6results about How to "Accurate spatial positioning" patented technology

The application discloses a hydrogen-oxygen mutual string early warning method and system based on an intelligent membrane and a sensing bipolar plate, direct sensing of mechanical stress by the intelligent membrane realizes earliest early warning of membrane damage, early warning time is tens to hundreds of hours earlier than that of a traditional electrochemical monitoring method, and sufficient time window is provided for taking protective measures. Through distributed measurement of the sensing bipolar plate, fault positioning accuracy is improved from a stack level to a centimeter level, precise spatial positioning is realized, and maintenance cost is greatly reduced. Through multi-modal data fusion, limitations of single signal monitoring are overcome, normal working condition fluctuations and real fault precursors are effectively distinguished, based on precise positioning and early warning, maintenance of the fuel cell system is changed from regular maintenance or post-failure maintenance to state-based predictive maintenance, maintenance resource allocation is optimized, and system availability and full life cycle economy are improved.

This invention discloses a method and system for machining curved workpieces. The machining method includes the following steps: setting theoretical three-dimensional coordinate information of the curved workpiece; setting a positioning base point, and ensuring that the contour corresponding to the theoretical three-dimensional coordinate information of the curved workpiece continuously passes through the positioning base point during movement; acquiring first target two-dimensional coordinate information and second target two-dimensional coordinate information; acquiring actual three-dimensional coordinate information; spatially positioning the curved workpiece; and machining the curved workpiece. This invention ensures that the contour corresponding to the theoretical three-dimensional coordinate information of the curved workpiece continuously passes through the positioning base point during movement, acquiring the actual three-dimensional coordinate information of the curved workpiece. This allows a multi-axis motion mechanism to quickly and accurately reposition the curved workpiece spatially based on the actual three-dimensional coordinate information, improving the positioning accuracy of the curved workpiece.

This invention belongs to the fields of cultural relic protection and computer vision technology. Addressing the problems of low efficiency, poor accuracy, and lack of spatial positioning in traditional methods for detecting defects in ancient city walls, this invention discloses an intelligent detection method and system for surface defects of ancient city walls based on 3D reconstruction. The method includes: processing the texture information of a loaded 3D reconstructed model of the ancient city wall using a multi-strategy texture restoration method; analyzing the model's geometric structure to determine the wall's orientation and dimensional parameters; generating virtual camera positions based on geometric features and a meshed serpentine scanning strategy; rendering RGB and depth images at each virtual camera position; performing defect detection using a YOLO model; mapping the 2D detection results to 3D space to generate 3D defect marker color blocks; and fusing multi-viewpoint results to output the detection results of surface defects on the ancient city wall. This invention achieves efficient, accurate, and automated detection of defects in ancient city walls, providing effective technical support for cultural relic protection.

The application discloses a device and a method for correcting visual errors of camera observation of particle sedimentation process, comprising an experimental setting module, an image acquisition module, a data processing module, a size correction module and the like, through the experimental setting module and the image acquisition module, only a ruler needs to be set and an image is shot, and the error correction can be completed by combining simple analysis of the data processing module, without complex equipment or tedious operation, so that the experimental implementation difficulty and the requirement for professional skills of the operator are significantly reduced; moreover, the whole device is easy to build, low in cost, and common equipment of a ruler and a camera is used, expensive instruments such as high-precision optical sensors are avoided, resource utilization is further optimized in combination with a modular design, experimental cost is reduced, and the device has higher economy and popularization and application value.

The application discloses a highway pavement damage self-recognition method and system based on deep learning, and relates to the technical field of road damage detection. The method comprises the following steps: continuously collecting images along the highway to construct an original pavement image sequence; performing multi-scale feature analysis on the sequence to draw a feature map; traversing the feature map to detect and generate a candidate damage area, and obtaining an initial recognition result through bidirectional analysis; constructing a pavement three-dimensional space based on three-dimensional mapping of the image sequence, synchronously positioning the result, and determining the damage spatial position; and generating a pavement condition report through position information inversion verification. The technical problem that the existing pavement damage detection is easily affected by light, noise and scale changes in the continuous collection scene, resulting in missed detection and false detection of the damage area, and cannot accurately position, is solved, the technical effects of improving the accuracy of damage recognition through multi-scale feature analysis and bidirectional verification of the candidate area, and realizing accurate positioning and automatic evaluation of the damage through three-dimensional space mapping and inversion verification, are achieved.