面向调线调坡的点云大数据分析及深度模型研究

已建成的隧道与原始的设计隧道之间的偏差信息对于地铁线路的安全调整非常重要。然而，目前还没有明确的数学公式能够准确地描述和度量这个偏差。目前主流的做法是通过人工测量具有相同间隔的截面的侵限值，并对这些侵限值进行累加求和，最终得到该偏差，这种方式存在误差大、耗时、成本高等缺点。为了解决这些问题，提出了一种新的基于深度神经网络的偏差表示方法，其能够基于点云大数据学习到设计线路的参数与侵限值之间的内在联系，进而预测出能够使得侵限值的和最小的参数，这些参数可以被用来辅助地铁线路的安全调整。在一个采集于实际地铁工程中的数据集上的实验结果表明，该方法能快速地计算出合适的调线调坡方案，并且只需要很少的计算机内存资源。

Point cloud big data analysis and deep model research for line and slope fine-tuning

The deviation information between the completed tunnel and the originally designed tunnel is very important for the safety adjustment of metro lines. However, there is no clear mathematical formula that can be used to accurately describe and measure the deviation. At present, the mainstream approach is to measure the invasion value of each section with the same interval manually and then sum up these values to get the deviation. This method has the disadvantages of large error, time-consuming and high cost. To solve these problems, a novel deviation representation method based on deep neural network is proposed, which can learn the internal relationship between the parameters of the designed tunnel and the invasion values based on the point cloud data, and then predict the parameters that can make the sum of the invasion values minimum. These parameters can be used to assist the safety adjustment of metro lines. The experimental results on a data set collected from a real subway project show that the proposed method can quickly obtain the appropriate adjustment scheme of the lines and slopes with only a small amount of computer memory resources.