231 results about "Delaunay triangulation" patented technology

The invention discloses an underground oil and gas well fault prediction method based on a multi-modal space-time diagram neural network, and aims to solve the problems of insufficient multi-modal feature coupling, weak space-time correlation modeling, poor real-time performance and the like of a traditional method. The method is characterized in that multi-frequency sensor data features and maintenance log semantic knowledge are respectively extracted through a time expansion convolutional network (TCN) and a BERT-BiLSTM model, and a cross-modal gating attention mechanism is designed to realize heterogeneous data dynamic fusion; an equipment space topological graph is constructed based on Delaunay triangulation, dynamic causal association between nodes is quantized in combination with transfer entropy to generate a time graph, and a fault propagation path is jointly modeled through residual space-time graph convolution; a hierarchical prediction module is constructed by adopting a bidirectional LSTM and a graph attention network (GAT), short-term fault classification and long-term equipment residual life prediction are respectively realized, and the fault prediction precision and industrial landing feasibility under complex working conditions are effectively improved.

Disclosed is a complex scene deformation monitoring and classifying method based on interferometric synthetic aperture radar (InSAR) and a deep learning self-attention model, including: generating a deformation interferogram by using an InSAR extraction technology combining persistent scatterer (PS) points and distributed scatter (DS) points and relying on a shuttle radar topography mission digital elevation model (SRTM DEM) image; constructing a Delaunay triangulation network and an annular zone, obtaining a time series deformation data of PS points and DS points through adaptive arc densification and omni-directional point expansion, and extracting settlement points on a bridge; decomposing a time series by an SAR-self-attention model, decomposing InSAR time series data into a trend component and a seasonal component, and interpreting and analyzing deformation of the sea-crossing bridge; and describing time series dynamics and a seasonal pattern by comparing a curve fitting method with a seasonal and trend decomposition method.

The invention relates to the technical field of building model design, in particular to a building three-dimensional model lightweight design method and system based on artificial intelligence, and the method comprises the steps: extracting geometric-semantic features of a building model through a multi-scale curvature filtering and semantic segmentation network, constructing a fusion feature vector matrix, and obtaining a fusion feature vector matrix; by utilizing an integrated graph convolutional network and a self-adaptive neural simplification network of a double-branch attention mechanism, differential resampling is executed based on vertex importance weight, a simplified intermediate model is generated, a surface microstructure is recovered by means of a generative adversarial network, grid holes are corrected by combining Delou inner triangulation, and a surface microstructure is obtained. A non-uniform rational B-spline curved surface is adopted to reconstruct a key decoration component and output a lightweight model, so that the problems of insufficient geometric feature retention and semantic information splitting in the traditional technology are solved, intelligent, efficient and lightweight of a building three-dimensional model is realized, visual fidelity is ensured while data compression is performed, and the construction quality is improved. And the digital management requirement of the whole life cycle of the building is met.

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a side slope slippage monitoring and early warning method based on an image recognition technology, which comprises the following steps: arranging a monitoring target and a reference target in a to-be-monitored area of a side slope, arranging two image displacement monitoring devices at opposite stable positions at equal height to acquire two-dimensional displacement data of the targets; after error correction, an equipment monitoring coordinate system included angle is calculated based on parameters such as equipment spacing, slope inclination displacement is calculated through vector synthesis, and three-dimensional slippage deformation is calculated in combination with vertical displacement; meanwhile, a multi-level early warning mechanism based on the slip rate and the accumulated slip amount is established, a Delaunay triangulation network is constructed, and an improved adaptive Kriging interpolation algorithm is adopted to realize deformation trend prediction. According to the method, binocular vision and multi-algorithm fusion are utilized to realize high-precision three-dimensional monitoring, the evaluation accuracy is improved through the dynamic weighting model and intelligent early warning, the method has the advantages of high monitoring precision, timely early warning, high adaptability and the like, and the safety of slope engineering can be effectively guaranteed.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention relates to the technical field of chemical safety and intelligent control, in particular to an explosion-proof intelligent temperature control method and system for a hydrogen peroxide storage tank based on multi-modal monitoring, and the method comprises the steps: collecting multi-dimensional data such as temperature, pressure and H2O2 concentration through a sensor array, carrying out the Kalman filtering and wavelet transform denoising, recognizing local abnormal points through an improved WMDS-DBSCAN algorithm, and obtaining the explosion-proof intelligent temperature control of the hydrogen peroxide storage tank based on the WMDS-DBSCAN algorithm. Predicting a hot spot diffusion trend in combination with an unsteady heat conduction equation and an RF-ADI algorithm; weighted Delaunay triangulation is utilized to construct a self-adaptive monitoring grid, risk area probability distribution is output through an SAM-LSTM-CNN hybrid neural network, and a dynamic amplitude limiting fuzzy PID controller adjusts the opening degree of a nozzle to achieve temperature control. According to the method, multi-modal data and an intelligent algorithm are fused, abnormal accurate detection, risk prediction and self-adaptive temperature control are realized, a closed-loop optimization mechanism is formed, and the operation safety and reliability of the hydrogen peroxide storage tank are effectively improved.

