38 results about "Three dimensional morphology" patented technology

The invention relates to the technical field of petroleum and natural gas engineering, in particular to a method, a system and equipment for acquiring artificial fracture parameters. The method comprises the following steps: acquiring initial fracture form data through a core experiment, wherein the initial fracture form data comprises the straightness, the branch number and the roughness parameter of a fracture; performing verification analysis based on morphology on the initial fracture morphology data, and adding morphological characteristics of irregular fractures to obtain fracture morphology data; according to the fracture form data, fracture conductivity simulation based on proppant distribution and stratum conditions is carried out, preliminary evaluation is carried out, and fracture conductivity data are generated; and carrying out dynamic adjustment processing based on crack pressure on the crack form data by using micro-seismic imaging to generate crack three-dimensional form model data. Through systematic core experiment and strict morphological verification, the obtained data has higher accuracy and reliability, and firm data support is provided for subsequent flow conductivity analysis and model construction.

Disclosed in the present invention is a method for identifying morphological features of an underwater pile group of a bridge on the basis of a three-dimensional sonar point cloud, comprising the following steps: collecting point cloud data of the three-dimensional morphology of underwater pile foundations of a bridge: using a three-dimensional real-time sonar system-mounted survey vessel to perform multi-directional scanning around the underwater pile foundations of the bridge and performing real-time stitching, so as to acquire point cloud data of the complete morphology of the underwater pile foundations; on the basis of the point cloud data of the complete morphology of the underwater pile foundations, performing pile foundation extraction by means of a point cloud minimum cut method, and on the basis of a single pile foundation extracted after segmentation, projecting the pile foundation point cloud to an XOY plane to obtain a planar pile foundation point cloud; on the basis of the planar pile foundation point cloud, performing pile foundation morphological feature identification by means of an iterative MAD method; and on the basis of the identification result, mapping a complete pile foundation morphological feature point cloud back to an original three-dimensional space on the basis of elevation information so as to obtain a three-dimensional pile foundation point cloud having complete morphological features. The present invention can implement accurate identification of complete pile foundation point cloud morphological features.

The invention discloses a cell fate distinguishing and differentiation prediction method based on a three-dimensional form, and belongs to the technical field of cell fate distinguishing and differentiation prediction. In order to solve the problem that cell fate differentiation is difficult to distinguish and predict under a small sample size, the method comprises the following steps: acquiring a living biological sample, and imaging by using a three-dimensional delay microscope to obtain cell three-dimensional image data; segmenting the cell membrane boundary and extracting geometric and high-order three-dimensional morphological characteristic parameters; labeling cell fate and differentiation states based on means such as cell lineage tracking or gene fluorescence labeling; and finally, determining an optimal morphological index and a minimum sample size through single-side double-sample t test, and applying the optimal morphological index and the minimum sample size to two new cell populations and outputting a cell fate distinguishing and differentiation prediction result. The method can realize cross-species, low-loss, low-cost and high-precision cell fate differentiation and differentiation prediction, and is suitable for development research, stem cell culture and disease diagnosis.

The invention provides a tailing particle classification method based on multi-dimensional morphological feature compression and clustering, and belongs to the technical field of geotechnical engineering, mineral processing and particle classification, and the method comprises the following steps: S1-S6, obtaining an STL file database of tailing particles; s7, calculating and extracting morphological parameters to form an original parameter data set; s8-S10: obtaining a three-dimensional morphological parameter set which retains the original morphological feature information; s11-S12, determining an optimal clustering number by adopting an elbow rule, a contour coefficient and Gap statistics, and performing particle morphology clustering grouping on the three-dimensional morphology parameters by adopting a K-means algorithm; and S13, carrying out stacking test simulation on the clustered particle categories by using a discrete element method, analyzing the stacking density and the average coordination number of different categories of particles, and verifying the physical significance of a classification result. The tailing particle classification method can systematically and efficiently integrate particle multi-dimensional form information, can directly associate the information with macroscopic physical and mechanical properties, and is data-driven, clear in mechanism and explainable in result.

