2592 results about "Mesh model" patented technology

The invention provides a news scene three-dimensional reconstruction and visualization method based on multi-source remote sensing data, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting multi-modal data feature points through employing a spatial position weighted iterative nearest point algorithm and a tensor projection algorithm; after bidirectional attention deep neural network processing and local geometric consistency constraint screening, registration multi-source remote sensing data is generated, data enhancement is carried out through a variational auto-encoder network and a generative adversarial network, the enhanced data is used for depth information generation and texture feature migration, an initial three-dimensional grid model is generated based on a Poisson reconstruction algorithm, and the initial three-dimensional grid model is constructed; and carrying out topological optimization by combining multi-view projection consistency constraint and a grid optimization algorithm to obtain an optimized three-dimensional scene model, setting a space division function, carrying out space division, constructing a grade model and a depth region, generating a scene semantic segmentation map, and updating a visual scene.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a multi-mode remote sensing data driven refined city three-dimensional reconstruction method and system, and the method comprises the steps: obtaining an original satellite optical image for providing city surface information; synthetic aperture radar (SAR) data is obtained and used for assisting reconstruction of a point cloud data missing region; the method comprises the following steps: acquiring laser radar data carried by a flight aerial carrier for acquiring high-precision three-dimensional point cloud; performing data preprocessing; carrying out fusion on the preprocessed multi-modal data; on the basis of the fused features, complementing space detail information; on the basis of the refined features, progressive reconstruction is carried out, vertexes and edges are gradually added from the initial vertexes, and a grid model is constructed; in the reconstruction process, performing semantic segmentation on the grid model by using a multi-category segmentation network, and judging the category of each triangular patch; time sequence reconstruction is completed through fusion of multi-temporal data, and a dynamic city change model is generated; and complementation of advantages of different modal data is realized.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

The invention relates to the technical field of quality monitoring, and discloses a diamond high-strength micro-powder quality detection method and system based on artificial intelligence. The method comprises the steps of obtaining a two-dimensional projection image sequence of diamond micro-powder particles, calculating a projection matrix based on camera calibration parameters and geometric constraints, obtaining a multi-view image data set of the particles, establishing a pixel-level corresponding relation, extracting three-dimensional space coordinates of the surfaces of the particles, and reconstructing dense point cloud data of the particles. Establishing a local coordinate system based on the dense point cloud data, determining attitude parameters of particles in a three-dimensional space, if the attitude parameters deviate from a normal range, performing attitude compensation processing to obtain standardized point cloud data, and performing three-dimensional grid model construction on the standardized point cloud data; and calculating geometrical characteristic parameters of the particles based on the three-dimensional grid model, performing defect detection on the surfaces of the particles, and generating a crystal integrity evaluation report of the particles. The quality detection accuracy of the diamond high-strength micro-powder particles is improved.

The invention provides a three-dimensional modeling processing method based on unmanned aerial vehicle oblique photography. The method is applied to the technical field of three-dimensional modeling, and comprises the following steps: obtaining a serialized image set containing geographical coordinate information according to original image data collected by an oblique photography camera carried by a multi-rotor unmanned aerial vehicle; according to time-space synchronization parameters of the serialized image set, determining a multi-view image matching relation matrix with an overlapping degree quantitative index; determining mixed three-dimensional point cloud data fusing sparse point cloud and dense point cloud according to geometric constraint conditions of the multi-view image matching relation matrix; determining an initial three-dimensional grid model with multi-level details according to the topological connection relationship of the mixed three-dimensional point cloud data; and determining an optimized three-dimensional model based on adaptive texture mapping according to the surface curvature distribution characteristics of the initial three-dimensional mesh model. In this way, the efficiency of three-dimensional modeling can be improved.