The invention discloses a software project development method based on a low-code platform, which belongs to the technical field of software development, and specifically comprises the following steps: receiving original demand description through an embedded natural language parser, and establishing a semantic incidence matrix to extract demand elements; encoding the demand elements into three-dimensional structured demand vectors; mapping the demand vector to a three-dimensional orthogonal feature space, calculating the coordinates of a functional module, and generating an initial topological structure based on a Delou inner triangulation algorithm; rendering an interactive three-dimensional network map on a visual interface; monitoring user operation, and recalculating the adjacency relation of the affected module when the displacement of the module exceeds a threshold value or the connection edge is reconstructed; compared with a historical version, when a key service path is interrupted or a data flow ring is broken, a compensatory connection bridge is activated; a deployable architecture with a version traceability identifier is output, and a traceability code is built in the module to reversely map an original demand vector.

The invention discloses a multi-mode intelligent linkage 3D visual data center operation and maintenance system and method, and relates to the technical field of data center operation and maintenance. The system comprises a multi-modal data fusion module used for mapping physical sensor data and video monitoring and operation and maintenance logs to a unified three-dimensional coordinate system and constructing a multi-modal fusion feature tensor; the three-dimensional particle modeling module is used for constructing a particle space structure based on a Voronoi diagram and Delaunay triangulation and adaptively adjusting the particle resolution; the multi-modal score analysis module is used for calculating a particle multi-dimensional score and generating a semantic heat map to recognize an abnormal region; the structured noise prediction module is used for simulating future responses under different instructions based on a diffusion model; the instruction generation and regulation module is used for realizing causal analysis and instruction optimization in combination with a knowledge graph; and the three-dimensional visual interaction module supports real-time rendering and interaction operation at a client. According to the method, the intelligence, the real-time performance and the visualization level of data center operation and maintenance can be improved.

The present invention relates to the technical field of natural ventilation in buildings, and in particular to a computationally assisted decision-making method and system for climate-adaptive building cavity design. The method comprises: using a Delaunay triangulation method to generate an initial mesh, using a Laplace operator-based mesh refinement method to adaptively refine the initial mesh, so as to obtain an adaptive mesh system, wherein the adaptive mesh system is used for dynamically adjusting the mesh density of conditional PINNs; constructing a multi-task learning framework within the conditional PINNs, wherein the multi-task learning framework is used for jointly predicting a plurality of physical field variables within the conditional PINNs; and iteratively training the conditional PINNs, and using a gradient descent algorithm to minimize a loss function until a predetermined number of training iterations or loss convergence is reached, thereby generating a key physical field variable prediction model in building cavity design. The present invention can implement efficient and accurate prediction of key physical quantities in building cavity design, and can be adapted to different building layouts and functional space characteristics.

