Full-process online detection system and method for solar module

Abstract

The invention provides a full-process online detection system and method for a solar module, and the system comprises a central control module which is used for coordinating and controlling the operation of the whole detection system, and is also used for data collection and instruction distribution; the distributed detection work stations comprise a series welding section detection work station, a stitch welding section detection work station, a pre-lamination detection work station, a post-lamination detection work station, a post-welding detection work station and a post-glue-pouring detection work station; the data management and analysis module is in communication connection with the central control module and the distributed detection work stations, and is used for receiving, storing, processing and analyzing the detection data from each work station and generating a quality report and tracing information; and the communication interface is used for realizing data interaction and instruction transmission among the central control module, the distributed detection work station and the data management and analysis module, and realizing full-process online detection, data interconnection and intercommunication, intelligent analysis and quality tracing of the solar module.

Description

Technical Field

[0001] This invention relates to an online testing system and method for the entire process of solar modules. Background Technology

[0002] With the continuous growth of global demand for renewable energy, the photovoltaic industry has developed rapidly. As the core component of solar power generation, the quality and reliability of solar modules directly affect power generation efficiency and lifespan. During the production process of solar modules, various defects can be introduced at multiple stages, from stringing and stacking of cells to lamination and encapsulation, such as microcracks, broken grids, solder strip misalignment, contamination, bubbles, foreign matter, and surface damage. If these defects are not detected and effectively controlled in a timely manner, they will seriously affect the performance and reliability of the final product, and may even lead to premature module failure.

[0003] Traditional inspection methods mainly rely on manual visual inspection or offline sampling inspection. Manual visual inspection is inefficient, susceptible to subjective factors, and cannot detect internal defects; offline sampling inspection cannot achieve full coverage of all products, has the risk of missed detection, and has a long inspection cycle, which is not conducive to real-time quality control on the production line.

[0004] In recent years, although some automated testing equipment has emerged, most of them are designed for single processes or specific defect types and lack systematic, end-to-end integration capabilities. Testing data from different processes are often stored independently, making it difficult to perform correlation analysis. This results in the inability to trace the source of defects and to macroscopically control and optimize the quality of the entire production process.

[0005] Therefore, an online testing system is needed that can cover the entire solar module production process, achieve data interconnection, and possess intelligent analysis and traceability capabilities to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a full-process online inspection system and method for solar modules, realizing online inspection, data interconnection, intelligent analysis, and quality traceability throughout the entire solar module production process. This objective is achieved as follows: This invention proposes a full-process online testing system for solar modules, comprising: a central control module for coordinating and controlling the operation of the entire testing system and for data collection and instruction distribution; distributed testing stations, including: a string welding section testing station, a lap welding section testing station, a pre-lamination testing station, a post-lamination testing station, a post-welding testing station, and a post-insertion testing station; a data management and analysis module, communicatively connected to the central control module and the distributed testing stations, for receiving, storing, processing, and analyzing testing data from each station, and generating quality reports and traceability information; and a communication interface for enabling data interaction and instruction transmission between the central control module, the distributed testing stations, and the data management and analysis module.

[0007] Furthermore, each testing station includes: an image acquisition module for acquiring image data of the component under test; an illumination module for providing illumination conditions for image acquisition; a local processing and control module for controlling the actuators of this station and processing the acquired image data; and an actuator interface for connecting and controlling the actuators on the production line.

[0008] Furthermore, the image acquisition module of the string welding section inspection station includes a high-resolution area array CCD camera, the execution mechanism includes a suction cup gripping mechanism for gripping the battery string and an energized clamp for applying bias voltage or current to the battery string, and the local processing and control module is an embedded industrial control computer, which has pre-stored image processing algorithms and defect judgment models for identifying defects such as hidden cracks, broken grids, and weld strip misalignment.

[0009] Furthermore, the image processing algorithm includes using a directional filter to enhance the texture features along the crack direction before detection.

[0010] Furthermore, the image acquisition module of the lap welding section inspection station includes a high-resolution linear scan camera, the illumination module is a bar-shaped linear scan light source, and the local processing and control module has a pre-stored image processing program based on template matching algorithm and geometric dimension measurement, used to detect busbar contamination and positional offset.

