Abstract: A method and system for artificial intelligence-based radiofrequency ablation parameter optimization and information synthesis are provided. The method is applied to a radiofrequency ablation controller including a processor and an artificial intelligence module. The processor of the radiofrequency ablation controller preprocesses sample data and sends the preprocessed sample data to the artificial intelligence module. The artificial intelligence module establishes an artificial neural network model according to the preprocessed sample data and a radiofrequency ablation control parameter for the sample data. The processor preprocesses signals collected by sensors on a plasma wand. The artificial intelligence module imports preprocessed sensor data into the artificial neural network model for analysis and fusion, to obtain the radiofrequency ablation control parameter.

Abstract: A method and a device of adaptive EMC-EMI radio frequency signal data processing are provided. The method includes: performing segmentation and preprocessing in response to a radio frequency signal; performing Hilbert-Huang transform on signals after segmentation; calculating EMC power, EMI radio frequency energy and mode, and a radio frequency signal-to-noise mode, and comparing the EMC power, the EMI radio frequency energy and mode, and the radio frequency signal-to-noise mode with corresponding thresholds; and adaptively adjusting energy parameters of a radio frequency ablation device, or prompting a user to adjust the energy parameters of the radio frequency ablation device. Instantaneous and dynamic radio frequency plasma is qualitatively and quantitatively detected and classified according to a signal-to-noise mode and pattern recognition of radio frequency emission, and an actual state of the knife head is perceived, to performs adaptive control or prompt the user to perform an adjustment operation.