The invention is suitable for the technical field of road detection, and relates to a semi-rigid base layer loose disease GPR image recognition and three-dimensional reconstruction method, which comprises the following steps: constructing a semi-rigid base layer loose disease random medium model, carrying out forward modeling on a road structure, generating simulation data, and expanding a data set in combination with a data enhancement technology; a DN module fusing a dynamic deformation convolution module and a RepNCSPELAN4 module is designed, meanwhile, a WIoU loss function is introduced, training is conducted through an obtained data set, and identification of the semi-rigid base layer loose disease is achieved; calculating the area of a disease region through pixel and physical size conversion based on an instance segmentation result obtained through identification; a three-dimensional point cloud is generated by adopting inverse distance weighted interpolation, and the disease volume is calculated in combination with a Delaunay triangulation method. According to the method, accurate disease recognition is achieved, meanwhile, two-dimensional recognition is converted into three-dimensional reconstruction, and a quantitative basis is provided for maintenance priority division and repair scheme formulation.

The invention provides a curved surface grid generation method based on discrete grid parameterization, and belongs to the technical field of computer aided engineering (CAE) and computational geometry. Aiming at the problems of tedious geometric operation, parameter space fusion distortion, insufficient grid fitness and the like in grid subdivision of a complex curved surface formed by a plurality of sub-curved surfaces, the invention provides the following technical scheme: firstly, carrying out triangular grid pre-subdivision on an original curved surface, and constructing a virtual topological structure for inhibiting non-key features; the discrete grids are mapped to a low-distortion two-dimensional parameter space by adopting least square conformal mapping (LSCM), and seamless combination of multiple sub-surface parameter domains is realized; dynamically calculating a parameter space size field, and controlling grid density distribution; after a parameter space adaptive grid is generated through Delaunay triangulation, grid points are accurately attached to an original geometric surface by using a dual projection correction mechanism. According to the method, complex CAD geometric operation is avoided, the automation degree is remarkably improved, meanwhile, physical space grid uniformity and strict fitness are guaranteed, and the method is suitable for efficient and high-precision grid generation of complex curved surfaces in the fields of aerospace, automobiles, ships and the like.

The invention discloses a flood risk map result data coupling and management method, and relates to the technical field of hydrological information processing and geographic information systems, and the method comprises the following steps: obtaining hydrological data and topographic data required by flood risk assessment, including water level time sequence data, submerging depth data and TIN topographic data, and carrying out standardized conversion on data in different formats, and carrying out unified alignment based on a coordinate reference system. According to the method, the coupling precision of the hydrological data and the topographic data is improved through the adaptive Delaunay triangulation algorithm, and simulation distortion caused by time-space mismatching is avoided; the storage is optimized by adopting a topological optimization GeoJSON data structure, so that the query and management efficiency is improved; gPU acceleration rendering and LOD layering technologies are combined, the real-time visualization capability of flood simulation is improved, and efficient and accurate data support is provided for flood prevention emergency management and water conservancy decision making.

The invention provides an airborne point cloud building contour point extraction method based on a constraint triangulation network and semantic ring analysis, and belongs to the field of airborne laser radar data processing, and the method comprises the steps: obtaining an airborne laser radar point cloud data set of a to-be-processed region; performing classification processing by using a filtering algorithm to obtain a surface point set and a non-surface point set; building candidate seed points are extracted, and building point clouds are extracted; a Delaunay triangulation network is constructed based on the building point clouds, and constraint is carried out based on the distance between the point clouds; constructing a concurrent triangle set, and extracting contour points and contour connecting edges; and constructing a topological graph, performing traversal search on the topological graph, determining the minimum inclusion rectangle of the closed loop, and judging the contour point type corresponding to the closed loop according to the geometrical shape parameter and the spatial distance parameter of the minimum inclusion rectangle. According to the method, the triangulation network is constructed by using the disordered contour points, and the sequence relationship and semantic information between the points are considered, so that accurate extraction of the semantic contour points is realized.

