37 results about "Geometric reconstruction" patented technology

The application provides a symmetric workpiece disorderly grabbing method and system based on hierarchical reasoning, relates to the technical field of industrial robots, and comprises the following steps: acquiring a multi-modal image of a stacking scene, and reconstructing a complete physical mask representation of each workpiece from the multi-modal image. Through multi-modal perception fusion and full-view geometric reconstruction processing, the application obtains complete physical boundary representation and unbalanced load feature data of each workpiece in a shielding state, and on this basis, a directed hierarchical topological model describing the physical pressing and stacking relationship between workpieces is constructed through spatial gradient analysis, a candidate workpiece in an absolute top layer in the current scene is screened out, and then, a grabbability quantitative evaluation is performed to determine a target to be grabbed, so that the robot can identify and eliminate the bottom shielding workpiece which is easy to cause the collapse of the material pile in advance based on the real physical support relationship before action execution, and interference and instability caused by blind grabbing are avoided from the root.

This invention relates to the field of image analysis technology, specifically to a machine vision-based quality inspection system for new energy vehicle wiring harnesses and cable ties assembly. The system includes: an image acquisition module that uses polarimetric stereo technology to acquire multi-angle images; a geometric reconstruction and reference extraction module that calculates the surface normal field of the insulation layer and separates the background field of the cable's cylindrical curvature and the micro-indentation residual field, thereby extracting the global axial and radial curvature moduli of the cable; a feature decoupling analysis module that calculates the contour-shaping migration flow field of the indentation micro-residual field relative to the standard contour, and performs anisotropic decomposition of the flow field based on the global axial direction of the cable to obtain the axial slip index; and a quality inspection module that determines an adaptive slip judgment threshold based on the cable's radial curvature modulus, thereby generating inspection results. This invention solves the problems of difficulty in sensing micro-deformation on reflective cylindrical surfaces and the difficulty in decoupling slip risk from assembly tolerances, thus improving the accuracy of wiring harness and cable tie assembly quality inspection.

This invention discloses a three-dimensional terrain construction method based on multimodal semantic constraints, belonging to the field of three-dimensional terrain modeling technology. This method acquires optical remote sensing images and digital surface models and constructs unified spatial reference data. Guided by multimodal semantic constraints, it performs joint semantic parsing of ground features to generate a set of semantic masks with structural consistency. Furthermore, based on the mapping relationship between semantic categories and surface physical attributes, it performs regional geometric reconstruction of the digital surface model to form three-dimensional terrain entities that conform to the spatial expression characteristics of ground features. Simultaneously, by parsing engineering cost documents and extracting spatially oriented engineering cost data, under spatial and semantic consistency constraints, it associates and maps engineering cost information with three-dimensional terrain entities, ultimately forming a three-dimensional terrain data result that integrates terrain geometric information and engineering economic attributes.

This invention discloses a method and system for pain threshold determination based on multimodal automatic laser stimulation and animal behavior analysis. The method includes: using an RVC-Pose convolutional neural network model to detect key points on the animal's foot and outputting the foot's spatial coordinates in real time; positioning a laser spot on the foot and outputting continuously adjustable laser stimulation according to a preset gradient; extracting facial key points and micro-expression features using a Light-Face facial recognition algorithm, and / or reconstructing the animal's three-dimensional skeleton using a multi-view geometric reconstruction algorithm and extracting pain-related behavioral features; inputting the multimodal behavioral data into a multi-parameter fusion judgment model to automatically identify pain responses, adaptively adjusting the stimulation intensity under gradient stimulation mode, terminating stimulation and recording the pain threshold when an effective pain response is detected. This invention achieves fully automatic closed-loop control for pain threshold determination, solving the problems of inaccurate positioning, imprecise stimulation, and subjective judgment in existing technologies, significantly improving the objectivity and accuracy of pain assessment.

This invention relates to the field of intelligent inspection and 3D visual reconstruction technology for automotive parts, specifically a method for 3D image reconstruction of lightweight internal high-pressure molded parts for automobiles. The method includes: acquiring a sequence of structured light images, multispectral reflectance texture images, and a priori simulation model of the target object, and completing spatial coordinate registration; dynamically allocating edge extraction weights based on the local curvature gradient of the 2D image to generate a non-uniform density 3D point cloud; constructing a surface reconstruction energy function using surface reflectance variation data as a penalty term, fitting the point cloud to the 3D surface, and generating a target 3D mesh model; fusing 3D geometric features, 2D texture features, and the priori simulation model, and outputting the wall thickness reduction rate and residual stress distribution via a graph neural network; generating and overlaying a risk heat map, and outputting the evaluation decision results. This invention extends from geometric reconstruction to risk semantic reconstruction, improving the comprehensiveness and reliability of online inspection of internal high-pressure molded parts.

