114 results about "Displacement field" patented technology

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 application relates to a deformation field prediction method based on image quality evaluation and adaptive pre-training. Wavelet packet decomposition is performed on a reference image and a deformation image of a collected component to obtain coefficients of each subband of each layer of the images, and Pearson correlation coefficients of each subband are calculated based on all the coefficients corresponding to the subband. The subbands are screened based on L2 norms of coefficient vectors of the subbands and the Pearson correlation coefficients, and the screened coefficient vectors of the subbands are reconstructed to obtain reconstructed reference images and reconstructed deformation images. Quality evaluation indexes of each reconstructed sample pair are calculated. Training branches to which the corresponding reconstructed sample pairs belong are determined based on the quality evaluation indexes, a U-Net model is preliminarily trained by using a preset supervised training strategy based on the reconstructed sample pairs of different training branches, the U-Net model is unsupervisedly trained, and displacement fields and strain fields are predicted based on the trained U-Net model.

This invention discloses a method and system for inverting mechanical parameters and intelligently analyzing the stability of tunnel surrounding rock, relating to the field of intelligent construction technology for tunnels and underground engineering. The method includes: collecting tunnel monitoring and measurement data and constructing a displacement field observation matrix; constructing a physical information neural network embedded with the geotechnical mechanics control equations to invert the mechanical parameters and stress field of the surrounding rock; automatically calling the finite element kernel through a programming interface and calculating the safety factor of the surrounding rock using the strength reduction method; using evidence theory to fuse multi-source analysis results and output the stability level; and driving online model updates and support optimization through prediction-monitoring comparison verification. This invention achieves the integration of parameter inversion, automated numerical simulation, and closed-loop verification, improving the accuracy, efficiency, and intelligence level of surrounding rock stability analysis.

The application discloses a pose-deformation joint estimation method and system for micro contact assembly process, a workpiece is scanned by a mobile device from multiple perspectives, and a global point cloud model of the workpiece is reconstructed. Next, the overall pose parameters and the local surface topography of the workpiece are solved synchronously, and then a displacement field and a strain field are constructed to quantitatively represent the deformation state. Subsequently, the image, point cloud, pose and deformation parameters are fused, and a pre-trained joint estimation model is used to realize rapid prediction of the current coupling state of the workpiece. Through the built-in consistency verification and online updating mechanism, the reliability of the prediction result is ensured, and the final reliable state estimation is converted into a robot assembly correction instruction in real time, thereby forming a closed loop of perception, estimation and control. The application can realize sub-millimeter level pose measurement and high-precision deformation prediction, significantly improve the dynamic adaptability, robustness and automation level of the assembly process, and is suitable for automatic assembly of high-precision components in the fields of aviation, aerospace and the like.

The application discloses a kind of crack propagation prediction method and system of water turbine runner under transient hydraulic excitation, belong to the technical field of water conservancy and hydropower engineering, to solve the technical problem that existing prediction method cannot simulate crack propagation under transient hydraulic excitation.The application is by constructing finite element grid and level set field, encryption grid, identify enhanced node and solve runner time sequence displacement field, then calculate the stress intensity factor and energy release rate of crack front node, and calculate crack propagation direction and rate based on this, thereby update and correct level set field, to obtain the crack after expansion, to realize the accurate simulation of crack propagation under transient hydraulic excitation, provide reliable basis for runner crack monitoring, expansion trend prediction and fracture life assessment.Further, the application can track the crack propagation process from the current small condition to the penetration by multiple iterations, and accurately predict the fracture life of the runner according to the propagation law.

This invention relates to the fields of injection molding quality control and artificial intelligence, specifically to a method and system for predicting warpage defects in injection molded products based on a physical information neural network. The method includes: acquiring the basic parameters of the injection molded product and a physical information neural network; collecting measured data and randomly selecting physical sampling points; substituting the measured data and physical sampling points into the network using a double loss function training method; the measured data includes the basic parameters and corresponding measured warpage displacement values; the physical sampling points output corresponding predicted temperature fields, predicted internal stress fields, and predicted warpage displacement fields through an output layer; inputting the basic parameters of the product to be predicted into the trained network, and outputting the warpage defect prediction results. This invention balances data-driven accuracy with the reliability of physical mechanisms, solving the technical problems of pure data-driven models relying on a large number of defect samples and the low efficiency of traditional physical simulation.