The invention relates to a numerical simulation method for multiphase fluid stratum seepage, which belongs to the technical field of numerical simulation and comprises the following steps: S1, generating an unstructured grid by adopting a mode of combining a leading edge propulsion method and Delaunay triangulation; s2, performing dynamic adjustment through an adaptive grid encryption technology according to the change of the seepage physical quantity; s3, establishing a dynamic phase change model, and obtaining a function of representing the phase change rate as temperature, pressure and concentration of each phase; s4, the mass and energy change of each phase in the phase change process is calculated in real time through a kinetic equation; s5, processing dynamic evolution of a phase interface by adopting a phase field method, and describing distribution conditions of different phases through phase field variables; and S6, coupling the grid system obtained by dividing the multi-scale unstructured grid with the dynamic phase change model through a numerical solution algorithm. According to the method, the complex geometrical shape of the stratum can be better fitted through multi-scale unstructured grid division, and then the change of the phase change process along with time and space can be tracked in real time through the dynamic phase change model.

The invention discloses a rock mass falling identification algorithm based on point cloud distance tolerance, and belongs to the technical field of rock mass monitoring, and the algorithm comprises the steps: obtaining image data of a collection region, and generating a live-action three-dimensional model and original point cloud data of the collection region; removing the water surface feature points in the point cloud data; performing density adaptive voxel downsampling on the point cloud without the water surface feature points; iCP registration is executed layer by layer by adopting a voxel size progressive decrease strategy, when ICP registration is executed on each layer, iteration convergence is controlled through a dynamic threshold value, and transformation matrixes of all layers are accumulated to obtain a final registration result; and calculating the Hausdorff distance tolerance of the point clouds after the two stages of registration, when the distance tolerance is greater than a preset threshold value, determining the rock mass falling area, and calculating the rock mass falling volume by using an Alpha-Shape algorithm in combination with Delaunay triangulation. The algorithm based on the point cloud distance tolerance is high in recognition precision in the rock mass falling area; the method can be effectively applied to rock mass monitoring and loosening and peeling detection.

The invention provides a hydropower station reservoir area three-dimensional terrain modeling method and device based on multi-source data fusion, and relates to the technical field of three-dimensional terrain modeling, and the method comprises the steps: obtaining a multi-source data set containing point cloud data, image data, texture data and actual measurement data of a hydropower station reservoir area; preprocessing the multi-source data set to obtain a preprocessed data set; and according to the preprocessed data set, constructing a three-dimensional terrain model by using an improved Delou inner triangulation algorithm, a non-uniform rational B-spline surface reconstruction technology and a Laplacian smoothing operator. The geometric accuracy and the natural form of the three-dimensional terrain model can be remarkably improved.

The invention provides a multi-arm spiral subarray cooperative adaptive acoustic imaging system and method. The system adopts a circular substrate to construct an Archimedes spiral array. The innovative dynamic processing strategy comprises low-frequency sub-array combination and aperture expansion, intermediate-frequency independent processing and load balance maintenance, and high-frequency cross-arm virtual focusing array construction. The key technology covers multi-channel dynamic sampling, frequency band conflict arbitration, phase compensation beam forming and confidence weighted fusion. In the data fusion stage, a blind area is eliminated by adopting Delaunay triangulation and radial basis interpolation, and edge features are reserved in combination with anisotropic diffusion filtering. A temperature-sound velocity correction model is integrated in the system, and delay summation, MVDR and compressed sensing hybrid beam forming algorithms are applied to real-time processing. The output unit generates a three-dimensional acoustic thermodynamic diagram fusing Gaussian smoothing and edge enhancement. According to the scheme, the spatial sampling efficiency is optimized through spiral topology, the resolution and precision in a complex scene are improved through a dynamic recombination mechanism, and the method is suitable for industrial monitoring and noise source positioning.

The invention discloses a remote sensing image zero-watermarking method based on depth features, relates to the field of digital watermarking, and aims to solve the problem of insufficient robustness during active feature design. The remote sensing image zero-watermarking method comprises the following steps: reading a remote sensing image, converting the remote sensing image into a gray scale, and normalizing the gray scale image; detecting feature points of the normalized image by using an SIFT algorithm, screening out feature points in a certain intensity interval, and constructing a Delaunay triangulation network according to a rule; calculating inscribed circles of triangles in the triangulation network one by one, constructing an inscribed circle mask set Masks, multiplying the mask set by the remote sensing image, and performing scale normalization processing on a result in order to facilitate feature extraction of the neural network; and inputting the feature domain into a trained U-Net neural network, and extracting robust features in the feature domain. According to the method, on the basis of good global stability and empty circle characteristics of the Delaunay triangulation network, each feature domain of the image exists independently, and good uniqueness of the watermark algorithm is guaranteed.

