638 results about "Mesh point" patented technology

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention discloses a three-dimensional scene and object reconstruction method, system and device based on SDF and a Gaussian field, belongs to three-dimensional scene reconstruction in the technical field of computer vision, and aims to solve the technical problem that the three-dimensional scene reconstruction quality is not high due to the fact that geometric precision and rendering quality cannot be achieved at the same time in the three-dimensional reconstruction process in the prior art. The method comprises the following steps: initializing and updating a TSDF voxel volume by using a depth map and an RGB image to obtain a camera external parameter and a scene three-dimensional initial model; sDF prediction is carried out on the scene three-dimensional initial model through an SDF neural network, scene surface points are generated according to the light direction, and a high-precision scene geometric model is obtained; training the SDF neural network; performing grid division on a bounding box of the scene three-dimensional initial model, outputting an SDF predicted value of each grid point by using the trained SDF neural network, and extracting a refined scene grid model; and performing Gaussian rendering by using the high-precision scene geometric model to generate a final rendered image.

The invention relates to the technical field of computer information processing, and discloses a high-fidelity terrain surface interpolation method and system for large-scale point cloud data, and the method comprises the steps: carrying out the multi-level cleaning of original point cloud data, and generating a cleaned point cloud data set; performing adaptive tile grid division based on the cleaned data to generate a tile set covering the target area; in each tile, taking the to-be-interpolated grid point as a query point, constructing and optimizing a local Gaussian process model, and obtaining a real elevation predicted value of the query point; processing all query points in the tiles in parallel to obtain a tile elevation interpolation result set; performing weighted average on each tile result to generate a global seamless terrain surface; according to the method, the problems of poor expandability and surface discontinuity caused by local interpolation in large-scale point cloud data processing are effectively solved, and high-precision and high-fidelity terrain surface construction is realized.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a one-dimensional plasma simulation method based on deep learning optimization and related equipment, and the method comprises the steps: firstly constructing a one-dimensional basic model containing specific parameters, and then constructing and training a neural network of a multi-layer sensor (MLP) structure for predicting a potential function; then, a particle simulation method (DL-PIC) based on deep learning optimization is executed. Under the condition that 1000 grid points are included, compared with a traditional particle simulation method (PIC), the simulation time of the DL-PIC neural network is shortened by about 33.37%. According to the invention, complex physical phenomena such as reflection oscillation caused by heat effect and space charges can be effectively processed, and the method has good expansibility. The method is not only suitable for one-dimensional simulation, but also can be popularized to higher-dimension and electromagnetic particle simulation. Therefore, the invention provides an efficient and accurate solution for plasma simulation research, and can be widely applied to the technical field of plasma simulation.

The invention discloses an electromagnetic field simulation grid adaptive generation method based on a neural network. The method comprises the following steps: performing mesh generation on a semiconductor device simulation model to obtain original generation data, and constructing node features; an undirected graph is constructed, graph nodes of the undirected graph adopt node features of grid points, and connecting edges adopt Euclidean distances between the grid points and adjacent grid points; inputting the undirected graph into a double-branch neural network, and outputting a predicted node position; and correcting each node of the original subdivision data by using the predicted node position obtained by the double-branch neural network to form new subdivision data for electromagnetic field simulation. According to the method, physical field information can be fused to efficiently adjust the node density, and the geometric boundary and topological structure characteristics of the device can be strictly kept.

The invention relates to an earthwork balance calculation method, system and device and a storage medium, and relates to the technical field of earthwork balance design. The method comprises the steps of performing fusion processing on oblique photography data and laser radar point cloud data to generate a live-action three-dimensional model; performing grid division on the live-action three-dimensional model to obtain a grid model; based on the gridding model, defining a decision variable set including the design elevation of each grid point, the overall slope of the site, the slope height of each slope unit and the earthwork allocation amount, and constructing a comprehensive objective function including an earthwork balance objective, an economical objective and a safety objective; and under the engineering constraint condition, performing multi-objective optimization solution on the comprehensive objective function to obtain a Pareto optimal solution set, and selecting a final implementation scheme from the Pareto optimal solution set. According to the method, the problem of topographic data missing of the dense vegetation region is effectively solved, the limitation of traditional single-target optimization is broken through, and the maximization of comprehensive benefits is realized.

