The setting and adjustment of ventilator parameters need to rely on a large amount of clinical data and rich experience. This paper explored the problem of difficult decision-making of ventilator parameters due to the time-varying and sudden changes of clinical patient's state, and proposed an expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment based on fuzzy control rule and neural network. Based on the method and the real-time physiological state of clinical patients, we generated a mechanical ventilation decision-making solution set with continuity and smoothness, and automatically provided explicit parameter adjustment suggestions to medical personnel. This method can solve the problems of low control precision and poor dynamic quality of the ventilator's stepwise adjustment, handle multi-input control decision problems more rationally, and improve ventilation comfort for patients.
呼吸机参数的设置与调节，需要依靠大量临床数据和丰富的经验。本文针对由于临床患者状态时变性、突变性而导致呼吸机参数决策困难的问题进行了探讨，依据模糊控制规则和神经网络，提出一种基于专家知识的呼吸机参数设置与无级自适应调节策略。本文依据该方法和临床患者实时生理状态，生成了具有连续性与平滑性的机械通气决策解集，自动地向医护人员提供明确的参数调节建议。该方法解决了呼吸机有级调节控制精度低、动态品质差等问题，可以更合理地处理多输入的控制决策问题，并提高了患者的通气舒适性。.
Keywords: Adaptive ventilation; Parameter setting; Stepless adjustment; Ventilator.