2results about How to "Avoid engineering accidents" patented technology

The application provides a deep-sea mining hydraulic lifting pipeline concentration monitoring method and system, and belongs to the field of deep-sea mineral resource development and detection. The application adopts indoor model experiment data and a small amount of field measurement data to construct a transfer learning neural network model, fuses a multi-scale convolution network, a multi-head attention mechanism and a transfer learning algorithm to form a full closed-loop system from indoor laboratory pre-training to deep-sea online precise concentration measurement, so that the purpose of significantly improving real-time monitoring accuracy is achieved. The deep-sea mining hydraulic lifting pipeline concentration monitoring system comprises a source domain data construction and model pre-training subsystem for constructing a land source domain data set and generating pre-training model parameters, and a target domain data acquisition and online transfer monitoring subsystem for acquiring deep-sea target domain data and loading pre-training parameters to perform online monitoring.

The invention discloses a Railway engineering reinforcement cage quality detection method based on Reighting fusion. The method comprises the following steps: calibrating a camera based on Reighting fusion; original images of the railway engineering reinforcement cage are obtained through a camera array and spliced; classifying and marking various types of reinforcing steel bar images in the reinforcing steel bar framework; performing instance segmentation learning based on an artificial intelligence algorithm, and generating a weight file; collecting railway engineering reinforcement cage production and processing image data; reinforcing steel bar framework data in the image are recognized through artificial intelligence; analyzing and extracting steel bar image data, and comparing with design to generate a detection result; and receiving and displaying detection result data through the platform. Geometric information identification is performed on the reinforcement cage based on a deep learning technology, high-precision and high-efficiency non-contact automatic reinforcement cage quality detection is realized through matching with design information, and data accuracy and operation safety are remarkably improved.