[0011] Furthermore, the pre-lamination inspection station integrates an imaging system for performing three-dimensional appearance inspection, electroluminescence (EL) inspection, and photoluminescence (PL) inspection. The three-dimensional appearance inspection system includes a structured light projector and a high-speed CCD camera for image acquisition, and calculates the height information of the component surface using triangulation. The PL inspection system includes a laser line source and a high-speed CCD camera for acquiring PL images. The station's local processing and control module is a GPU server used to run multimodal fusion analysis algorithms.

[0012] Furthermore, the data management and analysis module includes a relational database and a data analysis engine. The data analysis engine is configured to: associate detection data from different workstations using the component ID as the key to form a full-process quality profile of the component, and perform defect distribution statistics and defect correlation analysis.

[0013] Furthermore, the solar module full-process online inspection system uses a solar module full-process online inspection method, which specifically includes: at each inspection point, using an integrated image acquisition device, image data of the module under test is acquired according to a preset, specialized inspection plan targeting the defect characteristics of that process; the acquired image data is transmitted to the processing unit in real time; the image data is analyzed and defect identified in real time using image processing algorithms and defect judgment models targeting the defect characteristics of different processes; inspection results are generated, and the result data is bound to the module identity information and uploaded to the central database; and based on the inspection results, predetermined quality control actions are executed.

[0014] Compared with the prior art, the beneficial effects of the present invention are: by setting up multiple distributed testing stations, the key links in the production of solar modules are covered, realizing online testing of the entire process from string welding to encapsulation, effectively avoiding the drawbacks of traditional manual testing and offline sampling, and significantly improving testing efficiency and coverage. The central control module, distributed testing workstations, and data management and analysis module interact and transmit commands through a communication interface, enabling centralized storage and management of all testing data.

[0015] The data management and analysis module can store, process, and intelligently analyze massive amounts of testing data from various workstations, generating detailed quality reports and traceability information. This helps to quickly locate problematic processes, optimize production processes, and improve product quality. Detailed Implementation

[0016] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0017] This invention provides a full-process online testing system for solar modules, comprising: a central control module, distributed testing stations, a data management and analysis module, and a communication interface.

[0018] Understandably, the central control module is responsible for coordinating and controlling the operation of all distributed inspection stations, as well as data collection and instruction distribution. For example, the central control module can issue inspection tasks to each station according to the production plan, receive inspection results uploaded by each station, and monitor the overall system operation status. Distributed inspection stations are deployed at different key process points on the solar module production line to achieve online inspection of the entire module production process. These stations include: string welding section inspection stations, lap welding section inspection stations, pre-lamination inspection stations, post-lamination inspection stations, post-welding inspection stations, and post-filling inspection stations. Each station inspects specific defects that may occur in its respective process. The data management and analysis module works in conjunction with the central control module. The central control module and distributed testing stations communicate with each other via a communication interface. Its main function is to receive, store, process, and analyze testing data from each station. Through in-depth analysis of this massive amount of data, the data management and analysis module can generate detailed quality reports, such as defect type distribution and defect rate statistics, and provide component quality traceability information, such as which component had what defect in which process and the severity of the defect. The communication interface serves as the intermediary for data interaction and command transmission between the central control module, distributed testing stations, and the data management and analysis module. It can employ various methods such as industrial Ethernet, fiber optic communication, and wireless communication to ensure the real-time performance, stability, and reliability of data transmission.

[0019] In a preferred embodiment, each distributed testing station includes: an image acquisition module, an illumination module, a local processing and control module, and an actuator interface. The image acquisition module is used to acquire image data of the component under test, such as visible light images, infrared images, electroluminescent images, etc. The illumination module provides sufficient and uniform illumination conditions for image acquisition to ensure image quality, such as using LED light sources, halogen lamps, laser light sources, etc. The local processing and control module is used to control the actuator of the station and perform preliminary processing on the acquired image data. The actuator interface is used to connect to and control the actuators on the production line, such as robotic arms, conveyor belts, suction cups, etc., to realize operations such as component gripping, positioning, and movement.

