Abstract
Navigation challenge corresponding development strategies to overcome the collection and application of the shipbuilding industry due to the control of various data-based energy efficiency measures. Ship performance data collection and navigation of ships navigate this energy efficiency strategy, an integral part of the management plan. The provisions of various navigation strategies in CNNs (Convolutional Neural Network) are believed to play an important role in ship performance in modern integrated bridge systems. Therefore, it realizes a large amount of onboard data to process the process. In general, navigation systems can provide guidance for the transmission in exercise, and provide effective guidance for the navigation information and direction of the ship. It can provide effective information and convenience for the exercise of the ship during the voyage of the ship. This paper mainly studies the methods and ways to provide some solutions through big data. The author suggests using the data flow chart recorded for the ship engine. This can easily monitor the main performance and some data of the ship during navigation, thus improving the guiding effect of navigation. Database image recognition is to monitor possible accidents through sensors, and classify and compress the monitored data. The classified ship dataset is then compressed to reduce the number of parameters passed to the coastal data center. A compressed dataset of ship performance and navigation information is expanded to its original size in the first step of the process. The two steps of data compression and expansion are done with another CNN technology (i.e., deep learning) of the autoencoder. The new dataset contains estimated ship performance parameters and navigation information, even if the compressed dataset has been expanded to its original size. Therefore, comparisons between actual and presumed datasets are made to change.
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