The invention relates to the technical field of 5G communication, in particular to a 5G network signal low-altitude coverage test method and system, and the method comprises the steps: obtaining regional geographic information and base station layout information; based on geographic information and base station layout, constructing a three-dimensional digital map, dividing a dynamic test grid, and determining an initial flight path; according to a multi-source sensor of the unmanned aerial vehicle, in combination with the initial flight path, collecting 5G signal parameters and environmental data of the grid points through a preset frequency; a wavelet transform filtering algorithm is adopted for calibration, abnormal values are removed, and feature vectors are generated; according to the feature vectors, a coverage evaluation model is constructed in combination with a three-dimensional digital map, and a low-altitude coverage blind area and an interference type are identified; and if a low-altitude coverage blind area or abnormal interference is detected, dynamically adjusting the flight path of the unmanned aerial vehicle through an A-star algorithm, testing a target area, and generating a coverage optimization scheme based on a test result. Therefore, the problems of low test efficiency, lagging data processing and analysis and the like in the prior art are solved.

The invention discloses a frozen soil roadbed hot melt settlement laser scanning measurement method and system, and relates to the technical field of measurement, the method comprises the following steps: obtaining gridding surface data, and obtaining an elevation data sequence of each grid point; acquiring an elevation change index of each grid point according to the elevation data sequence of each grid point; acquiring a plurality of grid points adjacent to the ith grid point and taking the plurality of grid points as a plurality of reference grid points of the ith grid point, acquiring each relative difference quantity of the ith grid point, and acquiring a correction proportion of the ith grid point according to the plurality of relative difference quantities of the ith grid point; and obtaining target elevation data of each grid point according to the correction proportion of each grid point and the measured elevation data at the current moment, and forming a measurement result including the gridding surface data according to the target elevation data of each grid point. The method has the advantages of identifying abnormal values, dynamically correcting measurement and improving data reliability.

The invention provides a high-resolution emission flux inversion system and method. The method comprises the following steps: collecting atmosphere multi-source data; determining a distance weighting function, and calculating the correlation between the grid points in the simulation area and the observation value; generating a set sample meeting physical constraints of the assimilation object; performing singular value decomposition and dimension reduction on the set samples; replacing a tangent line and an adjoint mode of a regional air quality mode with a mixed assimilation method, and obtaining a simulated regional space grid point analysis increment; obtaining corrected concentration and flux distribution data; the data is used for carrying out emission flux inversion, and the unit time emission flux of different positions is estimated. According to the method, a traditional four-dimensional variation mode is replaced with a mixed assimilation method, and the calculation and programming difficulty is reduced; generating a set sample by using a four-dimensional sliding sampling algorithm, reducing dimensions, and calculating resource consumption; a joint assimilation algorithm is introduced to optimize the concentration and flux field, and accurate inversion of the high-temporal-spatial-resolution emission flux is achieved.

The invention relates to a complex medium inversion imaging method and system guided by a physical information neural network, and the method comprises the steps: constructing a two-dimensional grid model of a block structure with different medium distributions, obtaining out-of-plane displacement data at grid points through staggered grid finite difference, and forming a wave field full-waveform data set; constructing a physical information neural network, wherein the physical information neural network comprises a first feed-forward network for modeling a mapping relation of a displacement field of the sample, a second feed-forward network for modeling a velocity field of the sample, and a residual network; based on the wave field full-waveform data set, training a physical information neural network by taking minimization of compound loss as a target; and taking the space-time coordinates of the sample as input, and utilizing the trained physical information neural network to obtain predicted displacement and sound velocity of the corresponding position of the space-time coordinates, so as to realize inversion imaging of sound velocity medium distribution. According to the method, the ultrasonic propagation model of the complex medium can be quickly and accurately established under the condition that a small amount of marked data is used.

