating an automated testing method for adapting to multi-size solar cells provided by the present invention. Figures 1-3 As shown, the automated detection method includes the following steps:

[0069] Step S101: The control unit controls the conveyor platform to move the battery component M under test and transport it to the preset detection area until the position sensor is triggered and then pauses.

[0070] For example, taking a 1.0m×1.2m perovskite module as an example, the perovskite module to be tested is transported to the testing area by the conveying platform 102. After the laser rangefinder (position sensor) is triggered, the conveying platform 102 stops running, and at the same time the automatic limit mechanism 103 can perform rough positioning based on the initially identified width.

[0071] Step S302: The control unit controls the size recognition unit to collect the size information and attribute information of the battery component M under test. The size information includes at least the length L, width W, and thickness H, while the attribute information includes whether it is a rigid component or a flexible component.

[0072] Specifically, the size recognition unit 30 is activated, the laser rangefinder scans the edge of the component, and collects the length L=1.2m and width W=1.0m; the visual positioning module captures the image and recognizes the thickness H=5mm, and the corner coordinates (0,0), (1.2m,0), (0,1.0m), (1.2m,1.0m). The size recognition unit 30 is activated, the laser rangefinder collects the length L and width W of the component, the visual positioning module collects the thickness H and corner positioning information, and the measurement parameters are transmitted to the control unit in real time.

[0073] Step S303: Based on the size information and attribute information fed back by the size recognition unit, the control unit generates a detection parameter control signal that is compatible with the battery component M under test, either from the pre-stored full-size-parameter mapping database or through interpolation algorithms.

[0074] Specifically, after receiving the parameters, the control unit 50 searches for the corresponding specification in the pre-stored full-size-parameter mapping database. If a perfect match exists, the corresponding detection parameter set is directly invoked. If no match exists, a detection parameter set adapted to the battery component M under test is calculated using an interpolation algorithm based on the parameters of adjacent specifications. This parameter set includes at least the three-dimensional spatial position of the adjustable detection component 20, the camera focal length, the exposure time, the filter band, and the width of the automatic limiting mechanism 103.

[0075] For example, the control unit 50 determines whether the component specifications are in the mapping database. If a matching specification exists, it directly calls the corresponding camera X / Y / Z axis position, focal length f, exposure time t, filter band, and width of the limiting mechanism 103, such as calling the detection parameters: X-axis position 0.6m, Y-axis position 0.5m, Z-axis height 1.5m, focal length 30mm, exposure time 20ms, filter 650nm, and width of the limiting mechanism 103 1.0m. If the specification does not exist, it calculates the adaptation parameters through an interpolation algorithm, such as determining the camera movement distance based on L and W, and adjusting the Z-axis height based on H to meet the focal length requirements.

[0076] Step S304: Control the drive unit to move the adjustable detection component to the target position, automatically adjust the detection parameters of the adjustable detection component, and control the EL camera to acquire EL images of the battery component M under test.

[0077] Specifically, the control unit controls the drive unit to move the perovskite component to be tested to the target position. Simultaneously, it controls the adjusting servo cylinder to adjust the width of the limit mechanism 103, switches the adapter filter, and adjusts the camera focal length and exposure time. For example, it drives the X-axis module to 0.6m, the Y-axis module to 0.5m, and the Z-axis module to 1.5m; the adjusting cylinder pushes the side stop bar to a width of 1.0m; the filter is switched to 650nm; and the camera focal length is adjusted to 30mm and the exposure time to 20ms.

[0078] After the detection parameters are adapted, at least one EL camera 201 is activated to capture images. Depending on the size of the perovskite component to be detected, either a single EL camera or a dual EL camera is used to obtain EL images. If EL camera capture is activated, a single-image capture mode is used to acquire the EL image.

[0079] Step S305: The control unit controls the defect analysis module to analyze and process the acquired EL image and generate a defect detection report.

[0080] For example, if a multi-image shooting mode is used, a complete component EL image is first synthesized using an image stitching algorithm. Then, the defect analysis module analyzes and processes the (synthesized) EL image to identify various defects and generate a defect detection report. As described above, the defect analysis module, based on a deep learning defect recognition model, performs noise reduction, geometric correction, and image enhancement preprocessing on the EL image. Then, through image segmentation and feature extraction algorithms, it identifies defects such as dark spots, cracks, poor welds, and uneven luminescence, and counts the location, size, and number of defects.

