2results about How to "Achieve real-time tracking" patented technology

This invention relates to a markerless real-time registration and tracking method and system for mandibular surgery. The method includes the following steps: the patient performs multiple opening and closing movements of the upper and lower jaws while the robotic arm is fixed, and the system synchronously records the changes in the angles of each joint of the robotic arm; based on the forward kinematics of the robotic arm, the continuous motion trajectory of the distal end of the jaw in space is calculated to form an actual jawbone trajectory point cloud; a preoperative image model is formed using the actual jawbone trajectory point cloud, a simulated jawbone trajectory is constructed in the preoperative image model, and the actual trajectory is aligned using the ICP registration algorithm to achieve automatic registration; the system establishes a mapping between angle changes and the spatial posture of the jawbone to achieve dynamic tracking and visual navigation during surgery. This invention solves the problems of large errors, high complexity, many limitations, and low efficiency in preoperative image data registration technology during maxillofacial surgery, avoids the need for preoperative implantation of markers, and achieves high-precision registration, high automation, and strong real-time performance under natural conditions.

The application discloses a big data-based intelligent education guidance evaluation system and method, belongs to the technical field of intelligent education and big data analysis, and comprises a multi-source heterogeneous education data acquisition module, a data preprocessing and feature fusion module, a multi-dimensional capacity evaluation module, a learning behavior sequence analysis module, an individualized guidance recommendation module and a multi-role visual display module. The method integrates multi-source data through a time sequence self-adaptive alignment algorithm, realizes five-dimensional comprehensive capacity evaluation by adopting a subjective and objective fusion weighting algorithm and a double-flow attention knowledge tracking algorithm, performs learning behavior time sequence modeling by using a key behavior node identification algorithm, and generates an individualized learning path by a cognitive load perception path optimization algorithm. The application solves the problems of single evaluation dimension, fragmented data acquisition and lack of data support for individualized guidance in the existing education evaluation system.