22 results about "Surface transformation" patented technology

The invention discloses a textile production defect detection method and system based on image processing, and belongs to the field of textile defect detection. According to the method, the point cloud data is generated by collecting the surface deformation stripes, and the penetrability reflection signal containing the sound wave propagation time is obtained through the multi-frequency ultrasonic probe. Denoising the point cloud data and then positioning a surface deformation candidate area based on a curvature gradient and a normal vector deflection angle; and an internal abnormal region is determined by separating and extracting a reflection wave amplitude value and a time delay parameter through an ultrasonic frequency band. And matching the surface point cloud coordinates with the ultrasonic propagation time by adopting an iterative nearest point algorithm to establish feature association between the normal vector deflection angle and the reflected wave amplitude. And dynamically distributing fusion weights for the surface features and the internal features according to the curvature gradient change rate and the amplitude attenuation rate to generate a multi-modal defect distribution diagram. And geometric shape and reflection intensity parameters are extracted and matched with a preset defect template to realize type and position judgment, and the method can be suitable for production defect detection of the thick textile with a multi-layer composite material structure.

The present invention relates to a method for the computer-aided generation of a data-driven model for the computer-aided processing of digital SAR data from a repeat-pass SAR system configured to determine surface deformations. The digital SAR data consist of a plurality of interferograms (Γ), each of which is obtained from a first SLC acquisition (s1) of the SAR system at a first time point and a second SLC acquisition (s2) of the SAR system at a second time point later than the first, and a Doppler center frequency (fdc), an interferometric phase (φ̂), and a coherence (P), all of which are obtained from a respective pair of first and second SLC acquisitions (s1, s2). The data-driven model is configured to generate a zero-Doppler phase (φzd), which represents the phase contribution associated with the deformation causing a zero-Doppler LOS change.to determine an along-track phase (φat), representing the phase contribution associated with deformation in an along-track direction, and an estimate of an associated along-track surface motion (uat). The generation of the data-driven model comprises the following steps: - Generating a synthetic training dataset (TDS) comprising a variety of interferograms (Γ), each formed by a pair of a first and a second SLC capture (s1, s2), by inducing a model-based and external 3D deformation into a given first SLC capture (s1) to obtain the associated second SLC capture (s2), which relates to a time point after a deformation event, and by applying a reverse approach,which is specified by a given coherence (ρ) and a noise-free interferometric phase (φtheo) after the deformation event; - Training a neural network (NN) with the training dataset (TDS), wherein the neural network (NN) comprises an input for inputting the plurality of interferograms (Γ) and an output for outputting the along-track phase (Φat), the zero-Doppler phase (φzd) and the associated along-track surface motion (uat) for the plurality of interferograms (Γ), wherein the trained neural network (NN) represents the generated data-driven model.

The invention provides a fluid dynamic action surface deformation calculation method based on a deep neural network, and the method comprises the steps: taking fluid characteristics and solid characteristic parameters as input data, taking surface deformation of a finite element calculation surface lift action as output data, and taking a constructed data set as a neural network training sample; and training a nonlinear high-order mapping relationship between corresponding input and output quantities of the neural network travel. According to the method, the mapping relation between fluid characteristics and solid characteristic parameters and fluid-solid coupling field physical parameters is established through machine learning, the difficulty of establishing a complex model can be reduced, the efficiency of establishing a unified design process can be improved, and the method can be popularized to different output physical quantities and has good application prospects. A new method is provided for solving a physical model which is high in nonlinearity and complex in equation form, and a new thought is provided for further analyzing the fluid mechanics law of the fluid-solid coupling phenomenon of complex fluid and complex surfaces.

The present invention relates to a method, device, equipment and storage medium for detecting a geometric model structure. The method comprises: receiving an input geometric model; after confirming that the number of vertices of two geometric surfaces to be detected in the geometric model is the same, and confirming that the vertices of the geometric surfaces are associated with arc edges, calculating a periodic surface rotation transformation matrix according to the arc edges; after confirming that there is a one-to-one correspondence between the boundary points of the two geometric surfaces according to the periodic surface rotation transformation matrix, confirming that the two geometric surfaces are a pair of rotational periodic surfaces. The solution provided by the present invention can quickly determine whether the two geometric surfaces to be detected in the geometric model are a pair of rotational periodic surfaces, which is conducive to improving the efficiency of mesh generation and improving the calculation efficiency and accuracy of numerical simulation.