The invention discloses a photovoltaic power station wind field evaluation method and system, electronic equipment and a storage medium, and relates to the technical field of photovoltaic power station engineering. The method comprises the following steps: carrying out classification processing on original point clouds, generating DEM data by using Delaunay triangulation in combination with a natural neighborhood interpolation method, and carrying out correction through ground control points and least square adjustment; and constructing a three-dimensional terrain model according to the corrected data and image data, determining a computational domain size and a boundary condition, performing terrain optimization, executing LES solution after applying near-surface turbulence inlet disturbance synthesized by PRFG3, and comparing and evaluating the obtained transient wind field data with field actually measured wind speed data. According to the method, accurate capture of complex terrain wind field features and dynamic adaptive correction of the model are realized, so that the evaluation period is shortened, the surveying and mapping cost is reduced, the precision and reliability of wind field evaluation are remarkably improved, and the demand of wind resource evaluation of a large-scale photovoltaic power station is met.

The invention provides a power collection system topology optimization method and device based on a variable weight minimum spanning tree, and relates to the technical field of offshore wind power topology optimization. The method comprises the following steps: grouping a plurality of fans based on the angle difference among the plurality of fans according to the position information of the offshore booster station to obtain a plurality of fan sets; a Delaunay triangulation method is adopted to construct a reference grid connecting line of each fan set; and based on the reference grid connection line of each fan set, generating the optimal topology of the to-be-processed offshore current collection system based on a variable weight minimum spanning tree algorithm. According to the method, the grouping clustering method, the Delaunay triangulation method and the variable weight minimum spanning tree algorithm are combined, and the topology design of the offshore wind plant current collection system is efficiently, economically and rapidly achieved. According to the invention, topological connection and cable specification type selection can be optimized synchronously, and a global optimal solution is sought between the cable path length and the type cost difference caused by the change of the current-carrying capacity, so that the fine control of the overall cost of the current collection system is realized.

The invention belongs to the technical field of civil engineering, geological engineering and artificial intelligence crossing, and particularly discloses a mountainous area airport soil-rock ratio evaluation method and system based on progressive spatial interpolation, and the method comprises the following steps: S1, data collection; s2, boundary elevation prediction: constructing an initial Delaunay triangulation network based on the collected data, and performing boundary elevation prediction at the gravity center of each triangle by adopting a regional adaptive spatial interpolation algorithm; s3, constructing a complete high-precision demarcation point set; s5, dynamically updating interpolation model parameters through an online learning mechanism in combination with real-time sensor data change, introducing a dynamic Bayesian network DBN and an evidence theory DST to model uncertainty of terrain disturbance and lithology change, and generating a soil-rock ratio confidence interval and prediction distribution in combination with Monte Carlo simulation. The method is suitable for efficiently and accurately determining the earth-rock interface and calculating the earth-rock ratio in the engineering investigation stage, and meanwhile, the uncertainty of the calculation result is quantified.

The invention, which relates to the technical field of mine geology and urban safety, discloses a refined collaborative prediction method and system for subsidence of a global goaf, comprising the steps of establishing a hierarchical storage system, and correcting a working face calculation boundary; extracting the maximum influence radius of all the working faces and four-dimensional coordinates of the mining boundary of each working face, and calculating to obtain a global prediction range to generate a global surface prediction grid; point set insertion and Delaunay triangulation are adopted to obtain an effective triangular unit, a local influence domain range of the effective triangular unit is calculated, and an influenced sub-grid is screened from the global surface prediction grid; and calculating a predicted point movement deformation value based on a parallel computing architecture, accumulating the predicted point movement deformation value to a global result array through a coordinate index mechanism, synthesizing a deformation component in a specific direction, generating a visual result, and performing statistical analysis. According to the method, the problems of poor adaptability and low efficiency of a traditional scheme are effectively solved, and the prediction precision and efficiency are remarkably improved.

