326 results about "Surface reconstruction" patented technology

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention discloses a prediction method for quantifying the development degree of undermining diseases of rammed earth cultural relics, and relates to the field of cultural relic protection. The method comprises the steps of obtaining three-dimensional laser point cloud data and close photography three-dimensional model data of the rammed earth cultural relics; fusion of the three-dimensional laser point cloud model and the close photography three-dimensional model is realized, and a rammed earth cultural relic undermining disease three-dimensional model is constructed; based on the rammed earth cultural relic undermining disease stereoscopic model, quantitative indexes of undermining disease development are extracted, and an undermining disease development prediction model is constructed and optimized; and generating and outputting a quantitative report of the development degree of the undermining disease of the rammed earth cultural relics. According to the method, three-dimensional laser scanning and close photogrammetry are combined, differentiated routes are designed for areas with different heights, alignment of cross section contours of different models is realized based on Fourier description, a Poisson curved surface reconstruction technology is combined with a hole filling algorithm to complement missing parts of the models, spatial distribution and morphological characteristics of undercut diseases are intuitively presented, and the method is suitable for large-scale popularization and application. The change rule of undercut diseases along with time is quantified, and protection work is promoted to be changed from passive repair to active prevention.

The invention discloses a multi-modal measurement system and method of a three-coordinate measuring machine, relates to the cross technical field of precision measurement, computer vision and intelligent manufacturing, and is used for solving the problems of low automation degree, strong subjective dependence and lack of assembly performance prediction capability in automobile covering part characteristic line detection. According to the method, point cloud data and positioning coordinates are synchronously collected through a multi-mode sensor, and a fusion point cloud sequence is generated through time sequence synchronization; constructing a high-precision three-dimensional point cloud model through noise filtering, registration and curved surface reconstruction; automatically extracting a feature line path based on curvature analysis and generating an analysis section; calculating a sharpness index through contour fitting, and generating a quality evaluation coefficient in combination with a CAD ideal model contour tolerance deviation; and finally, defects are identified through virtual assembly analysis, a sensor remeasurement path instruction is generated, and a quality report is output in combination with a quality coefficient, so that the detection precision, the efficiency and the assembly quality prediction capability in the measurement process of the three-coordinate measuring machine are improved.

The invention discloses a medicine package online detection system based on three-dimensional reconstruction, and relates to the technical field of three-dimensional reconstruction, the system is matched with a binocular industrial camera through a numerical control rotary table to collect a double-view group covering the whole surface of a medicine package and containing an overlapping surface; carrying out feature point registration by adopting a polarization fusion SIFT algorithm, and positioning a packaging area through a model fusing packaging topology priori and edge gradient constraint; generating a disparity map based on a semi-global block matching algorithm, and combining camera calibration parameters with a rotary table rotation angle for splicing to obtain a complete packaging point cloud; a three-dimensional grid model is generated through Poisson curved surface reconstruction, accurate detection of surface concave-convex defects is achieved through curvature sudden change analysis and a tolerance threshold comparison method, and finally a defect information set is output and linked with a sorting mechanism to complete qualification judgment. The system can carry out full-surface and high-precision detection on medicine packages, is suitable for multi-specification packages, and meets the quality control requirements of industrial production.

The invention relates to the technical field of data processing, and provides a BIM model continuous grid bottom surface extraction method, device and equipment and a storage medium, according to the scheme of the invention, grid bottom surface extraction is carried out based on a user-defined data structure after BIM model triangularization, the influence of a professional design software production environment and a multi-source heterogeneous storage format is eliminated, and universality is achieved; according to the method, the cleaned and corrected triangularized grid model is subjected to space division and an elevation extraction algorithm, so that the grid bottom surface extraction efficiency is greatly improved; according to the method, curved surface reconstruction is carried out according to the spatial point cloud normal set and the point cloud data set, so that the construction accuracy of grid bottom surface extraction in the projection direction is ensured, a finer and more continuous geometric expression is provided, and the problem of model bottom surface image missing is solved.