The application belongs to the field of three-dimensional reconstruction, and particularly relates to a micro-morphology analysis method based on wavelet multi-layer frequency band decomposition. The method comprises the following steps: firstly, a multi-focus image sequence of a micro scene to be measured is collected, and multi-scale spatial features are obtained through feature coding and projection mapping; then, a two-dimensional discrete wavelet transform method is introduced, the spatial features are decoupled into a low-frequency approximate subband representing global geometry and a high-frequency detail subband representing fine texture, and structure feature correction and directional texture enhancement are respectively implemented; on this basis, inverse discrete wavelet transform and residual fusion are used to construct time-frequency enhanced features, a deep regression network is used to analyze pixel-level focus probability distribution to generate a continuous depth map; finally, a cascading mechanism driven by three-dimensional geometric reconstruction for scene morphology perception is established, and depth geometric edge constraints are used to reversely guide the high-fidelity generation of a full-focus image. Through the wavelet multi-layer frequency band decomposition strategy, the application effectively solves the problem of confusion between texture details and out-of-focus noise in micro imaging, and realizes the synchronous robust analysis of high-precision geometric structure and full-depth clear texture of micro morphology.

This disclosure provides a cross-platform parametric design method, apparatus, device, medium, and program, relating to the field of computer technology. The method includes: acquiring parametric design data from a first application software, the parametric design data including a geometric model file generated by the first application software and a corresponding parameter configuration file; inputting the parametric design data into a pre-built two-layer processing framework consisting of a geometry processing layer based on an OCCT geometric kernel and a scene description layer based on a USD format, used to map parameters in the parametric design data to geometric entities and encapsulate them into USD assets containing parametric feature information; outputting the USD assets to a second application software, enabling the second application software to modify parameters based on the parametric feature information in the USD assets, and triggering the two-layer processing framework to perform geometric reconstruction and synchronization of the modified parameters. This disclosure realizes cross-platform application of parametric design data and improves parametric design efficiency.

The application provides a three-dimensional scene geometry, material and light decoupling and editing system, the system comprises: a geometry remodeling module, a decoding module and an editing module, wherein: the geometry remodeling module is used for reconstructing the geometry of a three-dimensional scene in an explicit triangular mesh according to a group of images with camera poses collected for the three-dimensional scene, and establishing the position relationship between the input images, their cameras and the triangular mesh; the decoding module is configured to determine the signed distance of a sampling point to the triangular mesh and the color of the sampling point according to the geometry features and material features on the vertices of the triangular mesh, and to render, update the geometry features, material features and learnable display environment light map on each vertex according to the pixel color of the input image and the rendering color of the corresponding pixel; the editing module is configured to obtain a user's custom view angle, and provide the user with: a geometry editing function, a material editing function and a light editing function.

The present application relates to the field of intelligent filling planning, and more particularly to a goaf intelligent filling planning method based on machine vision and autonomous navigation. The content includes: collecting point cloud data of the goaf environment and registering to obtain point cloud data under the global coordinate system; based on the point cloud data under the global coordinate system, introducing a local spectral weighted index algorithm to generate a structure weighted geometric reconstruction index; based on the structure weighted geometric reconstruction index, identifying the filling points and constructing the filling point set; based on the filling point set, combining the structure weighted geometric reconstruction index, introducing a progressive filling planning algorithm to select the next filling point and generate an ordered filling path. The problems of traditional point cloud processing methods that are difficult to automatically identify structural abnormalities or high-risk areas, and filling path planning that fails to make comprehensive decisions combining structural risk and spatial distance, and is difficult to achieve continuous, controllable and risk-prior intelligent filling planning are solved.