This invention discloses a method and system for predicting the safety status of a construction formwork using a digital twin model. The method includes: extracting the component topology of the construction formwork; establishing a finite element simulation model of the construction formwork based on the component topology; and constructing a topological tension matrix for each node on the component. During construction, the geometric drift of each node on each component is acquired in real time. All geometric drifts of a node within a certain period are combined into a geometric drift vector. Based on the topological tension matrix, the geometric drift vector is mapped to an equivalent driving vector of the node, serving as an external excitation for the node. The displacement field of the node is calculated based on the equivalent driving vector. The axial stress of the component is calculated based on the displacement field. The state field of each node of the component is calculated based on the axial stress. The corresponding node is then judged to be unstable based on the state field.

The application relates to the technical field of unmanned aerial vehicle structure design and optimization, and discloses a layout optimization method based on an unmanned aerial vehicle landing gear system. The method comprises the following steps: a three-dimensional design model of the landing gear is constructed; real flight attitude data and a load spectrum are imported; stress nephograms and displacement fields under multiple working conditions are dynamically generated to accurately identify a region to be optimized; preset layout rule library is used for iterative adjustment; component layout is optimized until mechanical indexes are met; the optimized model is compared with a historical failure case database; a risk component list with geometric or mechanical similarity is extracted; and local reinforcement or topological reconstruction is carried out according to the risk component list to generate a final scheme. The method fuses dynamic load analysis and historical failure data, improves the accuracy of layout design, and identifies and prevents potential structural failure risks.

The present application relates to a kind of displacement reconstruction method based on improved inverse finite element method suitable for arbitrary boundary floating raft structure, the present application constructs unified constraint least square mathematical framework fusing fixed boundary and elastic boundary, based on displacement measurement data, angle measurement data, strain measurement data, the constraint condition updating of mathematical framework is carried out, based on elimination method, it is proposed that arbitrary boundary inverse finite element method develops displacement field solution, effectively expands the universality of existing inverse finite element method to special boundary constraint (such as elastic support). Compared with prior art, the present application fuses strain data and displacement data, effectively solves the displacement reconstruction problem of floating raft structure under the constraint of arbitrary boundary condition, can be widely popularized in the field such as ship, aerospace and other fields facing large raft structure deformation needs to carry out deformation reconstruction.

The application discloses a nonlinear Lamb wave excitation deviation evaluation method and system under dispersion constraint, belongs to the technical field of ultrasonic nondestructive testing and structural health monitoring, and is based on a Rayleigh-Lamb frequency equation and a second-order perturbation theory to construct a second harmonic displacement field model, divides a phase velocity matching mode pair into an intersection type, a longitudinal wave type and a cutoff type; the contraction law of a propagation distance and a mode order on an excitation window is quantitatively analyzed, an excitation tolerance coefficient and a relative gain coefficient are introduced, a two-dimensional quantitative evaluation system of mode pairs is established, three typical mode pairs are divided and are adapted to different detection working conditions, and an aluminum alloy sheet experiment is completed to verify the application. The application adopts the above-mentioned nonlinear Lamb wave excitation deviation evaluation method under dispersion constraint, establishes a standardized mode evaluation system, and significantly improves detection sensitivity and engineering reliability.

The present application relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps: collecting data according to a collection template, extracting pulse timing by adaptive threshold peak detection, calculating instantaneous phase by linear interpolation, comparing quantitative indicators with preset threshold values, judging steady state by combining peak loss rate and mutation detection rules, and verifying by calculating inter-channel phase consistency; splitting the collected data according to concept entities to form a data relationship model, implementing fast global rigidity estimation, applying affine transformation, estimating pixel-level displacement field by using a pyramid dense optical flow network, applying the displacement field to original pixels, and performing time domain fusion by taking optical flow confidence and registration residual as weights; cropping the short-time steady image sequence after registration compensation, taking a mixed network of a convolution front end and a space-time Transformer backbone as a prediction model, and outputting a pixel-level risk heat map, a candidate lesion list and confidence intervals of each output.

