619 results about "Model reconstruction" patented technology

The invention discloses a three-dimensional stomatognathic model reconstruction system based on multi-modal data fusion. The system comprises a multi-modal data input unit, a template deformation reconstruction unit, a registration fusion unit, a multi-source data integration unit and an output unit. Through fusion processing of a CBCT image, an oral cavity vision measurement model and facial scanning data, a body deformation algorithm is adopted to couple biomechanical characteristics to realize craniojaw template deformation, and a non-rigid ICP algorithm is combined for dynamic regulation and control to realize facial template adaptation. A deep neural network is innovatively constructed to segment CBCT gingival data, the CBCT gingival data is fused with an oral cavity vision measurement model, and high-precision tooth reconstruction is realized by applying a differential geometry multi-scale curvature field segmentation and adversarial edge optimization technology. Through a composite registration strategy combining adaptive rigid registration and non-rigid registration, an occlusal plane constraint mechanism and an orbital curvature extreme point matching algorithm are innovatively introduced, finally, multi-source data high-precision registration fusion is realized, and a three-dimensional oral-jaw system model with anatomical structure integrity and clinical precision can be generated.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

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 relates to the technical field of electric digital data processing, in particular to a display process temperature management optimization system which comprises a data acquisition module, a dynamic modeling module, a cooperative compensation module and an early warning module. The data acquisition module acquires temperature data of a heating area through a multi-type temperature sensor array, and generates a temperature field matrix with a confidence label through multi-source verification; the dynamic modeling module receives the matrix, operates a plurality of machine learning algorithms in parallel to generate a thermal coupling strength distribution diagram, and outputs a region coupling coefficient and a confidence coefficient thermodynamic diagram; the cooperative compensation module calls knowledge base strategies in a layered mode according to the thermodynamic diagram and feeds back early warning signals when abnormity is detected. According to the system, a cross-module closed-loop control mechanism is formed through confidence label driving algorithm weight distribution, confidence thermodynamic diagram control strategy priority, abnormal signal triggering model reconstruction and knowledge base updating, and millisecond-level cooperative suppression of the thermal crosstalk effect of multiple heating areas is achieved.

The invention relates to the technical field of battery management, and discloses a forklift battery charging method and device, and the method comprises the steps: collecting the real-time multi-dimensional physical state data, including vibration data, of a forklift battery; dynamically reconstructing a multi-physical field coupling model matched with the current working condition in a preset model library according to the data; performing predictive multi-target optimization based on the model to determine an optimal charging equalization strategy considering charging time, energy loss, health attenuation and thermal risk; charging is executed according to the strategy, the deviation between physical response and model prediction is compared in real time, and online self-correction is conducted on model parameters. The system comprises a state acquisition module, a model reconstruction module, a strategy optimization module and a charging control and correction module. According to the method, the digital twin model capable of self-evolution is constructed and closed-loop feedback correction is carried out, so that the charging strategy can actively adapt to working condition changes caused by factors such as vibration and temperature, and safer and more efficient charging management of the forklift battery is realized.

The invention relates to the technical field of three-dimensional model reconstruction, in particular to an existing building three-dimensional model reconstruction system based on point cloud data, and the system comprises a point cloud data processing module which carries out the octree recursive space subdivision of input point cloud data, builds a hierarchical index structure, and generates a multi-view fusion point set. In the method, octree recursive space subdivision is performed on the input point cloud data to establish a hierarchical index structure, so that the key area of the building surface can be positioned more accurately in subsequent data processing, data redundancy is reduced, and the precision and efficiency of three-dimensional model construction are improved; then voxel depth field data is obtained by calculating a truncation symbol distance function value of voxels, the description accuracy of a building surface structure is improved, and an initial surface grid is formed through contour surface extraction; meanwhile, plane slicing is conducted on the multi-view fusion point set in the preset building main axis direction, and contour details of all building structure sections are captured.