The invention discloses a dragging positioning spraying track planning method for a local area of the surface of special equipment and a storage medium, and belongs to the technical field of industrial robot automation. The method comprises the steps of obtaining point set data of a to-be-sprayed area; calculating a convex hull based on the point set, and solving a minimum enclosing rectangle by using a rotary caliper method to determine an accurate rectangle spraying boundary; constructing a rectangular amplitude limiting sedimentary model through spraying testing and parameter identification; generating a grid track with a buffer area based on the model and the boundary, and optimizing the spraying speed by taking the minimization time as a target; and for the curved surface, performing B-spline curved surface reconstruction and extracting a normal vector to control the attitude of the spray gun. According to the method, the complex multi-parameter trajectory optimization problem is simplified into single-speed optimization through the rectangular amplitude limiting deposition model, the problems that in local spraying, trajectory planning is complex, edge control is not precise, a coating is not uniform and the like are effectively solved, and the spraying efficiency and quality are remarkably improved.

The invention provides a three-dimensional reconstruction method for a traffic accident scene in combination with a ground-air view angle, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: carrying out the image collection of the traffic accident scene through employing cameras of two platforms, namely an unmanned plane and an unmanned vehicle, carrying out the semantic segmentation of the image, and generating a pixel-level mask of a key target; extracting an ROI from the image according to the pixel-level mask of the key target, and performing image enhancement and denoising processing on the ROI to obtain a processed ROI image; and optimizing internal and external parameters of cameras of the unmanned aerial vehicle and the unmanned vehicle, performing absolute scale recovery, reconstructing a three-dimensional surface model by combining an SfM method, an MVS method and a surface reconstruction method of a combined camera optimization objective function based on a pixel-level mask of a key target and a processed ROI image, and performing digital quantitative analysis of an accident scene. The method can solve the problem that the existing three-dimensional reconstruction method is insufficient in camera calibration optimization and lacks absolute scale information.

The invention belongs to the technical field of vision measurement, and discloses a high-reflection curved surface reconstruction method and system based on multi-view point cloud fusion and optical optimization. The method comprises the following steps: acquiring multi-source heterogeneous data such as a stripe image, a polarization image and anchor point depth, and mapping the multi-source heterogeneous data to a unified workpiece coordinate system to form a standardized data set; calculating the normal direction of a sampling point based on data, establishing a slope field, and recovering the height through integration to obtain a geometric initial description of a curved surface; the method comprises the following steps: constructing a candidate light path through reverse ray tracing, generating a credibility weight in combination with a polarization degree, a reflection frequency, uncertainty and an incident angle index, and normalizing the credibility weight in a local neighborhood; fusing multi-source point clouds under the constraints of weights and anchor points, establishing a parameterized curved surface model, and constructing a joint optimization objective function containing geometric errors and optical consistency errors; and further adaptively adjusting a weight rule based on residual distribution, carrying out closed-loop iterative optimization, and outputting the initial surface shape of the high-reflection curved surface with high precision and good stability.

The invention relates to the technical field of fault identification, in particular to a three-dimensional fault occurrence intelligent identification method and system based on multi-source remote sensing, and the method comprises the steps: obtaining a high-resolution optical image, a penetrating radar profile and gravity gradient data of a target region, and generating a three-dimensional geological response body fusing earth surface and underground information; inputting the three-dimensional geological response body into a vertical adaptive enhancement module, and constructing a unified structured fault feature tensor; and inputting the fault feature tensor into a dynamic curvature constrained curved surface reconstruction module to generate a fault space curved surface, and resolving trend, tendency and dip angle change functions of the curved surface at different depths. According to the three-dimensional fault occurrence intelligent identification method based on multi-source remote sensing, the high-resolution optical image, the penetrating radar profile and the gravity gradient data are fused, a structured three-dimensional geological response body is constructed, the cooperative expression ability of earth surface-underground fault information is remarkably improved, and the fault occurrence identification accuracy is improved. And high-fidelity modeling of complex fault bending and abrupt change areas is realized.

The invention discloses a drilling-measurement constrained three-dimensional geothermal geologic attribute modeling and quantification method, and relates to the technical field of data analysis and processing, and the method comprises the following steps: 1, constructing a drilling-measurement constrained three-dimensional geothermal geologic voxel basic model, and forming a three-dimensional geothermal geologic voxel basic model covering a modeling range; 2, constructing an implicit signed distance field on the three-dimensional geothermal geologic voxel basic model to express fracture geometry, generating a unified implicit geothermal geologic voxel field, and redividing voxel categories according to the unified implicit geothermal geologic voxel field; and step 3, executing moving cube surface reconstruction and prior sensitivity analysis on the unified implicit geothermal geologic voxel field to give a space confidence body and a volume interval, and dividing the space confidence body and calculating the volume interval according to the voxel occurrence frequency. Evaluation of the geothermal body is changed from traditional single volume estimation to interval and confidence expression, and a reliable basis is provided for well position deployment, risk management and development planning.

