464 results about "Model reconstruction" patented technology

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 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 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 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 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 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.

Disclosed is a three-dimensional reconstruction method based on kinematic calibration of line structured light point set individualization, and the method includes the following steps: rigidly assembling an area-array camera and a line structured light emitter, and mounting the assembly on a motion platform; calibrating structural parameters of the motion platform of a measuring device by using a reference radius of a standard sphere and line structured light scanned data; registering a spatial position relationship of a line structured light point set by taking a calibrated spherical center position of the standard sphere and the reference radius of the standard sphere as constraints; controlling the five-axis motion platform to move and acquiring scanned data of a measured member under different poses; and completing high-quality three-dimensional model reconstruction of the measured member subjected to multi-pose scanning by using the scanned data and a real-time point cloud registration matrix.

The invention discloses a construction hidden danger automatic detection method and system based on point cloud and image multi-mode. The method comprises the following steps: step 1, acquiring point cloud data and RGB image data and respectively preprocessing the point cloud data and the RGB image data; 2, safety equipment detection is carried out by establishing an improved YOLOv5 model; establishing a 3D-CNN model to qualitatively judge the abnormal behavior of the personnel; 3, combining a semantic segmentation model and point cloud data processing to carry out quantitative judgment of height calculation and coordinate conversion of the fence; and 4, realizing three-dimensional live-action model reconstruction based on point cloud feature matching, Poisson surface reconstruction and GPU rendering, and detecting potential safety hazards of a construction site. The accuracy of qualitative detection is further improved, the precision error of quantitative detection is reduced, the recognition efficiency and precision of potential safety hazards are greatly improved, intelligent and all-around guarantee is provided for safety management of a construction site, remote decision is assisted, the management cost is reduced, and the method is suitable for various construction scenes.

The invention discloses a BIM / CIM-based engineering design whole-process auxiliary decision-making method, relates to the technical field of engineering design, and discloses a BIM / CIM-based engineering design whole-process auxiliary decision-making method, which comprises the following steps: responding to a project red line range, obtaining geographic space data, and carrying out preprocessing, model reconstruction and lightweight processing on the geographic space data to obtain a project red line range; generating a macroscopic current situation scene; obtaining at least two candidate schemes, and performing spatial registration and fusion on the BIM models corresponding to the candidate schemes and the macroscopic current situation scene to generate a corresponding fusion decision scene; based on the fusion decision-making scene, carrying out collaborative decision-making on the candidate schemes, and determining a bid-winning scheme; and packaging the BIM model corresponding to the bid winning scheme and associated data, and transmitting the packaged BIM model and associated data to a downstream construction management system through an interface. The scheme collaborative decision-making efficiency is effectively improved, the information fault problem in the design and construction stages is solved, and the whole-process decision-making closed loop of the engineering project is achieved.

The invention relates to a railway track abrasion detection method and system, and aims to realize accurate and efficient detection of turnout sharp point rail abrasion. A detection device carrying a 3D camera, a speed sensor, a signal processing module and a data processing terminal is arranged in a turnout area to be detected, parameter configuration is completed, in the constant-speed moving process, the speed sensor generates a trigger signal according to the specified distance, the 3D camera is driven to synchronously collect point cloud data of the two-dimensional contour of the cross section of the sharp point rail, and a three-dimensional data stream is formed; after data are denoised, section registration splicing and three-dimensional model reconstruction are completed in combination with distance stamps, an actual measurement model and a standard contour model are superposed and compared, abrasion parameters are calculated, and the maximum abrasion point and position are recognized; and finally presenting the abrasion data in the forms of a color cloud picture, a table and the like, and automatically generating a standard detection report containing the contents of line information, defect types and the like. According to the method, automation and visualization of abrasion detection are achieved, the detection precision is high, the efficiency is high, and reliable data support is provided for track maintenance.