This invention discloses a deformation analysis method for multi-bolt structural connections based on displacement field equivalence and machine learning, relating to the field of advanced aircraft connection technology, and aimed at solving the technical problems of high computational load and low efficiency in existing technologies. The method includes: establishing a refined model of a single bolt connection process; analyzing the deformation of the connected components during the connection process based on the refined model; obtaining the equivalent load based on the deformation displacement field; constructing a multi-bolt connection deformation analysis model and creating connection elements within the analysis model; simulating the overall component by inputting the equivalent load to obtain a sample dataset; training a machine learning model using the sample dataset and learning the mapping relationship between load boundary conditions and deformation response from the simulation samples; and predicting the deformation of the multi-bolt structural connection based on the trained machine learning model.

The application provides a double-line shield tunnel optimization design method, device and equipment considering soil-water-structure coupling, and relates to the technical field of underground engineering and tunnel engineering. The method comprises the following steps: obtaining a construction stage stratum displacement field considering double-line mutual disturbance; establishing a groundwater seepage model to obtain a pore water pressure distribution and determine a consolidation additional displacement field; superimposing the construction displacement field and the consolidation additional displacement field to obtain a comprehensive stratum displacement field; analyzing the internal force and deformation response of the lining structure based on the comprehensive stratum displacement field; determining design indexes according to the response; if the design indexes do not meet the preset allowable value, adjusting the design parameters and repeating the above steps until all indexes meet the requirements. The application takes the stratum displacement field as a unified carrier, realizes continuous transmission and closed-loop optimization of construction effect, consolidation effect and structure response, and improves the reliability and consistency of double-line shield tunnel design.

This invention discloses an online visual inspection and laser dressing closed-loop control system for grinding wheel allowance, comprising a multi-axis precision linkage platform, a laser ablation dressing unit, a dual-modal composite visual sensing component, a real-time data processing unit, and a closed-loop feedback control unit. The dual-modal composite visual sensing component consists of a structured light scanning module and an infrared thermal imaging module connected by a coaxial optical path, and uses the same trigger signal to achieve spatial alignment and temporal synchronous acquisition, obtaining registered geometric point cloud and temperature field data of the grinding wheel surface. The real-time data processing unit incorporates a real-time dynamic decoupling model for thermally induced deformation, calculates the thermal expansion displacement field based on the temperature field, and separates it from the comprehensive radial deviation of the geometric point cloud to obtain the true geometric allowance. The closed-loop feedback control unit generates adjustment commands based on the deviation between the true allowance and the target contour, achieving high-precision closed-loop dressing. This invention solves the technical challenge of detecting thermal expansion interference accuracy during laser dressing, improving dressing accuracy and consistency.

This invention discloses a method for identifying dynamic loads on structures based on dynamic displacement inversion and motion control equations. The method includes: dividing the structure under test into several four-node inverse shell elements; deploying sensors at the centroid positions of the upper and lower surfaces of each element; reconstructing the displacement values ​​of each element node based on the collected strain data; predicting the displacement values ​​at any position of the structure under test using a high-resolution spatial domain reconstruction algorithm based on the reconstructed displacement values ​​of each element node; discretizing the fourth-order spatial deflection derivative of the displacement field of the structure under test using the finite difference method; approximating the second-order derivative of the strain data using the second-order central difference method to obtain the higher-order spatiotemporal deflection derivative; and establishing a model of the relationship between the dynamic displacement and load distribution of the structure under test by combining the motion control equations of the structure under test, thereby achieving dynamic load identification of the structure under test. Given the known geometry and material properties of the structure, this method can not only reconstruct the time history of the dynamic loads on the structure but also simultaneously identify the load application positions.

The application provides a multi-modal medical image registration method and system, which comprises the following steps: performing rigid coarse registration on a target multi-modal medical image after aligning the respiratory phase, obtaining an initial deformation field based on an initial radio transformation matrix of a generated global image and an initial grid spacing of the target multi-modal medical image, and calculating a normalized cross-correlation (NCC) gradient value of the global image; then identifying a local image of the target multi-modal medical image, dynamically updating the grid spacing of the local image, updating the deformation field, and calculating an NCC gradient value of the local image; determining a local displacement field according to the NCC gradient value of the local image and a preset gradient value, and superimposing the initial radio transformation matrix on the local displacement field to obtain a final registration result. The rigid registration of the target multi-modal medical image can ensure the end-expiratory phase alignment of the image, the non-rigid registration can further optimize the deformation field, and the registration accuracy is improved.

