385 results about "Point projection" patented technology

The invention relates to the technical field of computer vision, in particular to a point projection type three-dimensional reconstruction and segmentation method and system based on semi-Gaussian pruning. The method comprises the following steps: respectively obtaining an SFM point cloud and a consistency label mask of a cross-view label based on an obtained multi-view image; initializing the obtained SFM point cloud into an identity semi-Gaussian point cloud, and performing rendering optimization by using a differentiable renderer; densifying the initial sparse point cloud by using a localized semi-Gaussian point management method, and identifying a local error region for resetting and repairing; using the obtained consistency label mask to supervise Gaussian identity feature learning by using cross entropy loss, and using unsupervised 3D regularization loss to force spatially adjacent gauss to maintain identity consistency; according to the method, the identity coding semi-Gaussian kernel method is adopted, the inherent representation fuzziness of a single opacity formula is eliminated, and the positive influence on the identity coding precision is generated.

The invention provides a laser and inertia fusion point cloud map construction method and equipment. Mobile laser point cloud data and inertial measurement data which are subjected to hardware time synchronization are obtained through a laser radar and an inertial measurement unit sensor; performing motion distortion correction on the laser point cloud in one frame according to inertial measurement data; projecting the laser observation point after distortion correction to a map coordinate system for feature association according to the position and attitude obtained by inertial measurement data integration, and calculating the position and attitude of a carrier by considering the laser positioning degradation condition; dividing the point cloud sub-graphs according to the time span, constructing the relative pose constraint between the sub-graphs, and optimizing the poses of the sub-graphs to obtain the high-precision point cloud map. The three-dimensional sparse sampling of the environment is directly obtained through the laser radar, the discrete inertia pose is corrected by using the continuous time model to obtain more accurate point cloud distortion correction, the carrier pose is updated by considering laser degradation, and the robustness and precision of point cloud mapping in the moving process are improved.

The invention belongs to the field of three-dimensional surveying and mapping, and particularly relates to a multi-precision three-dimensional surveying and mapping data fusion method based on dynamic modeling, which comprises the following steps of: assigning low-level semantic tags to LiDAR point cloud geometric features, and assigning high-level semantic tags to optical image texture features; defining a semantic tree structure, and establishing a cross-scale semantic association initial anchor point; constructing a cross-modal graph structure, projecting LiDAR point cloud nodes to optical image neighborhood superpixel nodes, and connecting and aggregating multi-scale semantic features; inputting geometric and image residual texture features by using a U-Net generator and outputting virtual textures; geometry and texture feature fusion and semantic and geometry collaborative optimization are realized through gating weighted feature fusion. According to the method, the problem of inconsistent multi-precision data semantic expression is systematically solved, semantic consistency is improved, texture deficiency is filled, and dynamic balance between geometric fidelity and semantic enrichment is realized.

The invention discloses a product model detection method and system based on welding spots. The method comprises the steps that a welding spot identification mark is obtained, a welding spot file matched with the welding spot identification mark is identified from a product model, and the welding spot file is a data file comprising a welding spot model; identifying a part model having an interference relationship with the welding spot model based on the position relationship of the welding spot file in the product model; taking an intersection surface obtained by intersecting the part models as a welding surface of the part models, projecting the welding spot model onto the welding surface to obtain a welding spot projection position, and detecting a shortest spot edge distance from the welding spot projection position to a welding surface boundary; based on the shortest point edge distance, the width of the welding face is determined through an auxiliary face intersection method; and detecting whether the product model is qualified or not based on the shortest point edge distance and / or the width of the welding surface. The technical problems that in the prior art, welding spot detection mainly depends on manual estimation, so that subjectivity is high, efficiency is low, and missing detection is prone to occurring are solved.

