基于深度学习的人工智能设计决策模型

To eliminate the influence of decision-maker's decision preferences on product development and further improve the design decision-making efficiency, a Residual Neural Network(ResNet)artificial intelligence design decision-making model was proposed. Based on artificial intelligence thinking, a design history scheme dataset based on product modeling semantics was built. After the semantic annotation of this design dataset, ResNet algorithm was applied to train the data set continuously to improve the accuracy of design decisions, which could transfer the general design decision problem into the image semantic recognition problem of the design schemes, so as to maximize the influence of decision-maker's decision preferences. The validity of ResNet artificial intelligence design decision model was verified by the example of crane design decision making experiment, which proved the feasibility and rationality of the model.