This invention relates to the field of electronic digital data processing technology, specifically to computer-aided engineering calculation and numerical simulation data processing technology. This invention discloses a design method for determining the maximum hanging weight of an ultra-thin lifting beam. In incremental iterative solutions, the equivalent stress gradient and strain energy density are extracted in real time to calculate local instability characteristic values. When the rate of change of element strain energy density exceeds a threshold, a mesh reconstruction mechanism is triggered. The nodal displacement field and stress field are extracted as initial boundary conditions. The coarse mesh is split into a fine mesh, and the coordinates of newly added nodes are smoothly interpolated based on the minimum potential energy principle. The local and global mesh degrees of freedom are coupled and assembled to continue nonlinear buckling analysis iterations until the load-displacement curve shows an extreme point. The corresponding load is then extracted as the maximum hanging weight. This invention avoids stiffness matrix singularities and computational divergence caused by distortion of the fixed mesh in local instability regions, ensuring the computational convergence of determining the maximum hanging weight.

This invention discloses a method for visualizing and detecting the internal flow field of a MEMS gas chromatograph column based on background schlieren imaging, relating to the fields of microelectromechanical systems (MEMS) and microfluidics. The method includes the following steps: S1, preparation of the MEMS micro gas chromatograph column chip; S2, establishment of the background schlieren optical link; S3, acquisition of the reference image; S4, acquisition of the flow image; S5, extraction of the displacement field; S6, inversion from displacement to refractive index / density gradient; S7, reconstruction and visualization output of flow field quantities such as velocity / pressure; S8, localization of local defects and updating of structural parameters; S9, experimental-simulation closed-loop iterative optimization. This invention applies BOS quantitative visualization technology to the internal diagnostics of MEMS-grade chromatographic columns, enabling rapid closed-loop correction and iteration of CFD models, reducing theoretical plate height, increasing the number of theoretical plates, reducing pressure drop, and increasing peak capacity. This provides a reliable experimental verification and optimization method for next-generation high-resolution, low-power, miniaturized aerospace-grade chromatographic columns.

The application discloses a UAV multi-modal image registration method and device, medium and product, relates to the technical field of remote sensing image processing and computer vision, and comprises the following steps: calculating the normalized mutual information value of each wave band of the image to be registered and a reference image, selecting a wave band with the maximum value as a reference wave band, solving a global affine transformation matrix by using an enhanced correlation coefficient algorithm, performing global geometric coarse correction, and obtaining the image to be registered after coarse registration; calculating the local principal direction of the image to be registered after coarse registration and the reference image, constructing a Gaussian derivative filter, extracting structural features based on the turning theorem, generating respective structural feature maps and dividing the structural feature maps into regular grids, performing grid-by-grid template matching to obtain a sparse displacement field, performing outlier elimination and interpolation smoothing to generate a dense linear deformation field, performing nonlinear geometric correction and applying the dense linear deformation field to all wave bands of the image to be registered after coarse registration, and obtaining a final registration result. The application realizes high-precision registration of multi-modal images.

The application discloses a fan blade grid dynamic matching method for two-way fluid-structure coupling analysis. The blade discrete deformation data output from a structure solver is used to extract the spanwise coordinate and displacement component, the neutral axis deflection sample is obtained by adopting the robust statistics of the box, the main deflection function is constructed by adaptively fitting Chebyshev polynomial, and the monotone regression constraint is applied. Then, the second-order deformation strength function is identified based on the curvature and the radial coordinate of the cross section, the axial projection shortening term is constructed by combining the large deflection geometric relationship, and the parametric analytical displacement field is formed. The above function is written into a parameter file in a small number of polynomial coefficients, the displacement is evaluated in real time according to the initial coordinates of the nodes in the dynamic grid of the fluid solver, and the coordinate-driven mapping without grid consistency is realized. The application effectively solves the displacement mapping difficulty caused by the inconsistent interface grid, reduces the calculation cost, avoids the dynamic grid distortion, and is suitable for the two-way fluid-structure coupling analysis of the fan blade.

