749 results about "Model reconstruction" patented technology

The invention belongs to the technical field of three-coordinate measurement, and particularly discloses a three-coordinate measurement method based on incomplete curved surface fitting, which comprises the following steps: hierarchically extracting geometric features from incomplete point clouds, and dividing a workpiece to be measured into a plurality of ordered curved surfaces; adding a curved surface label to each point cloud data, and generating a plurality of ordered point cloud sets based on the curved surface labels; generating virtual points based on the geometric features to fill the missing region; reconstructing a three-dimensional sub-model of each ordered curved surface based on the ordered point cloud set, the virtual points and the edge point cloud; and fitting the three-dimensional sub-model to generate a continuous curved surface model, and obtaining workpiece measurement parameters based on the continuous curved surface model. By extracting geometric features, classifying point clouds and generating virtual points, the limitation that a traditional method depends on a prior model is broken through, unknown or non-standard geometry can be processed, global continuous modeling of incomplete point clouds is achieved through ordinal curved surface division, three-dimensional sub-model reconstruction and continuous curved surface fitting, and the accuracy of measurement parameters is ensured.

The invention discloses a three-dimensional stomatognathic model reconstruction system based on multi-modal data fusion. The system comprises a multi-modal data input unit, a template deformation reconstruction unit, a registration fusion unit, a multi-source data integration unit and an output unit. Through fusion processing of a CBCT image, an oral cavity vision measurement model and facial scanning data, a body deformation algorithm is adopted to couple biomechanical characteristics to realize craniojaw template deformation, and a non-rigid ICP algorithm is combined for dynamic regulation and control to realize facial template adaptation. A deep neural network is innovatively constructed to segment CBCT gingival data, the CBCT gingival data is fused with an oral cavity vision measurement model, and high-precision tooth reconstruction is realized by applying a differential geometry multi-scale curvature field segmentation and adversarial edge optimization technology. Through a composite registration strategy combining adaptive rigid registration and non-rigid registration, an occlusal plane constraint mechanism and an orbital curvature extreme point matching algorithm are innovatively introduced, finally, multi-source data high-precision registration fusion is realized, and a three-dimensional oral-jaw system model with anatomical structure integrity and clinical precision can be generated.

The invention relates to the technical field of three-dimensional model reconstruction, and discloses a three-dimensional model reconstruction method based on point cloud data processing, which comprises the following steps: step 1, point cloud data acquisition and multi-modal fusion: acquiring point cloud data through a laser radar, an RGB camera and multi-view three-dimensional reconstruction to obtain a point cloud set, aligning the point cloud data with an RGB image to obtain a point cloud image set; establishing a mapping relation between point cloud coordinates and image pixel coordinates through an internal parameter matrix and an external parameter matrix of the camera to obtain aligned multi-modal point cloud data; 2, preprocessing the point cloud data: performing cleaning and downsampling processing on the multi-modal point cloud data obtained in the step 1; by combining point cloud data obtained by a laser radar, an RGB camera and multi-view three-dimensional reconstruction, accurate alignment of different sensor data is realized, high-precision and complete multi-modal point cloud data is obtained, blind areas of all sensors are effectively supplemented, and the precision and reliability of a three-dimensional reconstruction result are improved.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention, which relates to the technical field of structure health monitoring, discloses a sensing data-based on-line intelligent detection system for the whole body and the periphery of a house, comprising: a distributed sensor module, which is composed of a fiber grating sensor array, an MEMS environment sensor and an image acquisition unit, the system is arranged in a house bearing structure, a peripheral foundation, a drainage system and a greening area. The edge computing node module is configured to preprocess sensor data, including noise filtering, temperature compensation and abnormal data marking; the dynamic topology communication network module adopts a LoRaMesh and 5G hybrid networking technology and supports node self-repairing and bandwidth self-adaptive distribution; and the cloud analysis platform module integrates BIM model reconstruction, multi-source data fusion analysis and early warning decision, and outputs structure deformation prediction, an energy consumption anomaly map and a disaster risk assessment report.