The invention discloses a three-dimensional automatic modeling method for a high-precision transformer substation. The method comprises the following steps: collecting laser point cloud data and multi-view pictures of the transformer substation; dense point cloud data registration, multi-view image camera parameter reconstruction, multi-view image projection and adaptive point cloud density control are carried out; approximating the point cloud data by using three-dimensional Gaussian distribution functions, summing all the three-dimensional Gaussian distribution functions to obtain a Gaussian distribution geometric field represented by the three-dimensional Gaussian distribution point cloud, and reconstructing and rendering a three-dimensional grid to obtain a three-dimensional grid model; setting constraint conditions of equipment on the basis of the three-dimensional grid model, and generating a three-dimensional equipment model, equipment coordinates and equipment classification names by using a volume segmentation algorithm and a three-dimensional marking algorithm; and converting the equipment model into a GIM model file, generating a PMS file from the equipment coordinate and the equipment classification name, and synchronizing the PMS file to a GIM and PMS 3.0 system. According to the method, the problems of low efficiency and inconsistent quality in the traditional manual modeling process can be effectively solved.

The invention provides a plastic product quality detection method and system based on machine vision, and relates to the technical field of optical intelligent detection.The method comprises the steps that information extraction is conducted on an image data set through a contour recognition channel, and the contour of an identification component is determined; performing fitting filling of the point cloud data stream by taking the contour of the identification component as a constraint, generating an identification component point cloud, and fusing and building a plastic grid model; comparing based on the plastic grid model, and positioning size deviation information; and performing defect extraction on the plastic grid model based on the defect identification elements, determining abnormal defect information, and generating a quality detection report. Through the method, the technical problems of poor detection precision, comprehensiveness and detection efficiency caused by insufficient precision, accuracy and integrity of surface defect and dimensional deviation detection and low automation degree of an existing method can be solved, the automation of quality detection can be improved, and the detection efficiency is improved. And the effects of improving the detection accuracy, comprehensiveness and detection efficiency are achieved.

The invention discloses a high-fidelity large-scale scene rendering method, system and device based on Gaussian splashing and a medium. The method comprises the steps of S1, collecting high-precision point cloud data of a large-scale scene; s2, generating an initial three-dimensional grid model, and introducing depth prior information to reduce floating artifacts in a rendering process; s3, constructing a multi-resolution three-dimensional Gaussian expression model, and adopting a self-adaptive scaling factor and random resolution level training scheme in each level; s4, in the rendering process, a hierarchical selection strategy is adopted based on the distance between the three-dimensional Gaussian and a rendering viewpoint, and high-quality and high-speed rendering on high-performance equipment is achieved; and S5, developing a lightweight renderer, and completing efficient rendering on a lightweight mobile device. According to the invention, by introducing a depth regularization technology, high-precision point cloud data and three-dimensional Gaussian representation are combined; the three-dimensional Gaussian model is optimized by using depth prior information, floating artifacts possibly occurring in the rendering process are reduced, and the rendering quality is remarkably improved.

The invention discloses a humanoid robot grabbing method based on three-dimensional vision, and the method comprises the following specific steps: S1, environment modeling and perception: carrying out the three-dimensional scanning of a working environment, and constructing a three-dimensional map of the environment; s2, object identification and 6D attitude estimation: identifying a target object, and estimating the position and attitude of the object in a camera coordinate system; s3, coordinate conversion and target positioning, wherein the posture of the object is converted into a robot base coordinate system from a camera coordinate system through TF conversion; and S4, path planning and motion control are conducted, specifically, path planning is conducted through a cumotion algorithm, a grabbing path is generated, and the two arms are driven to move according to the planned track to achieve grabbing. By means of environment modeling and sensing, the robot can update environment information in real time, conduct three-dimensional scanning on the working environment and generate the three-dimensional grid model of the environment in real time, the model is used for obstacle detection and avoidance planning, and the dynamic environment adaptability of the robot to object grabbing is improved.

The invention relates to the technical field of three-dimensional modeling, and provides a satellite three-dimensional reconstruction method in a complex illumination environment, which comprises the following steps: acquiring images under different illumination conditions, and extracting image features by combining multi-view collaborative observation; performing feature point and multi-view data registration, generating a three-dimensional model and analyzing illumination change; performing system initialization by using the initially matched feature points, and establishing an initial three-dimensional point cloud model; sparse point cloud is recovered through multi-view stereoscopic vision, and motion recovery is carried out; a 2D diffusion model is used to complement the shielded area, and laser radar data is combined to carry out densification processing on the sparse point cloud; a three-dimensional grid model is generated, and texture mapping is carried out by a multi-view image; light compensation is adopted, a polarized light separation technology is used, and ambient light and specular reflection light are separated; according to the method, through multi-view-angle collaborative observation, the shielding problem under a single view angle is reduced, the 2D diffusion model is used for complementing the shielded area, and the completeness and precision of three-dimensional reconstruction are ensured.