The invention belongs to the technical field of robot control, and discloses a multi-dimensional touch decoupling method and system based on displacement field streamline and phase consistency, and the method comprises the steps: obtaining displacement or strain data of a flexible surface in continuous time, constructing a time sequence, and generating a displacement vector field and a displacement field streamline; establishing a joint motion topological stability reference through streamlines in continuous time, and comparing the current streamlines to detect topological anomaly so as to determine a touch candidate area; a displacement signal instantaneous phase is extracted in the candidate area, a phase gradient direction field is constructed, and finally the shear touch or torsion touch type is judged in combination with the cooperative relation between the streamline direction and the phase gradient direction. According to the method, stable decoupling of touch and movement can be realized under the background of strong joint movement, and the distinguishing accuracy of high-similarity touch types is improved.

This invention relates to the field of image recognition technology, specifically to a method and system for intelligent recognition of abnormal behavior at construction sites based on deep learning. The method acquires continuous image frames and extracts shallow feature maps, which are then input into a parallel decoupling network of a background dynamic decoupling network. A background motion estimation branch predicts the background pixel-level displacement field based on deformable convolution, and the shallow feature map of the current frame is reverse-distorted and aligned to generate an aligned feature map. A foreground human body separation branch extracts the foreground human body motion mask based on the aligned feature map. The spatiotemporal feature similarity matrix of the foreground human body motion masks across multiple consecutive frames is calculated, and pseudo-motion masks caused by sudden changes in illumination are removed, retaining temporally consistent motion masks. The temporally consistent motion masks are then input into a behavior classification network to output the recognition result. This invention removes non-human dynamic background interference at the feature level, eliminates mask anomalies caused by illumination changes, and ensures the purity of the input features to the behavior classification network.

This invention provides a high-fidelity optical deformation measurement technology and image generation method and system driven by a cross-attention mechanism, including: a high-precision deformation measurement step, which uses a feature extraction network to map a reference image and a deformed image to a high-dimensional feature space, constructs key-value vectors and query vectors respectively through learnable transformation matrices, constructs a similarity loss function based on a cross-attention mechanism, and calculates the displacement field and strain field by combining a preset parameterized representation mode; and a high-fidelity image generation step, which defines the deformed image as a pixel-level optimizable tensor, calculates the gradient and iteratively corrects the pixel gray-level distribution of the tensor under the constraint of a preset displacement field, and derives a high-fidelity deformed image that conforms to physical laws. This invention transforms image generation into an optimization problem under physical constraints, solving the defect of traditional interpolation methods that do not conform to physical laws, and providing a high-quality training dataset for deep learning-driven algorithms.

The application discloses a kind of three-dimensional deformation field measuring device and measuring method of projectile body in penetration process, it is related to projectile body penetration test technical field, its technical solution key points are: including: light gas gun, for launching solid projectile;Special target store, its lateral wall is equipped with observation window, inside is equipped with target plate and the projectile to be measured fixed on target plate;Three-dimensional digital image acquisition equipment, symmetrically arranged outside observation window, including two high-speed cameras of synchronous trigger, matching lens, high-brightness light source and synchronous trigger;Magnetic speed measuring instrument, set in the tail of the barrel of light gas gun;Data processing equipment is used to receive the image sequence collected by three-dimensional digital image acquisition equipment, based on calibration parameter calculation the three-dimensional displacement field of the surface of the projectile to be measured, full-field strain field and velocity field, visual output deformation evolution process.The application can realize the non-contact, high space-time resolution measurement of three-dimensional dynamic deformation field of projectile body itself in the transient process of projectile body penetrating target plate.

This invention relates to the field of intelligent manufacturing technology, and more particularly to a self-aligned stack positioning method and system for welding multi-layer structures. The method first extracts the microstructure of the flow channels and the geometric features of the edges on the plate, constructing a spatial constraint model to eliminate false features. Then, the features are mapped to a global coordinate system, and the pose deviation is calculated based on image signal-to-noise ratio weighting. Simultaneously, a thermo-mechanical coupling model is constructed to obtain the nonlinear displacement field of the plate under welding thermal cycling, and the relative expansion vector is calculated using the thermal expansion center as a reference to generate a thermal pre-compensation value to offset thermal deformation. By superimposing the pose deviation and the thermal pre-compensation value, the motion platform is driven to perform the initial alignment. Finally, feature frequencies at different depths are stripped in the frequency domain to infer the cumulative offset of the multi-layer stack and construct an error evolution curve, achieving adaptive correction of subsequent positioning parameters.