The invention relates to the technical field of virtual reality, in particular to an indoor structure reconstruction method based on a panoramic image scene understanding algorithm, and the method comprises the steps: S11, obtaining a plurality of equidistant columnar projection panoramic images of a current indoor scene through a panoramic camera or a panoramic image splicing algorithm; s12, performing semantic segmentation on the collected panoramic image by using SAM, deducing indoor ceiling, floor and wall areas of the panoramic image on the basis of a semantic segmentation result, and generating a multi-channel semantic graph; and S13, inputting the acquired RGB panoramic images and the multi-channel semantic map into a pre-trained panoramic image depth estimation model to obtain a depth map corresponding to each panoramic image. The method is used for automatically generating an indoor structure model, the scheme comprises the core steps of semantic segmentation and layout reasoning, depth estimation and point cloud construction, structure optimization and indoor model reconstruction and the like, and a complete indoor panoramic image scene understanding scheme is formed.

The invention relates to the technical field of point cloud processing, and discloses a point cloud data processing method, system and device for industrial three-dimensional model construction. The method comprises the following steps: carrying out denoising and down-sampling processing on original point cloud data of an industrial part to obtain preprocessed point cloud data, calculating a local geometric descriptor, converting the local geometric descriptor into an industrial feature recurrent plot through a recurrent plot algorithm, inputting the recurrent plot into a convolutional neural network to extract a high-dimensional geometric feature vector, and mapping the feature vectors into B-spline curved surface control parameters through a deep belief network, and performing point cloud reconstruction quality evaluation according to the control parameters to obtain geometric accuracy error data and curved surface reconstruction integrity data. According to the method, the problems that in existing industrial three-dimensional model construction, a point cloud data processing method lacks intelligent feature recognition, the reconstruction precision is insufficient, and manufacturing constraints are not fully considered are solved, and the recognition accuracy of geometric features of complex industrial parts and the precision of three-dimensional model reconstruction are improved.

Embodiments of the present application provide a three-dimensional model reconstruction and image generation method, a device, a storage medium, and a program product. In the method, multi-stage three-dimensional reconstruction is performed on the basis of a single image of a target object; in a first stage, a plurality of view images are generated on the basis of an image generation model, and an initial three-dimensional model is reconstructed on the basis of the plurality of view images; and in a second stage, on the basis of the plurality of view images and an initial prompt containing set marker information, a text-to-image model is used to learn an association relationship between the target object and the set marker information, and a plurality of scene images are generated on this basis. Compared with the plurality of view images, the scene images generated in the second stage have higher resolution and richer image details, and then the initial three-dimensional model is optimized on the basis of the plurality of scene images, so that a target three-dimensional model having higher resolution and clearer model details can be obtained, thereby paving the way for practical application of a three-dimensional reconstruction solution based on a single image.

The invention discloses a rotating body ancient building part model reconstruction method based on a least square method, and relates to the technical field of model reconstruction analysis, and the method comprises the steps: obtaining ancient building part point cloud through employing a three-dimensional laser scanning technology; the historic building part point cloud is sliced and projected to a horizontal plane, a circle is fitted by using a least square method, circle center coordinates are extracted, and the position of a rotating shaft is determined. According to the method, the precision of model reconstruction is effectively improved by accurately extracting the rotating shaft and fitting the rotating generatrix, high-precision point cloud data is obtained by using a three-dimensional laser scanning technology, and accurate extraction of the rotating shaft and the rotating generatrix is ensured through the steps of slicing, projection, least square fitting and the like; a non-uniform rational B-spline curve is adopted to fit a rotating bus, the rotating bus is rotated around a rotating shaft to reconstruct a three-dimensional model, the three-dimensional model of the ancient building part model is parameterized, the geometric accuracy of the model is further improved, and the reconstructed model can more accurately reflect the actual shape and size of the ancient building part.

The invention belongs to the technical field of deformation measurement, and provides a distributed optical fiber sensing casing damage monitoring system and monitoring method based on an OFDR (Optical Frequency Domain Reflectometer) technology. An armored optical cable consisting of a multi-core single-mode optical fiber and two multi-mode optical fibers of which the tail ends are welded into a U shape is spirally wound on the outer wall of a casing; a single-channel DSS / dual-channel DTS modulation-demodulation system of a dynamic distributed optical fiber strain and temperature composite sensing technology is used for real-time acquisition of strain and temperature sensing data of the casing pipe under the action of ground stress, and a cylindrical casing pipe three-dimensional model reconstruction algorithm is combined to automatically generate a spiral line. The three-dimensional model of the cylindrical casing is in one-to-one correspondence with the position of the optical fiber on the real casing, casing damage possibly occurring to the underground casing under the action of ground stress is monitored and measured in real time for a long time, early warning and forecasting information about that the casing damage is about to occur or is occurring is issued in time, and well site operators can conveniently handle and repair the casing damage in time.