The invention provides a wearable exercise and rehabilitation monitoring system based on multi-modal AI, which relates to the field of electrical digital data processing and comprises a distributed multi-modal sensing acquisition module, a hierarchical wireless data aggregation and transmission module, a 3D human body dynamics reconstruction and modeling module and an intelligent action evaluation and feedback module. The distributed multi-modal sensing acquisition module is responsible for realizing multi-dimensional cooperative sensing of human body motion through heterogeneous sensors, and the hierarchical wireless data aggregation and transmission module adopts a master-slave base station architecture to realize efficient aggregation and low-delay transmission of heterogeneous sensor data. The 3D human body dynamics reconstruction and modeling module is used for realizing high-fidelity 3D skeleton model reconstruction based on inverse kinematics solution of multi-sensor fusion; the intelligent action evaluation and feedback module is used for providing personalized training guidance by constructing a standard action library and realizing multi-dimensional similarity quantitative scoring; according to the system, deep fusion of the multi-modal sensor can be realized, and accurate motion monitoring is provided.

The invention provides a factory three-dimensional digital twin model reconstruction and incremental updating method for industrial space intelligence, and the method comprises the steps: obtaining a factory multi-view basic image for a factory panoramic scene, so as to train a bifurcated structure neural implicit field network containing a radiation field branch and an SDF branch, and generating an initial three-dimensional digital twin base; acquiring factory real-time inspection images acquired by the inspection robot under different inspection poses, rendering the initial three-dimensional digital twin base by using a radiation field branch to obtain a virtual reference view, and performing difference analysis on the virtual reference view and the factory real-time inspection images to position a structural change area in a factory; determining an axis alignment bounding box for changing the physical range of the entity in the area, taking the axis alignment bounding box as an effective boundary of the local space patch, and determining matched radiation field network weight and SDF network weight according to the effective boundary; and adjusting the radiation field network weight and the SDF network weight, and updating the radiation field network weight and the SDF network weight to the network in an incremental updating manner to generate a factory three-dimensional digital twinning model based on the implicit three-dimensional geometric field.

Some embodiments of the present application include obtaining a trained machine learning model that was trained using training data. Production data may be obtained from a data feed selected based on the trained machine learning model. In some embodiments, feature sets and observed results may be extracted from data output from the data feed, and one or more features or results may be masked to generate the production data. Predicted results data may be generated with the trained machine learning model based on the production data and an accuracy score for the trained machine learning model may be determined based on the predicted results data. If the accuracy score satisfies a threshold accuracy condition, the trained machine learning model may be caused to be rebuilt or the training data may be caused to be updated.

The invention relates to the technical field of three-dimensional reconstruction, in particular to an electric power facility surrounding environment three-dimensional reconstruction method and system. The method comprises the following steps: acquiring electric power facility data, performing electric power facility layout structure analysis, and constructing an electric power facility three-dimensional model; obtaining electric power facility environment data, carrying out dynamic environment evolution, and constructing an electric power facility environment dynamic evolution model; performing rendering and model alignment embedding processing on the electric power facility three-dimensional model based on the electric power facility environment dynamic evolution model to generate an environment-electric power facility fusion model, and performing electric power facility corona loss fluctuation detection and environment fluctuation factor analysis; corona environment disturbance area detection and error calculation are carried out on the environment-electric power facility fusion model, disturbance error data are generated, and corona environment disturbance reconstruction processing is carried out on the environment-electric power facility fusion model. According to the invention, the electric power facility environment combined corona loss dynamic three-dimensional model reconstruction can be realized.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention discloses a hyperspectral anomaly detection method based on background attention and anomaly suppression, and relates to the technical field of hyperspectral image processing, and the method comprises the following steps: S1, employing a pyramid similarity downsampling module to reduce the anomaly content in an image; s2, multi-scale background attention is calculated for the image after abnormity reduction; s3, performing pixel reconstruction on the obtained feature map through depth separable blind block convolution; and S4, using an l1 norm as a loss function, and using a reconstructed image difference to guide model training. And S5, reconstructing an image by using the trained model, and taking a reconstruction error as a detection result. According to the invention, a pyramid similarity down-sampling module is used for reducing the abnormal degree of an image; the multi-scale attention module is used for improving the attention of the network to the background; the depth separable blind block convolution shields the perception of the network on a central blind block, and reduces the possibility of abnormal reconstruction; the reconstructed image difference is used to determine an optimal training round.

