Zhang, S., 2019. Large-scale ship fault data retrieval algorithm supporting complex query in cloud computing. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, and Engineering of Coastal, Port, and Marine Systems. Journal of Coastal Research, Special Issue No. 97, pp. 236–241.

In the cloud computing environment, the mass ship fault data retrieval is easy to be interfered by the association rule items, the fuzzy clustering of the data retrieval is not good, the fault diagnosis efficiency of the ship is reduced, and in order to improve the fault diagnosis capability of the ship, The invention provides a mass ship fault data retrieval technology based on complex query support in a cloud computing environment. The distributed storage structure analysis of mass ship fault data is carried out by adopting a vector quantization characteristic coding technology, the spectral characteristic analysis of the mass ship fault data is carried out by adopting a subsection adaptive regression analysis method, the quantitative recursive analysis model is used for extracting the mass ship fault data, the method comprises the following steps of: extracting an association rule feature set reflecting the attribute of a mass ship fault data category, carrying out data classification retrieval on the extracted mass ship fault data feature quantity by using a BP neural network classifier, introducing a machine learning factor to perform convergence control on a support vector machine, And the global stability of the mass ship fault data retrieval is improved. The simulation results show that the accuracy of the data retrieval is high, the error rate is small, and the fault diagnosis ability of the ship is improved.

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