The embodiment of the invention discloses a three-dimensional landform model reconstruction method and device based on geometry and texture combination. A specific embodiment of the method comprises the following steps: carrying out self-adaptive grid division on a landform three-dimensional point cloud and then carrying out geometric super-resolution to obtain a target three-dimensional landform sub-grid; performing color texture mapping and texture correction enhancement on the target three-dimensional landform sub-grids to obtain an enhanced landform texture map; performing collaborative bidirectional optimization on the enhanced landform texture map and the target three-dimensional landform sub-grids to obtain a super-resolution landform texture map and a super-resolution three-dimensional landform sub-grid; performing model recombination and fusion on the plurality of super-resolution landform texture maps and the plurality of super-resolution three-dimensional landform sub-grids to obtain a three-dimensional texture landform grid model; and performing density control reconstruction processing on the three-dimensional texture landform grid model to obtain a three-dimensional density texture landform model, and storing the three-dimensional density texture landform model. According to the embodiment, the quality of the three-dimensional density texture landform model can be improved, and waste of storage resources is reduced.

The invention discloses a stainless steel stator blade composite repairing method based on model reconstruction, which is characterized by comprising the following steps of: acquiring a blade profile section and parameters of a stainless steel stator blade; blade profile size data of a new blade are collected, and a point cloud model is established; acquiring an optimal point cloud model based on the data of the point cloud model, and establishing a blade reference grid model as a blade modification standard model; adopting UG software to reconstruct the blade modification standard model; detecting an old blade of which the blade profile contour is within the baseline deviation range of the reference model according to the reconstructed blade modification standard model for repairing; and blade corrosion pit repairing and chord length repairing are conducted on the old blade based on the blade modification standard model, and blade modification and X-ray detection are conducted after repairing is completed. According to the method, the size precision of the established blade modification standard model is within 0.02 mm; the repairing area can effectively avoid the problem of secondary corrosion, and the service life of the blade is prolonged.

The invention discloses a laser point cloud-based industrial equipment digital twin model rapid reconstruction method and system, and the method combines a model matching mode and a point cloud reconstruction mode, and effectively solves the problem of twin model reconstruction in different scenes. According to the first mode, on the basis of an existing three-dimensional model, through shape matching, feature extraction and model retrieval methods, registration of point cloud data and the three-dimensional model is combined, and rapid reconstruction of the equipment twinborn model is successfully achieved. The mode is suitable for equipment with an existing standard three-dimensional model, the repeated utilization rate of the model can be improved, and the reconstruction time and cost are reduced. In the second mode, the reverse grid model construction method based on the point cloud data is provided for equipment which cannot be retrieved through an existing model. Through an improved greedy projection triangulation algorithm, point cloud data is converted into a grid model, and the precision and authenticity of the model are improved in combination with a grid optimization algorithm and a surface texture mapping technology. According to the method, the digital twinborn model of the industrial equipment can be efficiently and accurately reconstructed, the modeling difficulty in an industrial scene is relieved, the actual demand of field modeling is met, and the method has high application value and practicability.

The invention relates to the technical field of three-dimensional digital modeling, in particular to an automatic real-time three-dimensional digital modeling method and system for a dike. The method comprises the following steps: collecting first image data of a dike area, and setting a three-dimensional coordinate system of the dike area; evaluating a plane median error and an elevation median error of the first image data, and obtaining field acquisition precision based on the plane median error and the elevation median error; adjusting shooting parameters of the unmanned aerial vehicle according to the field acquisition precision, and acting the adjusted shooting parameters of the unmanned aerial vehicle on the unmanned aerial vehicle; acquiring second image data of the dike area through the adjusted unmanned aerial vehicle; executing spatial triangulation calculation on the second image data, and generating aerial triangulation calculation result data; and performing three-dimensional model reconstruction based on the aerial triangulation result data to obtain a dike three-dimensional digital model. According to the method, the frequency and the precision of obtaining the geometric information of the dike can be improved, and high-resolution and dynamically-updated three-dimensional modeling is realized.