[0020] In a preferred embodiment, the image acquisition module of the string welding section inspection station includes a high-resolution area array CCD camera for capturing the overall image of the battery string. The execution mechanism includes a suction cup gripping mechanism for gripping the battery string and an energized clamp for applying bias voltage or current to the battery string. The suction cup gripping mechanism can accurately position and grip the battery cells or battery string, while the energized clamp can electrically excite the battery string during the inspection process to expose potential defects. The local processing and control module is an embedded industrial control computer, which has pre-stored image processing algorithms and defect judgment models for identifying defects such as hidden cracks, broken grids, and weld strip misalignment. The embedded industrial control computer has the characteristics of small size, low power consumption, and strong real-time performance, making it suitable for fast and real-time image processing and defect judgment on the production line.

[0021] In a preferred embodiment, the image processing algorithm includes using directional filters to enhance the texture features of crack directions before detection. For example, Gabor filters or gradient direction histograms and other directional filters can be used to perform multi-directional filtering on the image to enhance the texture features of linear defects such as hidden cracks or broken grids, making them stand out more in the background, thereby improving the detection accuracy and robustness of defects.

[0022] In a preferred embodiment, the image acquisition module of the lap weld section inspection station includes a high-resolution linear scan camera for continuous scanning imaging of the high-speed moving components. The illumination module is a strip-shaped linear scan light source that works in conjunction with the linear scan camera to provide uniform linear illumination, thereby eliminating ambient light interference and ensuring image quality. The local processing and control module has a pre-stored image processing program based on template matching algorithm and geometric dimension measurement for detecting busbar contamination and positional offset. The template matching algorithm can be used to identify the shape and position of the busbar, while the geometric dimension measurement can accurately calculate the offset of the busbar and detect whether there are any abnormalities such as contamination on its surface.

[0023] In a preferred embodiment, the pre-lamination inspection station integrates an imaging system for performing 3D appearance inspection, electroluminescence (ELE) inspection, and photoluminescence (PL) inspection. The 3D appearance inspection system includes a structured light projector and a high-speed CCD camera for image acquisition. It calculates the height information of the component surface using triangulation to detect surface defects such as bubbles, foreign objects, and scratches. The PL inspection system includes a laser line source and a high-speed CCD camera for acquiring PL images. It excites the solar cells to generate photoluminescence and acquires their emission images to detect internal defects such as microcracks, broken grids, and efficiency degradation. The EL inspection system applies current to the component to make it emit light and acquires its electroluminescence images to detect electrical defects such as microcracks, broken grids, poor solder joints, and short circuits. The station's local processing and control module is a GPU server used to run multimodal fusion analysis algorithms. The GPU server can efficiently process image data from multiple modalities such as 3D, EL, and PL, and comprehensively judge the quality status of the component through multimodal fusion analysis algorithms, improving the accuracy and comprehensiveness of defect detection.

[0024] In a preferred embodiment, the data management and analysis module includes a relational database and a data analysis engine. The relational database is used to store structured inspection data from various workstations, such as defect type, location, severity, component ID, timestamp, etc. The data analysis engine is configured to: use the component ID as the key to associate inspection data from different workstations to form a full-process quality profile of the component, and perform defect distribution statistics and defect correlation analysis. Through the component ID, the inspection results of the same component in different processes can be associated, thereby tracing the generation and evolution of defects. Defect distribution statistics can help identify high-incidence defect types and high-risk processes.