The invention discloses a four-dimensional Gaussian model generation method, system and equipment based on Gaussian sputtering, and belongs to the field of computer vision processing. The method comprises the following steps: firstly, generating a target video consistent with a given text prompt by using a pre-trained text-to-video diffusion model, then converting the target video into a three-dimensional grid model frame by frame by using an image-grid generation model, and rendering the three-dimensional grid model in a focal length range under a main view angle, the method comprises the following steps: firstly, calculating a rendering matching focal length, training a static Gaussian model at an initial moment by adopting a Gaussian sputtering method, then training a deformation network, and finally generating a four-dimensional Gaussian model under a given timestamp through the trained static Gaussian model and deformation network. By means of the method, the vivid high-dynamic 4D model which is fine in texture, accurate in geometrical relation and capable of accurately conforming to the semantics of the characters input by the user can be generated within obviously short time.

The invention provides an intelligent electric energy distribution cabinet heat flow field prediction method and system, and relates to the technical field of cabinet heat flow field prediction, and the method comprises the steps: carrying out the clustering analysis of the obtained operation data and material attributes of each region in an intelligent electric energy distribution cabinet, and the element layout information of equipment in the cabinet; respectively establishing a thermal-mechanical coupling model, a fluid dynamic model and an electromagnetic model; and according to the final refined grid model, simulating fluid dynamics by using a motion constraint smoothing particle fluid dynamics method so as to obtain an optimal layout scheme of the internal structure of the intelligent electric energy distribution cabinet. And calculating the optimal layout scheme by using an analog simulation technology to obtain an optimization scheme of air flow and heat transfer in the intelligent electric energy distribution cabinet, and realizing prediction of the heat flow field of the intelligent electric energy distribution cabinet. Accurate prediction and optimization of the heat flow field of the power distribution cabinet are realized, the reliability and safety of a power system are improved, and the accident risk and loss are reduced.

The invention relates to the technical field of coal gangue classification and recognition, in particular to a coal gangue recognition method and system based on hyperspectral imaging and laser ranging, and the method comprises the steps: mapping hyperspectral image data to a three-dimensional point cloud of laser ranging data, and obtaining a mapping result; adopting the trained network model to extract multi-scale spatial features and multi-scale spectral features from the mapping result; the extracted features are fused, and then candidate target areas are generated based on the fused features; classifying the candidate target areas to obtain a classification result of the coal gangue; constructing a three-dimensional model of the coal surface based on laser ranging data; generating a triangular mesh model for the three-dimensional model of the coal surface; smoothing the triangular mesh model to obtain a reconstructed model; determining the granularity of the coal briquettes based on the reconstruction model; and based on the spectral data, identifying the lithofacies characteristics of the coal, and distinguishing different types of coal. And the coal gangue sorting accuracy and efficiency are improved.

The invention discloses a high-precision granary volume calculation method and system, and relates to the technical field of laser radar technology and point cloud processing, and the method comprises the following steps: data collection; laser radar equipment is used for carrying out omnibearing scanning on a target object, and a laser point cloud data set is obtained; preprocessing the data; performing point cloud segmentation; object recognition; point cloud precision compensation: performing precision compensation on the point cloud data, the precision compensation including error compensation of laser radar precision along with distance and temperature changes; volume calculation: performing volume calculation on the recognized regular object by adopting a volume calculation formula; for the recognized irregular object, a three-dimensional grid model of the object is created through a three-dimensional model reconstruction algorithm, the grid model is adjusted, and then the volume of the grid model is calculated; according to the method, optimization is carried out from the two aspects of radar distance and temperature compensation, an error model is established, in the model, primary functions are designed from the two aspects of temperature and distance, and the precision is improved from the two aspects at the same time.