Disclosed is a three-dimensional reconstruction method based on kinematic calibration of line structured light point set individualization, and the method includes the following steps: rigidly assembling an area-array camera and a line structured light emitter, and mounting the assembly on a motion platform; calibrating structural parameters of the motion platform of a measuring device by using a reference radius of a standard sphere and line structured light scanned data; registering a spatial position relationship of a line structured light point set by taking a calibrated spherical center position of the standard sphere and the reference radius of the standard sphere as constraints; controlling the five-axis motion platform to move and acquiring scanned data of a measured member under different poses; and completing high-quality three-dimensional model reconstruction of the measured member subjected to multi-pose scanning by using the scanned data and a real-time point cloud registration matrix.

The invention relates to an industrial CT composite material defect reconstruction method based on an unsupervised neural network. The method comprises the following steps: carrying out industrial CT shooting to obtain a cross section two-dimensional image of a composite material component; an unsupervised model for converting an industrial CT composite material image into a defect model for reconstruction and discrimination is constructed, sampling is performed through a multi-scale sampling model, a channel attention mechanism is introduced for weight adjustment, and a multi-scale residual module is introduced for reconstruction; constructing a joint loss function for calculating the unsupervised model; training the constructed unsupervised model by using a data set, wherein a loss function is used for updating parameters; and inputting a to-be-detected industrial CT composite material image into the model, so that three-dimensional reconstruction of the defects of the composite material can be completed. According to the method, the defects of the composite material are reconstructed with high precision, nondestructive testing of the composite material is achieved, and the method can be used for structural health monitoring and defect detection of aerospace vehicles such as airplanes and rockets.

The invention discloses a construction hidden danger automatic detection method and system based on point cloud and image multi-mode. The method comprises the following steps: step 1, acquiring point cloud data and RGB image data and respectively preprocessing the point cloud data and the RGB image data; 2, safety equipment detection is carried out by establishing an improved YOLOv5 model; establishing a 3D-CNN model to qualitatively judge the abnormal behavior of the personnel; 3, combining a semantic segmentation model and point cloud data processing to carry out quantitative judgment of height calculation and coordinate conversion of the fence; and 4, realizing three-dimensional live-action model reconstruction based on point cloud feature matching, Poisson surface reconstruction and GPU rendering, and detecting potential safety hazards of a construction site. The accuracy of qualitative detection is further improved, the precision error of quantitative detection is reduced, the recognition efficiency and precision of potential safety hazards are greatly improved, intelligent and all-around guarantee is provided for safety management of a construction site, remote decision is assisted, the management cost is reduced, and the method is suitable for various construction scenes.

The invention discloses a BIM / CIM-based engineering design whole-process auxiliary decision-making method, relates to the technical field of engineering design, and discloses a BIM / CIM-based engineering design whole-process auxiliary decision-making method, which comprises the following steps: responding to a project red line range, obtaining geographic space data, and carrying out preprocessing, model reconstruction and lightweight processing on the geographic space data to obtain a project red line range; generating a macroscopic current situation scene; obtaining at least two candidate schemes, and performing spatial registration and fusion on the BIM models corresponding to the candidate schemes and the macroscopic current situation scene to generate a corresponding fusion decision scene; based on the fusion decision-making scene, carrying out collaborative decision-making on the candidate schemes, and determining a bid-winning scheme; and packaging the BIM model corresponding to the bid winning scheme and associated data, and transmitting the packaged BIM model and associated data to a downstream construction management system through an interface. The scheme collaborative decision-making efficiency is effectively improved, the information fault problem in the design and construction stages is solved, and the whole-process decision-making closed loop of the engineering project is achieved.

The invention relates to a map data processing method and device based on model reconstruction, equipment and a medium, and the method comprises the steps: generating a building model grid according to map data after the to-be-processed map data is obtained, and enabling the building model grid to represent the geometric shape of a building group; according to the splitting result of the building model grid, constructing a directed bounding box about each independent building; according to a preset segmentation size, segmenting the directed bounding box into a plurality of proxy cubes for model reconstruction; and for each proxy cube, replacing the proxy cube with a target building model according to the neighborhood state of the proxy cube to obtain a model reconstruction result, and generating new map data according to the model reconstruction result. Through the method, the problems of single building model form, serious appearance homogenization and the like in map data can be effectively solved.