This invention relates to the field of bridge displacement and stress field measurement technology, specifically to an existing bridge deflection and stress monitoring method based on digital image correlation technology. It includes deploying a speckle field on the surface of a key cross-section of the bridge to be monitored, setting up a high-speed camera to acquire time-series image sequences, processing the image data using a digital image correlation algorithm to calculate the full-field displacement field and full-field strain field of the key cross-section surface, verifying the full-field strain field to generate full-field deformation data, and using a physical driving model to invert the full-field deformation data into the stress distribution of the key cross-section, identifying stress concentration areas, and providing dynamic early warning based on a preset safety threshold. This invention provides full-field, dynamic observation of key areas of the bridge, avoiding the damage and long-term maintenance problems caused by drilling holes and attaching sensors to the structure. Theoretically, the measurement point density is only limited by the camera resolution, enabling seamless coverage of the entire monitoring area and ensuring that local damage is detected.

This invention discloses a method and system for identifying and reconstructing suspended spans of subsea pipelines based on distributed fiber optic strain sensing, belonging to the field of subsea pipeline integrity management and structural health monitoring technology. The invention deploys distributed fiber optic strain sensors longitudinally along the monitored pipeline segment, dispersing them circumferentially to form measurement positions, thereby separating strain and determining the principal bending plane and curvature. The curvature energy ratio extrapolation is used as the convergence criterion to determine the boundary of the suspended span influence region. A simply supported conjugate beam model is established within the suspended span influence region to obtain the foundation displacement field and rotation field reflecting the elastic deformation of the suspended span. Addressing the limitation of the conjugate beam method in reconstructing the rigid body displacement effect generated by pipe-soil interaction, a zero-curvature rigid body follow-up displacement correction method is proposed. This method compensates for the missing rigid body displacement components in the real displacement field by constructing a piecewise linear correction term. This invention overcomes the difficulty of conventional numerical reconstruction relying on ideal boundaries and improves the computational accuracy of spatial displacement field reconstruction.

A method for full-field inversion of corrected Mohr-Coulomb constitutive parameters based on automatic differentiation of computational graphs is presented, relating to the field of numerical simulation in geotechnical engineering. This method constructs a smoothed, differentiable corrected Mohr-Coulomb computational graph. After inputting initial data of the soil and rock mass to be analyzed, a forward finite element simulation is performed to obtain the simulated displacement field. The loss function is calculated, and its change is checked to determine convergence. If convergence is not achieved, the gradient is automatically calculated via backpropagation, and the parameters are updated using the L-BFGS optimization algorithm, with TV regularization constraints used to maintain the smoothness of the parameter field. The forward finite element simulation is repeated, and this process is iterated until convergence is achieved. The inverted parameter field is then output. By utilizing automatic differentiation technology, the accurate gradient of the full-field parameters can be obtained with a single backpropagation, introducing the automatic differentiation technology of deep learning frameworks into the field of geotechnical engineering, realizing the transformation from manual gradient derivation to automatic machine differentiation.

This invention relates to the field of image processing technology, and particularly to a three-dimensional surface flattening method, apparatus, electronic device, medium, and product. The method includes the following steps: acquiring multi-view surface images of a target object and reconstructing a three-dimensional dense point cloud; after preprocessing, flattening a reference plane by plane fitting and aligning the point cloud to its coordinate system to obtain a unified coordinate point cloud; constructing a discrete point cloud mesh model and compressing it to the reference plane to obtain a particle displacement field; and finally generating a two-dimensional flattened image of the target object based on this displacement field and the target three-dimensional dense point cloud. This invention solves the problems of incomplete display of two-dimensional flattened images and low flattening efficiency when processing surfaces with severe wrinkles, tears, or complex curvatures, thereby improving the accuracy of two-dimensional images of three-dimensional surface flattening and increasing the efficiency of three-dimensional flattening.