The application discloses an XCT image-based CFRP defect automatic identification and classification method and system, and belongs to the field of carbon fiber reinforced composite material manufacturing and processing quality detection.The application obtains the three-dimensional features of defects through binarization three-dimensional label statistics classification, obtains the two-dimensional features of defects through manual labeling based on the three-dimensional label of defects, and after fusing the three-dimensional features and the two-dimensional features of defects, combines the features with a deep learning method to obtain an efficient real defect identification and classification model, which is simple and efficient and effectively improves the CFRP defect identification precision.The application uses the three-dimensional morphological parameters of defects when labeling the data set, and avoids the unreliability introduced by directly labeling on two-dimensional slices.The application trains a semantic segmentation model for identifying and classifying CFRP defects based on a small amount of two-dimensional CT images, and the semantic segmentation model is used for predicting internal defects of similar materials, so that a large amount of tedious and time-consuming manual image processing for obtaining internal defect data of materials is avoided, and a large amount of labor cost is saved.

This application discloses a method, device, and medium for measuring the three-dimensional morphology of the temporomandibular joint (TMJ), relating to the field of image processing. The method comprises: inputting target TMJ CBCT data into a mandibular three-dimensional segmentation model to obtain a mandibular three-dimensional segmentation result; determining an image of the TMJ center region based on the mandibular three-dimensional segmentation result and the target TMJ CBCT data; inputting the TMJ center region image into a condyle and fossa two-dimensional segmentation model to obtain two-dimensional segmentation results of the condyle and fossa regions; and calculating condyle morphological information, fossa morphological information, and position information of the condyle in the fossa based on the two-dimensional segmentation results of the condyle and fossa regions; wherein the mandibular three-dimensional segmentation model and the condyle and fossa two-dimensional segmentation model are both obtained by training a deep learning network. This application can achieve automatic and accurate measurement of the three-dimensional morphology and position of the condyle and fossa of the TMJ.

The invention relates to the field of aggregate morphology detection and image processing, and discloses an aggregate particle morphology quantification method and system based on Fourier spectrum decoupling, and the system comprises a three-dimensional point cloud collection module which is used for obtaining the three-dimensional point cloud data of aggregate particles; the point cloud data processing module is used for processing the three-dimensional point cloud data to obtain the two-dimensional contour data; the morphological feature analysis module is used for processing the two-dimensional contour data to obtain aggregate decoupling feature data; and the detection and analysis module is used for associating the modules. According to the method, the RGB-D depth point cloud system is used for collecting multi-view point cloud, and the complete three-dimensional morphology point cloud is obtained through noise reduction and registration. Through Monte Carlo simulation sectioning, two-dimensional equal circumference mapping, contour Fourier series fitting, gradient operator definition and the like, a method for performing decoupling calculation on the shape, the corner angle and the texture of the aggregate through aggregate form parameters is established, and the accuracy and the accuracy of aggregate three-dimensional form test analysis are improved.

The invention relates to the technical field of biological information processing and automatic sorting, and discloses a chick gender automatic identification method and system based on multi-modal fusion, and the method comprises the steps: collecting original three-dimensional point cloud data through a three-dimensional form sensing module, and calculating the key region coordinates of chicks; based on the coordinates, controlling the addressable micro sampling array to execute active sampling adaptation, and obtaining measurement response time sequence data of the target volatile matter; acquiring background response time sequence data by utilizing a background channel sensor array; and performing differential processing on the two to generate a differential biochemical response time sequence vector. And then, respectively extracting morphological feature vectors and biochemical fingerprint feature vectors, inputting the morphological feature vectors and the biochemical fingerprint feature vectors into respective independent classifiers to obtain prediction categories and confidence coefficients, finally, judging a bimodal prediction result through preset cross validation gating fusion logic, outputting a final decision, and controlling a sorting execution module to complete physical sorting. And the accuracy of biochemical sampling is improved through active sampling adaptation.