Approaches presented herein provide for the unconditional generation of novel three dimensional (3D) object shape representations, such as point clouds or meshes. In at least one embodiment, a first denoising diffusion model (DDM) can be trained to synthesize a 1D shape latent from Gaussian noise, and a second DDM can be trained to generate a set of latent points conditioned on this 1D shape latent. The shape latent and set of latent points can be provided to a decoder to generate a 3D point cloud representative of a random object from among the object classes on which the models were trained. A surface reconstruction process may be used to generate a surface mesh from this generated point cloud. Such an approach can scale to complex and / or multimodal distributions, and can be highly flexible as it can be adapted to various tasks such as multimodal voxel- or text-guided synthesis.

The invention provides a hydropower station reservoir area three-dimensional terrain modeling method and device based on multi-source data fusion, and relates to the technical field of three-dimensional terrain modeling, and the method comprises the steps: obtaining a multi-source data set containing point cloud data, image data, texture data and actual measurement data of a hydropower station reservoir area; preprocessing the multi-source data set to obtain a preprocessed data set; and according to the preprocessed data set, constructing a three-dimensional terrain model by using an improved Delou inner triangulation algorithm, a non-uniform rational B-spline surface reconstruction technology and a Laplacian smoothing operator. The geometric accuracy and the natural form of the three-dimensional terrain model can be remarkably improved.

This invention provides a method and system for monitoring surrounding rock deformation in underground coal mines based on three-dimensional point clouds, relating to the field of coal mine safety monitoring. The invention includes the following steps: installing a reference target in the monitoring area and collecting point cloud data; preprocessing the point cloud data, performing coarse registration using the reference target and fine registration using the iterative nearest point algorithm, and generating a triangular mesh model using the Poisson surface reconstruction algorithm; parameterizing the triangular mesh model of the monitoring area into a two-dimensional analysis mesh, calculating the three-dimensional displacement vector of each node, and establishing a displacement field matrix; calculating the displacement gradient tensor based on the displacement vector, deriving the Green-Lagrange strain tensor, and obtaining the full-field strain distribution; recording the strain value of each mesh node, and determining whether an instability warning is triggered based on the relationship between the strain value and the warning threshold; this method can achieve high-precision monitoring of surrounding rock deformation in underground coal mines.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention discloses a multi-machine collaborative three-dimensional map updating method based on mobile augmented reality, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the following steps: carrying out the collection of initial collection data of a plurality of target regions through the wearing of AR glasses by a plurality of people; performing frame extraction on the original video image, and calculating to obtain a data grouping result; a sparse pairing list is generated by adopting a hybrid pairing strategy, and sparse point cloud data is obtained by combining incremental SfM calculation; performing distortion removal processing on the frame extraction image, and obtaining pure dense point cloud data through a multi-view solid geometry algorithm, a clustering algorithm and statistical filtering; a triangular mesh model is generated through Poisson surface reconstruction, and a high-precision three-dimensional model is obtained through Markov random field optimization view selection; and constructing a unified space-time reference conversion matrix to obtain a real-time AR visual navigation and map updating result. By adopting the method, the problems that the three-dimensional map is poor in current situation, virtual and real registration is misplaced, the modeling efficiency is low and the precision is insufficient are solved.

The invention belongs to the technical field of computer vision and three-dimensional reconstruction, and particularly relates to a building intelligent three-dimensional reconstruction method and system based on point cloud, and the method comprises the steps: obtaining the point cloud data of a building, and carrying out the preprocessing of the point cloud data; constructing an intelligent three-dimensional reconstruction model based on an intelligent algorithm selection mechanism, an adaptive surface reconstruction technology and an anti-mold-penetration processing algorithm; and three-dimensional reconstruction of the building is realized based on the preprocessed point cloud data and the intelligent three-dimensional reconstruction model. Through an intelligent algorithm selection mechanism, a self-adaptive surface reconstruction technology and an anti-mold-penetration processing algorithm, the technical problems of blind algorithm selection, unstable reconstruction quality, geometric mold penetration and the like in the prior art are solved, and high-quality and high-efficiency building three-dimensional reconstruction of point cloud data of various formats is realized.