The invention discloses a scene-level synthetic point cloud enhanced semantic segmentation method and system based on a diffusion model, and aims to solve the problem of insufficient point cloud training samples in deep learning. Comprising the following steps: firstly, generating a high-quality panoramic image based on a text through a diffusion model; thirdly, performing depth prediction on the panoramic image by using a deep learning visual converter, and performing semantic segmentation labeling in combination with an SAM intelligent labeling method; then, mapping the depth information and the semantic label to the panoramic image by adopting a cylindrical projection method, and generating a three-dimensional point cloud containing space coordinates, RGB color values and the semantic label; and combining the generated synthetic point cloud with the real point cloud to train a deep learning model, thereby improving the precision of semantic segmentation. And finally, performing semantic segmentation prediction on a target scene through the trained model, generating a semantic tag, and importing a segmentation result into BIM software to complete three-dimensional model reconstruction, thereby assisting intelligent operation, maintenance and monitoring of a building.

The invention relates to the technical field of deep learning, and particularly provides a slope deformation real-time early warning algorithm and system based on deep reinforcement learning, and the method comprises the steps: generating a standardized state representation vector, and building a state quality evaluation index; receiving the state representation vector as input, constructing a dual-mode strategy architecture, generating a preliminary decision and confidence evaluation thereof, and forming a quantitative index of decision reliability; forming a final early warning instruction and a strategy updating suggestion, and generating a strategy performance evaluation report; continuously tracking the change trend of state feature distribution, constructing a strategy degradation early warning system, and triggering a model reconstruction mechanism; the updated policy network parameters are fed back to the dual-mode policy architecture, and the optimized feature extraction suggestions are fed back to the establishment of the state quality evaluation indexes. The system comprises an index establishment module, a report generation module and a reconstruction mechanism module. According to the slope deformation real-time early warning algorithm, a monitoring-decision-optimization end-to-end closed-loop control system is realized through a deep reinforcement learning framework.

The invention belongs to the technical field of robotics and computer vision, and relates to a multi-source information fusion outdoor inspection scene three-dimensional color geometric model reconstruction method, which comprises the following steps: (1) inertial measurement unit IMU internal reference calibration initialization, (2) laser radar and inertial measurement unit IMU combined external reference calibration, (3) state estimation, (4) using an i-Octree structure to store an updated map, and (5) performing multi-source information fusion. (5) carrying out loopback detection to solve the mileage drift problem; (6) carrying out data fusion of the laser radar and the color camera; and (7) carrying out 3D global positioning. The method has the advantages that the information of the laser radar and the information of the color camera are fused, the one-to-one mapping relation between each space point in the three-dimensional point cloud and a certain pixel point in the color image is established, visual information is more completely described, and the visual information is more accurately and accurately positioned by using a 3D global positioning method. And high-precision reconstruction of the three-dimensional color geometric model is realized.

The invention provides a CAD model reconstruction method and device for a topological optimization result, and relates to the technical field of computer modeling, and the method comprises the steps: carrying out grid preprocessing, carrying out component type grid segmentation, carrying out patch type grid segmentation, obtaining at least two sub-grid patches, carrying out parameterization on each sub-grid patch, and obtaining a final parameterization result. And performing uniform interpolation sampling based on the final parameterization result to obtain sampling points, generating a spline surface by using the sampling points, assembling the spline surface, and generating a reconstructed CAD model corresponding to the three-dimensional topological optimization fruit. The grid model reconstruction method has the advantages of being high in automation degree, high in realizability, easy to operate and the like, is not limited to a tubular topological optimization structure, has no obvious difference in effect when processing tubular and non-tubular model characteristics, has higher universality, can adapt to the diversity requirements of industrial structure optimization, and is suitable for large-scale popularization and application. And meanwhile, the reconstruction efficiency and the reconstruction effect from the triangular mesh model to the CAD model can be improved.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention relates to the technical field of electric digital data processing, in particular to a display process temperature management optimization system which comprises a data acquisition module, a dynamic modeling module, a cooperative compensation module and an early warning module. The data acquisition module acquires temperature data of a heating area through a multi-type temperature sensor array, and generates a temperature field matrix with a confidence label through multi-source verification; the dynamic modeling module receives the matrix, operates a plurality of machine learning algorithms in parallel to generate a thermal coupling strength distribution diagram, and outputs a region coupling coefficient and a confidence coefficient thermodynamic diagram; the cooperative compensation module calls knowledge base strategies in a layered mode according to the thermodynamic diagram and feeds back early warning signals when abnormity is detected. According to the system, a cross-module closed-loop control mechanism is formed through confidence label driving algorithm weight distribution, confidence thermodynamic diagram control strategy priority, abnormal signal triggering model reconstruction and knowledge base updating, and millisecond-level cooperative suppression of the thermal crosstalk effect of multiple heating areas is achieved.

