Mooring Cable Force Prediction Method Based On Wavelet Transform And Neural Network

Under the situation of economic globalization,the rapid development of the maritime industry is accompanied by an increase in the number of ships and a large-scale accelerated construction of open terminals.In these open docks,due to climate and water conditions and other factors,cable breakages occur frequently when the ships are moored.In order to avoid the occurrence of cable breaking events and to ensure the safety of ship mooring operations,it is an indispensable industry requirement to predict the mooring force of ships.In this paper,the Db-GRU algorithm is designed and implemented to improve the accuracy of mooring cable force prediction.In this algorithm,the Daubechies wavelet in the wavelet transform is used to process the mooring cable force data with significant nonlinear characteristics,and finally the prediction results are reconstructed to obtain the prediction data we want.In the experiment of implementing the algorithm,we found that the accuracy of the prediction using the algorithm is significantly higher than the accuracy of directly predicting the mooring line force data through the GRU,and there is no excessive error between the prediction results and the real data that affects the judgment..Subsequently,this paper considers that a large amount of cable force data can be generated in a short time when the ship is moored.In order to verify that the analysis ability and prediction effect of the Db-GRU algorithm when facing a large amount of data are the same as before,it is specially selected under the big data Hadoop platform Use the MapReduce parallel computing framework for large amounts of data for experimental verification.From the results of the experimental run,we can see that the accuracy of the prediction of our Db-GRU mooring line force prediction model under the MapReduce to a million-level data volume can still be guaranteed.Finally,this paper proposes a Db-GRU mooring cable force prediction model based on quantum computing to improve the prediction efficiency of the algorithm.In the quantum model,we combine the originally used Daubechies wavelet and GRU with quantum computing to generate quantum Daubechies wavelet and QGRU,and then combine the quantum Daubechies wavelet and QGRU to generate our improved prediction model.In this part,we illustrate through theory that the algorithm can significantly increase the prediction speed on the basis of ensuring the accuracy of the original algorithm.With the further development of quantum computing,the improved model can be applied to ship mooring lines in practical engineering practice.To make predictions.