The present invention belongs to the field of urban functional area classification, and in particular relates to a functional area classification method, system, medium and equipment based on multi-source data fusion. Combining the two- and three-dimensional morphological features of the city and POI data, supplemented by road network data, a functional area classification study based on multi-source data fusion is carried out. Based on the differences in spatial landscape structure and socio-economic activities reflected by different functional areas, the two- and three-dimensional morphological features of each functional area are extracted to describe its spatial landscape structure, and POI data is used to describe basic human activities; the two- and three-dimensional morphological features of the city and POI features are fused, different functional area classification schemes are designed, and the impact of different features on the accuracy of functional area classification is compared and analyzed, which significantly improves the urban functional area classification results.

This invention provides a tailings particle classification method based on multi-dimensional morphological feature compression and clustering, belonging to the fields of geotechnical engineering, mineral processing, and particle classification technology. The method includes: S1-S6: acquiring an STL file database of tailings particles; S7: calculating and extracting morphological parameters to form an original parameter dataset; S8-S10: obtaining a three-dimensional morphological parameter set retaining the original morphological feature information; S11-S12: determining the optimal number of clusters using the elbow rule, silhouette coefficient, and Gap statistic, and using the K-means algorithm to cluster the three-dimensional morphological parameters into particle morphology groups; S13: using the discrete element method to conduct a stacking test simulation on the clustered particle categories, analyzing the stacking density and average coordination number of different particle categories, and verifying the physical meaning of the classification results. This tailings particle classification method can systematically and efficiently integrate multi-dimensional particle morphological information and directly correlate this information with macroscopic physical and mechanical properties. It is data-driven, has a clear mechanism, and the results are interpretable.

The invention relates to the technical field of smart animal husbandry and computer vision, and discloses a live pig weight estimation method based on semantic key point guidance and three-dimensional morphological feature fusion, which comprises the following steps: step 1, joint perception and semantic decoupling; step 2, space mapping and point cloud cleaning; step 3, carrying out self-adaptive attitude normalization; 4, orthogonally intercepting the rigid trunk; step 5, deep extraction of morphological characteristics; and step 6, intelligent regression prediction. According to the invention, a common fixed-proportion excision method or a neck positioning method depending on geometric depression in the prior art is abandoned; by introducing an orthogonal truncation mechanism guided by a semantic anchor point (ear root), a virtual'adaptive rigid collar 'is constructed on a front-end truncation plane, and the truncation boundary can be dynamically adjusted along with swinging of the head of a pig and is always perpendicular to the spine axis. According to the mechanism, interference of non-rigid deformation such as large-amplitude head torsion and head lowering of the pig on trunk volume calculation is eliminated in principle, and the stability of effective trunk point cloud is ensured.

The invention provides a detection and rating method for non-metallic inclusions, which comprises the following steps: scanning a sample by adopting an X-ray tomography system to obtain three-dimensional gray data, and marking the non-metallic inclusions; obtaining three-dimensional morphological parameters and spatial position information of the non-metallic inclusions; counting the obtained three-dimensional morphological parameters and spatial positions to obtain the volume fraction, size distribution concentration ratio, spatial distribution uniformity and shape distribution data of the inclusions; calculating the data by adopting an evaluation model to obtain an inclusion evaluation index, and rating the nonmetallic inclusions of the steel grade on the basis of the inclusion evaluation index. According to the method, the problems of contour distortion and form misjudgment of a traditional method are solved, the objectivity of inclusion rating is effectively improved, an IEI rating result is directly output, a visual basis can be provided for steel quality control and performance pre-judgment, and industrial application is supported.