The invention relates to an earthquake fracture inversion method based on multi-source observation data. The method comprises the following steps: acquiring a seismic source mechanism solution of a specified earthquake event; according to the seismic source mechanism solution, geometric parameters of a fault of the seismic event are obtained; generating a three-dimensional grid model of a fault plane to which the fault belongs according to a pre-constructed geometric shape model of the fault and the geometric parameters of the fault; according to the limiting condition of each sub-fault in the three-dimensional grid model, obtaining the slippage of the sub-fault to which the seismic fracture belongs; and according to the observation data to which the seismic event belongs and the slippage of the sub-faults, performing inversion verification by means of an inversion model, and if the verification is passed, taking the slippage of all the sub-faults as a seismic fracture inversion result when the verification is passed. According to the method, the stability and reliability of an inversion result are improved, the sliding vector at each sub-fault can be better estimated, and the sliding process of the whole seismic fault can be more comprehensively understood.

The present application relates to the technical field of image processing. Disclosed are a texture mapping method and apparatus based on three-dimensional (3D) face reconstruction, a device and a storage medium. The method comprises: registering a standard 3DMM model with a 3D grid model, and performing region division on the 3D grid model; obtaining a texture image of a target object, and matching 2D face key points of the texture image with 3D face key points of the registered 3DMM model to obtain head posture information of the texture image; and on the basis of whether the head posture information meets preset requirements or not, determining whether to use the texture image for mapping. According to the technical solution, a standard 3DMM model is used to perform region division on a 3D grid model of a target object, and on the basis of head posture information of texture images, a texture image with the head posture meeting preset requirements is selected to perform mapping processing on a corresponding region, thereby making the texture of the mapping more consistent, and constructing a 3D face model having a better visual effect.

The invention discloses a gas tank base stress finite element analysis method and system, electronic equipment and a storage medium, and the method comprises the steps that for a complex geometric structure of an LNG gas tank base, a self-adaptive mesh generation technology is adopted, a stress concentration area and stress gradient changes are comprehensively considered, refined modeling is carried out on the gas tank base, a high-quality finite element mesh model is obtained, and in the mesh generation process, the stress concentration area and the stress gradient changes are comprehensively considered; the grid density of a stress concentration area under the load effect is focused, the grid quality of the area is improved, and the stress gradient change is accurately captured; in an LNG gas tank base finite element model, key load working conditions are identified, characteristic parameters such as types, sizes, directions, frequencies and durations of typical loads such as the self weight, thermal stress and wind load of a gas tank are mainly analyzed, load-time historical curves under different working conditions are obtained through load spectrum testing, and a load combination method is adopted, so that the load-time historical curves under different working conditions are obtained. And the stress distribution rule of the gas tank base under the multi-load coupling effect is researched.

The invention provides a CAD model reconstruction method and device for a topological optimization result, and relates to the technical field of computer modeling, and the method comprises the steps: carrying out grid preprocessing, carrying out component type grid segmentation, carrying out patch type grid segmentation, obtaining at least two sub-grid patches, carrying out parameterization on each sub-grid patch, and obtaining a final parameterization result. And performing uniform interpolation sampling based on the final parameterization result to obtain sampling points, generating a spline surface by using the sampling points, assembling the spline surface, and generating a reconstructed CAD model corresponding to the three-dimensional topological optimization fruit. The grid model reconstruction method has the advantages of being high in automation degree, high in realizability, easy to operate and the like, is not limited to a tubular topological optimization structure, has no obvious difference in effect when processing tubular and non-tubular model characteristics, has higher universality, can adapt to the diversity requirements of industrial structure optimization, and is suitable for large-scale popularization and application. And meanwhile, the reconstruction efficiency and the reconstruction effect from the triangular mesh model to the CAD model can be improved.