The invention relates to the technical field of multi-sensor fusion, in particular to an automatic calibration method and calibration device for laser radar-camera external parameters, and the method comprises the steps: synchronously collecting the point cloud data and image data of a road scene based on a vehicle-mounted laser radar and a camera; performing semantic segmentation on the image data to generate a semantic mask set of each image; based on the point cloud data, geometric attributes of a road scene are extracted from the vectorized map, a laser radar coordinate system of the vehicle-mounted laser radar and points in the vectorized map are projected into the image in combination with the semantic mask set, and a target consistency function is constructed; and adjusting the external parameter matrix based on the target consistency function until the target consistency function reaches a maximum value, and completing automatic calibration. Therefore, the problems that the calibration precision is insufficient and the high-precision perception requirement of automatic driving is difficult to meet due to the fact that related technologies depend on a large amount of labeled data or a specific environment, the generalization ability is poor, and a high-precision map and road prior information cannot be combined are solved.

The invention discloses a live pig body size and weight measuring method based on RGB-D. The method comprises the following steps: S1, obtaining a left vision rotation translation matrix and a right vision rotation translation matrix; s2, acquiring a three-view RGB image and a depth image of a single pig; s3, acquiring a body scale measurement key point; s4, acquiring a left viewpoint cloud, a right viewpoint cloud and an overlook point cloud, and performing point cloud registration to obtain a complete pig point cloud C1; projecting the body size measurement key points to the complete pig point cloud C1; s5, obtaining a final complete pig point cloud C2; s6, calculating the body length, the body width, the body height and the abdominal girth of the pig by using the final complete pig point cloud C2, and forming body size data; and S7, substituting the body size data of the pig into the multiple linear regression prediction model to predict the weight. According to the method for measuring the body size and the weight of the live pig, the accuracy and the stability of extracting the position of the body size measuring point are improved, and the accuracy of the body size and the weight measuring result of the pig is improved.

The embodiment of the invention provides an indoor vehicle positioning method and device and a storage medium, and the method comprises the steps: obtaining a scene image corresponding to a target vehicle at a current moment when it is detected that the target vehicle drives into a positioning region of a target scene; edge point detection is carried out on the scene image to determine a target image area corresponding to a target calibration lattice, and the target calibration lattice is a calibration lattice assembled on the target vehicle. And extracting pixel coordinates corresponding to each pixel point in the target image area from the scene image. And according to a pre-calibrated internal reference matrix of the camera, the pixel coordinate corresponding to each pixel and the height from the target calibration grid to the ground, determining the camera coordinate of each pixel point projected to the camera coordinate system. And according to the pre-calibrated external parameter matrix of the camera, converting the camera coordinate corresponding to each pixel point into the world coordinate in the world coordinate system to determine the position information of the target vehicle in the positioning area, so that the vehicle position can be accurately positioned in the indoor scene.

An apparatus for generation of a 3D representation of a scene includes a memory for storing a plurality of images depicting a scene; and processing circuitry in communication with the memory. The processing circuitry is configured to generate a depth map for the plurality of input images depicting a scene and unproject the depth map to a three-dimensional (3D) point cloud. The processing circuitry is also configured to project a plurality of 3D points from the 3D point cloud to a plurality of two dimensional (2D) planes and extract a plurality of triplane features from the plurality of 2D planes. The processing circuitry is further configured to generate a 3D representation of the scene based on combining the plurality of triplane features and a plurality of image features extracted from the plurality of input images.

The invention discloses a weld ROI segmentation method based on active and passive vision fusion. The method comprises the following steps: synchronously acquiring a two-dimensional image and a three-dimensional point cloud of the surface of a welded workpiece; inputting the trained deep learning network, and outputting a weld joint candidate region and a pixel-level mask graph thereof; establishing a cross-modal mapping relation through geometric calibration, and projecting the three-dimensional point cloud to a two-dimensional image to complete registration and fusion; cutting from the three-dimensional point cloud by using a cross-modal mapping relation to obtain a candidate point cloud region; generating a gray matrix for ray odd-even discrimination according to the pixel-level mask pattern; and each point of the candidate point cloud is projected to a pixel coordinate point by point, ray odd-even discrimination is carried out on the projection points according to the gray matrix, the points conforming to an odd-even rule are determined as welding line points and are aggregated into a welding line ROI point cloud, and the welding line ROI point cloud is output as a segmentation result. According to the method, the adaptive capacity to the unstructured welding seam is effectively improved, the point cloud processing complexity is reduced, and good real-time performance and robustness are achieved.