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 relates to the field of aviation safety, and provides a flight bumping area prediction method and system based on air-ground data fusion, and the method comprises the steps: obtaining a measurement area, and obtaining a barometric altitude through meteorological data; acquiring air detection data of the measurement aircraft so as to calculate a horizontal airspeed component; checking according to the horizontal airspeed component and the ground speed component to obtain a high-altitude wind speed component; generating an airborne data matrix, calculating the distance between a measurement point in the airborne data matrix and a grid point in a measurement area to obtain a latitude and longitude distance, and performing weight accumulation to obtain a grid point wind speed; wind field deformation is calculated, a jolting index is calculated according to the wind field deformation and normalized, a normalized jolting index is obtained, and a comprehensive jolting index is obtained through the normalized jolting index; and obtaining a target jolting index according to the ground measurement jolting index and the comprehensive jolting index to realize jolting prediction. According to the invention, accurate flight bumping prediction can be realized.

The invention discloses a thin-wall structure layered body grid generation method based on surface grid alignment, which comprises the following steps of: reading an integral surface grid with a thin-wall structure characteristic and a mapping relation, and pairing a source surface grid and a target surface grid in the integral surface grid according to the mapping relation; aligning the source surface grid and the target surface grid which are paired, updating the target surface grid, and extracting triangular mapping information of an aligned region; according to the mapping information, advancing a source surface grid to a target surface grid layer by layer to obtain a layered body grid; extracting the outer surface of the stratified body mesh, combining the outer surface of the stratified body mesh with the overall surface mesh to obtain a combined surface mesh, and performing mesh point classification; normalizing the unit normal direction of the combined surface grid, calculating the normal direction at grid points needing to be propelled according to the grid point classification, and interpolating points according to distances in the normal direction to form a constraint point set; and generating a cavity area body grid by taking the constraint point set and the combined surface grid as constraints, and combining the cavity area body grid with the layered body grid to obtain the layered body grid of the overall thin-wall structure.

The present invention discloses an unified multiple access (MA) based system for uplink transmission of data by multiple users. The system comprises an user interface integrated transmitter modules for transmitting the data from multiple user transmitters over waveforms through a cooperative unified data grid structure including grid points, said unified data grid structure including each user transmitter allocation of a few non-overlapping grid points for loading respective data bearing symbols, with zeros loaded onto unallocated grid points. The user transmitter is configured to transmit a sparsely loaded grid resembling to the unified two-dimensional data grid structure involving data-bearing Quadrature Amplitude Modulation (QAM), where a part of available grid points of the grid as allocated is loaded with QAM symbols and remaining grid points are loaded with zero symbols. A waveform modulator corresponding to user transmitter is also provided in the present system for waveform modulation of said sparsely loaded grid as per the waveforms enabling transmission for multi-antenna reception of the waveforms to a base station or an access point.

The invention discloses a GNSS (Global Navigation Satellite System) nonlinear deformation correction method based on a full-spectrum three-dimensional thermoelastic model. The GNSS nonlinear deformation correction method comprises the following steps: carrying out discrete Fourier decomposition on a temperature time sequence of each grid point; carrying out space spherical harmonic expansion and coefficient projection on each decomposed frequency; based on a space spherical harmonic expansion and coefficient projection result, considering the periodicity of the temperature, and performing sine / cosine representation of a frequency-by-frequency temperature item; performing spherical harmonic representation and time expansion on the deformed displacement field corresponding to the periodic temperature based on the spatial spherical harmonic expansion and coefficient projection result; solving a deformation equation in spherical coordinates in a Love number form to obtain an expression of a complex Love number; based on the complex spherical harmonic coefficient and the complex Love number of the temperature, full-spectrum synthesis of three-component displacement at the spherical surface is carried out; and performing internal consistency and external dispersion verification based on a full-spectrum synthesis result.

The invention discloses a pressure vessel lower chamber model order reduction method combining sparse grids and POD, and the method comprises the steps: adding one to the grade of the sparse grids after iteration initialization, and obtaining test grid points according to important grid points; executing the lower chamber full-order computational fluid mechanics model corresponding to the test grid points to obtain a first snapshot; reconstructing a second snapshot by using the mapping relation and the POD modal matrix; calculating a root-mean-square error of the two snapshots, and if the root-mean-square error is smaller than a threshold value, completing construction of a reduced-order model Otherwise, the grid points with the errors meeting the requirements are stored in a non-important grid point set, the grid points which do not meet the requirements are marked as important grid points, and the snapshot matrix is updated. Performing singular value decomposition on the snapshot matrix, updating the mapping relation, judging whether the maximum sparse grid grade is reached or not, and continuing iteration if the maximum sparse grid grade is not reached. According to the method, the field distribution of the thermal hydraulic parameters in the lower chamber can be quickly obtained through matrix algebraic operation, and the method can be used for application scenes such as parameterization analysis, design optimization and digital twinning.

