In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

In recent years, research on new technologies for expressing and exchanging information through tactile vibration has been the focus of researches. In this paper, by choosing a suitable coding scheme and a vibrating motor arrangement, we designed a device to express semantic information through vibrotactile. Three types of experiments were designed to test the usability of the encoding scheme and the device. Firstly, the vibration intensity was experimented when designing the encoding scheme, and the results showed that the encoding scheme was better with Braille units of 0.2 and 0.3 vibration intensities. In addition, the learning experiment and sentence recognition accuracy experiment were carried out to verify the usability of the device. The learning experiment results show that subjects were able to memorize Braille characters with an accuracy more than 90%, and to recognize a Chinese character (consisting of two Braille cells) with an average of 90.8% accuracy. The sentence recognition accuracy test experiment results show that the average recognition rate of the three poems used for the test was 93.33%. The device can be used for semantic information expression and touch-reading of Braille, and it can realize the reading experience of paper Braille.