The invention relates to a railway track abrasion detection method and system, and aims to realize accurate and efficient detection of turnout sharp point rail abrasion. A detection device carrying a 3D camera, a speed sensor, a signal processing module and a data processing terminal is arranged in a turnout area to be detected, parameter configuration is completed, in the constant-speed moving process, the speed sensor generates a trigger signal according to the specified distance, the 3D camera is driven to synchronously collect point cloud data of the two-dimensional contour of the cross section of the sharp point rail, and a three-dimensional data stream is formed; after data are denoised, section registration splicing and three-dimensional model reconstruction are completed in combination with distance stamps, an actual measurement model and a standard contour model are superposed and compared, abrasion parameters are calculated, and the maximum abrasion point and position are recognized; and finally presenting the abrasion data in the forms of a color cloud picture, a table and the like, and automatically generating a standard detection report containing the contents of line information, defect types and the like. According to the method, automation and visualization of abrasion detection are achieved, the detection precision is high, the efficiency is high, and reliable data support is provided for track maintenance.

The invention discloses a production decision adaptive optimization method and system, particularly relates to the technical field of industrial production process optimization, and is used for solving the problems of strategy stiffness and limited adaptive ability caused by historical strategy data dependence of an existing optimization system. Model reconstruction is dynamically triggered by monitoring strategy entropy in real time, candidate strategies are generated in combination with parameter space resetting and multi-physics field simulation verification, strategy exploration priorities are dynamically allocated based on KL divergence difference, and a strategy set is continuously optimized through a closed-loop feedback mechanism. According to the method, path dependence can be actively broken when strategy diversity is attenuated, a dynamic priority distribution balance strategy is utilized to inherit and explore, a data acquisition window is adjusted in combination with deviation rate feedback, a self-adaptive optimization closed loop is formed, the adaptability and decision elasticity of a production strategy are effectively improved, and the efficiency stability in long-term operation is guaranteed.

The invention provides a wearable exercise and rehabilitation monitoring system based on multi-modal AI, which relates to the field of electrical digital data processing and comprises a distributed multi-modal sensing acquisition module, a hierarchical wireless data aggregation and transmission module, a 3D human body dynamics reconstruction and modeling module and an intelligent action evaluation and feedback module. The distributed multi-modal sensing acquisition module is responsible for realizing multi-dimensional cooperative sensing of human body motion through heterogeneous sensors, and the hierarchical wireless data aggregation and transmission module adopts a master-slave base station architecture to realize efficient aggregation and low-delay transmission of heterogeneous sensor data. The 3D human body dynamics reconstruction and modeling module is used for realizing high-fidelity 3D skeleton model reconstruction based on inverse kinematics solution of multi-sensor fusion; the intelligent action evaluation and feedback module is used for providing personalized training guidance by constructing a standard action library and realizing multi-dimensional similarity quantitative scoring; according to the system, deep fusion of the multi-modal sensor can be realized, and accurate motion monitoring is provided.

The invention provides a factory three-dimensional digital twin model reconstruction and incremental updating method for industrial space intelligence, and the method comprises the steps: obtaining a factory multi-view basic image for a factory panoramic scene, so as to train a bifurcated structure neural implicit field network containing a radiation field branch and an SDF branch, and generating an initial three-dimensional digital twin base; acquiring factory real-time inspection images acquired by the inspection robot under different inspection poses, rendering the initial three-dimensional digital twin base by using a radiation field branch to obtain a virtual reference view, and performing difference analysis on the virtual reference view and the factory real-time inspection images to position a structural change area in a factory; determining an axis alignment bounding box for changing the physical range of the entity in the area, taking the axis alignment bounding box as an effective boundary of the local space patch, and determining matched radiation field network weight and SDF network weight according to the effective boundary; and adjusting the radiation field network weight and the SDF network weight, and updating the radiation field network weight and the SDF network weight to the network in an incremental updating manner to generate a factory three-dimensional digital twinning model based on the implicit three-dimensional geometric field.