A method includes obtaining images of an environment captured by imaging sensors associated with a passthrough AR device and position data and depth data associated with the images. The method also includes generating a point cloud representative of the environment based on the images, position data, and depth data. The method further includes generating a mesh for a specified image. The mesh includes grid points at intersections of mesh lines. The method also includes determining one or more depths of one or more grid points of the mesh. The method further includes transforming the mesh from a viewpoint of a specified imaging sensor that captured the specified image to a user viewpoint of the passthrough AR device based on the depth(s) of the grid point(s). In addition, the method includes rendering a virtual view of the specified image for presentation by the passthrough AR device based on the transformed mesh.

The invention discloses an aircraft simulation method, device and equipment based on parallel block sparse matrix incomplete LU decomposition and a medium, and relates to the technical field of simulation, and the method comprises the steps: building a block sparse matrix comprising a plurality of zero sub-blocks and non-zero sub-blocks based on the topological relation of each grid point in an aircraft grid and simulation physical parameters, determining dependency pairs respectively corresponding to a plurality of non-zero sub-blocks from the non-zero sub-blocks, and storing the dependency pairs by adopting a preset data structure; wherein the dependent pair comprises a pair of matched non-zero sub-blocks; respectively distributing the plurality of non-zero sub-blocks to a plurality of GPU thread bundles for parallel processing so as to update the corresponding non-zero sub-blocks based on the updated dependency pairs after the GPU thread bundles complete updating on the dependency pairs corresponding to the corresponding non-zero sub-blocks; and after the plurality of non-zero sub-blocks are updated, updating the block sparse matrix based on the plurality of updated non-zero sub-blocks, and determining an incomplete LU decomposition result according to the updated block sparse matrix so as to perform aircraft simulation.

The invention provides a thermal imaging automatic calibration and key region segmentation method. The method comprises the steps of obtaining a thermal imaging image, and then performing preliminary denoising processing on the image to obtain a denoised image; constructing a high-density grid in an image pixel dot matrix by adopting a dense sampling method according to the de-noised image so as to determine an initial pixel coordinate of each grid point; obtaining neighborhood feature vectors through an abnormal point set, and grouping the vectors by adopting K-means clustering so as to determine a temperature change mode, and fusing a temperature gradient to extract a boundary extraction basis as grid optimization input; and extracting a boundary extraction basis of the key part from the temperature change mode, adjusting the high-density grid according to the boundary extraction basis to obtain an optimized grid, and starting relocation iteration adjustment through coordinate deviation calculation. According to the method, a closed-loop optimization process of high-density grid construction, abnormal point identification, clustering analysis, grid dynamic optimization and iterative relocation is constructed, so that refinement and adaptive segmentation of a thermal anomaly region are realized.

The invention discloses a super-set deterministic weather forecast method and device based on machine learning, and the method comprises the steps: obtaining multi-source meteorological data of a target region, carrying out the meshing of the multi-source meteorological data, carrying out the historical static feature analysis and dynamic feature analysis of the meshing features, obtaining the feature weight of each mode, and carrying out the recognition of the multi-source meteorological data. The method comprises the following steps: constructing a prediction sub-model according to an extreme event, obtaining enhanced numerical prediction data, obtaining posterior probability distribution of grid points through a conditional generative adversarial network and a Bayesian neural network based on the numerical prediction data, a gridding feature and a feature weight, taking a maximum probability value as a deterministic weather forecast, and calculating a confidence interval. And obtaining a joint probability product including wind speed and rainfall joint distribution and the characteristic contribution degree. According to the method, numerical forecasting set products of different mode centers are utilized to fuse probability forecasting information, deterministic weather forecasting is obtained, smoothing of extreme events is reduced, deterministic maximum value output is provided, and meanwhile good interpretability is achieved.

