36662results about "3D modelling" patented technology

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention relates to the technical field of three-dimensional model adjustment, in particular to a three-dimensional model adjustment method and system and a medium. The method comprises the following steps: obtaining a multi-angle image of an original model, carrying out multi-view normalization on the multi-angle image, generating a normalized view image set, extracting feature points of the original model, carrying out parallax correction on the feature points, reconstructing a simulation three-dimensional model, collecting basic purpose data of the model, carrying out ideal demand mapping through the data, and carrying out ideal demand mapping. The method comprises the following steps: determining an ideal three-dimensional model structure, carrying out core region segmentation on a reconstruction model according to basic purpose data to obtain key region model slices, carrying out highlight region comparison with the ideal model structure, analyzing model differences, determining a structure adjustment amplitude interval according to a comparison result, and carrying out cyclic fine adjustment correction on the key region model slices to obtain a three-dimensional model. And the three-dimensional model is consistent with the ideal three-dimensional model in structure, so that the optimized three-dimensional model is generated. According to the invention, efficient and accurate three-dimensional model adjustment and optimization are realized.

An example operation may include at least one of receiving, via a user interface of a device, an activation input from a user to initiate a session, capturing, by a camera of the device, a scan of a body of the user, wherein the capturing comprises recording at least one image and / or at least one video of the user, processing the at least one image and / or video to generate a three- dimensional model of the user comprising measurements and contours of the body, retrieving, from a database, at least one clothing item associated with the user, the at least one clothing item comprising dimensional attributes and texture attributes, rendering, by a graphics processing unit, the at least one clothing item onto the three-dimensional model to generate a visual representation, wherein the rendering simulates draping behavior, movement, and light interaction of the at least one clothing item relative to the three-dimensional model, and displaying, on the user interface, an interactive visualization comprising the visual representation of the three-dimensional model with the at least one clothing item from multiple viewing angles.

The invention, which relates to the technical field of image processing, discloses a city component automatic three-dimensional reconstruction method based on a sub-meter high-resolution remote sensing image, comprising the following steps: acquiring a sub-meter high-resolution remote sensing image of a target area, and establishing an initial ground-image projection mapping relation based on a rational polynomial coefficient model; acquiring a ground control point, correcting the mapping relation, and outputting a registration image; performing multi-view geometric matching on the registered image to generate dense earth surface point cloud, and inputting the dense earth surface point cloud into a semantic neural radiation field network to generate dense point cloud; performing cross-modal feature alignment and fusion on the dense point cloud and the vector data, and outputting a fused multi-source dense point cloud; according to the method, through fine correction of the multi-source remote sensing image, the problems of large registration error, more point cloud sparse noise and semantic deficiency are solved, and high-precision and automatic three-dimensional reconstruction of urban components is realized.

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention relates to the technical field of part size detection, and discloses a part size detection method based on machine vision. The method comprises the following steps: synchronously acquiring multi-angle original image data of a to-be-detected part by using a plurality of industrial cameras; preprocessing the original image data to obtain a de-noised and enhanced standard image set; performing three-dimensional point cloud reconstruction on the set to obtain spatial point cloud model data of the part; through a spatial frequency domain registration algorithm, registering the spatial point cloud model data with a preset part standard CAD model, and outputting a difference point cloud distribution diagram; extracting a key dimension feature vector in the image, inputting the key dimension feature vector into the trained optical feature fusion model, and generating a multi-dimensional dimension deviation quantitative index; and according to the index matching process compensation parameter set, outputting a size detection decision report. The method can comprehensively capture the part features, improves the detection precision, achieves the cooperation of detection and production technologies, and is suitable for the field of part size detection.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

In implementation of techniques for three-dimensional reconstructions based on Gaussian primitives, a computing device implements a reconstruction system to receive a first digital image depicting an object from a first angle and a second digital image depicting the object from a second angle. The reconstruction system segments the first digital image and the second digital image into patches. The reconstruction system then generates, using a machine learning model, three-dimensional Gaussian primitives that predict parameters of points of the object in a three-dimensional space that correspond on a per-pixel basis to pixels of the patches. The reconstruction system then forms a three-dimensional reconstruction of the object for display in a user interface by merging the three-dimensional Gaussian primitives.

The present disclosure relates to the field of computer vision and discloses a three-dimensional dynamic scene reconstruction method and apparatus, and a storage medium. The three-dimensional dynamic scene reconstruction method comprises: acquiring synchronized videos of a plurality of viewpoints of a dynamic scene; computing matching points between video images of different viewpoints, and estimating intrinsic and extrinsic parameters of each camera; obtaining a Gaussian splatting point set {p0} on the basis of a sparse point cloud constructed according to the depth of each matching point; for the first image frame of each video, using {p0} to perform static training thereon, to obtain a Gaussian splatting point set {p}; for the remaining image frames, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, and constructing a dynamic Gaussian splatting point set {P} from {p}, {S}, and the final {D}; and, in view of the intrinsic and extrinsic parameters of each camera, rendering {P} using a Gaussian splatting rendering pipeline, to obtain rendered images at different moments from new viewpoints.

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

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 discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

A method and apparatus for reconstructing a three-dimensional (3D) scene from a two-dimensional (2D) input image of the scene using a fully-differentiable transformer-based encoder-decode. A 2D input image encoded into a set of image features using a pre-trained vision transformer model, wherein the vision transformer model is pre-trained with multi-view RGB image supervision and point cloud supervision. The set of image features is projected onto a 3D triplane representation using a transformer decoder to obtain output triplane tokens. A triplane representation is created from the tokens and queried. 3D point features of color and density for volumetric rendering re predicted using a multi-layer perceptron. The geometry of the generated 3D asset is represented with a surface mesh including vertices and triangular faces. A texture map by is created with a multichannel image in UV space. Multiple views of the 3D scene are simultaneously generated based on the surface mesh.

