Research on Operation Reliability Prediction Platform of Large-Scale Medical Equipment Based on Visual Internet of Things
Addressing medical device reliability in clinical practice,several shortcomings that have become increasingly prominent in clinical practice,developed the large-scale medical equipment operation reliability prediction platform based on the visual Internet of Things.First,introduce the visualization of Internet of Things simulation modeling technology,use sensor clusters to obtain real-time multi-dimensional status data of large-scale medical equipment operation,and form a sample set of core elements of large-scale medical equipment operation reliability;Then pool the sample set of the core elements of the reliability of the operation of large medical equipment,and construct the data pool of the core elements of reliability that combines the pre-training set and the post-test set;Finally,the deep convolutional neural network DCNN is used to identify the characteristics of the feature data pool,and build the accurate prediction mechanism for the sample set of the core elements of the reliability of large-scale medical equipment in positive time sequence.The nuclear magnetic resonance imaging equipment of the tertiary first-class hospital was selected as the case analysis carrier,and the platform was verified in clinical application practice,the results show that the platform satisfies the needs of intelligent transformation of large-scale medical equipment operation reliability prediction,and greatly optimizes the intelligent controllable perception mechanism of large-scale medical equipment operation reliability prediction,and the core parameters of the large-scale medical equipment operation reliability prediction platform meet the requirements of clinical practice.
visual Internet of Thingslarge-scale medical equipmentreliability predictionDCNN algorithmclinical practice verification
NETL
NSTL
万方数据