The invention relates to the technical field of battery management, and discloses a forklift battery charging method and device, and the method comprises the steps: collecting the real-time multi-dimensional physical state data, including vibration data, of a forklift battery; dynamically reconstructing a multi-physical field coupling model matched with the current working condition in a preset model library according to the data; performing predictive multi-target optimization based on the model to determine an optimal charging equalization strategy considering charging time, energy loss, health attenuation and thermal risk; charging is executed according to the strategy, the deviation between physical response and model prediction is compared in real time, and online self-correction is conducted on model parameters. The system comprises a state acquisition module, a model reconstruction module, a strategy optimization module and a charging control and correction module. According to the method, the digital twin model capable of self-evolution is constructed and closed-loop feedback correction is carried out, so that the charging strategy can actively adapt to working condition changes caused by factors such as vibration and temperature, and safer and more efficient charging management of the forklift battery is realized.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention discloses an instance segmentation and model reconstruction method and system based on deep learning and synthetic point clouds, and the method comprises the steps: firstly, carrying out the sampling of a BIM model, and generating an instance-level synthetic point cloud; comprising the following steps: uniformly sampling to generate a non-occlusion point cloud, generating an occlusion point cloud by applying a hidden point removal algorithm, and selecting candidate viewpoints to simulate the occlusion point cloud in a real scene. And then, the generated synthetic point cloud is used for training a deep learning instance segmentation model, and the trained model is used for segmenting an actual point cloud scene, so that component-level accurate identification is realized. And finally, parametric modeling is carried out on the segmented point cloud through Dynamo software, a building component is reconstructed according to an instance segmentation result, and three-dimensional model reconstruction in a complex scene is completed. The method can effectively improve the precision and modeling efficiency of point cloud segmentation in a complex environment, and is widely applied to the fields of building intelligent operation and maintenance, electromechanical equipment management and the like.

The invention relates to the technical field of virtual reality, in particular to an indoor structure reconstruction method based on a panoramic image scene understanding algorithm, and the method comprises the steps: S11, obtaining a plurality of equidistant columnar projection panoramic images of a current indoor scene through a panoramic camera or a panoramic image splicing algorithm; s12, performing semantic segmentation on the collected panoramic image by using SAM, deducing indoor ceiling, floor and wall areas of the panoramic image on the basis of a semantic segmentation result, and generating a multi-channel semantic graph; and S13, inputting the acquired RGB panoramic images and the multi-channel semantic map into a pre-trained panoramic image depth estimation model to obtain a depth map corresponding to each panoramic image. The method is used for automatically generating an indoor structure model, the scheme comprises the core steps of semantic segmentation and layout reasoning, depth estimation and point cloud construction, structure optimization and indoor model reconstruction and the like, and a complete indoor panoramic image scene understanding scheme is formed.

The present invention proposes a three-dimensional (3D) model reconstruction and rendering device, which includes a data collection module for obtaining required data from two-dimensional images captured by different photographic equipment, a model training module configured to obtain data from the data collection module to train the 3D modeling model and learn the ability to reconstruct and render 3D models and scenes, a 3D model and scene rendering module utilizing the 3D modeling model completed with training to convert the collected data into three-dimensional models and scenes, and a rendering and 3D effect module being used to process the generated three-dimensional models and scenes through the three-dimensional modeling and rendering software for further applications.

The invention relates to the technical field of point cloud processing, and discloses a point cloud data processing method, system and device for industrial three-dimensional model construction. The method comprises the following steps: carrying out denoising and down-sampling processing on original point cloud data of an industrial part to obtain preprocessed point cloud data, calculating a local geometric descriptor, converting the local geometric descriptor into an industrial feature recurrent plot through a recurrent plot algorithm, inputting the recurrent plot into a convolutional neural network to extract a high-dimensional geometric feature vector, and mapping the feature vectors into B-spline curved surface control parameters through a deep belief network, and performing point cloud reconstruction quality evaluation according to the control parameters to obtain geometric accuracy error data and curved surface reconstruction integrity data. According to the method, the problems that in existing industrial three-dimensional model construction, a point cloud data processing method lacks intelligent feature recognition, the reconstruction precision is insufficient, and manufacturing constraints are not fully considered are solved, and the recognition accuracy of geometric features of complex industrial parts and the precision of three-dimensional model reconstruction are improved.

