The automatic sorting machine for photovoltaic (PV) cells plays a crucial role in the PV industry. It employs automated and intelligent means, combined with image recognition and machine learning technologies, to efficiently and accurately sort PV cells.

　　‌In terms of image recognition‌:

　　When a PV cell is placed on the inspection workbench, the equipment first captures its appearance using a high-definition CCD camera or other camera systems to obtain image information. During this process, a light source provides sufficient illumination for accurate imaging. Subsequently, the image processor performs operations such as preprocessing, feature extraction, and classification recognition on these images. Preprocessing may include distortion correction, image enhancement, and other steps to ensure image quality. Feature extraction identifies key characteristics on the cell surface, such as defects, flaws, and color.

　　‌In terms of machine learning‌:

　　The equipment utilizes advanced machine learning algorithms to analyze and classify the extracted image features. These algorithms may include support vector machines, neural networks, etc., which can automatically classify PV cells into different quality grades based on預(yù)設(shè) classification standards and algorithms. Furthermore, machine learning technology continuously learns and optimizes the classification model, improving the accuracy and efficiency of sorting.

　　By integrating image recognition and machine learning technologies, the PV cell automatic sorting machine achieves comprehensive, rapid, and accurate detection and sorting of PV cells. This not only enhances production efficiency but also ensures the quality and performance of PV components, providing strong support for the sustained development of the PV industry.