This invention discloses a single-view 3D photometric reconstruction method, system, device, and medium based on machine learning, relating to the field of computer vision technology. The method includes acquiring single-view images of a target object under different light source conditions and light source direction information; preprocessing the images and light source direction information to generate standardized multi-dimensional input data; performing feature extraction and fusion to obtain a fused feature representation; performing preliminary 3D surface normal estimation; outputting preliminary normal vectors; iteratively optimizing the preliminary normal vectors; combining light source reliability assessment and material prior knowledge constraints to obtain accurate normal vectors; and constructing a 3D model of the target object using a 3D geometric reconstruction algorithm. This invention significantly improves the accuracy, robustness, and direct usability of 3D reconstruction in complex lighting and heterogeneous material scenarios by combining multi-scale feature learning with channel attention and incorporating physical optimization based on light source reliability assessment and material prior knowledge.

This invention discloses a method and system for representing and rapidly generating spatial cable geometry. By providing an analytical representation of the centerline of the spatial cable under axial parameters, the cable geometry is uniformly represented as a function combining a linear overall trend and local geometric perturbations, thus avoiding numerical oscillations and geometric inconsistencies caused by direct interpolation or high-order polynomial fitting. Through parameter solving and analytical calculation of the given analytical representation, efficient generation of spatial cable geometry is achieved, avoiding complex geometric reconstruction or iterative calculation processes. The spatial cable geometry obtained based on this invention can accurately reflect the actual spatial orientation of cable bundles in electrical engineering and facilitates subsequent electromagnetic coupling analysis, electromagnetic compatibility assessment, and related engineering applications, thereby enhancing the practical value of spatial cable geometry modeling methods in the field of electrical engineering.

This invention belongs to the field of structural optimization design and proposes a topology optimization and reconstruction method for axisymmetric structures based on pixel grayscale. Based on the feature contour pixel information or mesh information of the topology optimization results, it studies the feature contour recognition algorithm of the topology optimization results and the geometric reconstruction method based on contour feature points to obtain a CAD geometric model. This construction method can directly identify boundaries and extract boundary points from topology-optimized structural images, and reconstruct boundary contours through boundary point coordinates. It solves the problem that commonly used topology optimization software cannot directly output vector structures and therefore cannot model structures. Through boundary tracking, it can achieve contour extraction of arbitrary boundary shapes. This method obtains the coordinates of all boundary contour points, and by adjusting the coordinate information, it can modify the structure, enabling secondary optimization design of the optimized structure.

This invention discloses a machine vision-based method for detecting the accuracy of holes in CNC machining of mobile phone frames, specifically relating to the field of mobile phone frame machining and inspection technology. First, images of the mobile phone frame under multi-angle lighting conditions are acquired and composite images of the holes are generated to identify abnormal contour regions caused by micro-flanging or reflective structures. Then, the composite images of the holes are decomposed into contour layers to extract candidate true hole boundaries. The continuity and curvature consistency of the boundaries are analyzed, and the true boundary curves of the holes are selected. A geometric reconstruction model of the holes is established, and the true boundary curves are geometrically fitted to obtain the center coordinates of the holes. A topological relationship of the hole array is constructed, and abnormally offset holes are corrected. Finally, the corrected hole coordinates are compared with preset standard hole coordinates to calculate the hole deviation and output the detection results. This invention can effectively suppress interference from micro-flanging and reflective structures at the holes, improving the stability and accuracy of hole accuracy detection in CNC machining of mobile phone frames.

This invention belongs to the field of geological exploration technology, specifically disclosing a mineral exploration method and system based on intelligent fusion of multi-source data and target area delineation. The method involves collecting multi-source mineral exploration information and preprocessing it under a unified spatiotemporal reference to obtain a multi-source fusion basic dataset; extracting features and performing multimodal representation on the aforementioned dataset to construct a fused feature vector field; obtaining candidate mineralized anomaly areas based on spatial pattern recognition and initial anomaly selection using the aforementioned vector field; extracting the target area boundary point set of the aforementioned anomaly areas using a graph-based geometric reconstruction method to generate an initial target area range; constructing a multi-objective optimization model to iteratively optimize the aforementioned range to obtain the optimal target area delineation result, which is then output to guide deep and peripheral mineral exploration work. The system includes modules for data acquisition, constructing a fused feature vector field, identification and initial selection, generating an initial target area range, and target area delineation. This invention features intelligent efficiency, high target area identification accuracy, and reasonable boundary delineation.

