Liu, X. and Ma, L., 2020. Real-time feedback method of ship sensor network learning monitoring. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 934–938. Coconut Creek (Florida), ISSN 0749-0208.

In view of the problem that the traditional real-time feedback method of ship sensor network learning monitoring has poor real-time effect, a new real-time feedback method of ship sensor network learning monitoring is designed. The monitoring real-time feedback data is divided into explicit feedback data and implicit feedback data. The random sampling method is used to select the data set, and SPSS Clementine tool is used to mine the explicit feedback data and implicit feedback data respectively. The data to be processed is introduced to realize preprocessing, including processing missing values, data exception detection and data conversion. The real-time feedback model of ship sensor network learning monitoring is designed to realize the real-time feedback of network learning monitoring. The real-time feedback model of ship sensor network learning monitoring is composed of learning control module, learning achievement evaluation module and learning real-time feedback module. In order to prove the real-time effect of the designed feedback method, the traditional ship sensor network learning monitoring real-time feedback method is compared with that of the designed method. Experimental results show that the real-time performance of this method is better than that of the traditional method.