Overall Design and Architecture of an Intelligent Blood Tube Sorting System

This paper constructs a blood tube sorting system based on intelligent algorithms, integrating mechanical design, visual recognition, and PLC control technology. It introduces the convolutional neural network (CNN) and particle swarm optimization algorithm (PSO) to enhance sorting efficiency and innovates the system architecture. Experiments show that the system exhibits excellent performance in accuracy, efficiency, and stability. The optimization algorithms significantly improve the system's performance, providing an effective solution for the automation of medical sample processing.