The invention discloses a pixel-level accurate texture generation method based on a multi-view artistic work. The method comprises the following steps: inputting an image file; performing multi-view projection on the 3d model for a plurality of views corresponding to the plane image to construct a texture space; aiming at coexisting points on the surface of the 3d model in the multiple visual angles, calculating a dot product of each visual angle direction and a normal of the surface of the 3d model so as to construct an initial texture map corresponding to the 3d model; projecting all contour points of the 3d model into a texture space to obtain corresponding texture information, and comparing the texture information with contour points contained in the initial texture map to construct a mask area; generating a shielding texture map corresponding to the mask area through a diffusion model; and carrying out rendering processing on the 3d model based on the initial texture map and the sheltered texture map so as to output a high-quality model with texture information. The invention further provides a pixel-level accurate texture generation device. According to the method provided by the invention, the high-quality texture can be generated for the grid, and meanwhile, the pixel-level consistency with the reference art is ensured.

An electronic device for performing calibration is provided. The electronic device includes a communication interface, memory storing one or more computer programs, and one or more processors communicatively coupled to the communication interface and the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors individually or collectively, cause the electronic device to obtain, via the communication interface, a first image of a user captured by a first camera, and a second image of the user captured by a second camera, extract first joint feature points, which are two-dimensional (2D) position coordinate values of joints of the user, from the first image, and second joint feature points, which are 2D position coordinate values of the joints, from the second image, obtain three-dimensional (3D) joint feature points of the joints by lifting the extracted first joint feature points to 3D position coordinate values, obtain a projection relationship for projecting the 3D joint feature points onto the 2D position coordinate values of the second joint feature points, and perform camera calibration by predicting a positional relationship between the first camera and the second camera based on the obtained projection relationship.

The invention discloses a dual-path non-cooperative spacecraft pose estimation method based on uncertainty perception, and belongs to the technical field of spacecraft pose estimation. According to the method, a dual-path prediction framework based on a shared backbone network is constructed and comprises a geometric reasoning path and a global context sensing path. In the training stage, prediction results of the two paths are aligned through a geometric consistency loss function, and joint optimization is carried out by combining multi-task losses such as key point regression, classification, rotation and translation regression and the like. In the inference stage, the pose estimation result is adaptively fused according to the predicted variance output by each path, when the variance is low, the output of the global context sensing path is directly adopted, otherwise, the key point projections of the two paths are combined, and the final pose is recovered. The method has higher estimation precision, robustness and interpretability in complex space environments such as shielding, illumination variation and large-scale variation, and provides reliable visual navigation support for on-orbit autonomous tasks.

The invention innovatively provides a laser cladding processing device control system which comprises the following steps: a target extraction step: collecting to-be-processed workpiece image data acquired by a visual sensor to obtain a total data set, and performing similarity analysis on the total data set in a rough classification strategy to obtain a target detection data set; an image analysis step: training the target detection data set to obtain a plurality of to-be-processed workpiece target detection models, obtaining a dimension reduction plane through point projection according to the to-be-processed workpiece target detection models, and constructing the dimension reduction plane into a plurality of grid matrixes; a defect extraction step: according to the grid matrix, a change grid is obtained through change ratio analysis in a fine analysis strategy, and the change grid reflects a change point of the workpiece to be machined; and a defect judgment step: obtaining the change grid and judging whether the workpiece to be machined has defects or not through a state analysis strategy.