[0025] In a preferred embodiment, the solar module end-to-end online testing system uses a solar module end-to-end online testing method, which specifically includes the following steps: S1: At each inspection point, an integrated image acquisition device is used to collect image data of the components to be tested according to a pre-set special inspection plan for the defect characteristics of the process. For example, a high-resolution area array CCD camera is used to collect images of battery strings in the string welding section, and a three-dimensional appearance inspection system, EL inspection system and PL inspection system are used to collect multimodal images before lamination. S2: The acquired image data is transmitted to the processing unit in real time. These processing units can be local processing and control modules of each workstation, or central control modules or data management and analysis modules. S3: Through image processing algorithms and defect judgment models targeting the defect characteristics of different processes, image data is analyzed and defects are identified in real time. For example, in the serial welding section, directional filters are used to enhance crack texture features for detection; in the stack welding section, template matching algorithms and geometric size measurements are used to detect busbar contamination and offset; and before lamination, multimodal fusion analysis algorithms are used to comprehensively judge defects. S4: Generate test results and upload them to the central database after binding the result data with the component identity information. The component identity information is a unique identifier throughout the entire production process, ensuring data traceability; S5: Based on the detection results, execute predetermined quality control actions. For example, for critically defective components detected, the system can automatically issue an alarm and instruct the actuators on the production line to reject or rework them; for minor defects, they can be recorded as a basis for subsequent process improvements.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A solar module full-process online testing system, characterized in that, include: The central control module is used to coordinate and control the operation of the entire detection system, and is used for data collection and instruction distribution; The distributed testing station includes: a string welding section testing station, a lap welding section testing station, a pre-lamination testing station, a post-lamination testing station, a post-welding testing station, and a post-application testing station; a data management and analysis module, which is communicatively connected to the central control module and the distributed testing stations, is used to receive, store, process, and analyze testing data from each station, and generate quality reports and traceability information; a communication interface enables data interaction and command transmission between the central control module, the distributed testing stations, and the data management and analysis module.

2. The online testing system for the entire process of solar modules according to claim 1, characterized in that, Each testing station includes: an image acquisition module for acquiring image data of the component under test; an illumination module for providing illumination for image acquisition; a local processing and control module for controlling the actuators of this station and processing the acquired image data; and an actuator interface for connecting to and controlling the actuators on the production line.

3. The online testing system for the entire process of solar modules according to claim 2, characterized in that, The image acquisition module of the string welding section inspection station includes a high-resolution area array CCD camera. The execution mechanism includes a suction cup gripping mechanism for gripping the battery string and an energized clamp for applying bias voltage or current to the battery string. The local processing and control module is an embedded industrial control computer. The embedded industrial control computer has pre-stored image processing algorithms and defect judgment models for identifying defects such as hidden cracks, broken grids, and weld strip misalignment.

4. The online testing system for the entire process of solar modules according to claim 3, characterized in that, The image processing algorithm includes using a directional filter to enhance the texture features along the crack direction before detection.

5. The online testing system for the entire process of solar modules according to claim 4, characterized in that, The image acquisition module of the lap welding section inspection station includes a high-resolution linear scan camera, the illumination module is a bar-shaped line scan light source, and the local processing and control module has a pre-stored image processing program based on template matching algorithm and geometric size measurement, which is used to detect busbar contamination and positional offset.

6. The online testing system for the entire process of solar modules according to claim 5, characterized in that, The pre-lamination inspection station integrates an imaging system for performing 3D appearance inspection, electroluminescence (EL) inspection, and photoluminescence (PL) inspection. The 3D appearance inspection system includes a structured light projector and a high-speed CCD camera for image acquisition, and calculates the height information of the component surface using triangulation. The PL inspection system includes a laser line source and a high-speed CCD camera for acquiring PL images. The station's local processing and control module is a GPU server used to run multimodal fusion analysis algorithms.

7. The online testing system for the entire process of solar modules according to claim 6, characterized in that, The data management and analysis module includes a relational database and a data analysis engine. The data analysis engine is configured to: associate detection data from different workstations with component ID as the key to form a full-process quality profile of the component, and perform defect distribution statistics and defect correlation analysis.

8. The online testing system for the entire process of solar modules according to claim 7, characterized in that, The solar module full-process online inspection system uses a solar module full-process online inspection method, which specifically includes: at each inspection point, using an integrated image acquisition device, image data of the module under test is acquired according to a preset inspection plan for the defect characteristics of that process; the acquired image data is transmitted to the processing unit in real time; the image data is analyzed and defect is identified in real time using image processing algorithms and defect judgment models for different process defect characteristics; inspection results are generated, and the result data is bound with the module identity information and uploaded to the central database; and based on the inspection results, predetermined quality control actions are executed.