The present application relates to the field of fan blade deicing, and provides a fan blade surface icing growth numerical simulation and distribution prediction method, comprising: establishing a fan blade three-dimensional geometric model based on actual operation conditions and setting a rotating field flow field initial condition, constructing a rotating blade micro-domain cloud water flow field impact characteristic dynamic analysis based on an azimuth function, establishing a dynamic boundary slip model for blade surface liquid film formation and migration, coupling a thermodynamic phase change model and performing ice type dynamic growth calculation, performing iterative convergence judgment and icing form output based on geometric reconstruction, and performing aerodynamic and load analysis based on the final converged icing geometric model. The present application realizes dynamic reconstruction of the instantaneous impact characteristics of supercooled water droplets at different spatial positions of the rotating blade, can capture the non-uniform distribution of local liquid water content and water droplet diameter caused by terrain shielding or lifting effect when the blade rotates to different azimuth angles, and makes the input conditions of the icing simulation closer to the actual operation scene of the fan.

The application relates to the technical field of image recognition, and discloses a 3D printing defect identification method and system based on OCT and deep learning, the method comprising the following steps: performing coherence gating benchmarking calibration on an OCT acquisition device to obtain a standardized image acquisition benchmark; based on the standardized image acquisition benchmark, performing coherence volume data tomographic reconstruction on real-time acquired OCT interference signals to obtain a coherent tomographic volume image; performing feature contrast enhancement on the coherent tomographic volume image to obtain a feature enhanced image; performing multi-scale feature discrimination on the feature enhanced image to obtain a defect identification result; performing three-dimensional geometric reconstruction on the defect identification result and the coherent tomographic volume image to obtain three-dimensional geometric representation data, and performing spatial configuration analysis on the three-dimensional geometric representation data to obtain a spatial topological atlas; and performing defect root cause tracing on a printer to obtain a process parameter adjustment strategy; the application can improve the efficiency of a 3D printing defect identification method based on OCT and deep learning.

This invention relates to the field of historical building conservation and digitization technology, and discloses a collaborative management platform for three-dimensional digital archives of historical buildings. The platform includes: a data acquisition module for acquiring multi-source heterogeneous data of the target historical building; a data processing module for receiving the raw data set, performing format unification and time-base alignment processing on different data sources, and respectively performing point cloud denoising, image feature extraction, and geometric reconstruction processing; a data integration module for establishing a unified spatial coordinate system and generating three-dimensional structural data that uniformly expresses the overall form and detailed features of the historical building; and a data application module for constructing a three-dimensional digital archive database of the historical building based on the fused three-dimensional structural data. This invention achieves closed-loop management of the entire process of historical building management, from raw data acquisition to digital archive construction and application analysis.

A method for segmenting and 3D visualization of chest CT images, belonging to the field of medical image processing and computer-aided diagnosis technology, comprises the following steps: S1, importing and standardizing preprocessing of medical image data; S2, segmenting of multiple tissue images of the thoracic cavity based on a hybrid strategy; S3, dual-mode 3D geometric reconstruction; and S4, interactive 3D visualization and rendering. Through these steps, this invention directly achieves accurate segmentation of multiple tissues and dual-mode 3D reconstruction of surfaces and volumes without relying on high-end hardware or complex manual operations, thus improving image analysis efficiency.

This application provides a training method for a motion control strategy network and a motion control method for a humanoid robot, relating to the field of robot control technology. The method includes acquiring a sequence of human motion images of a preset scene; the sequence of human motion images includes multiple consecutive frames of human motion images; performing multimodal perception on the human motion image sequence to obtain an environmental geometric reconstruction model and a human geometric motion model; obtaining the effective contact area in the preset scene from the environmental geometric reconstruction model; constructing a local motion simulation scene with a simulated robot based on the human geometric motion model and the effective contact area; and training a motion control strategy network using the local motion simulation scene to obtain a target motion control strategy network. Through the method of this application, a motion control strategy with terrain perception capability can be trained, enabling the humanoid robot to adaptively adjust its motion according to terrain geometric features, improving motion stability and task success rate in real complex environments.

This invention provides a programmable mechanical metamaterial structure generation method and system based on a reaction-diffusion morphogenesis mechanism. By establishing a reaction-diffusion kinetic model, the structural morphology self-organizes under the coupling effect of local reaction and spatial diffusion, thereby generating structural morphologies with complex topological features and aperiodic spatial distribution characteristics. By setting spatial parameter fields such as diffusion coefficient, supply rate, and disappearance rate within the design domain, and introducing a direction control field to adjust the diffusion direction, the scale characteristics, connectivity, and anisotropy characteristics of the generated structure can be regionalized and continuously controlled. Through threshold transformation, boundary extraction, and geometric reconstruction of the morphology generation results, the generated structure can be directly converted into a two-dimensional or three-dimensional geometric model. This invention expands the design space of mechanical metamaterial structures and realizes an effective connection between structure generation and engineering design and manufacturing.