The invention discloses a water quality monitoring intelligent early warning method and system based on multi-dimensional data analysis, and belongs to the technical field of water quality monitoring. The method comprises the following steps: processing missing values and noise through a dynamic weighted data filling algorithm to obtain complete water quality data; carrying out multi-parameter collaborative dimensionality reduction by adopting an improved t-SNE algorithm, and mapping high-dimensional water quality parameters to a three-dimensional feature space; constructing a dynamic watershed partition model based on a Delaunay triangulation network and DEM data; calculating a spatial anomaly score through a weighted local outlier factor; performing time anomaly detection in combination with STL (Standard Template Library) decomposition and a Transform model; multi-scale fluctuation detection is realized by using wavelet packet decomposition; fusing space, time and parameter dimensions to construct a three-dimensional judgment matrix, and generating a comprehensive anomaly score; and triggering a multi-level early warning mechanism according to a scoring result. The system can monitor the water quality change in real time, and gives out early warning in time when abnormity occurs, so that the water quality safety is guaranteed.

The invention discloses an ocean terrain digital display method based on three-dimensional modeling. The ocean terrain digital display method comprises the following steps of 1, constructing a multi-source ocean data set; 2, performing space fusion by adopting a self-adaptive interpolation method based on position weight to generate a three-dimensional space density field; 3, performing contour surface extraction by adopting an improved Marching Cubes algorithm to generate an initial three-dimensional terrain model; 4, performing local modeling by adopting a Delaunay triangulation network reconstruction method, and constructing a complete three-dimensional terrain model; 5, obtaining a texture terrain model; step 6, constructing an LOD tree structure and realizing dynamic loading in combination with view cone cutting and a screen space error control strategy; and step 7, based on a dynamic loading result of the LOD tree structure, driving node switching through a time controller, and performing visual display. According to the method, three-dimensional modeling and an LOD tree structure are fused, and high-precision reconstruction and evolution display of the ocean topography are achieved.

The invention discloses a miniature intelligent terminal intestinal image processing method and device, a storage medium and computer equipment. The method comprises the following steps: preprocessing an original intestinal tract image set acquired by a monocular endoscope, calculating according to the preprocessed intestinal tract image set to obtain a motion calibration scale factor and a texture calibration scale factor, and adaptively fusing to obtain a conversion coefficient from a pixel to an actual size; extracting polyp region key feature points of each image of the preprocessed intestinal image set, constructing a Delaunay triangulation graph according to each polyp region key feature point, and performing non-rigid deformation matching based on a cross-graph convolutional network and an optimal transmission algorithm to obtain a matched feature point set; and sparse 3D reconstruction is carried out according to the matched feature point set to obtain a polyp point cloud, and the polyp size is calculated according to the Euclidean distance between two farthest points in the polyp point cloud and the conversion coefficient. According to the method, the problems of monocular image scale ambiguity and non-rigid deformation can be solved, and the more accurate polyp size can be obtained.

The invention discloses an HBIM reconstruction method in a historic building digital twinning technology. Firstly, fine modeling is carried out, and a standard parameter model and a triangulated irregular network model are fused to construct a basic geometric model; digital-analog decoupling is carried out, geometric space and semantic attribute information are separated, and an IFC format geometric model is generated; then entering a geometric conversion link, dividing a high-risk region, a medium-risk region and a low-risk region according to the ratio of the number of vertexes per unit area to the area, subdividing the high-risk region, merging the low-risk regions, converting the high-risk region and the medium-risk region into an OBJ format by adopting a geometric element extraction and reconstruction algorithm and a Delaunay triangulation algorithm, and finally converting the OBJ format into a glTF format; and finally, carrying out attribute mapping, associating attribute data with the glyTF-format geometric model by utilizing a unique identifier of the component, setting an updating mechanism to keep synchronization, completing HBIM reconstruction, and building a foundation for digital protection and application of the ancient building.