The invention discloses an aircraft surface geometric reconstruction method based on deep learning. The method comprises the following steps: acquiring a multi-view two-dimensional image of a target aircraft through a camera, extracting a target area mask, and constructing a feature corresponding relation between view angles to estimate camera parameters; constructing a multi-source loss function of fusion scene prior knowledge of the neural rendering network, and inputting camera parameters and the preprocessed two-dimensional image into the network for training until loss convergence to obtain network implicit output; point cloud reconstruction of the target aircraft is carried out, and an initial reconstruction point cloud is obtained after preprocessing; and inputting the initial reconstruction point cloud into a differentiable parametric curved surface model for optimization, outputting a three-dimensional parametric curved surface model after processing, extracting appearance parameters of the target aircraft to reconstruct the surface of the aircraft, and performing geometric reconstruction. The method effectively improves the precision and efficiency of model reconstruction, has good visualization, interaction and engineering integration capabilities, and is suitable for various application scenes such as industrial design and medical modeling.

The present application relates to the technical field of transmission scheduling in the industrial Internet, and discloses a trusted scheduling method and apparatus for a dynamic blockchain-based industrial wireless network. The method comprises: constructing a dynamic blockchain mechanism-based industrial wireless network system; on the basis of preset constraint conditions respectively corresponding to a scheduling stage and a consensus stage, performing model construction with the goal of maximizing the trusted processing efficiency of tasks, and obtaining an optimization model, the optimization model carrying parameters to be optimized; on the basis of a preset multi-agent Markov decision process model, performing model reconstruction on the optimization model, and obtaining a target optimization model; and, on the basis of observation information acquired in real time of each industrial device in the industrial wireless network system, using a preset rotation multi-agent deep reinforcement learning algorithm model to optimize the target optimization model, and obtaining a target parameter value corresponding to each parameter to be optimized. The method of the present application improves the trusted processing efficiency of tasks in an industrial wireless network system.

The invention belongs to the technical field of aero-engines, and particularly relates to a design method of an air inlet casing thin-wall reinforced structure based on rigidity requirements. The invention relates to a design method of a thin-wall reinforced structure of an air inlet casing based on a rigidity requirement. Comprising the following steps of model establishment, parameter setting, load and boundary determination, topological optimization mathematical model establishment, topological optimization calculation, model reconstruction, structural strength evaluation, manufacturability evaluation and preparation, and preparation of the air inlet casing by adopting a selective laser melting additive manufacturing technology. According to the design method for the thin-wall reinforced structure of the air inlet casing based on the rigidity requirement, the topological optimization structure design method and the additive manufacturing technology are combined, the material utilization rate and the specific rigidity of the air inlet casing are greatly improved, and the light weight level of the whole machine is remarkably improved.

The invention discloses a blood vessel three-dimensional reconstruction method and device based on a multi-view angiography image, computer equipment and a storage medium, and relates to the field of blood vessel simulation, and the method comprises the following steps: firstly, selecting a key frame from a multi-angle angiography sequence, carrying out high-precision blood vessel tree segmentation, and determining a reference image; secondly, constructing a three-dimensional projection cone by extracting projection parameters, taking an intersection of the three-dimensional projection cone and discretizing the intersection, iteratively screening by using a reference image and other visual angle information, and precisely defining a blood vessel tree spatial domain; the three-dimensional model reconstruction method is characterized in that a spatial dispersion and traversal projection thought is introduced, so that the calculation complexity is not remarkably improved when the number of visual angles is increased, and the limitation of the number of visual angles in traditional multi-visual-angle reconstruction is broken through; the method has the advantages of high precision and high expansibility, and can meet the harsh requirements of hemodynamic simulation and other high-end clinical applications on the precision of the blood vessel model.