The present invention provides a method and system for constructing a digital periodontal lesion model. The method uses an intraoral scanner and a CBCT to scan the oral cavity to be measured respectively, and obtains the first three-dimensional morphological data including the dental crowns and gums in the patient's oral cavity and the second three-dimensional morphological data including the patient's teeth and alveolar bone respectively; the three-dimensional morphological data are fused by the same crown morphology to obtain a three-dimensional visualization model of the oral cavity to be measured including oral soft and hard tissue information; generating a target marking line in the oral cavity model to be measured; obtaining a target area according to the target marking line; automatically measuring the target data of the representative measurement sites of each tooth and outputting the results; rendering the target area and outputting the rendered oral cavity model to be measured with the target area. The present invention shortens the periodontal examination and diagnosis time, reduces the waste of human resources, improves work efficiency, and enables the accurate and intuitive three-dimensional display of the subgingival periodontal lesion areas that are not directly visible.

The invention relates to the technical field of erosion gully parameter conversion, in particular to an erosion gully multi-dimensional morphological parameter conversion method and related equipment. And constructing a multi-dimensional factor database comprising ditch shape parameters, terrain, rainfall, vegetation, soil, land utilization, lithology and management factors. And adopting a recursive feature elimination algorithm to screen main control factors influencing the three-dimensional form, inputting a screening result into a pre-trained prediction model, and outputting an elevation value of the cross section of the erosion gully. And finally, generating a three-dimensional form spatial distribution result of the erosion gully in the target area by combining the elevation value and a factor database. According to the method, accurate prediction from two-dimensional data to three-dimensional form is realized, and a scientific basis is provided for erosion gully treatment and water and soil conservation.

The invention provides an automatic identification method for a black tide three-dimensional structure based on a flow velocity gradient, and the method comprises the steps: calculating the flow velocity gradient line by line according to latitude and layer by layer according to depth, and precisely determining the boundary points of the black tide three-dimensional structure in each layer and each line through combining with the longitude difference constraint and candidate point screening technology. In order to adapt to different marine environments, the method supports flexible selection of whether the influence of topographic factors is included or not, a two-dimensional field obtained by subtracting an average value from a resultant velocity is constructed, the bottom boundary depth is determined by using a zero isoline, finally, distribution of a black tide structure is visualized in a three-dimensional scatter diagram form, and the three-dimensional form of the black tide structure is comprehensively presented. According to the method, the limitation that a traditional method is limited to surface analysis and a black tide structure is difficult to accurately extract in real time is broken through, the defects of forced division of water area features and fixed depth setting in a clustering method are overcome, real-time structural form acquisition based on the data time resolution is realized, and meanwhile, the calculation complexity is remarkably reduced.

A method 500 for printing on a substrate 102 for producing solar cells, the method comprising: printing 501 a wet pattern on the substrate 102; extracting 503 three-dimensional morphological data of the wet pattern 104′ in real time using an online profilometer 101; wherein the printing of the wet pattern on the substrate is controlled in real time at least in part based on previous three-dimensional morphological data obtained by extracting the three-dimensional morphological data.

The present invention provides a method for identifying flaky and elongated aggregate particles based on multi-view image fusion, including: collecting an aggregate image sequence from multiple perspectives, preprocessing the images and performing multi-morphology data association of the same aggregate particle, adjusting the angle of the axis where the minimum moment of inertia of the aggregate particle is located, fusing the multi-view morphologies of the aggregate particle into one aggregate image, establishing an aggregate shape classification model based on a deep learning network and classifying the aggregate shape. By fusing the aggregate morphologies in different perspectives, the present invention constructs a multi-view fusion image capable of reflecting the three-dimensional morphological characteristics of the aggregate, improves the three-dimensional morphological representation ability of the two-dimensional aggregate image, and uses the adaptive feature extraction ability of the deep learning model to construct a classification model for aggregate shape classification, effectively improving the recognition accuracy of flaky and elongated aggregates.

