Research On Full Range Energy Saving Control Of Ship Based On Big Data Technology

The shipping industry is a high-energy-consumption industry.In recent years,the problem of energy consumption caused by ship transportation has become more and more prominent.The energy-saving problem of ships has drawn great attention from various countries.At the same time,with the rapid development of emerging technologies such as mobile networks,cloud computing,and the Internet of Things,global data has exploded,and the collection and application of shipping big data have also been vigorously developed.In order to further promote the energy conservation and emission reduction of ships,and the innovative application of the current development of big data technology,this paper conducts research on energy conservation control during ocean shipping.First,through the analysis and research of shipping big data from the perspective of the full range of the ship,a multi-objective optimization model of ship speed with the lowest operating cost and the lowest greenhouse gas emissions of the ship is established through the traditional ship resistance and power models,and the evaluation is optimized.The ship’s most economical and energy-saving speed during the whole voyage achieves the best fuel consumption rate.Secondly,by using the method of shipping big data mining analysis and BP neural network model prediction,the fuel consumption of the ship’s main engine and its related factors were visually displayed,and the fuel consumption data of a future ocean-going ship within a certain sailing time was predicted.At the same time,a comparison analysis between BP neural network prediction and traditional physical model fuel consumption prediction is added to the fuel consumption prediction part.The analysis and prediction of the fuel consumption of the ship’s main engine integrated with the big data of ship navigation have various ship navigation conditions,and the predicted change trend of fuel consumption is clear.Finally,through the establishment of a big data-based decision support system,the results of multi-target speed optimization results,BP neural network fuel consumption prediction,and host fuel consumption factor analysis are presented to decision makers intuitively to assist ship energy-saving control decision makers in ship navigation management.Thereby reducing ship energy consumption.