The invention discloses a broadband scattering RCS acquisition method and system based on an adaptive integral method and a pseudo-spectral derivative method, and mainly solves the problems of overlarge memory occupation, low calculation efficiency and unstable precision in the prior art. According to the implementation scheme, boundary conditions are applied to a composite electromagnetic target to obtain an EFIE-PMCHWT integral equation of an electromagnetic field, and discretization, Galerkin testing and fast Fourier transform are carried out on the EFIE-PMCHWT integral equation to obtain a near-field impedance matrix; selecting GLC interpolation sampling points at the center frequency based on a pseudo-spectral method to generate a pseudo-spectral derivative matrix, and performing linear combination on the pseudo-spectral derivative matrix by a near-field impedance matrix to obtain a high-order impedance matrix; setting a rectangular grid used for surrounding the composite target, projecting current and charges of the composite target to grid points, and expanding the current into Taylor series and expanding the frequency band of the current by using an asymptotic waveform technology; and the broadband electromagnetic scattering characteristic is calculated according to the frequency band and the moment coefficient. According to the method, memory occupation is reduced, calculation efficiency and precision are improved, and the method can be used for analyzing the broadband characteristic of the dielectric / metal composite object.

The invention provides a new energy automobile chassis structure visual detection method and system, and relates to the technical field of computer vision. The method comprises the following steps: firstly, carrying out partition semantic annotation on a target chassis image, extracting the center and the area of each detection region, calculating an attention degree coefficient and generating a dynamic sampling step length; constructing a grid shooting point set in the communication reserved area and establishing a local coordinate system; constructing a graph G based on all the grid points, generating a shortest path by adopting a path planning algorithm, and controlling the shooting trolley to move in sequence according to the path to complete chassis image acquisition; then image filtering and splicing are carried out to generate a complete chassis map; and finally, performing state identification and action judgment on the image blocks based on a Markov decision model, and outputting a chassis structure defect detection result. According to the invention, through full-process fusion of structure sensing sampling, shortest path optimization and Markov intelligent detection, the efficiency, accuracy and intelligent level of new energy automobile chassis structure detection are significantly improved.

The invention discloses an oil and gas resource spatial distribution prediction method of a tree enhanced naive Bayes classifier, and belongs to the field of petroleum geology and artificial intelligence, and the method comprises the steps: S1, collecting exploratory well, earthquake and geological information; s2, oil and gas resource space distribution prediction master control geological factors are extracted, and an exploratory well data set and a grid data set are made; s3, establishing a tree enhanced Bayesian network model according to the exploratory well data set; s4, calculating the oil-gas probability of the grid points by using the model; and S5, drawing an oil and gas resource space distribution probability graph by using an interpolation method. The method can achieve the accurate and efficient prediction of the spatial distribution of oil and gas resources through optimizing the selection strategy of the dependency relationship of the main control geological factors and combining the geological data features, can provide quantitative decision support for oil and gas exploration and deployment, and improves the exploration efficiency and economic benefits.

The invention discloses a structured grid optimization method and device and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: determining regular family fragments in a structured grid, calculating a spacing distance corresponding to the regular family fragments, and obtaining the optimized structured grid according to the spacing distance and a pre-stored ideal spacing distance. And optimizing the regular family fragments, determining optimized grid point coordinates, and finally updating the structured grid according to the grid point coordinates. When the spacing distance is adjusted, the spacing distance of the regular family fragments is optimized through the ideal spacing distance, the optimized grid point coordinates are obtained, and finally the structured grid is updated according to the grid point coordinates, so that the accuracy of a grid optimization mode can be improved.

The invention discloses a meteorological element spatial interpolation method based on terrain coupling and a hybrid model. The meteorological element spatial interpolation method comprises the following steps: acquiring meteorological station observation data and digital elevation model (DEM) data of a research area; converting the longitude and latitude coordinates of the meteorological stations into a projection coordinate system which is the same as the DEM data, and extracting DEM values corresponding to the positions of the stations; the longitude, latitude and DEM value of a meteorological station are used as input characteristics, and a thin-plate spline function is adopted to carry out preliminary interpolation; constructing input features of the machine learning model; an XGBoost algorithm is adopted to train a machine learning model; creating a regular grid according to the target resolution, and performing interpolation prediction on grid points; and performing Gaussian filtering smoothing processing on the prediction result to obtain a final interpolation result. According to the method, terrain information is integrated, adaptive parameter optimization for different meteorological elements is realized, calculation efficiency and interpolation precision are balanced, nonlinear characteristics and spatial heterogeneity of meteorological data are processed, and spatial continuity and precision of an interpolation result are improved.