The application discloses a kind of stem filament three-dimensional morphology characterization and filamentous degree evaluation method and system, belong to tobacco processing technical field.The method includes: obtaining stem filament three-dimensional point cloud data, after pre-processing, using cascade segmentation strategy, combined with normal vector distribution entropy criterion and improved watershed algorithm realizes the accurate separation of adherent stem filament;To single stem filament point cloud cluster extraction three-dimensional space skeleton line;Along the skeleton line method plane is analyzed to cross section projection point set, calculates real three-dimensional length, average width and thickness, bending degree, cross section uniformity index and structural integrity coefficient and other basic morphological parameters;On this basis, construct the comprehensive filamentous degree index of fusion multi-attribute penalty function, and calculate thickness unevenness index, structural defect suspicion index and other derivative parameters to assist process diagnosis.The application realizes the objective quantitative evaluation of stem filament "thin, long, straight, even, integral" quality, solves the problem of traditional two-dimensional measurement distortion and subjective evaluation misalignment.

The present invention provides a method for automatically identifying the three-dimensional structure of the Kuroshio based on the velocity gradient, including: calculating the velocity gradient row by row according to latitude and layer by layer according to depth, and precisely determining the boundary points of the three-dimensional structure of the Kuroshio in each layer and each row by combining the longitude difference constraint and the candidate point screening technique. To adapt to different ocean environments, this method supports the flexible selection of whether to incorporate the influence of topographic factors. By constructing a two-dimensional field of the combined velocity minus the average value and using the zero isocline to determine the bottom boundary depth, the distribution of the Kuroshio structure is finally visualized in the form of a three-dimensional scatter plot, comprehensively presenting its three-dimensional shape. The present invention breaks through the limitation of the traditional method which is limited to surface analysis and difficult to accurately extract the Kuroshio structure in real time, avoids the deficiencies of forced division of water area characteristics and fixed depth setting in the clustering method, realizes the acquisition of real-time structural morphology based on the time resolution of data, and significantly reduces the computational complexity.

The invention provides a lingual surface three-dimensional shape reconstruction method and system based on deep learning, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: activating an image collection array based on a light source illumination parameter to carry out the multi-angle two-dimensional sensing of a target tongue part, and obtaining an original lingual surface image sequence; performing visual cutting, extracting a plurality of lingual surface target images, performing deep learning, and constructing a lingual surface depth map; performing three-dimensional shape analysis, constructing lingual surface three-dimensional point cloud coordinates, mapping the coordinates to a plurality of lingual surface target images for identification, and performing comparative analysis on an identification result and a standard image library to generate a comparative parameter set; and performing correction, performing three-dimensional reconstruction on the target tongue according to a correction result, and constructing a three-dimensional morphological model. The technical problems that a lingual surface analysis method in the prior art mostly depends on two-dimensional images or manual observation, subjectivity is often high, information loss is serious, and the real three-dimensional form of the lingual surface is difficult to obtain are solved.

The present application relates to the field of aggregate morphology detection and image processing, and discloses a method and system for quantifying aggregate particle morphology based on Fourier spectrum decoupling, including: a three-dimensional point cloud acquisition module for acquiring three-dimensional point cloud data of aggregate particles; a point cloud data processing module for processing the three-dimensional point cloud data to obtain the two-dimensional contour data; a morphological feature analysis module for processing the two-dimensional contour data to obtain aggregate decoupling feature data; and a detection and analysis module for associating various modules. The present invention uses an RGB‑D deep point cloud system to collect multi-view point clouds, and obtains a complete three-dimensional morphological point cloud through noise reduction and registration. Through Monte Carlo simulation sectioning, two-dimensional isoperimetric circle mapping, contour Fourier series fitting and gradient operator definition, a calculation method for decoupling the shape, edges and texture of aggregates using aggregate morphological parameters is established, thereby improving the precision and accuracy of aggregate three-dimensional morphological testing and analysis.

