Artificial Intelligence-Based Early Warning Method for Abnormal Operation and Maintenance Data of Medical and Health Equipment

When traditional early-warning methods for abnormal operation and maintenance data of medical care equipment are used to process nonlinear abnormal data in the operation and maintenance process of medical care equipment, the data classification accuracy is poor, resulting in insufficient reconciliation level of early-warning methods. Therefore, an artificial intelligence based early-warning method for abnormal operation and maintenance data of medical care equipment is proposed. First, the article establishes the overall framework of data anomaly early warning, including communication network layer, smart contract layer, equipment layer, and application layer. Based on artificial intelligence technology, it establishes the anomaly data detection model, uses RNN cyclic neural network as the basis, designs the anomaly data detection process, and analyzes whether medical and health care equipment is in an abnormal operating state by comparing the real value of current measurement points with the predicted value of RNN neural network model. The experimental results show that: combined with the experimental results of nonlinear data, it can be determined that the data classification accuracy of the designed early-warning method is high, the early-warning data is more comprehensive and complete, and the detection method is superior to the common detection methods.