Embodiments of the present application provide a three-dimensional model reconstruction and image generation method, a device, a storage medium, and a program product. In the method, multi-stage three-dimensional reconstruction is performed on the basis of a single image of a target object; in a first stage, a plurality of view images are generated on the basis of an image generation model, and an initial three-dimensional model is reconstructed on the basis of the plurality of view images; and in a second stage, on the basis of the plurality of view images and an initial prompt containing set marker information, a text-to-image model is used to learn an association relationship between the target object and the set marker information, and a plurality of scene images are generated on this basis. Compared with the plurality of view images, the scene images generated in the second stage have higher resolution and richer image details, and then the initial three-dimensional model is optimized on the basis of the plurality of scene images, so that a target three-dimensional model having higher resolution and clearer model details can be obtained, thereby paving the way for practical application of a three-dimensional reconstruction solution based on a single image.

The invention discloses a rotating body ancient building part model reconstruction method based on a least square method, and relates to the technical field of model reconstruction analysis, and the method comprises the steps: obtaining ancient building part point cloud through employing a three-dimensional laser scanning technology; the historic building part point cloud is sliced and projected to a horizontal plane, a circle is fitted by using a least square method, circle center coordinates are extracted, and the position of a rotating shaft is determined. According to the method, the precision of model reconstruction is effectively improved by accurately extracting the rotating shaft and fitting the rotating generatrix, high-precision point cloud data is obtained by using a three-dimensional laser scanning technology, and accurate extraction of the rotating shaft and the rotating generatrix is ensured through the steps of slicing, projection, least square fitting and the like; a non-uniform rational B-spline curve is adopted to fit a rotating bus, the rotating bus is rotated around a rotating shaft to reconstruct a three-dimensional model, the three-dimensional model of the ancient building part model is parameterized, the geometric accuracy of the model is further improved, and the reconstructed model can more accurately reflect the actual shape and size of the ancient building part.

The invention relates to the technical field of underwater concrete three-dimensional reconstruction, in particular to an underwater concrete three-dimensional reconstruction method and system based on linear structured light and stereoscopic vision fusion. The method comprises the steps of obtaining a depth map and a texture map of underwater concrete based on a line structured light module and a stereoscopic vision module, and obtaining a preliminary depth data sequence and a surface texture sequence; performing environment compensation and image enhancement on the depth data and the texture data to generate data after environment correction; based on the corrected data, performing registration and matching on the depth and texture data by using a multi-modal fusion algorithm to generate a fusion point cloud data model; motion compensation and deformation repair are carried out on the point cloud model, deformation and structure offset caused by water flow are corrected, and a repaired point cloud model is generated; the repaired point cloud is input into a three-dimensional model reconstruction engine, a high-precision underwater concrete three-dimensional model is generated, intelligent analysis is conducted on the surface of the model, and damage and structural integrity of the model are evaluated. The underwater concrete three-dimensional reconstruction method effectively improves the precision of underwater concrete three-dimensional reconstruction, solves the problem of model distortion in complex environments such as water flow and turbid water quality, and has efficient and accurate structure detection and maintenance functions.

The invention discloses a method and a system for measuring the distance between cross positions of carrier cables of a rail contact network based on machine vision, and relates to the technical field of measurement of the distance between the cross positions of the carrier cables of the rail contact network. A laser radar and an infrared sensor are used for collecting depth information and temperature distribution data, and a multi-view image and depth temperature data are obtained; collecting the multi-view image and the depth temperature data to a central processing unit through a high-speed data transmission channel to obtain an initial data set; preprocessing the initial data set to obtain a high-quality data set; based on a deep learning algorithm and the high-quality data set, constructing a prediction model of the carrier cable and the intersection point, inputting sample data in the high-quality data set, and outputting prediction results of the carrier cable and the intersection point; and based on prediction results of the carrier cables and the intersection points, three-dimensional model reconstruction is carried out on the intersection positions of the carrier cables in combination with stereoscopic vision and structured light technologies.