The invention discloses a building detail extraction method and device based on oblique photography and composite modal features, and the method comprises the steps: calculating a tilt parameter of a midpoint of a point cloud model based on the point cloud model of oblique photography data of a target building, and converting the tilt parameter into a color parameter; projecting points on the point cloud model to a target building view area of a two-dimensional plane to obtain a plurality of superpixel blocks, inputting features of the superpixel blocks into a variational auto-encoder, and performing optimization to obtain encoder optimization weight data; and inputting the features of the super-pixel blocks into an encoder to obtain corresponding optimized latent variables, clustering the super-pixel blocks based on the optimized latent variables, and mapping a clustering result to the three-dimensional model of the target building to obtain details of the target building. By adopting the technical scheme, the three-dimensional features and the color features are compounded, and the structure details and the distribution characteristics of the complex building are captured by utilizing the relevance between the features; an encoder of a pre-trained variational auto-encoder is used for aggregation, so that the accuracy of an aggregation result is improved.

The invention relates to the technical field of robots, and discloses an obstacle sensing method and a storage medium, and the method comprises the steps: obtaining image data of an environment in front of a mobile robot and three-dimensional point cloud data generated through laser radar scanning; pixel-level semantic segmentation is carried out on the image data, and a semantic segmentation map with each pixel having a semantic tag is generated; projecting each three-dimensional point in the three-dimensional point cloud data to a semantic segmentation map based on a calibration relationship between a camera and a laser radar, and generating a semantic point cloud with a semantic tag; for a detection blind area of the laser radar in front of the robot, generating a virtual three-dimensional point cloud in the detection blind area by using obstacle semantic information corresponding to an image area of the detection blind area in the semantic segmentation map; and determining the obstacle point cloud based on the three-dimensional point cloud of which the semantic label is the obstacle category in the semantic point cloud and the virtual three-dimensional point cloud. According to the invention, the obstacle detection precision in a complex environment can be improved.

The invention provides a power equipment three-dimensional modeling and route planning method based on laser point cloud and image fusion. The method comprises the steps that an original point cloud sequence and an original image sequence collected by an unmanned aerial vehicle and pose information of the unmanned aerial vehicle are acquired; preprocessing the original point cloud sequence to generate a clean point cloud sequence; preprocessing the original image sequence to generate an enhanced image sequence; performing rough matching on the clean point cloud sequence according to the pose information to generate a panoramic scene point cloud; projecting points of the panoramic scene point cloud to the enhanced image sequence; mapping the pixel color to the panoramic scene point cloud by applying an L-M algorithm to generate a color point cloud; inputting the enhanced image sequence into a pre-trained SegFormer semantic segmentation model to obtain semantic probability distribution of each image; mapping the color point cloud to a corresponding image according to the semantic probability distribution to generate a semantic point cloud, the semantic point cloud including color information and semantic information; and generating a three-dimensional scene model according to the semantic point cloud to output an inspection route.

The invention relates to a 3D target detection method and device for adaptively optimizing BEV features, computer equipment and a storable medium. The 3D target detection method for adaptively optimizing the BEV features comprises the following steps: acquiring all-round view image data from a Nuscenes data set; performing feature extraction on the look-around image by using a feature extraction backbone network to obtain image features; raising the dimension of the image features to a BEV space, and compressing a z-axis to obtain BEV features; n 3D query boxes to be queried are uniformly arranged in a BEV space, candidate boxes are projected to a BEV plane to obtain a 2D rotating frame, BEV features in the 2D rotating frame are extracted as prior features, the center points of the query boxes are used as query points, camera parameters are utilized to project the query points to an image feature plane for interpolation to obtain corresponding query features, and the prior features and the query features are fused on a channel level; the method comprises the following steps of: predicting a perd box and a label score by using fusion features, manually generating optimization points at the centers of six surfaces of k pred boxes before the score, adding offset, optimizing and updating BEV features by using the corresponding fusion features, sending the optimized BEV features into a positioning detection head and a classification detection head, and respectively predicting a 3D object bounding box and an object category. The method has the advantage that the accuracy of 3D target detection of the look-around image is greatly improved.