The invention provides a geological envelope body automatic generation method. The method comprises the following steps: defining a target work area; performing sparse horizon picking of a top interface and a bottom interface on the geological envelope in the defined target work area to determine a top interface horizon, a bottom interface horizon and corresponding top interface and bottom interface data; based on the grid positions corresponding to all the pickup point positions of the top interface horizon and the bottom interface horizon, according to the range of the top interface data and the range of the bottom interface data, boundary lines of the geological envelope body at the top interface and the bottom interface in the target work area are determined; determining horizon depth values in all grid points of the top interface and the bottom interface; constructing a top interface boundary constraint and a bottom interface boundary constraint according to the horizon depth value and based on boundary lines of the geological envelope body at the top interface and the bottom interface to determine an intersection point between the top interface and the bottom interface of the envelope body so as to optimize the boundary lines of the geological envelope body at the top interface and the bottom interface; and generating a three-dimensional visualization model of the geological envelope based on the optimized boundary line.

The invention discloses a shot point arrangement method. The method comprises the following steps: acquiring a work area in which shot points are to be arranged and theoretical shot points in the work area; creating a first grid in the work area according to a preset size to obtain a plurality of grid points; screening out a first grid point which can be used as a candidate shot point according to the gradient of each grid point and the position of each grid point in the work area; constructing a second grid by taking each theoretical shot point as a center; and one first grid point is selected from each second grid to serve as a target shot point. The invention further discloses a system, computer equipment and a readable storage medium. According to the scheme provided by the invention, the difficulty of field construction of shot points is reduced while obstacles in a work area can be avoided, gridding uniform distribution of the shot points in the work area is realized, uniformity of surface element attributes of an observation system is ensured, and the quality of three-dimensional seismic acquisition data can be improved.

The invention relates to a pollutant monitoring method, device and equipment based on multi-point source superposition diffusion, and belongs to the technical field of atmospheric pollutant diffusion analysis. The latitude and longitude coordinates of the emission source, the evaluation point and the evaluation area are represented by a geographic coordinate system, and the latitude and longitude coordinates are converted into plane coordinates under a projection coordinate system; a two-dimensional rectangular grid is adopted to cover an evaluation area, and a continuous space is discretized into a plurality of grid units; establishing a reference coordinate system and a diffusion coordinate system; the constructed Gaussian plume diffusion model is adopted to calculate pollutant concentration data of each emission source to each grid point, concentration contributions of the multiple emission sources are superposed, and total concentration data of the grid points are obtained; and enabling the pollutant concentration data of the evaluation point to be equivalent to the function output of the total concentration data of one or more grid points in the grid unit where the evaluation point is located, so as to obtain the pollutant concentration data of the target position. The invention provides a refined technical scheme for pollution diffusion simulation of complex multi-point sources.

The invention discloses an arbitrary structure fast electromagnetic scattering analysis method based on time domain finite difference, and the method comprises the steps: obtaining the triangular patch data of a scatterer model, generating an extended bounding box, and improving the adaptability of a complex structure; an initial grid point set is generated by adopting an interpolation method based on a preset grid side length, and grid data is screened and optimized, so that grid division is more accurate, and calculation redundancy is effectively reduced; the optimized grid data is mapped into a three-dimensional array, and a material type and electromagnetic parameter distribution are defined, so that efficient modeling of a complex target is realized; the electromagnetic scattering characteristics are calculated based on time domain finite difference iterative calculation in combination with the surface equivalence principle and the Poynting theorem, the calculation precision and stability are improved, meanwhile, the calculation process is optimized to reduce the storage requirement and the calculation time, and therefore efficient and accurate electromagnetic scattering analysis of any structure is achieved, the calculation efficiency is improved, and the engineering application requirement is met.