Development of Costing and Budget Control Strategy for Shipbuilding Based on Machine Learning

Due to the complexity of the ship product structure and process, long production cycle and other factors, ship enterprises are plagued by the problem of profitability.Strengthening cost prediction and budget control is a very important means for ship enterprises to improve their profit margins.By analyzing the cost structure of shipbuilding, this paper proposes a rolling forecast model of shipbuilding cost based on long and short-term memory neural network (LSTM) as the estimation method of shipbuilding cost.Meanwhile, the traditional earned value method and target cost method are combined to sort out the shipbuilding cost control process and prepare the cost control plan as the control strategy of shipbuilding cost.Then we take the manufacturing data of a shipyard as the experimental object, use this paper's model for data mining, compare the data performance of this paper's model with similar algorithms, and verify the feasibility of this paper's model.Finally, the model of this paper is applied to real cases.In the comparison of the estimation results between this paper's model and the commonly used algorithms, the average error of cost estimation of this paper's model is 4.95%, which is better than the average error of the commonly used algorithms.The superior accuracy of this paper's model in shipbuilding cost estimation is verified.