Disclosed in the present invention is a three-dimensional reconstruction method based on kinematic calibration for the monomerization of a line-structured light point set. The method comprises: rigidly combining an area-array camera with a linear-line-structured light emitter, and mounting same on a motion platform; using a standard radius value of a standard sphere and line-structured light scanning data to calibrate structural parameters of the motion platform of a measurement apparatus; with the calibrated spherical center position of the standard sphere and the standard radius of the standard sphere as constraints, calibrating a spatial position relationship of a line-scanned point set; controlling the motion of the five-axis motion platform, and acquiring scanning data of a measured piece in different poses; and using the scanning data and a real-time point cloud calibration matrix to complete high-quality three-dimensional model reconstruction of the measured piece from its multi-pose scanning. The system comprises: an apparatus fixing module, a calibration module, a point cloud calibration module, a scanning module, and a point cloud reconstruction module. By means of using the present invention, the reconstruction accuracy of a line-structured light three-dimensional point cloud model can be improved. The present invention can be widely applied to the field of three-dimensional reconstruction.

A lung CT blood vessel image segmentation and reconstruction system based on multiple interpretable features performs further fine-grained segmentation on the basis of main airway features through a feature extraction module to obtain a plurality of connected domains, performs multi-stage feature extraction on the connected domains, and outputs multi-scale feature information; the blood vessel segmentation module generates an information matrix according to the indexes and the pulmonary parenchyma mask, generates a blood vessel network segmentation result through an adversarial network and performs post-processing; and the model reconstruction module performs three-dimensional reconstruction on the blood vessel network segmentation result and the pulmonary parenchyma mask to obtain a 3D model. According to the method, on the basis of anatomical priori knowledge of pulmonary vessels and airways, a large amount of multi-scale feature information is utilized, the information loss of the two-stage segmentation network is reduced, and the network adaptability and the learning ability of the relation between pixels under the complex condition are improved. In the aspect of system design, a modular design and management mode is adopted, efficient resource access is achieved, and meanwhile the operability and usability of the system are improved through end-to-end process design.

The invention relates to the technical field of building block arrangement control, in particular to a building block arrangement dynamic control system based on Rhino and Grasshopper collaboration, in the building block arrangement dynamic control system based on Rhino and Grasshopper collaboration, building blocks are parameterized into topological nodes through a constraint network construction module, a zero-spacing constraint relation is established, a dynamically updated network node set and a topological link relation are generated, and the network node set and the topological link relation are dynamically updated; the problems of structure gaps and low model reconstruction efficiency in traditional arrangement are solved; the dynamic response module positions an influence node range in real time based on an event source, generates a state event carrying a change type, realizes localized precise conduction, and avoids resource waste of global recalculation; the integrated solving module calls three types of rules in a chain manner through a fixed time sequence, utilizes a geometric continuity rule to force continuous position updating, utilizes a conflict-free rule to correct space interference, optimizes a rule to adjust size combination, and drives an interface to generate an arrangement scheme after cooperatively updating node parameters; the technical problems of continuous damage, space interference and high proportion of non-standard components in masonry design are solved.

The invention relates to the technical field of ultrasonic detection, and discloses an implicit neural representation-based model generation method and device, equipment and a medium, and the method comprises the steps: obtaining a two-dimensional image sequence collected by detection equipment and spatial pose information corresponding to image frames, analyzing periodic motion, screening the image frames in a stable motion stage, and generating a to-be-reconstructed image sequence. And processing the to-be-reconstructed image frame, and extracting segmentation data of the target object. And training an implicit neural representation model based on the to-be-reconstructed image sequence, the spatial pose information and the target object segmentation data, inputting the three-dimensional space coordinates into the model, generating corresponding image information and semantic information, and generating a three-dimensional model of the target object. According to the method, the stable image frames are screened and the implicit neural representation model is trained by analyzing the spatial pose information, so that the data utilization efficiency and the precision and continuity of three-dimensional model reconstruction are improved, and high-precision continuous three-dimensional reconstruction is realized without external registration or breathing cooperation.