[0081] Based on the above embodiments, this application also provides adaptation verification for components of different sizes. For example:

[0082] First, for small-sized samples (10cm×10cm): set parameters such as Z-axis height 0.5m, focal length 12mm, exposure time 10ms, filter 450nm, single shot, and detection time ≤3s;

[0083] Second, medium-sized component (60cm×80cm): set parameters Z-axis height 1.0m, focal length 20mm, exposure time 15ms, filter 550nm, single shot, detection time ≤5s;

[0084] Third, large-size components (1.2m×1.6m): set parameters Z-axis height to 1.8m, focal length to 35mm, exposure time to 25ms, filter to 650nm, stitched image capture (2×2 images), and detection time to ≤8s;

[0085] Verification results: EL images of all components of all sizes are clear, defect identification is accurate, and the fitting process requires no manual intervention.

[0086] Based on the above embodiments, the control unit 50 can also automatically select the shooting mode according to the size of the battery component M under test. For example, the size of the battery component M under test is compared with a preset threshold. If it is smaller than the preset threshold (e.g., 0.5m × 0.5m), a single-camera single-shot mode is used; if it is greater than or equal to the preset threshold, for large-sized components exceeding the field of view of a single camera, a dual-camera shooting mode or a single-camera multi-shot stitching mode is used. For example, a single camera is used for small-sized components, and a dual camera is used for large-sized components, using single-shot (small-sized) or stitching (large-sized) modes to obtain clear EL images.

[0087] Based on the above embodiments, after generating a defect detection report, the control unit can also control the conveying platform 102 to divert the detected components to the qualified area or rework area according to the analysis results of the defect detection report, and reset or adjust the adjustable detection component 20 to adapt to the next component to be detected, so as to achieve continuous automated detection.

[0088] Specifically, the control unit 50 sends a signal to the conveying platform 102, and qualified components are conveyed to the next process, while unqualified components are diverted to the rework area. The test components are automatically reset or adapted and adjusted according to the parameters of the next set of components to enter the next round of testing, so as to achieve continuous automated testing.

[0089] In summary, the automated testing method for solar cells of various sizes provided in this application has at least the following advantages compared with the prior art:

[0090] 1. Highly adaptable: Through automatic size recognition, three-dimensional adjustment, and parameter self-adaptation, it covers the full size range from 10cm×10cm to 1.6m×2.4m, without the need for manual adjustment, and can be directly adapted to multiple specifications of perovskite production lines.

[0091] 2. High detection accuracy: High-precision size recognition unit (positioning ≤0.5mm) + linear module (repeat positioning ±0.02mm) + customized filter, effectively eliminating stray light interference and operational errors, improving defect recognition accuracy by more than 40%.

[0092] 3. Automation and Efficiency: The entire process is automated (no manual measurement, adjustment, or judgment required), improving testing efficiency by more than 30% and meeting the needs of large-scale continuous production.

[0093] 4. Compatibility and versatility: The modular design allows for parameter adjustment to adapt to EL testing of other photovoltaic modules (such as crystalline silicon modules), making it widely applicable.

[0094] 5. Targeted optimization: Customized optical systems and algorithms are developed for the photoelectric properties of perovskite materials to solve the problem of inaccurate identification of perovskite-specific defects by existing equipment.

[0095] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Features of various embodiments of the present invention can be partially or wholly coupled or combined with each other, and can cooperate and be technically driven in various ways. Various obvious changes, readjustments, combinations, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An automated testing device adaptable to solar cells of various sizes, characterized in that, include: frame; A conveying platform, mounted on the frame, is used to carry and convey the battery components to be tested; A size recognition unit is fixedly installed on the entrance side and above the conveying platform to collect the size and attribute information of the battery assembly under test. An adjustable detection assembly includes at least one EL camera, a zoom lens module, a filter assembly, and a movable mounting base; A drive unit, connected to the movable mounting base, is used to drive the adjustable detection component to move in three-dimensional space; The control unit is electrically connected to the size recognition unit, the drive unit, the adjustable detection component, and the conveying platform, respectively. It receives feedback information from the size recognition unit and searches in a pre-stored full-size-parameter mapping database or calculates and generates detection parameter control signals adapted to the battery assembly under test using an interpolation algorithm. This controls the drive unit to move the adjustable detection component to the target position and automatically adjusts the detection parameters of the adjustable detection component. Simultaneously, the adjustable detection component is activated to acquire EL images of the battery assembly under test. The detection parameters include focal length, exposure time, and filter band. The defect analysis module, connected to the control unit, is used to analyze and process the acquired EL images and generate a defect detection report.