This invention discloses a fully automated, high-speed, and highly accurate three-dimensional reconstruction method and system, relating to the field of three-dimensional dynamic reconstruction. The invention introduces the construction of a spectral-point cloud fusion model and the calculation of a dynamic reflectance field by Monte Carlo ray tracing. By combining this with Fresnel's equation and dynamic ray tracing techniques, the reflectance calculation conforms to physical laws, thereby improving the accuracy of spectral mapping. Furthermore, the invention optimizes spectral data based on an error correction matrix using a spectral error compensation network, reducing the accumulation of errors caused by factors such as measurement angle and surface roughness. Moreover, in the three-dimensional reconstruction process, the invention improves the continuity and spectral consistency of the three-dimensional model by using Poisson surface reconstruction and a spectral reflectance-driven mesh optimization method based on spectrally enhanced point cloud data.

The invention discloses a self-adaptive point cloud implicit curved surface reconstruction method based on a truncation level B spline, and the method comprises the steps: constructing a first-layer truncation level B spline primary function space, and obtaining an initial implicit function through solving an optimization model; evaluating the fitting error of each point of the input point cloud, and determining the fitting error as a region which needs to be further refined; establishing a new truncation hierarchy B-spline primary function set, and expanding a current function space; performing truncation operation on the THB spline primary function which is completely positioned in the refined region; combining the truncated primary function with the primary function set generated by the new layer, and updating the whole primary function library; re-optimizing the model, and solving the updated implicit function; refining and optimizing layer by layer, and outputting a final implicit function and a zero level set thereof; and performing triangular meshing on the final zero level set to finish curved surface reconstruction. According to the method, an extra training process is not needed, excellent point-to-surface distance and curvature indexes can be kept at the noise level up to 10%, and the memory and calculation overhead can be greatly reduced.

The invention relates to geological three-dimensional reconstruction, in particular to a self-adaptive parameter optimization and rapid imaging method in complex geological body three-dimensional modeling, which comprises the following steps of: integrating multi-source geological data to generate an initial three-dimensional geological model; determining an objective function and constraint conditions of adaptive parameter optimization of the three-dimensional modeling of the complex geologic body, and constructing an adaptive parameter optimization model; solving the adaptive parameter optimization model by adopting an improved crocodile ambush optimization algorithm to obtain an optimal parameter combination; substituting the optimal parameter combination into the initial three-dimensional geologic model, and generating a final three-dimensional geologic model through curved surface reconstruction; according to the technical scheme provided by the invention, the optimal parameter combination is substituted into the initial three-dimensional geologic model, and the final three-dimensional geologic model is generated through curved surface reconstruction.

The invention provides a video point cloud data fusion and curved surface reconstruction method based on a digital twin scene, and belongs to the technical field of video processing. Then, inertial data is mapped to a Lie group manifold space, and a continuous trajectory model is constructed by using B spline fitting; based on the model, accurately calculating a first pose transformation matrix at the exposure moment of each row of pixels and a second pose transformation matrix of each laser point; then, projecting the point cloud to a global coordinate system by using the second matrix, and generating multi-dimensional feature mapping data of space-time registration by combining the first matrix, the internal reference and a perspective principle; and finally, inputting the data into a voxelization truncated symbol distance field, synchronously updating a distance value and a color weight, and generating a high-fidelity digital twin three-dimensional curved surface by extracting a zero level set contour surface.

The invention relates to a structured light illumination confocal super-resolution measurement system and method, and relates to the technical field of optical measurement. The system comprises a digital micromirror device (DMD), a sample platform, an electric displacement platform, a microscopic imaging assembly, a complementary metal oxide semiconductor (CMOS) imaging assembly and computer equipment, and the DMD is used for adjusting the point light source array and the structured light field. According to the DMD-based confocal super-resolution measurement method, a structured light field and a signal-noise separation technology are combined, and high-precision and high-resolution three-dimensional surface reconstruction can be realized. By using the flexibility of the DMD and the advantages of the super-resolution technology, the spatial resolution and speed of confocal imaging are effectively improved, so that the measurement process is more accurate. In addition, low-rank decomposition and self-supervised deep learning methods are fused, the image reconstruction process is optimized, noise interference is suppressed, and the overall performance of the system is further improved.