The present invention discloses a multi-point projection device for improving the accuracy of laser ranging and its measurement method, belonging to the technical field of laser ranging; it first emits at least three laser beams in sequence through a laser emission unit, and the laser irradiates the surface of the object to be measured at different angles in sequence through an optical scanning unit, and after being reflected by the surface of the object to be measured, it is received by a laser receiving unit through an optical receiving unit; the data processing unit obtains the ranging value of each laser beam according to the received data, and calculates the spatial coordinate data of each measurement point by combining the emission angle of each beam; constructs a Hermite matrix from the obtained spatial coordinate data to obtain the minimum eigenvalue and eigenvector, and obtains the coefficients of the plane equation according to the minimum eigenvalue and eigenvector; substitutes the obtained coefficients of the plane equation into the projection distance formula from a point to a plane to obtain the actual distance between the laser ranging device and the object to be measured; maximally eliminates external errors and improves the ranging accuracy.

The invention discloses a lunar surface target three-dimensional point cloud detection and positioning method and system based on semantic information, relates to the technical field of three-dimensional object detection, and receives three-dimensional point cloud data and a two-dimensional image detection result, the two-dimensional image detection result is obtained by detecting acquired image data based on a pre-established two-dimensional target detection model, performing ground segmentation on the three-dimensional point cloud data to obtain non-ground point cloud data, mapping the non-ground point cloud data into a two-dimensional grid with a preset size to obtain a two-dimensional clustering result, and performing clustering on the non-ground point cloud data to obtain a clustering result; mapping the two-dimensional clustering result to the three-dimensional point cloud data to obtain a plurality of point cloud clusters; the points of each point cloud cluster are projected into an image coordinate system, dynamic weighted scores are calculated based on the number and confidence of the projection points of each point cloud cluster falling into the detection frame and the geometrical characteristics of the point cloud clusters, the point cloud cluster with the highest dynamic weighted score is selected for distance calculation, and the distance of the point cloud cluster with the highest dynamic weighted score is calculated; and obtaining a lunar surface target three-dimensional point cloud detection and positioning result.

The invention provides a method for obtaining a relative position relation between linear welding seams and related equipment, and the method comprises the steps: obtaining a projection distance from an end point of a current linear welding seam to a reference linear welding seam which is any welding seam in repeated linear welding seams corresponding to the current linear welding seam; the projection distance comprises a starting point projection distance and an ending point projection distance; and determining a relative position relationship between the current linear weld and the reference linear weld based on the projection distance. The projection distances from the starting point and the end point of the current linear welding seam to the reference linear welding seam are obtained, the relative position relation between the current linear welding seam and the reference linear welding seam is determined through the obtained projection distances, and the purpose of rapidly and accurately obtaining the relative position relation between the welding seams is achieved.

The invention provides a method and system for judging a boss according to the distance between welding spots of a welding edge, and the method comprises the steps: detecting the welding spots having an interference relation with a to-be-detected boss part in a digital model, and extracting all boundary curves of the outer surface of the part; selecting an outer contour line; the outer contour line is segmented at the projection of the welding spot to form a dispersion curve; selecting one welding spot corresponding to two welding spots in the model; the gap value h'of the maximum gap between the projection curves is measured and calculated, the projection curves are intersected and then disassembled, two curves are obtained, the sizes d1 and d2 of the two curves are measured respectively, and boss detection is completed. The boss detection method can automatically achieve boss detection judgment, detection is more accurate, the risk is reduced, and efficiency is improved.

The invention provides an automatic marking method for a target GCP. The automatic marking method comprises the following steps: establishing a rough mesh model based on an aerial photo set of a specified area; determining a marking point of the target GCP on a reference picture; projecting the marking point to the surface of the rough mesh model and intercepting a first projection surface element; searching and determining the optimal position and the optimal orientation of the first projection surface element based on the correlation of images in a re-projection window obtained by re-projecting the first projection surface element to the plurality of evaluation pictures; and projecting an image in a reference window on the reference picture to the first projection surface element with the optimal position and the optimal orientation to obtain a second projection surface element, and searching and determining a corresponding imaging position of the target GCP on the to-be-marked picture based on an expected image obtained by re-projecting the second projection surface element to the to-be-marked picture. According to the method provided by the invention, the credibility and accuracy of automatically marking the target GCP on the to-be-marked picture are effectively improved.