The invention relates to a method for characterizing an intraoral organ to be characterized, comprising the following steps: 1) modeling the intraoral organ to be characterized in the form of a digital three-dimensional model or "model to be characterized", the model to be characterized comprising a mesh of points defining a surface; 2) setting the model to be characterized in a standardized configuration relative to a digital three-dimensional model of a reference intraoral organ, called "initial reference model", the initial reference model comprising a mesh of points called "initial reference points"; then 3) determining for each initial reference point a final reference point by means of a morphing algorithm, then determining a set of values determining the position of the final reference points and / or determining a final basic surface from the final reference points, the morphing algorithm determining the position of the final reference points so that the final reference model composed of the mesh of final reference points matches as closely as possible the model to be characterized; 4) generating a feature vector grouping in an ordered manner the values determined in step 3) for all the initial reference points.

The invention discloses a rock structure and three-dimensional stress-strain field evolution visualization method based on digital twinning. The rock digital twinborn model is constructed by fusing an in-situ loading CT scanning experiment, three-dimensional digital image correlation method (3D-DIC) surface deformation field measurement, digital volume image correlation method (DVC) internal deformation field measurement, digital modeling and numerical simulation technologies and methods. And obtaining mesomechanics parameters of the numerical model in real time, synchronously performing mirror image simulation, generating a four-dimensional dynamic body image with space-time continuity, and realizing visualization of the whole evolution process of the rock internal structure and the three-dimensional stress-strain field.

Mesh tracking-based dynamic 4D modeling for machine learning deformation training includes: using a volumetric capture system for high-quality 4D scanning, using mesh tracking to establish temporal correspondence on a sequence of 4D scanned face and full-body meshes, using mesh registration to establish spatial correspondence between the 4D scanned face and full-body meshes and a 3D CG physics simulator, and using machine learning to train surface deformations as increments of the physics simulator. Deformations for natural animation can be predicted and synthesized using a standard MoCAP animation workflow. Animation and machine learning-based deformation synthesis using a standard MoCAP animation workflow includes using single-view or multi-view 2D video of a MoCAP actor as input, solving for 3D model parameters for animation (excluding deformation) (3D solve), and predicting 4D surface deformations from ML training given the 3D model parameters obtained by 3D solve.

This invention discloses a method for monitoring the surface deformation evolution process of rock undergoing uniaxial compression creep failure, belonging to the field of geotechnical and rock mechanics testing technology. The steps are as follows: Step 1: Set up a test platform, which includes a speckle spraying system, an imaging and illumination module, a test device, and a host computer; Step 2: Fabricate rock test specimens and arrange the imaging and illumination module; Step 3: Obtain the compressive strength and set the graded loading levels; Step 4: Form a white base layer and black speckles on the surface of the rock specimen, meeting the requirements; Step 5: Conduct a uniaxial graded loading creep test on the rock specimen and acquire image data; Step 6: Process the image data to obtain the strain field evolution process on the specimen surface. This invention uses the above method to record the changes of the white base layer and black speckles during compression using an imaging device, thereby obtaining the deformation creep failure characteristics and evolution law of the rock surface.

The invention discloses a deformation detection method based on three-dimensional point cloud data of a river sluice, and the method comprises the steps: 1), carrying out the observation station arrangement of the river sluice, scanning the surface of the river sluice through a ground three-dimensional laser scanner, and obtaining the point cloud data of the river sluice; 2) extracting contour points of the river sluice based on a river sluice stable area control point extraction method; 3) registering two-stage river sluice point cloud data based on a registration algorithm optimized by a symmetric objective function; and 4) based on a multi-scale river sluice surface deformation algorithm of a point cloud roughness confidence interval, calculating a deformation amount between two registered river sluice point clouds. According to the method, accurate control points are extracted as feature input based on a non-deformation stable region in an overall environment, a registration correction algorithm objective function is optimized, the spatial position of the river sluice is corrected, and a confidence interval is set by using each part of the river sluice and an overall point cloud roughness distribution rule; and accurate detection of actual deformation of the river sluice is completed based on multi-period prototype observation data.

The application discloses a hydropower station slope surface deformation monitoring method and system based on multiple ground-based radars, and belongs to the field of slope safety monitoring. A plurality of ground-based radars are arranged outside a slope deformation area, and a monitoring target point is determined, then through reference calibration, satellite second pulse time synchronization and space geometric constraint calibration, the time and space references of multiple radars are unified, and the measurement data is converted to a unified geodetic coordinate system; then, the synchronization correlation matching of the monitoring target point across time and radars is completed; finally, based on the matching result, the three-dimensional coordinates of the monitoring target point are calculated by combining the space forward intersection principle, the error is dynamically corrected through a Kalman filtering model, and the three-dimensional coordinates and displacement time series of the monitoring target point are obtained. The method can realize high-precision time and space unification of multiple radars, improve the matching success rate of the monitoring target point, effectively suppress various monitoring errors, and realize full coverage and automatic precision monitoring of the slope.