2. The automated testing equipment according to claim 1, characterized in that, The conveying platform is equipped with automatic limit mechanisms on both sides, and the automatic limit mechanisms include side bars, adjustment servos, and guide rails; The adjustment servo is used to drive the side stop bar to move along the guide rail to a position that matches the width of the battery assembly under test, according to the control signal from the control unit.

3. The automated testing equipment according to claim 1, characterized in that, The size recognition unit includes: Laser rangefinders are symmetrically installed on both sides of the entrance of the conveying platform to scan the edge of the battery assembly under test in order to obtain the length and width of the battery assembly under test. The visual positioning module includes an industrial camera and an image analysis module. The industrial camera is used to capture surface images of the battery assembly under test. The image analysis module is used to identify the corner positions and thickness information of the battery assembly under test through an edge detection algorithm, and to identify the rigidity and / or flexibility properties of the battery assembly under test based on image features.

4. The automated testing equipment according to claim 1, characterized in that, The movable mounting base is fixedly connected to the drive unit; The filter assembly includes at least three filters in different wavelength bands, which can be quickly switched by an electric rotary wheel or slider mechanism to adapt to the light emission characteristics of different battery components under test.

5. The automated testing equipment according to claim 1, characterized in that, The drive unit includes an X-axis linear module, a Y-axis linear module, and a Z-axis lifting module, used to drive the adjustable detection component to move in three dimensions.

6. The automated testing equipment according to claim 1, characterized in that, The control unit includes: An industrial computer with a built-in full-size-parameter mapping database; the database pre-stores detection parameters corresponding to solar cell modules of various specifications and supports users to add new specifications of modules and their corresponding optimal detection parameters. The PLC controller is used to receive signals from the industrial computer and control the conveying platform and the adjustable detection component to work together.

7. The automated testing equipment according to claim 6, characterized in that, The control unit is also used for: When the size and attribute information of the battery component under test do not have a complete match in the full-size-parameter mapping database, the optimal adaptation parameters are calculated and generated based on the parameters of adjacent specifications using an interpolation algorithm.

8. The automated testing equipment according to claim 1, characterized in that, In the automatic EL image acquisition step, for large-sized components that exceed the field of view of a single camera, multiple images are acquired by using dual-camera collaboration or single-camera partitioning, and a complete component EL image is synthesized by an image stitching algorithm.

9. An automated testing method adaptable to solar cells of various sizes, characterized in that, The automated testing method for adapting to solar cells of various sizes, as described in any one of claims 1-8, includes the following steps: The control conveyor platform moves the battery component under test and transports it to the preset detection area until the position sensor is triggered and then pauses. The control size recognition unit collects the size information and attribute information of the battery component under test; the size information includes at least length, width and thickness, and the attribute information includes rigid components or flexible components; Based on the size and attribute information fed back by the size recognition unit, a detection parameter control signal adapted to the battery component under test is generated from the pre-stored full-size-parameter mapping database or by interpolation algorithm. The control drive unit moves the adjustable detection component to the target position and automatically adjusts the detection parameters of the adjustable detection component, thereby controlling the EL camera to acquire EL images of the battery component under test. The control defect analysis module analyzes and processes the acquired EL images and generates a defect detection report.

10. The automated detection method according to claim 9, characterized in that, Before controlling the EL camera to acquire images, the following steps are also included: The size of the battery assembly under test is compared with a preset threshold. For sizes smaller than the preset threshold, use a single-camera single-shot mode; for sizes greater than or equal to the preset threshold, use a dual-camera shooting mode or a single-camera multi-shot stitching mode.

11. The automated detection method according to claim 9, characterized in that, After generating the defect detection report, the following is also included: Based on the analysis results of the defect detection report, the conveying platform is controlled to divert the detected components to the qualified area or the rework area, and the adjustable detection component is reset or its state is adjusted to adapt to the next component to be detected, so as to achieve continuous automated detection.

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