The invention discloses a non-structural surface grid assembling method and device, equipment and a medium, and relates to the technical field of computer aided design, and the method comprises the steps: repairing an initial CAD model, and building a watertight grid data structure to obtain a virtual surface; dividing the target area of the repaired CAD model into a plurality of closed areas according to the connection relation of the virtual edge data on the ring on the virtual surface; projecting the three-dimensional ring point of each closed area to a target plane, performing two-dimensional calculation on each projection point in the target plane to obtain a two-dimensional ring point corresponding to each projection point so as to generate an initial boundary grid, and performing inverse mapping processing on the initial boundary grid by using a mapping relation among the two-dimensional ring points, the corresponding projection points and the three-dimensional ring points so as to generate an initial boundary grid; obtaining an initial three-dimensional grid; and performing two-dimensional mapping processing on the optimized initial three-dimensional grid to obtain an optimized two-dimensional grid, and inversely mapping the optimized two-dimensional grid back to a three-dimensional space to obtain a three-dimensional unstructured grid. A three-dimensional unstructured grid may be generated without a complete geometric entity.

The invention relates to a multi-form central point fused cloud particle detection method, which mainly comprises the following steps of: firstly, performing morphological processing of different levels on a cloud particle image to generate a plurality of morphological processing results; then, for each piece of morphologically processed image data, extracting different types of center points, including a geometric center point, a minimum circumscribed rectangle center point, a maximum connected region center point, a contour centroid and a minimum circumscribed circle center point; then abnormal center point filtering and fusion are carried out on center points in each maximum connected area, and a final target center point position is obtained; and projecting the central points onto the original image data to generate a predetermined number of anchor frames, establishing a deep learning detection model to predict the anchor frames, and screening the anchor frames to obtain a detection result. According to the method, the cloud particle region detection positioning precision is improved, and a reliable basis is provided for cloud microphysical parameter inversion.

The invention belongs to the technical field of unmanned aerial vehicle positioning, and particularly discloses an unmanned aerial vehicle target positioning system, which comprises an image acquisition module arranged at the bottom of an unmanned aerial vehicle and used for acquiring ground image information; the target identification module is used for identifying a target point in the ground image information; the coordinate conversion module is used for converting the GPS coordinates of the unmanned aerial vehicle into UTM coordinates; converting the projection coordinate of the target point into a geographic coordinate; the parameter calculation module is used for calculating a ground distance corresponding to a unit pixel; and based on the yaw angle of the unmanned aerial vehicle and the position of the target pixel point relative to the image center point, calculating the coordinate of the target point in the projection coordinate system by using the ground distance corresponding to the unit pixel. According to the method, the types and the number of sensors are simplified, only a basic visible light camera needs to be carried, the complexity and the cost of the system are reduced, image data can be rapidly processed through an optimization algorithm and an efficient data processing technology, and rapid recognition and positioning of a target are achieved.

The invention relates to a weighted adaptive anchor frame cloud particle detection method, which mainly comprises the following steps of: firstly, carrying out different morphological processing on a cloud particle image, extracting multiple types of central points and carrying out first fusion; then, according to the expansion corrosion degree of morphological processing, different weights are given to each fused center point, the weight given to the center point with the larger expansion degree is lower, and the weight given to the center point with the larger corrosion degree is higher; projecting the weighted central points back to the original image, and performing secondary central point fusion and weight updating; and finally, adjusting the size of an anchor frame according to the weight of the central point, generating the anchor frame adaptive to the size of cloud particles, and establishing a deep learning model for detection. According to the method, through adaptive weight and anchor frame size adjustment, the precision of cloud particle target detection is remarkably improved, and the use efficiency of the anchor frame in the detection process is optimized.