The invention belongs to the technical field of robot picking, and discloses a soft fruit picking slip calculation method, device and equipment and a storage medium, and the method comprises the steps: obtaining surface deformation field data and distribution force field data when a soft fruit is picked, determining a slip contact area based on the distribution force field data, according to the displacement difference formed by all the contact points in the picking process, in combination with surface deformation field data, a contact surface slippage field is calculated, according to the rotation angular speed generated by all the contact points in the picking process, a rotation slippage component caused by fruit stem torsion is calculated, distribution force field data is input into a preset grabbing quality function, and the grabbing quality is obtained. A grabbing quality evaluation value of the end effector is obtained through calculation, and based on the sliding contact area, the contact surface sliding field, the rotating sliding component and the grabbing quality evaluation value, picking sliding information of the soft fruits is obtained through summarizing; by means of the method, the microscopic slippage phenomenon of the soft fruits in the picking process can be quantified.

Methods for detecting areas of localized tilt on a sample using imaging reflectometry measurements include obtaining a first image without blocking any light reflected from the sample and obtaining a second image while blocking some light reflected from the sample at the aperture plane. The areas of localized tilt are detected by comparing first reflectance intensity values of pixels in the first image with second reflectance intensity values of corresponding pixels in the second image.

The present application discloses a surface deformation detection method, device, equipment and medium based on phase gradient, the method comprising: obtaining a time series interference pattern by synthetic aperture radar interferometry; calculating a deformation phase gradient according to the time series interference pattern; superimposing the calculated deformation phase gradient, and performing an unwrapping operation on the superimposed deformation phase gradient to obtain an unwrapped absolute phase gradient; inputting the unwrapped absolute phase gradient into a pre-trained local surface deformation recognition model to identify the deformation area. The surface deformation detection method based on phase gradient provided in the embodiment of the present application not only improves the accuracy and reliability of surface deformation detection, but also reduces the difficulty of identifying local deformation signals from the rate map obtained from the time series analysis, greatly improves the detection efficiency of local deformation signals in a large range, and at the same time the computational cost is also greatly reduced.

The present invention discloses a tunnel surface deformation monitoring method based on multi-baseline close-range photogrammetry, comprising the following steps: S1: original image, using a CCD camera installed at a position in the tunnel that needs to be monitored, to collect its original image and store it; S2: reconstruction of the original image three-dimensional point cloud model, to reconstruct the three-dimensional model of the collected original image; S3: real-time image, using the CCD camera installed at the position in the tunnel that needs to be monitored in S1 to take regular photos of the position of the tunnel and store it; S4: reconstruction of the real-time image three-dimensional point cloud model. The present invention effectively realizes real-time monitoring of the use of the tunnel by monitoring the difference between the real-time image and the original image, can well control the service life and safety of the tunnel, and effectively reduce the losses caused by the use of the tunnel. At the same time, the method can realize real-time unmanned monitoring, and its monitoring is reliable.

The application discloses a soil liquefaction surface deformation field measuring system based on digital image technology, which comprises a control platform, a servo control and data acquisition system, a workbench, a support table, a soil box, an angle adjusting mechanism, a power loading system and a digital image deformation measuring system. The system can non-contact, full-field and high-resolution acquire the evolution data of the displacement field and the strain field of the soil surface in the vibration process, and can adapt to model tests of different sizes and inclination angles, thereby providing an advanced technical means for in-depth research on the liquefaction mechanism of saturated sand.

The invention discloses a reflector surface shape error calculation method based on a stiffness weighting method and related equipment. The method comprises the following steps: constructing a reflector finite element model; reading coordinate data and displacement data of mirror surface nodes of the mirror finite element model; calculating the displacement gradient of each node according to the displacement data, and calculating the rigidity weight of each node based on the displacement gradient; based on the rigidity weight, a rigidity weighting method is adopted to calculate the rigid body motion amount of the reflector; and deducting the rigid body motion amount from the total displacement to obtain a surface deformation amount, and calculating a reflector surface shape error based on the surface deformation amount. The objective of the invention is to overcome the defects that an extraction result deviates from a real overall motion trend and an actual surface shape error of an optical mirror surface cannot be accurately represented due to the fact that abnormal displacement of a local deformation node is considered when rigid body motion is extracted by a traditional equal-weight method, and to realize faster and more accurate surface shape error analysis and evaluation of a reflector deformation condition.