The invention discloses a power scene multi-source and multi-state model reconstruction method, medium and system, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data, and constructing a twin according to the multi-source heterogeneous data; s2, obtaining quasi-static data, and constructing an equipment parameterized twin model according to the quasi-static data; and S3, generalizing the parameterized twinborn model, establishing a physical simulation model, obtaining a simulation result according to the physical simulation model, and mapping the simulation result into the twinborn body. The power grid model of the power scene constructed by the model reconstruction method provided by the invention can perform fusion processing on multi-source heterogeneous three-dimensional space data to form a multi-source multi-state power grid three-dimensional digital model, so that a uniform data structure is provided for rapid construction and later operation and inspection of the power grid, and a power grid system platform can conveniently call and analyze.

The invention belongs to the technical field of deformation measurement, and provides a distributed optical fiber sensing casing damage monitoring system and monitoring method based on an OFDR (Optical Frequency Domain Reflectometer) technology. An armored optical cable consisting of a multi-core single-mode optical fiber and two multi-mode optical fibers of which the tail ends are welded into a U shape is spirally wound on the outer wall of a casing; a single-channel DSS / dual-channel DTS modulation-demodulation system of a dynamic distributed optical fiber strain and temperature composite sensing technology is used for real-time acquisition of strain and temperature sensing data of the casing pipe under the action of ground stress, and a cylindrical casing pipe three-dimensional model reconstruction algorithm is combined to automatically generate a spiral line. The three-dimensional model of the cylindrical casing is in one-to-one correspondence with the position of the optical fiber on the real casing, casing damage possibly occurring to the underground casing under the action of ground stress is monitored and measured in real time for a long time, early warning and forecasting information about that the casing damage is about to occur or is occurring is issued in time, and well site operators can conveniently handle and repair the casing damage in time.

The invention discloses a dynamic projection method and system for vehicle-mounted laser point cloud data, and the method comprises the steps: obtaining the point cloud data through vehicle-mounted laser, firstly, according to a principle that a point close to a laser radar has higher density than a point far away from the laser radar, recognizing a radar scanning track through a region with high point cloud density; and secondly, aligning the projection direction with the radar scanning direction, adaptively constructing a dynamic coordinate axis and a horizontal cylindrical projection for each point cloud area, and obtaining fitted projection parameters (such as radius, center point, height and the like), thereby better enhancing ground feature retention in the dense point cloud, relieving ground feature deformation in the projection process to a greater extent, and improving the robustness of the radar. Compared with the prior art, the method has the advantages that less ground objects are shielded, semantic confusion is reduced, the effect of the point cloud semantic segmentation network is improved, the artificial intelligence reasoning speed is increased, and automatic driving map construction, three-dimensional model reconstruction, smart city development and the like can be assisted.

The embodiment of the invention discloses a three-dimensional landform model reconstruction method and device based on geometry and texture combination. A specific embodiment of the method comprises the following steps: carrying out self-adaptive grid division on a landform three-dimensional point cloud and then carrying out geometric super-resolution to obtain a target three-dimensional landform sub-grid; performing color texture mapping and texture correction enhancement on the target three-dimensional landform sub-grids to obtain an enhanced landform texture map; performing collaborative bidirectional optimization on the enhanced landform texture map and the target three-dimensional landform sub-grids to obtain a super-resolution landform texture map and a super-resolution three-dimensional landform sub-grid; performing model recombination and fusion on the plurality of super-resolution landform texture maps and the plurality of super-resolution three-dimensional landform sub-grids to obtain a three-dimensional texture landform grid model; and performing density control reconstruction processing on the three-dimensional texture landform grid model to obtain a three-dimensional density texture landform model, and storing the three-dimensional density texture landform model. According to the embodiment, the quality of the three-dimensional density texture landform model can be improved, and waste of storage resources is reduced.

The invention discloses a positioning puncture surgery robot system with a reconfigurable working space, and the system comprises a multi-mode image collection module which is used for obtaining a high-resolution image of a target region of a patient; the three-dimensional anatomical model reconstruction module is used for generating a three-dimensional anatomical model containing a skeleton structure and a soft tissue structure; the target point selection and puncture path generation module is used for automatically recommending puncture target points according to operation types and clinical requirements and automatically generating a plurality of preliminary puncture paths; the multi-plane segmentation and path injection module is used for segmenting the three-dimensional anatomical model from a coronal plane, a sagittal plane and an axial plane and generating slices, and injecting puncture paths into the corresponding slices; by integrating multi-modal image acquisition, three-dimensional anatomical model reconstruction and automatic path planning and optimization setting, the operation efficiency and accuracy are remarkably improved, the operation risk is reduced through real-time data analysis, and a more personalized treatment scheme is provided for a patient.

