医疗急救设备是保障病患生命健康的基础，以降低船舶医疗急救设备故障率，最大限度保障船舶航行过程中人员的人身安全为目的，提出基于人工智能的船舶医疗急救设备状态检测与维护方法。采集船舶医疗急救设备在正常运行状态与故障运行状态下的相关信息，以及各项机械性能参数，并预处理所采集信息，简化数据。构建人工智能领域中的卷积神经网络，将预处理后的设备信息作为输入，通过学习与训练过程构建决策模型，将决策模型应用于全部船舶医疗急救设备，获取不同船舶医疗急救设备的运行状态与维护信息，实现精准维护目的。实验结果显示该方法的状态检测率达到99%，能够降低65%设备故障率。
Medical first-aid equipment is the basis for ensuring the life and health of patients. In order to reduce the failure rate of medical first-aid equipment on ships and maximize the personal safety of personnel during the navigation of ships, research the state detection and maintenance methods of medical first-aid equipment on ships based on artificial intelligence. Collect the relevant information of the ship's medical first-aid equipment under normal and fault operating conditions, as well as various mechanical performance parameters, and preprocess the collected information to simplify the data. Build a convolutional neural network in the field of artificial intelligence, take the preprocessed equipment information as input, build a decision model through the learning and training process, apply the decision model to all ship medical emergency equipment, obtain the operation status and maintenance information of different ship medical emergency equipment, and achieve the purpose of accurate maintenance. The experimental results show that the state detection rate of this method reaches 99%, and the equipment failure rate can be reduced by 65%.
2023,45(3): 141-144 收稿日期：2022-09-16
DOI：10.3404/j.issn.1672-7649.2023.03.026
分类号：TP393
基金项目：江苏省高等学校自然科学研究面上项目（20KJD580006）；江苏高校哲学社会科学专题研究项目（2020SJB0802）
作者简介：庞辉(1983-),男,硕士,讲师,研究方向为船舶医疗急救及急救医疗设备检测与维护
