Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement.
