Research on heavy metal ion detection method based on convolutional neural network

The severe contamination caused by heavy metal ions presents a significant risk to ecosystems and human well-being, making the pursuit of precise and efficient detection a focal point in contemporary research. Conventional detection techniques depend on intricate chemical assays and costly analytical instruments, which struggle to fulfill the need for swift assessment. This study introduces a convolutional neural network (CNN)-based approach for detecting heavy metal ions, leveraging deep learning models to autonomously extract characteristics from spectral or sensor data, thereby enhancing recognition accuracy and generalizability. An experimental dataset comprising samples with varying heavy metal ion concentrations is established, followed by normalization, denoising, and augmentation processes to refine data quality. A CNN framework is architected and fine-tuned, employing convolutional operations for feature extraction and optimization strategies to bolster model resilience and precision. Empirical findings indicate that this methodology surpasses conventional techniques in terms of identification accuracy, stability, and computational efficacy, offering an innovative technological avenue for ecological surveillance and contamination mitigation.