The invention provides a rapid design model reconstruction method and system based on parameterized feature decoupling, and relates to the technical field of data processing, and the method comprises the steps: selecting a product template from a parameterized product template library constructed according to a feature decoupling idea; on the basis of the selected product template, a new design scheme is obtained through saving operation; based on the new design scheme, determining a process type selection calculation book through project requirements, inputting main body parameters of the design scheme according to the process type selection calculation book, and storing the main body parameters to obtain a parameterized design scheme; on the basis of a parameterized design scheme, parameters of key parts are sequentially input and stored according to predefined part types in a product template, and complete parameter configuration is obtained. Normalization and standardization of product design are achieved, and finally the purposes of shortening the development period and reducing the product cost are achieved.

The invention discloses a BIM model reconstruction method based on multi-source data fusion and semantic recognition, and the method comprises the steps: automatically generating an orthogonal projection snapshot image, and carrying out the component recognition and segmentation through a visual large language model, thereby effectively reducing the manual intervention, and remarkably improving the modeling efficiency; meanwhile, point cloud data and a model output result are combined, an orthogonal mapping algorithm and space intersection and clustering analysis are utilized, rapid conversion from a two-dimensional image to a three-dimensional model is achieved, the modeling process is further accelerated, accurate mapping of pixel points is ensured through the orthogonal mapping algorithm, and the geometric accuracy of the model is guaranteed; in addition, the visual large language model improves the accuracy of component recognition and segmentation, reduces human errors, and enhances semantic precision. In addition, multi-source data fusion and semantic recognition enable modeling to be more intelligent, different building requirements can be automatically met, the whole process from data acquisition and processing to model generation is automatic, the error rate is reduced, and the high standard of modern building engineering for precision and efficiency is fully met.

The invention relates to the field of numerical simulation, in particular to a porous ceramic model reconstruction and filtering performance simulation method, which comprises the following steps of: S1, obtaining internal structure image data of a ceramic material through micro-CT (Computed Tomography) scanning with micron-level resolution, and reconstructing a real three-dimensional microstructure of the ceramic material; s2, performing REV extraction and dynamic processing on the three-dimensional geometric model generated in the S1; s3, establishing a flow field model on the CFD, and setting corresponding boundary conditions; s4, the DPM is started, a particle injection source is configured, and particle physical parameters are set; s5, bidirectional coupling solution is executed, and high-precision particle motion and deposition process simulation is achieved; and S6, extracting simulation result data, and if the difference between the simulation result and the reference value exceeds a preset threshold value, returning to S4 and / or S5 to carry out parameter adjustment and model correction. According to the method, effective prediction of the particles in the porous structure can be realized.

The invention belongs to the technical field of robot testing, and provides an intelligent robot testing method and system based on a virtual environment, electronic equipment and a storage medium. The method comprises the steps of building data acquisition, virtual test environment reconstruction, digital twin robot construction, multi-dimensional test task setting, quantitative evaluation formula system construction, virtual test and quantitative evaluation. According to the method, the three-dimensional reconstruction model is improved, the multi-dimensional point cloud data is utilized for collaborative reconstruction, and the actual remote sensing image is utilized for dynamic adjustment of the reconstruction structure, so that a large amount of manual reconstruction data is not used, the situation that the model reconstruction effect is reduced due to unsupervised reconstruction is avoided, and the test scene reduction degree is greatly improved; the deviation between test and actual operation is reduced; by coupling and binding the digital twin robot and the target robot, the problem that the reliability of the test result is low due to excessive idealized setting is avoided, and the test result better fits the actual working condition.

The present application provides a three-dimensional model reconstruction method based on multi-source input, which is applied to a three-dimensional model reconstruction platform, and comprises: acquiring a mapping result comprising image information and point cloud information of an object to be reconstructed; establishing a three-dimensional model; from the mapping result, determining target image information and target point cloud information of said object under observation at a target pose; instructing the three-dimensional model to generate a target depth image and a target digital image on the basis of the target pose; then separately determining a corresponding geometric residual on the basis of the target point cloud information and the target depth image, and determining a corresponding optical residual on the basis of the target image information and the target digital image; and adjusting the three-dimensional model on the basis of the geometric residual and the optical residual. Therefore, the efficiency and precision of three-dimensional model reconstruction are improved, allowing rendered products of obtained three-dimensional models to be more realistic.