A method and system for detecting the three-dimensional morphology of aggregates based on two-dimensional particle parameters is disclosed, relating to the fields of aggregate detection and image processing technology. The method mainly involves acquiring two-dimensional morphological data of aggregates using an aggregate image measurement system (AIMS), acquiring three-dimensional morphological data of aggregates using a three-dimensional blue light scanner and visualization software (AVIZO), and then establishing a correlation mapping between two-dimensional and three-dimensional indicators using a gradient boosting regression tree method. This enables rapid and accurate prediction of the three-dimensional morphology of aggregates, facilitating refined management of mineral aggregate classification, identification, storage, and accurate retrieval, providing scientific support for engineering design, and promoting the intelligent and automated development of engineering management.

The application discloses a kind of based on morphological extraction point cloud local surface feature method, steps are as follows: S1 acquires point cloud data and carries out data pretreatment to point cloud data;S2 searches the neighborhood point of each point of point cloud in three-dimensional space, calculates normal information, then based on normal information, the curvature of each point is calculated, and curvature value is obtained;Then refer to the curvature value range corresponding to local surface feature and filter out target surface;S3 uses progressive morphological filter to adjust filter parameter constantly, according to the curvature change of selected local area, the size of filter window is adjusted accordingly, non-target point in point cloud data after curvature screening is filtered out, and the point of target local area is extracted;S4 uses dilatation, corrosion three-dimensional morphological operator and dilatation corrosion combination open operation and close operation to optimize target local area, and extract the local surface feature area of complete smooth point cloud data.The method is extracted to local area with obvious curvature change, high accuracy, and wide adaptability.

This invention relates to a three-dimensional defect segmentation method, system, and storage medium, belonging to the interdisciplinary fields of computer vision, deep learning, and nondestructive testing. The three-dimensional defect segmentation result is obtained by inputting the industrial CT volume data of the object to be inspected into a 2.5D segmentation network composed of an encoder, a spatial feature displacement module, and a decoder. This invention achieves efficient three-dimensional context awareness, significantly improving defect segmentation accuracy and three-dimensional morphological integrity. The segmentation result has topological connectivity and smooth boundaries, and its computational complexity and memory usage are far lower than those of a full 3D network. It can meet the real-time requirements of industrial online inspection and has broad application prospects in high-end manufacturing.

The invention relates to the technical field of machine vision, in particular to a multiple verruca plantaris morphological data acquisition system based on a machine vision system, and aims to generate an accurate verruca plantaris three-dimensional morphological graph. The system comprises a plantar wart image acquisition module, a plantar wart area identification module, an image enhancement module, an image restoration module and a plantar wart three-dimensional form reconstruction module. The plantar wart image acquisition module is used for shooting from three different visual angles to obtain a multi-visual-angle plantar wart image; the verruca plantaris area identification module identifies a verruca plantaris area in the multi-view verruca plantaris image and analyzes the verruca plantaris area to obtain key morphological characteristics; the image enhancement module constructs an image enhancement model to perform multi-region enhancement processing on the plantar wart region image to obtain a second enhanced plantar wart image; the image repairing module is used for repairing depth information of the second enhanced plantar wart image to obtain a repaired plantar wart depth map; and the plantar wart three-dimensional shape reconstruction module generates a plantar wart three-dimensional shape graph according to the repaired plantar wart image and the multi-view plantar wart image.

This invention relates to a three-dimensional defect segmentation method, system, and storage medium, belonging to the interdisciplinary fields of computer vision, deep learning, and nondestructive testing. The three-dimensional defect segmentation result is obtained by inputting the industrial CT volume data of the object to be inspected into a 2.5D segmentation network composed of an encoder, a spatial feature displacement module, and a decoder. This invention achieves efficient three-dimensional context awareness, significantly improving defect segmentation accuracy and three-dimensional morphological integrity. The segmentation result has topological connectivity and smooth boundaries, and its computational complexity and memory usage are far lower than those of a full 3D network. It can meet the real-time requirements of industrial online inspection and has broad application prospects in high-end manufacturing.

