Quantum and deep learning combined method for adaptively identifying dangerous rock mass and finely representing holographic projection visualization

Abstract

The invention discloses a quantum and deep learning combined dangerous rock block adaptive identification and fine representation holographic projection visualization method, and belongs to the technical field of slope stability and geological disaster monitoring, and the method comprises the steps: obtaining slope point cloud data based on data collected by an unmanned plane; preprocessing the point cloud data to obtain a preprocessed data set; constructing a quantum random forest model and an environment factor self-adaptive algorithm, and training the quantum random forest model based on the preprocessed data set to obtain a dangerous rock block self-adaptive automatic identification model; obtaining fine representation information based on a quantum machine learning algorithm, a big data model and the automatic dangerous rock block recognition model; and performing personalized high-precision modeling on the fine representation information, and importing modeling data into a holographic projection model to generate a three-dimensional visual model. According to the method, the technical problem of automatically identifying dangerous rock blocks and fine features thereof on high and steep slopes is effectively solved.

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