The invention relates to a method for the computer-aided generation of a data-driven model for the computer-aided processing of digital SAR data from a repeat-pass SAR system for determining surface deformations. The SAR data consist of a plurality of interferograms (Γ), each of which is obtained from a first and a second SLC acquisition (s1, s2), as well as a Doppler center frequency (f). dc ), an interferometric phase (ϕ̂) and a coherence (ρ̂), all obtained from a respective pair of first and second SLC acquisitions (s1, s2). The model is configured to produce a zero-Doppler phase (ϕ̂). zd ), an along-track phase (ϕ at ) and an estimate of an associated along-track surface motion (u at ) to determine. The generation of the model comprises the following steps: - Generating a synthetic training dataset (TDS) comprising a multitude of interferograms (Γ), each formed by a pair of a first and a second SLC acquisition (s1, s2), by inducing a model-based and external 3D deformation into a given first SLC acquisition (s1) to obtain the associated second SLC acquisition (s2), which relates to a time point after a deformation event, and by applying a reverse approach defined by a given coherence (ρ) and a noise-free interferometric phase (ϕ). theo ) is specified according to the deformation event; - Training a neural network (NN) with the training data set (TDS), wherein the neural network (NN) has an input for inputting the multitude of interferograms (Γ) and an output for outputting the along-track phase (ϕ). at ), the zero-Doppler phase (ϕ zd ) and the associated along-track surface motion (uat ) for the multitude of interferograms (Γ), where the trained neural network (NN) represents the generated data-driven model.

The utility model discloses an outer wall surface transformation structure, and belongs to the technical field of building outer wall surface transformation. The structure comprises an original wall body with a hollow drum being chiseled away, and an adhesion agent layer, an anti-crack waterproof mortar layer, a fine and smooth sublayer, a primer layer and a decorative surface layer are sequentially arranged on the original wall body from inside to outside. The old wall surface is modified by the interface agent layer, the anti-crack waterproof mortar layer, the fine and smooth sublayer, the primer layer and the decorative surface layer mainly through coating or spraying construction, punching on the original wall body is not needed, the labor intensity of outer wall surface modification is reduced, and the safety risk is low.

The application belongs to the technical field of robot picking, and discloses a soft fruit picking sliding calculation method, device, equipment and storage medium, the method comprises the following steps: acquiring surface deformation field data and distributed force field data when picking soft fruits, determining a sliding contact area based on the distributed force field data, calculating a contact surface sliding field according to the displacement difference formed by each contact point in the picking process, combining the surface deformation field data, calculating a rotational sliding component caused by the twisting of the fruit stem according to the rotational angular velocity generated by each contact point in the picking process, inputting the distributed force field data into a preset grasping quality function to calculate the grasping quality evaluation value of the end effector, and based on the sliding contact area, the contact surface sliding field, the rotational sliding component and the grasping quality evaluation value, the picking sliding information of the soft fruit is summarized. Through the above method, the micro sliding phenomenon of the soft fruit in the picking process can be quantified.

The invention provides a flexible body surface grid processing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grid distribution optimization of a flexible body surface deformation region and a potential deformation region through combining an energy optimization algorithm and a mass point model on the basis of not changing the topological relation of a grid, and obtaining a grid distribution optimization result; and the optimized grid has smoothness and directivity required by simulation. Compared with a related scheme, complicated topological operation is not needed, the topological relation of the grids does not need to be changed, the calculation amount in the physical simulation process is greatly reduced, and the distribution of the grids can be optimized in real time according to the deformation characteristics of the flexible body, so that subsequent simulation with higher precision is facilitated.

The invention belongs to the technical field of material life prediction, and particularly relates to a structure low-cycle fatigue life prediction method based on an image and a deep learning method, which comprises the following steps of: 1, selecting a target structure material, preparing DIC speckles on the surface of the material, and setting N1 groups of low-cycle fatigue test loading conditions; and continuously shooting surface deformation images of the selected area by using an industrial camera to obtain speckle images. According to the structure low-cycle fatigue life prediction method based on the image and the deep learning method, structure low-cycle fatigue life prediction based on any strain image can be realized according to the prediction model provided by the invention only through image data set construction and convolutional neural network model training. According to the method, the structural fatigue life information can be obtained directly through image acquisition, the method is simple and convenient, the method has the advantages of intelligent calculation, real-time result output and the like, and the prediction accuracy is high.

The invention discloses a method and a device for establishing a full-parameterized model of a three-dimensional special-shaped spherical object. The method comprises the following steps: generating a spherical grid according to an input bubble foreign matter parameter; analyzing the deformation parameters of the spherical network to perform parameter format and content verification to obtain analysis parameters; whether random parameters are applied or not is determined according to the analysis parameters, if not, surface deformation operation is executed according to the analysis parameters, if yes, the random parameters are generated based on the analysis parameters, and the surface deformation operation is executed according to the analysis parameters and the generated random parameters; after the spherical grid is subjected to surface deformation operation, distribution characteristic parameters of final space grid nodes are generated and obtained; and according to the distribution characteristic parameters of the space grid nodes, generating a Delou inner triangle patch.