The invention provides a method and a system for drawing a regional energy consumption map of an electric vehicle. Wherein the acquired driving data and external meteorological information comprise multiple parameters and temperature gradients in the GB32960 standard. A dynamic grid model is constructed based on administrative division and a road network, track points are mapped into space grid units by using an adaptive algorithm, and the units combine information such as gradient, temperature and traffic flow. A trajectory analysis technology is adopted to segment a driving segment crossing a grid boundary, and feature vectors are generated; by establishing a mapping relationship between the feature vector and the energy consumption contribution degree, the original energy consumption is calculated, and multi-factor correction is performed to generate a dynamic energy consumption reference value. And associating the distribution density of the charging piles with the traffic flow weight by using a space-time coupling diffusion model, and generating a three-dimensional dynamic map through a rendering engine. According to the technical scheme provided by the embodiment of the invention, the accuracy of electric vehicle energy consumption estimation and the scientificity of charging facility layout are improved.

The invention relates to the technical field of image processing, and discloses a machine vision-based rare metal processing quality detection method, which comprises the following steps of: acquiring a multispectral polarization image and a surface normal graph of a to-be-detected workpiece; reconstructing a three-dimensional mesh model on the surface of the workpiece, and mapping pixel information of the multispectral polarization image into multidimensional physical attributes of vertexes on the three-dimensional mesh model; extracting depth features of the surface of the machined part by adopting a graph convolutional neural network; the depth features are input into a segmentation decoder and a normalized stream model in parallel, and a defect probability graph and a likelihood graph are generated; performing joint judgment on the defect probability graph and the likelihood graph; and extracting geometric information and multi-dimensional physical attributes corresponding to the judged defect area, and inputting the geometric information and the multi-dimensional physical attributes into a defect classifier to determine the type of the defect. According to the method, known defect types can be accurately identified, novel defects which are not learned can be effectively detected, and the generalization ability and robustness of detection are greatly enhanced.

The invention relates to the technical field of geological exploration and three-dimensional modeling, in particular to a mining unmanned aerial vehicle high-precision three-dimensional geological modeling system which comprises a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a three-dimensional geological model construction module and a geological attribute visualization output module. Wherein the multispectral imaging acquisition module is used for acquiring registration image data and original point cloud data; the point cloud-image high-precision registration module is used for carrying out consistency matching and outputting a registration point cloud-image data set; the geological feature fusion extraction module is used for identifying geological boundaries and outputting geological feature vector data; and the three-dimensional geologic model construction module is used for generating a three-dimensional grid model with attribute topology. According to the method, through accurate three-dimensional modeling and lithology visualization, geological features and lithology information are efficiently combined, and reliable data support is provided for rock mass stability analysis.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention provides a chip bonding wire three-dimensional measurement method and system based on an inner cavity reflection camera system, and relates to the technical field of three-dimensional measurement. The method comprises the following steps: performing one-time exposure imaging on a chip bonding wire to be detected through an inner cavity reflection camera system to obtain at least three mutually independent visual angle images in the same frame; performing brightness normalization compensation and geometric calibration on the visual angle image to obtain a visual angle data set with uniform brightness and correlation; pairing the view angle data sets in pairs, extracting parallax information by using a feature matching algorithm based on optical flow, and fusing multiple pairs of parallax to generate a lead three-dimensional point cloud; performing noise filtering and curved surface reconstruction on the three-dimensional point cloud to obtain a continuous three-dimensional grid model of a lead; and performing texture mapping on the three-dimensional grid model by adopting any visual angle in the visual angle image or the combination of the visual angles, and outputting a lead three-dimensional reconstruction result with textures. According to the invention, high-precision, large-depth-of-field and rapid three-dimensional measurement of the chip bonding wire is realized.

The invention discloses a computer numerical simulation method, system and equipment for a composite material, particularly relates to the technical field of analog simulation, and is used for solving the problem of simulation distortion caused by neglecting the randomness of a microstructure in the prior art. The method comprises the following steps: acquiring microstructure image data of a composite material, extracting fiber arrangement direction and pore distribution parameters, and generating a fiber direction probability density function and a pore topological relation; constructing a non-uniform grid model constrained by a local coordinate system according to the fiber direction probability density function; identifying a weak connection area based on a pore topological relation and marking nodes; applying dynamic decoupling constraint to the weak connection area, wherein the dynamic decoupling constraint comprises initial rigidity and rigidity attenuation rate; applying an external load to the model and calculating a real-time stress coefficient of the weak connection area; dynamically adjusting the constraint parameter through threshold judgment and iterative correction until the stress coefficient is converged; and finally, outputting a corrected macroscopic mechanical property result.