Various embodiments comprise a multi-agent spacecraft system for rendezvous and proximity operations. The spacecraft system comprises a chief spacecraft and a deputy spacecraft. The chief spacecraft maneuvers to the orbit of a Resident Space Object (RSO) and deploys a deputy spacecraft to rendezvous with the RSO. The deputy spacecraft maneuvers to the RSO and senses the RSO during the rendezvous and proximity operations maneuver. The deputy spacecraft generates a Three-Dimensional (3D) surface reconstruction of the RSO based on the sensing. The deputy spacecraft determines a roll rate and a relative trajectory of the RSO based on the sensing. The deputy spacecraft returns to the chief spacecraft.

The invention belongs to the technical field of cigarette production, and provides a cigarette three-dimensional shape reconstruction method and system based on laser three-dimensional scanning and application, and the method comprises the steps: collecting three-dimensional point cloud data of the whole surface of a cigarette based on a laser triangular reflection principle; preprocessing the three-dimensional point cloud data by adopting a multi-stage filtering algorithm strategy to obtain reference data; the method comprises the following steps of: dispersing reference data into a plurality of two-dimensional sections along the axial direction of a cigarette, synchronously extracting upper and lower semicircular feature points of the cigarette in different two-dimensional sections by adopting a multi-thread technology, performing offset vector calculation and mirror image translation transformation so as to reconstruct a plurality of continuous two-dimensional reference sections in a virtual space, and performing curved surface reconstruction on the continuous two-dimensional reference sections, obtaining a three-dimensional shape model of the cigarette; based on the three-dimensional shape model, the perimeter and the length of the cigarette are extracted through a mixed measurement model. The cigarette shape detection efficiency can be improved, and the detection accuracy and the data accuracy are improved.

The invention relates to a personalized cap body digital jet printing method based on a three-dimensional curved surface model in the technical field of intelligent manufacturing, and the method comprises the steps: obtaining cap body three-dimensional scanning data, and processing the data through a curved surface reconstruction algorithm to obtain a complete three-dimensional curved surface model; wherein the model comprises geometric features of all curvature change areas for subsequent analysis; calculating a local curvature value of each area according to the three-dimensional curved surface model, determining a curvature change distribution diagram by adopting a Gaussian curvature calculation method, and marking a boundary line of a high-curvature area and a low-curvature area in the distribution diagram to guide pattern adjustment; and nozzle movement track data is extracted from the diffusion control parameter set, a three-dimensional printing path is generated through a path planning algorithm, and the path ensures that movement of a nozzle on a curved surface is synchronous with curvature change so as to avoid position deviation.

The invention discloses an industrial complex curved surface point cloud reconstruction method based on adaptive projection, and aims to solve the problem of surface reconstruction of high-noise, non-uniformly-distributed and complex geometric point clouds. The method comprises the following steps of firstly performing down-sampling based on spatial distribution on an original three-dimensional point cloud, and then denoising and homogenizing the point cloud through an adaptive weighted local optimal projection algorithm; fitting a local plane by adopting a local least square method, projecting the point cloud to two dimensions, and then carrying out Delaunay triangularization; mapping the two-dimensional triangulation network topological relation to three dimensions to generate a three-dimensional triangulation network; and finally, abnormal triangles are eliminated through side length constraint, and the grid quality is optimized. According to the method, through whole-process cooperative processing, the point cloud data quality and complex curved surface adaptability are remarkably improved, it is ensured that reconstructed grids are smooth, regular and consistent in precision, and the high-precision requirement of complex curved surface reverse engineering in the fields of aviation, molds and the like can be met.