This application relates to the field of environmental monitoring and safety technology, and discloses a VOC cloud concentration inversion system based on self-calibrated infrared imaging. The system includes: a multi-view synchronous sensing module configured to simultaneously acquire raw infrared images of a gas cloud containing volatile organic compounds from at least two different viewpoints; an artificial intelligence inversion module configured to receive the raw infrared images and, using a pre-trained deep learning model, invert and generate a corresponding path integral concentration map for each viewpoint's raw infrared image; and a geometric reconstruction module configured to perform feature matching based on the path integral concentration maps from the multiple viewpoints. This invention overcomes the dependence of traditional infrared imaging technology on the background-gas temperature difference through a deep learning model, and, combined with a multi-view geometric model, decouples concentration and optical path, achieving three-dimensional quantitative inversion of VOC clouds under low signal-to-noise ratio conditions.

This invention discloses an adaptive rapid modeling method for tunnels based on the fusion of inspection and monitoring data and design data. The method utilizes numerical simulation to construct a multi-source heterogeneous dataset containing tunnel geometry, physical properties, and an optimal mesh size field. A geometric reconstruction neural network is built and trained to transform tunnel inspection and monitoring point cloud data into implicit geometric representations. A semantic parsing module extracts key physical elements from the design data and establishes a standardized information set. A feature fusion mechanism combines physical elements with geometric representations, inputting the data into a trained adaptive field prediction network to predict the posterior error distribution after structural stress. This predictor then calculates the target mesh size field, including absolute size references and local refinement features, directly generating a finite element model. This invention overcomes the shortcomings of traditional modeling methods, such as geometric-physical separation and reliance on manual experience for mesh generation, through multi-source data fusion and deep learning technology, achieving high-precision and high-efficiency automated modeling of tunnel structures.

This invention relates to the field of environmental model construction technology, and in particular to a data and knowledge fusion-driven integrated mine modeling method and system. The method first preprocesses multi-source heterogeneous data to establish a unified dataset, and then constructs a mine knowledge graph based on physical spatial information, linking objects, attributes, and the environment. Next, initial geometric models of the surface, ore body, and tunnels are constructed, and a mapping is established between the models and the knowledge graph. The semantic rules in the knowledge graph are used to enforce geometric constraints and logical optimization on the initial models, automatically correcting spatial conflicts. Finally, multi-element spatial integration is achieved under a unified coordinate system. This method achieves deep integration of geometric reconstruction and geological semantics, effectively solving the problem of missing model logic, and constructing a high-precision three-dimensional model with dynamic evolution capabilities and logical self-consistency, providing reliable support for intelligent mine management.

This invention discloses a multi-source information-based 3D modeling method, system, equipment, and medium for power operation, relating to the field of 3D modeling and information fusion technology. It includes acquiring raw point cloud and 2D image data of the power operation site using synchronously calibrated LiDAR and cameras, achieving spatiotemporal alignment through preprocessing, extracting point cloud and image features respectively, inputting them into a feature fusion model, performing association and deep fusion operations at the deep semantic level using a cross-modal alignment mechanism, and performing 3D geometric reconstruction, mesh model optimization, and texture mapping based on the generated fusion features to output a 3D model of the power operation site. This invention utilizes a cross-modal attention mechanism to achieve semantic-level alignment and fusion of geometric and texture features, generating a 3D power operation model with high geometric accuracy, rich semantic information, lightweight structure, and high visual realism, providing efficient and high-fidelity 3D digital assets for digital power grid operation and maintenance.

The application discloses an axial slice-based underground mine pipeline point cloud completion method, relates to the technical field of point cloud data processing, and is used for data completion of pipeline point cloud, and comprises the following steps: slicing a pipeline point cloud data after preprocessing along an axial direction, reducing three-dimensional point cloud to two-dimensional plane processing, calculating related geometric parameters of the pipeline, extracting a pipeline center line based on improved DBSCAN clustering and least square circle fitting, constructing a skeleton model under geometric topological constraints, combining local geometric features and global topological constraints, realizing geometric reconstruction of a missing area through a rotary surface generation and local interpolation algorithm, and realizing robust repair of the missing area. The application extracts the pipeline center line and geometric parameters in combination with a convex hull and a RANSAC method, realizes completion of the pipeline point cloud data through a continuous rotation correction method, and significantly improves the completion quality of the pipeline point cloud.

