The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer path analysis) method and CAE (Computer-Aided Engineering) method to analyze the road noise problem has the problems of complex transfer path, difficult acquisition of modeling parameters, long duration and high cost. Therefore, based on the road noise hierarchy constructed according to the road noise transmission path, the LSTM (Long Short-Term Memory) network is introduced to establish a data-driven prediction model, which effectively avoids the defects of the TPA method and CAE in analyzing road noise problems. Based on the LSTM prediction model, the AFW (adaptive feature weight) method is introduced to improve the model’s attention to the key features in the input data and finally improve the accuracy and robustness of the road noise prediction model. The results show that the accuracy (RMSE = 1.74 (dB)) and generalization ability (MAE = 2.60 (dB), $R^2$ = 0.924) of the AFW-LSTM model are better than other models.

Based on the design and construction data of the Nanjing Yangtze River Tunnel project, a 3D information inquiry system which adopted the ArcGIS Engine techniques was developed. We proposed a classification modeling method which is based on whether or not query the attribute information of the 3D models. We used ArcGIS Engine 3D module to create MultiPatch models so that we can query easily about the attribute, and the other models were generated using 3D modeling softwares. We can change view angles by switching 2D and 3D visual windows to express the plane and spatial relationship of objects at any time. The problem that the query function of the past 3D system was weak was solved by developing the 3D query function of ArcGIS Engine.