This application relates to a display color management method and system based on federated learning and digital twins. The method includes: collecting local color performance data from multiple medical display nodes to train a color prediction model and obtain local model update parameters; aggregating the encrypted local model parameters using a federated learningserver to generate a global optimization model; constructing and maintaining a digital twin for each medical display node using a digital twin platform, driving the digital twin to predict future color drift trajectories based on the global optimization model and real-time ambient light data, and generating a predictive calibration instruction when the prediction result exceeds a preset tolerance; executing the predictive calibration instruction to calibrate the target display, acquiring key target data and generating a data fingerprint, and uploading the transaction record containing the data fingerprint to the blockchain by calling a blockchainsmart contract. This application achieves predictive calibration, ensuring color consistency of medical displays.
The gas thermal decomposition unit (2) of the present application is provided with: a honeycomb structure (20) having one or more honeycomb structure portions (24) having an outer peripheral wall (240) and a partition wall (241) provided on the inner side of the outer peripheral wall (240) and partitioning a plurality of cells (241a) to form flow paths extending from one end face to the other end face; and an induction heating coil (21) disposed on the outer periphery of the honeycomb structure (20), at least one of the one or more honeycomb structure portions (24) containing a conductor and / or a magnetic body (25).