The invention discloses a virtual reality level simulation method based on a material point method and gridding surface reconstruction, and belongs to the crossing field of computer graphics, physical simulation and virtual reality technologies. The method comprises the following steps: S1, classifying and storing position coordinates of material points according to material numbers of the material points, and traversing the coordinates to obtain a boundary of a bounding box of the material points; s2, selecting a grid size and a boundary extension distance for reconstructing the surface in a self-defined manner, and generating a grid; s3, selecting a Gaussian function to calculate the contribution of each substance point to the overall substance point density field, and obtaining parameters; s4, calculating the contribution of each substance point to the overall substance point density field, and performing accumulation to obtain the overall substance point density field; and S5, obtaining a contour surface of the overall material point density field, and exporting the contour surface as a rendering engine general three-dimensional model to obtain a simulation visual effect. According to the method, the problems that in the prior art, tens of millions of substance points cannot be processed, reconstruction parameters are difficult to select, and connection with a high-end rendering engine is not smooth can be solved, and the virtual reality level simulation visual effect is achieved.

Optical sensors (e.g., cameras) and (e.g., IR) illumination sources may be distributed in an environment (e.g., an interior space such as a cabin or cockpit of an ego-machine) and synchronized to generate frames of sensor data, which may be used to reconstruct 3D geometry and / or 3D pose of an occupant, operator, or other object in the environment. For example, stereo vision may be used to generate one or more depth maps from image data generated using different cameras, the depth map(s) may be transformed into a 3D point cloud, and surface reconstruction may be applied to reconstruct the 3D geometry of surface(s) in the environment. A 3D pose, one or more keypoints (e.g., facial landmarks), or some other representation of the shape of the reconstructed surface(s) may be extracted from the reconstructed surface and used in one or more downstream tasks, such as driver and / or occupant monitoring tasks.

The invention discloses a reverse modeling method and device for rapidly evaluating and counting turbine blade profile deviation and a medium, and the method comprises the steps: obtaining coordinate data of a limited section through three-coordinate measurement, and processing key areas such as a film hole and a trailing edge in a data processing stage to form measurement point data of a complete section; according to the multiple pieces of section coordinate data, curved surface modeling is conducted through a curved surface reconstruction mode fitting method, and a complete machined blade shape is obtained; comparing the actually processed blade with the designed blade to obtain the deviation between the two blades, and finally obtaining the deviation of the processing model; the key parameters are adjusted through blade grid modeling software to be fitted with the machined blade, and then the variable quantity of the key design parameters is obtained; through statistics of batch blades, the change range of key design parameters of the blades is obtained, a machining deviation blade profile is generated, and a turbine cooling blade entity is generated, so that pneumatic, heat transfer and strength analysis is carried out according to a turbine blade entity model sample.

The invention discloses a multi-robot process scheduling and cooperative control method based on artificial intelligence, and the method comprises the steps: carrying out the modeling of a process structure and robot resources, and generating a unified task information queue; environment perception and multi-source point cloud acquisition are carried out to generate multi-source point cloud data; carrying out Poisson curved surface reconstruction to obtain a three-dimensional reconstruction result and an occupied grid map; selecting a projection view angle and a grid resolution, and generating a multi-channel two-dimensional environment grid map; constructing an improved HarDNet network, and generating an environment semantic and risk map; performing task allocation and path planning to form a cooperative control plan; and performing cooperative execution, controlling the physical robot to execute tasks, and controlling the software robot to process businesses. Through combination of Poisson curved surface reconstruction and improved HarDNet network, unified scheduling and cooperative control of multi-robot tasks are realized.

The invention relates to the field of optical precision detection, in particular to a sub-aperture splicing detection method based on a frequency domain and space domain cooperative strategy, which comprises the following steps: collecting discrete phase data of sub-apertures; constructing a continuous physical field model; performing coarse alignment on the adjacent sub-apertures by using a frequency domain phase correlation technology to obtain integer pixel displacement between the adjacent sub-apertures; performing sub-pixel fine alignment through a numerical gradient method based on a normalized cross-correlation function of a continuous physical field model and an embedded geometric error elimination operator; constructing a global robust energy functional to carry out global optimization; and solving the zero-rotation pose under the floating reference, and performing Gaussian gradient weighted fusion and adaptive orthogonal substrate surface shape reconstruction to obtain the full-aperture surface shape of the large-aperture optical element. According to the method, the problems of high initial value dependence, displacement-inclination degeneracy, accumulative error warping, abnormal value sensitivity and the like of a traditional sub-aperture splicing detection method are solved, and ultrahigh precision and high robustness of large-aperture optical element sub-aperture splicing detection are realized.