The invention provides a slope excavation engineering quantity calculation system and method based on point cloud, and the method comprises the steps: reading the slope geological data of hydropower engineering to construct an initial three-dimensional model, and collecting the multi-stage point cloud data through a three-dimensional laser scanner; performing equidistant gridding on the point clouds of the model region according to an X / Y axis, calculating the spatial similarity and color similarity of the point clouds in grids, and dividing sub-data sets according to threshold values; fitting an excavation surface through a Delaunay triangulation network, calculating an XY plane projection area, and calculating an average Z-direction distance from a subset to the surface of the initial model; the node weight of the triangulation network is corrected by fusing rock stratum interface parameters, finally, the excavation engineering amount is dynamically output through summation of the product of the projection area of each subset and the average distance, and when the deviation of adjacent stages exceeds 10%, cross validation is conducted in combination with a profile method. According to the method, the precision and the real-time performance of slope excavation engineering quantity calculation can be improved, and automatic integration of lithologic characteristics and mechanical attributes is realized.

The invention relates to the technical field of laser welding, and particularly discloses a laser welding system and a laser welding spot position determining method.The laser welding system comprises a welding execution module, a motion execution module, a data acquisition module and an industrial personal computer; the welding execution module is used for carrying out laser welding operation on a welding seam position according to a welding instruction issued by the industrial personal computer; the motion execution module is used for driving the mechanical arm to complete mechanical actions according to the welding seam position issued by the industrial personal computer; the data acquisition module is used for acquiring contour information of a weld joint position of a to-be-welded workpiece and transmitting the contour information to the industrial personal computer; point cloud information of a to-be-welded workpiece is obtained through a laser sensor and a CCD camera, three-dimensional model reconstruction and point cloud segmentation are carried out in combination with an advanced Delaunay triangulation technology and an RANSAC algorithm, so that feature points of a welding seam are accurately extracted, then noise reduction processing is carried out on the extracted feature points by adopting a mean value replacement algorithm, and the feature points of the welding seam are accurately extracted. And the final welding spot position is more accurate.

The invention discloses a substation danger area judgment method and system based on electric field gradient perception, and relates to the field of safety monitoring, and the method comprises the steps: arranging an electric field sensor array in a key equipment area of a substation; performing noise reduction processing on the data of each sensor node to obtain an electric field vector set; obtaining the electric field intensity change rate of each monitoring point through a space vector differential algorithm, and generating an electric field gradient tensor; dividing the electric field gradient value, and constructing a dynamic danger level mapping rule; performing spatial interpolation through a Delaunay triangulation algorithm to generate an electric field gradient equipotential surface; constructing an electric field gradient space distribution model, and generating a dynamic danger boundary envelope body in real time; and correcting the boundary of the dangerous area through the pre-trained gradient compensation coefficient matrix and triggering a graded early warning signal. The method has the advantages that the electric field gradient change of the transformer substation is monitored in real time through the electric field sensor array and the wavelet denoising technology, the dangerous area is dynamically evaluated, and the accuracy and real-time performance of dangerous area judgment are effectively improved.

The invention relates to the technical field of three-dimensional modeling, in particular to a terrain modeling method and device based on a domestic three-dimensional graphic platform Railworks, and the method comprises the steps: collecting terrain data along a rail transit engineering project, and generating a structured original data pool; the method comprises the following steps: extracting and processing a discrete three-dimensional point set through a Railworks platform, and generating a triangular mesh by adopting a Gift Wrapping algorithm and a Delaunay triangulation algorithm; reconstructing a three-dimensional terrain surface model, adding an attribute database, and integrating elevation and gradient attributes according to industrial standards; and a downstream bridge and roadbed design module is connected through a data synchronization mechanism, so that design parameters are automatically updated. The device correspondingly realizes functions of each step. According to the method, the problems of high data security risk and low collaboration efficiency caused by dependence on foreign software are solved, an autonomous solution is provided, and the data processing security and the cross-professional design collaboration efficiency are improved.