The invention discloses an intelligent water conservancy three-dimensional twinborn scene construction method and system, and relates to the technical field of intelligent water conservancy, and the method comprises the steps: collecting scene data in real time through a multi-source sensor disposed at a water conservancy facility site, including hydrological parameters, facility structure data, environment image data and ecological monitoring data; performing space-time alignment and deviation correction on the scene data by using a deep learning driven multi-modal feature fusion algorithm to generate a three-dimensional water conservancy grid model of a unified coordinate system; according to the real-time data updating frequency and the environment change trend, a local or global updating strategy of the three-dimensional water conservancy grid model is adaptively adjusted, the optimized three-dimensional water conservancy grid model and a virtual reality engine are integrated, and a twin scene supporting gesture interaction is constructed; an ecological simulation module is embedded in the twinborn scene to dynamically evaluate the influence of water conservancy activities on water quality and biological habitats.

The invention provides a three-dimensional data synthesis method, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of texture-free three-dimensional grid models, carrying out the rendering of each three-dimensional grid model, obtaining a plurality of view images, and constructing a training sample pair in combination with a semantic description text; based on the training sample pair, utilizing a low-rank adaptation technology to carry out fine tuning on the first text graph model to obtain a second text graph model with multi-view consistency understanding ability; modeling a latent space semantic difference between the rendered image of the to-be-deformed three-dimensional grid model and the target semantic text based on the second text graph model, constructing an optimization constraint and updating parameters, and obtaining a deformed three-dimensional grid model conforming to target semantics; and generating a texture image by using the texture generation model, and attaching the texture image to the surface of the deformed three-dimensional grid model to obtain synthetic three-dimensional data with textures. The reconstruction precision, the diversity expression ability and the cross-category generalization ability of the synthesized three-dimensional data are obviously enhanced.

The invention provides a structure surface multi-disease three-dimensional measurement method and device, equipment and a medium. The method comprises the steps that an image sequence carrying GPS coordinate information is collected; performing depth calculation on the image sequence by using a three-dimensional reconstruction technology, and performing three-dimensional reconstruction on a scene to generate an initial three-dimensional model; carrying out disease determination and point cloud processing to obtain a three-dimensional point cloud model of a specified color and each disease point cloud instance; using a skeleton extraction method and a boundary point extraction algorithm to extract a boundary point set and a skeleton point set of the skeleton point set corresponding to the crack point cloud, and according to each obtained skeleton point set and the corresponding boundary point set, performing quantitative calculation on the length and the width of each crack; generating a spalling surface grid model by using a Poisson surface reconstruction algorithm and a hole filling algorithm, and calculating and determining the volume of the three-dimensional spalling instance according to the spalling surface grid model; and determining the normal direction of each point of the corrosion cloud instance, generating a grid model by using a Poisson surface reconstruction algorithm, and calculating the corrosion area.

The invention provides an urban groundwater inrush risk assessment method based on a three-dimensional grid model and an XGBoost algorithm, and belongs to the technical field of geological disaster early warning. The method comprises the following steps: acquiring geological, environmental and historical disaster data in real time through a multi-source heterogeneous data interface; constructing a heterogeneous grid unit based on three-dimensional geological modeling, and dynamically binding parameters such as underground water level, fault activity and lithologic permeability coefficient; an XGBoost algorithm is combined with second-order Taylor expansion to optimize gradient calculation, and a nonlinear risk assessment model is constructed through Bayesian hyper-parameter tuning and SHAP value feature interpretive analysis; identifying contiguous dangerous areas based on spatial autocorrelation analysis and an adaptive threshold segmentation algorithm; and a risk thermodynamic diagram, geological section superposition and real-time early warning are realized through a 3D GIS platform. According to the method, the problems that a traditional method depends on a linear model, data is single and local risk description is insufficient are solved, and the accuracy, efficiency and engineering practicability of risk assessment are remarkably improved.