The invention relates to a teaching-free vertical weld joint identification method, medium and equipment, and the method comprises the steps: collecting the surface point cloud of a to-be-welded workpiece, determining a bottom plane equation according to the surface point cloud of the to-be-welded workpiece to obtain upper surface feature points, carrying out the linear fitting of the upper surface feature points to obtain a plurality of linear equations, and obtaining the inner points of each linear equation; constructing an optimized feature point cloud, carrying out vector analysis on the optimized feature point cloud to obtain a plurality of welding seam end points, obtaining intersection points of points in each linear equation, determining a welding seam type according to the welding seam end points, projecting the intersection points of the points in each linear equation to a bottom plane equation to obtain a plurality of vertical welding seams, and obtaining a plurality of vertical welding seams according to the intersection points of the points in each linear equation and the welding seam end points. And determining the orientation of each vertical welding seam to complete welding seam identification. The problems that in the prior art, computing resource consumption is high, adaptability and generalization ability in practical application are insufficient, and recognition accuracy and recognition efficiency are low are solved.

The invention discloses an unmanned aerial vehicle sensing house safety appraisal management system and method based on BIM, and relates to the technical field of data processing. The method comprises the following steps: training a U-Net + + model, and based on a house three-dimensional BIM model, generating a preset track by combining a space grid sampling algorithm with a convex hull optimization algorithm; the unmanned aerial vehicle carries the dual-mode sensing pod to fly along a preset flight path, and image virtual columns and metadata are collected; de-noising enhancement processing is carried out on the image sequence, precision alignment is realized by utilizing BIM feature point projection to guide SIFT feature bidirectional matching, after Euclidean distance threshold verification, a pre-trained U-Net + + model is input to generate a crack binary mask, and the crack binary mask is converted into a physical crack parameter; the inclination angle of the wall is calculated through the SfM point cloud and ICP registration technology; integrating crack parameters, inclination angles and BIM attributes, and automatically triggering graded early warning through a risk rule base; the full-process automation from data acquisition, crack identification to risk assessment is realized, the detection precision and efficiency are remarkably improved, and the high-risk omission ratio is effectively reduced.

The invention discloses a partition fitting method for a free hyperboloid curtain wall skin, and aims to solve the problem that an optimal result of fitting sampling cannot be obtained by an existing hyperboloid curtain wall skin fitting method. The method comprises the following steps: setting arc edge sampling points on two arc edges II of an initial plate model, projecting a sampling line midpoint to the model to generate a sampling projection point, generating a first plane by the arc edge sampling points and the sampling projection point, projecting two arc edges I of the model to the first plane to generate an arc projection line and an arc edge projection line, obtaining a midpoint of a connecting line of corresponding nodes on the two projection lines and generating a middle curve, calculating a distance value from the nodes of the two projection lines to the middle curve, locking the positions of the arc edge sampling point and the middle curve when the distance value is minimum, forming a pressure arc line by the connecting line of two end points of the middle curve and the midpoint, and stretching to form a cylinder, and enabling the model to coincide with the normal of the side wall of the cylinder, projecting the arc edge of the model to the cylinder to form a single-curved-surface plate model, and outputting a pressure arc line, an angular point deviation value and an arc edge midpoint deviation value.

The invention discloses an automobile front anti-collision beam radian detection system, and relates to the technical field of anti-collision beam detection. The arc detection device comprises a detection table, a clamping assembly, a positioning assembly and a plurality of arc detection assemblies. The front anti-collision beam is reliably fixed to the clamping table through the clamping air cylinder and the rubber head, and automatic in-place and center correction are achieved through the conductive contact and the positioning conductive inclined block. When the position deviates, judgment is carried out through the resistance value difference, and early warning is given out. The arc measuring assembly is composed of image sensors, an imaging plate, a light shield and an adjusting fluted disc mechanism, the distance between the two sensors can be automatically adjusted according to detection requirements, multi-point projection imaging is conducted on a front anti-collision beam calibration point, and an overall radian curve is calculated. Meanwhile, the system is provided with a centrifugal impeller active heat dissipation mechanism, thermal noise of the image sensor is reduced, and detection stability is guaranteed.