Research on the conversion efficiency and preparation technology of monocrystalline silicon cells based on statistical distribution

Monocrystalline silicon solar cells are still one of the best choices for large-scale commercial use, and occupy a dominant position in large-scale applications and industrial production. In this paper, the conversion efficiency of monocrystalline silicon cells is studied based on the statistical distribution law, and the preparation process is analyzed, and a forensic algorithm for distinguishing between natural images and computer-generated images is proposed. Moreover, this paper focuses on the real digital image source forensics by designing GLRT, and establishes an image source forensics algorithm based on Gaussian distribution model to classify and detect analog data. In addition, based on the current industry commonly used texture manufacturing process, this paper introduces the electrochemical etching technology used to prepare porous silicon into the preparation of monocrystalline silicon solar cells. Finally, this paper conducts research on the conversion efficiency of monocrystalline silicon cells through process research, conducts data analysis through mathematical statistical methods, and verifies the statistical methods of this paper through experiments to provide references for related research.