Some embodiments of the present application include obtaining a trained machine learning model that was trained using training data. Production data may be obtained from a data feed selected based on the trained machine learning model. In some embodiments, feature sets and observed results may be extracted from data output from the data feed, and one or more features or results may be masked to generate the production data. Predicted results data may be generated with the trained machine learning model based on the production data and an accuracy score for the trained machine learning model may be determined based on the predicted results data. If the accuracy score satisfies a threshold accuracy condition, the trained machine learning model may be caused to be rebuilt or the training data may be caused to be updated.

The invention relates to the technical field of three-dimensional reconstruction, in particular to an electric power facility surrounding environment three-dimensional reconstruction method and system. The method comprises the following steps: acquiring electric power facility data, performing electric power facility layout structure analysis, and constructing an electric power facility three-dimensional model; obtaining electric power facility environment data, carrying out dynamic environment evolution, and constructing an electric power facility environment dynamic evolution model; performing rendering and model alignment embedding processing on the electric power facility three-dimensional model based on the electric power facility environment dynamic evolution model to generate an environment-electric power facility fusion model, and performing electric power facility corona loss fluctuation detection and environment fluctuation factor analysis; corona environment disturbance area detection and error calculation are carried out on the environment-electric power facility fusion model, disturbance error data are generated, and corona environment disturbance reconstruction processing is carried out on the environment-electric power facility fusion model. According to the invention, the electric power facility environment combined corona loss dynamic three-dimensional model reconstruction can be realized.

The invention relates to the technical field of three-dimensional reconstruction, and discloses a three-dimensional reconstruction method and terminal equipment, and the method comprises the steps: carrying out the unmanned plane route planning and ground end scanning planning of a scanning region; acquiring an aerial image and a ground image; performing air model reconstruction according to the air image to obtain an air sparse point cloud and first pose information, and performing ground model reconstruction according to the ground image to obtain a ground sparse point cloud and second pose information; matching a target aerial image and a target ground image with an overlapping region in the aerial image and the ground image to obtain relative pose information between the target aerial image and the target ground image; and according to the first pose information, the second pose information and the relative pose information, utilizing global binding with control point constraint to adjust and optimize pose information and sparse point positions of each image in the target aerial image and the target ground image with the overlapping region, and obtaining a fusion model. According to the invention, the space-ground coordinate alignment precision of three-dimensional reconstruction is improved.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention relates to the technical field of fault section identification, and particularly discloses a fault section identification method and system for a multi-branch hybrid power transmission line system, and the method comprises the steps: collecting multi-modal parameters, carrying out the dynamic topology modeling, outputting an enhanced feature matrix and a dynamic impedance thermodynamic diagram of a multi-branch hybrid power transmission line, performing spatial-temporal feature decoupling and credibility evaluation to obtain a decoupling feature vector group and a feature credibility label, performing intelligent path traceability and conflict resolution on the multi-branch hybrid power transmission line, constructing multi-source decision fusion and anti-interference verification, labeling coordinates of a core fault section and verifying a credibility certificate, and performing knowledge distillation and closed-loop self-learning. A typical fault fingerprint database is established, and whether model reconstruction is triggered or not is judged. According to the method, the problems of long consumed time, high cost, low efficiency and prolonged power failure time of traditional power transmission line fault section identification are solved, the fault positioning time can be effectively shortened, and the difficulty of troubleshooting is reduced.

The invention relates to a method for automatically generating a multi-professional pipe network BIM model based on a CAD drawing of a graph neural network, comprising: pipe network CAD drawing information analysis and multi-professional pipe network BIM model generation, the pipe network CAD drawing information analysis comprising graph neural network model construction, CAD drawing analysis based on the graph neural network model, legend table analysis and multi-graph information aggregation; the multi-professional pipe network BIM model is generated based on the extracted pipe network component information, and different reconstruction strategies are adopted for different types of pipe network components. According to the method, an automatic building pipe network CAD drawing analysis technical route combining deep learning driving and knowledge rule constraint is constructed, symbol primitive semantic extraction is carried out based on a graph neural network, high-accuracy symbol and layer automatic identification is realized, a pipe network system expressed by multiple CAD drawings is converted into a graph structure, and the method is suitable for large-scale popularization and application. And entity matching and information aggregation are carried out on a plurality of drawings and a plurality of graph structures, so that an automatic, efficient and high-stability building pipe network system information model reconstruction task is realized.