The application relates to a method, device, equipment and medium for detecting the mechanical property of a porous material, which comprises the following steps: reconstructing tomographic images from a plurality of projection data obtained by scanning the porous material, performing denoising processing on the tomographic images to obtain selected tomographic images, inputting the selected tomographic images into a trained network model to identify surface-attached particles, obtaining final images after removing the surface-attached particles, performing geometric reconstruction based on all the final images to obtain a three-dimensional digital model of the porous material, determining a plurality of target rods in each rod direction from the three-dimensional digital model, analyzing the radius of the plurality of target rods in each rod direction to obtain a rod defect characteristic curve in each rod direction, and performing finite element modeling based on the three-dimensional digital model and the rod defect characteristic curve in each rod direction to obtain a mechanical property curve diagram of the porous material. The application improves the accuracy of mechanical simulation of the porous material.

The application belongs to the technical field of millimeter wave radar. The application provides a millimeter wave radar room boundary robust geometric reconstruction method for multipath suppression. The embodiments of the present disclosure realize stable extraction of the real wall structure, effectively suppress the multipath echo and outlier point interference, and complete room boundary reconstruction and size accurate estimation by combining geometric constraints through antenna calibration, construction of a spatial point cloud geometric model, introduction of a robust straight line fitting and sequential model separation strategy. Through the sequential fusion and sequential robust geometric fitting strategy, the room wall boundary can be stably and accurately extracted, and the real wall and false echo can be effectively separated. In terms of wall detection integrity, anti-outlier point capability and size estimation accuracy, the embodiments of the present disclosure have obvious advantages, the room length and width estimation error is low, the boundary recognition success rate is significantly improved, and the embodiments of the present disclosure have good robustness and engineering applicability.

The present application belongs to the technical field of unmanned aerial vehicle remote sensing, photogrammetry and deep learning semantic segmentation, and particularly relates to a big panda staple bamboo species mapping method based on unmanned aerial vehicle images and deep learning, aiming to solve the problems of high cost, long cycle and discontinuous spatial coverage in traditional big panda staple bamboo survey relying on manual investigation. It includes obtaining unmanned aerial vehicle optical images, extracting and fusing ExG, and constructing R, G, B and ExG four-channel data sets; inputting the four-channel data into a four-channel Swin-UPerNet model adapted and modified, and combining a composite loss function to complete image pixel-level semantic segmentation; building a multi-view projection model to realize spatial matching and geometric reconstruction of two-dimensional recognition results and three-dimensional DSM data; and using a majority-voting fusion strategy to generate a spatially continuous and high-precision staple bamboo species distribution map. The method enhances the recognition of bamboo species in complex backgrounds, accurately identifies the distribution of staple bamboo, and significantly improves the recognition accuracy and spatial mapping continuity of big panda staple bamboo monitoring.

The present application relates to the field of power plant management, and discloses a full-process digital archive system based on face recognition, which is used for providing personnel identity verification and business compliance management method. The system comprises the following steps: constructing a digital geometric model based on collected face image data, generating a topological structure diagram, and generating a business-related digital archive by associating target personnel compliance business information. In view of the complex scene of the power plant, when constructing the face feature point cloud, the continuity of curvature is used for geometric reconstruction and interpolation in the area blocked by protective equipment. In real-time application, the output fusion decision signal triggers the access or authorization action. The present application effectively improves the accuracy of personnel identity recognition and the efficiency of business compliance management in the power plant, and ensures the safe and stable operation of the power plant.

This invention discloses a CAD geological drawing parsing and modeling system and method based on multimodal adaptive recognition. The system includes a user interface layer, an API gateway layer, a core processing engine layer, a 3D modeling and analysis layer, and a data persistence layer. The core processing engine layer includes a deep CAD parsing module, an anchor point connected domain bounding box search module, a multimodal fingerprint template library, a semantic table processing module, and a 3D geological modeling engine. The method flow is as follows: deep parsing of CAD drawings, intelligent bounding box positioning and clustering, template fingerprint matching, table geometric reconstruction and data extraction, batch processing and export steps, and 3D geological modeling and analysis. This invention achieves full-process automation from drawing recognition to 3D decision-making, significantly improving the efficiency and accuracy of exploration data processing.