The invention discloses a stainless steel stator blade composite repairing method based on model reconstruction, which is characterized by comprising the following steps of: acquiring a blade profile section and parameters of a stainless steel stator blade; blade profile size data of a new blade are collected, and a point cloud model is established; acquiring an optimal point cloud model based on the data of the point cloud model, and establishing a blade reference grid model as a blade modification standard model; adopting UG software to reconstruct the blade modification standard model; detecting an old blade of which the blade profile contour is within the baseline deviation range of the reference model according to the reconstructed blade modification standard model for repairing; and blade corrosion pit repairing and chord length repairing are conducted on the old blade based on the blade modification standard model, and blade modification and X-ray detection are conducted after repairing is completed. According to the method, the size precision of the established blade modification standard model is within 0.02 mm; the repairing area can effectively avoid the problem of secondary corrosion, and the service life of the blade is prolonged.

The invention discloses a laser point cloud-based industrial equipment digital twin model rapid reconstruction method and system, and the method combines a model matching mode and a point cloud reconstruction mode, and effectively solves the problem of twin model reconstruction in different scenes. According to the first mode, on the basis of an existing three-dimensional model, through shape matching, feature extraction and model retrieval methods, registration of point cloud data and the three-dimensional model is combined, and rapid reconstruction of the equipment twinborn model is successfully achieved. The mode is suitable for equipment with an existing standard three-dimensional model, the repeated utilization rate of the model can be improved, and the reconstruction time and cost are reduced. In the second mode, the reverse grid model construction method based on the point cloud data is provided for equipment which cannot be retrieved through an existing model. Through an improved greedy projection triangulation algorithm, point cloud data is converted into a grid model, and the precision and authenticity of the model are improved in combination with a grid optimization algorithm and a surface texture mapping technology. According to the method, the digital twinborn model of the industrial equipment can be efficiently and accurately reconstructed, the modeling difficulty in an industrial scene is relieved, the actual demand of field modeling is met, and the method has high application value and practicability.

The invention discloses an unmanned aerial vehicle image camera self-calibration method, system, medium and device, and relates to the technical field of camera self-calibration, and the method comprises the steps: carrying out the feature extraction and image matching of an unmanned aerial vehicle image, and constructing an undirected graph # imgabs0 #; according to the undirected graph # imgabs 1 #, generating an undirected graph # imgabs 2 # based on a minimum spanning tree; constructing an epipolar line geometric cost function based on three images of a triangular structure of an undirected graph # imgabs3 # and visible object space points in the three images; based on an undirected graph # imgabs4 #, seed three views and an optimal image of the incremental SFM are selected, an epipolar geometric cost function is used to carry out incremental unstructured bundle adjustment, and in the process, weighted iteration self-calibration is carried out on camera parameters to be calibrated; gNSS-assisted absolute orientation is utilized, GNSS constrained beam adjustment is carried out, the self-calibration precision is further improved, and optimal camera parameters after self-calibration are selected. The method can improve the precision of model reconstruction.

The invention relates to the technical field of three-dimensional digital modeling, in particular to an automatic real-time three-dimensional digital modeling method and system for a dike. The method comprises the following steps: collecting first image data of a dike area, and setting a three-dimensional coordinate system of the dike area; evaluating a plane median error and an elevation median error of the first image data, and obtaining field acquisition precision based on the plane median error and the elevation median error; adjusting shooting parameters of the unmanned aerial vehicle according to the field acquisition precision, and acting the adjusted shooting parameters of the unmanned aerial vehicle on the unmanned aerial vehicle; acquiring second image data of the dike area through the adjusted unmanned aerial vehicle; executing spatial triangulation calculation on the second image data, and generating aerial triangulation calculation result data; and performing three-dimensional model reconstruction based on the aerial triangulation result data to obtain a dike three-dimensional digital model. According to the method, the frequency and the precision of obtaining the geometric information of the dike can be improved, and high-resolution and dynamically-updated three-dimensional modeling is realized.