The invention relates to the technical field of image recognition, in particular to an aggregate three-dimensional morphological feature analysis method and system based on laser scanning, and the method comprises the steps of data intelligent collection, three-dimensional point cloud data intelligent preprocessing, morphological feature parameter calculation, intelligent analysis and association and application. In the prior art, morphological analysis, virtual experiment and performance prediction of aggregate are generally regarded as relatively independent links, or only forward prediction from process, material to performance can be carried out, and closed-loop analysis cannot be formed; according to the method, on the basis of analyzing the aggregate accumulation characteristics through a virtual compaction experiment, a double-prediction model architecture is introduced, the raw material ratio is reversely deduced from morphological characteristics and the process through the first prediction model, and the process parameters are inversed through the second prediction model in combination with morphological and raw material information; and finally, inputting all the features into a performance prediction model for comprehensive performance prediction. And unprecedented deep analysis capability is provided for aggregate quality diagnosis, production process optimization and performance customization.

This invention relates to the field of cloud-edge collaborative computing technology, specifically to a method and system for processing integrated air-space-ground metal mineral exploration data. The method includes the following steps: extracting node resistance anisotropy features to generate corrected topological weights and constructing a Laplace tensor matrix to output a reduced-rank profile matrix; establishing a high-order tensor for cross-domain routing record flow by combining the profile spatial gradient and the network node signal-to-noise ratio; constructing an anisotropic correction matrix based on local covariance matrix analysis to extract three-dimensional normal vectors; solving the implicit scalar field and extracting isosurfaces to finally complete three-dimensional reconstruction. In this invention, by analyzing the reduced-rank structural features of the multi-dimensional exploration network and integrating node dynamic attributes to determine link steady-state scores to guide the priority cross-domain flow of compressed tensors, and by relying on the proportion of spatial local distortion to correct the grid gradient and reshape the implicit surface constraint field, this effectively avoids long-distance congestion transmission of massive heterogeneous data and improves the accuracy of three-dimensional morphological reproduction of complex underground ore bodies.

This invention provides a digital intelligent MSE automatic design method based on biomechanical simulation, belonging to the field of biomechanical simulation technology. The method includes: acquiring three-dimensional morphological data and physiological load data of the target anatomical structure and mapping them to obtain a load distribution matrix; performing tensor operations on the load distribution matrix and tissue mechanical response characteristics to obtain a stress prediction field; identifying high-stress concentration areas and low-stiffness weak areas based on the stress prediction field; extracting contour curves and calculating the normal gradient to determine the expansion constraint direction; minimizing the stress distribution variance and the total tissue deformation energy and determining the spatial configuration parameters of the MSE device; using the spatial configuration parameters as boundary conditions to iteratively calculate the displacement field and strain field; correcting the spatial configuration parameters based on the difference between the cumulative deformation and a preset physiological threshold and the iterative deformation energy change rate; and outputting the corrected configuration parameters as design instructions when the convergence condition is met.

The invention relates to the field of ultrasonic non-destructive testing, and discloses an underground thread digital modeling method and system.The method comprises the steps that firstly, a thread area is wrapped with a self-adaptive coupling module; secondly, an ultrasonic phased array probe is fixed to a self-adaptive coupling module to do composite motion, and full-matrix data are obtained; then, performing imaging processing on the full-matrix data to obtain a two-dimensional image; then, extracting a thread three-dimensional feature point cloud based on the two-dimensional image; and finally, reconstructing a three-dimensional digital model of the thread based on the obtained three-dimensional feature point cloud. The method has the acoustic beam deflection and focusing capability of a phased array technology, can better adapt to threads of different specifications and surface states by combining flexible coupling, is high in universality, can judge whether defects exist or not, can accurately reconstruct the three-dimensional form of the defects, and provides unprecedented data support for safety evaluation and life prediction.