The invention relates to the technical field of cultural heritage digital protection, in particular to a super-volume historic building three-dimensional simulation modeling method based on multi-source heterogeneous data, and the method comprises the steps: firstly collecting node multi-source heterogeneous data such as laser point cloud, images, structural mechanical parameters and historical repair records, and then carrying out node feature enhancement through a node feature enhancement module; using an improved generative adversarial network to strengthen node edge features, adopting an adaptive threshold segmentation algorithm to extract surface texture features, converting mechanics and size data into a three-dimensional constraint condition parameter matrix, then using a topological relation verification algorithm, using a graph neural network to traverse and verify a component connection relation, and obtaining a three-dimensional confrontation model; and a re-calibration mechanism is triggered when the deviation exceeds the limit, the weight is adjusted based on a Bayesian optimization algorithm, fusion verification is carried out again, finally, hierarchical grid division is adopted to construct high-precision sub-models, and the sub-models are spliced into an integral three-dimensional model, so that the model precision and reliability are improved, and reliable digital support is provided for ancient building protection.

The invention discloses an underground water infiltration replenishment evaluation method and equipment based on digital twinning, and relates to the technical field of underground water digital twinning evaluation, and the method comprises the steps: traversing a target area to carry out multi-source sensing collection; performing underground water infiltration simulation to generate an infiltration full-process data set, and obtaining an infiltration coupling model; geologic structure parameters of the target area are extracted, and a three-dimensional geologic structure model is constructed; performing association mapping on the infiltration coupling model and the three-dimensional geological structure model to obtain a dynamic simulation result; deviation analysis is carried out, automatic correction optimization is carried out, and an underground water infiltration supply evaluation report is generated. According to the method, the technical problems of insufficient multi-source data fusion, low space-time simulation precision and weak model dynamic correction capability in underground water infiltration supply evaluation in the prior art are solved, and underground water infiltration supply dynamic simulation and digital twinborn automatic correction of multi-source data coupling are realized; and the technical effect of improving the space-time precision of groundwater infiltration supply evaluation is achieved.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention relates to the field of geological exploration, in particular to an intelligent prospecting prediction method and system for an ore deposit three-dimensional geological model, a terminal and a medium. The method comprises the steps of obtaining multi-source geological data of a target area to construct an ore deposit three-dimensional geological model, inputting the ore deposit three-dimensional geological model into a trained intelligent prospecting prediction model for ore-forming potential analysis, and optimizing the model or generating an intelligent prospecting prediction scheme according to a predicted resource quantity confidence degree condition; when the model is constructed, three-dimensional inversion calculation, element anomaly field construction and the like are carried out, the model can be optimized through transfer learning, exploration data can be accessed in real time to realize dynamic updating, and a multi-target optimization model is established to output an exploration scheme; the invention also relates to a corresponding system, a terminal and a storage medium. The method achieves the technical effects of improving the accuracy and efficiency of prospecting prediction, dynamically optimizing the model according to the actual situation, reasonably planning the exploration scheme, and reducing the exploration cost and risk.

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention discloses an AR home experience method in a large scene. On the basis of combination of a natural feature identification-based three-dimensional registration method and a binocular tracking positioning and local mapping method, the camera attitude is estimated by using feature points of a real-time scene and corresponding three-dimensional points thereof under the binocular trackingpositioning and local map construction technology. According to the mode, on-site environment features shot in real time are used as recognition tracking objects, a virtual home model can still be normally positioned and tracked under the condition that no identification graph exists, the problems that an existing AR home experience application is small in use range and poor in stability are solved, and therefore the AR home experience of virtual and real fusion can be met in a wider range and more truly.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention relates to the technical field of collaborative optimization, in particular to a geometric parameter collaborative optimization method of a taper hole machining cutter, which comprises the following steps: by constructing a high-fidelity digital twin model, integrating multi-physics field coupling and machine tool dynamic characteristics based on a finite element method, generating a geometric parameter-performance data mapping set and training and calculating an agent model; outputting a Pareto solution set through multi-target global optimization; and constructing a constraint range based on the solution set, and calling a digital twin model to carry out local optimization to obtain an optimal geometric parameter combination. The method comprises a self-correction mechanism: correcting a material constitutive relation and a friction coefficient through experimental data; staged adaptive learning, NSGA-II and DBSCAN clustering are adopted, and the efficiency is optimized along with the method; and a Pareto stability index and transfer learning are introduced, so that the result robustness and the cross-task reusability are improved. According to the method, high-precision and high-efficiency geometric parameter collaborative optimization of the taper hole machining tool can be realized.

The invention relates to the technical field of coast engineering, in particular to a beach change evolution inversion method combining numerical simulation and actually measured data, which comprises the following steps: S1, acquiring an actually measured shoreline data set of a current period; s2, establishing a wave-tidal current-sediment coupling numerical model; s3, outputting a simulated shoreline data set; s4, performing spatial matching on the actually measured shoreline data set and the simulated shoreline data set to generate a spatial discrete residual field; s5, taking the spatial discrete residual field as a constraint condition, and reversely solving an optimal hydrodynamic parameter combination; and S6, inputting the optimal hydrodynamic parameter combination into the wave-tide-sediment coupling numerical model, and outputting the beach evolution form in the future period. According to the method, dynamic coupling of the hydrodynamic parameters and the beach evolution process is realized by constructing an inversion optimization mechanism based on actual measurement data driving, and the accuracy of shoreline change simulation and the reliability of prediction are greatly improved.

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses a PCBA anomaly detection method and system based on three-dimensional modeling and AI fusion, and a medium. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.