147 results about "Grid mapping" 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 provides a method and a system for actively monitoring, regulating and controlling a clean room, which are characterized in that environmental parameters in the clean room and spatial state information of a dynamic object are acquired through cooperation of a fixedly arranged sensor and a sensor carried by a mobile device, and the environmental parameters comprise airflow velocity, temperature and humidity, pressure difference, particulate matter concentration, illuminance and the like; the dynamic objects comprise positions of the personnel and the equipment and geometric models of the personnel and the equipment. And cleaning and fusing the collected data to generate a standardized parameter sequence with a time label, and constructing a three-dimensional dynamic environment model by combining a clean room structure and dynamic object information to realize grid mapping and visual management of environment parameters in the model. And on the basis of the model, an environment state change trend is predicted, risk grade evaluation is executed, a hierarchical regulation and control instruction is generated, closed-loop regulation and control and parameter adaptive adjustment of the clean room environment are finally realized, and the intelligent and refined level of clean room environment regulation and control is improved.

The invention provides a low-altitude flight accident intelligent identification method based on multi-source data fusion, and the method comprises the steps: carrying out the encryption processing of flight control data, dynamically mapping environment data through an airspace grid mapping model, carrying out the time-space correlation of the two kinds of data, and obtaining multi-source data; constructing a five-dimensional evidence evaluation system through the information entropy of the multi-source data, and carrying out dynamic weight adjustment on the evaluation system according to a real-time data triggering condition to obtain a causal contribution degree; an accident liability contribution degree is obtained through a weight modeling algorithm; then evidence conflict detection is carried out to obtain recombination credibility; and finally, training a dynamic weight distribution model by recombining credibility, and identifying a low-altitude flight accident to obtain a responsibility division result. According to the method, multi-dimensional data are integrated in real time, a complete flight accident evidence chain is constructed, and a dynamic evaluation and multi-party collaborative management mechanism is introduced, so that a scientific basis is provided for responsibility division, and the responsibility affirmation efficiency and credibility are improved.

The invention belongs to the technical field of electronic equipment thermal management, and discloses an electronic equipment thermal field rapid prediction method for multi-physical field strong coupling. According to the method, a layered heat source intensity distribution model is constructed, a grid mapping relation topological graph technology and a boundary node energy conservation compensation mechanism are developed, a multi-time scale coupling transfer function is designed, and a dynamic adjustment strategy based on phase deviation analysis is introduced. According to the method, the non-uniform heat source distribution and thermal diffusion dynamic characteristics under the high-frequency condition are accurately captured, the energy consistency of multi-physical field coupling is ensured, the thermal field prediction precision is improved, particularly, the method has outstanding advantages in the aspect of phase and amplitude prediction of transient thermal response, and meanwhile calculation resource consumption is greatly reduced.

The invention provides a robot production line article grabbing method based on visual localization, which comprises the following steps: preprocessing a multi-modal image to obtain an original image; performing grid mapping on the original image to obtain a multi-level feature descriptor; based on the multi-level feature descriptors, a dynamic mapping relation among the three coordinate systems is established through a non-rigid coordinate system alignment algorithm, and coordinate offset of movement of the conveyor belt is compensated in real time; performing hierarchical feature matching on the multi-level feature descriptors and an article template library, identifying article categories and extracting contour geometric features; based on the contour geometric features and the surface curvature distribution, the three-dimensional pose and candidate grabbing points of the object are obtained through a geometric constraint optimization algorithm; and generating a robot obstacle avoidance track according to the candidate grabbing points and the robot motion model. According to the method, accurate coordinate compensation is realized through multi-modal data fusion and dynamic adaptive grid mapping, and the article positioning accuracy is improved in combination with hierarchical feature matching.

The invention discloses a water pollution cross-domain traceability method based on multi-chain collaboration and stable isotope source analysis, and relates to the technical field of water environment monitoring, the method comprises the following steps: pollution source chemical fingerprint construction: collecting an environmental water sample and a potential pollution source sample in a drainage basin; adopting a mass spectrometer to detect stable isotope compositions such as delta 13C, delta 15N, delta 18O and the like in the pollutants; and performing morphological division on the pollutants by using a morphological grading extraction technology. Homology judgment precision is greatly improved through isotope and morphological analysis; cross-chain credible collaboration is achieved, multi-party data interaction is achieved, and timeliness and accuracy of pollution tracing are guaranteed; the space-time model traceability is combined with an evolution path and gridding mapping, so that the pollution inversion accuracy is improved; and ecological compensation support provides a quantitative basis for pollution intensity and diffusion range so as to support a compensation model.

The invention discloses an offshore wind turbine structure health monitoring method and system, and the method comprises the steps: an offline stage: constructing a strain snapshot matrix through finite element simulation, extracting a low-dimensional feature subspace basis matrix through intrinsic orthogonal decomposition, and mapping a structural grid into a graph; and meanwhile, Fourier feature mapping of node coordinates is carried out, and after splicing with reconstructed strain features, a spectrogram neural network based on Chebyshev graph convolution is trained. In the online monitoring stage, according to unilateral sparse strain data collected in real time, a modal coefficient is rapidly solved through a mask matrix and a pre-stored basis matrix, full-field strain is preliminarily reconstructed, then the trained network model is input, and a high-fidelity full-field displacement field and a high-fidelity full-field stress field are deduced in real time. The method can effectively adapt to any change of sensor layout, real-time and accurate reconstruction of full-field physical response of a complex structure is achieved under the harsh condition of single-side sparse measurement, and the problem that a traditional technology depends on fixed point distribution and double-side measurement is solved.

The invention relates to the technical field of computer vision and automatic driving, in particular to a self-supervised aerial view perception method fusing Gaussian spattering and time sequence modeling, electronic equipment and a readable storage medium, and the method comprises the following steps: S1, constructing a BEV model; s2, feature grid mapping is carried out; s3, rendering a two-dimensional image; s4, performing self-supervised learning and optimization; s5, downstream task application; according to the method, BEV features are mapped into three-dimensional Gaussian parameters, end-to-end self-supervised learning is achieved through differential rendering, the method is applied to downstream tasks, and three-dimensional target detection, semantic segmentation or occupancy prediction are carried out; downstream tasks are trained and reasoned through self-supervision loss, no manual data annotation is needed, the data construction cost is remarkably reduced, and meanwhile the perception performance and generalization ability of the model in an automatic driving scene are improved.

The invention discloses a BMS hybrid battery management system based on AI machine collaboration, and relates to the technical field of battery management, and the system comprises a signal collection module which synchronously collects multi-dimensional signals, carries out the interpolation alignment, and generates a synchronous feature matrix; the graph generation module is used for calculating a correlation coefficient based on the feature matrix and filtering edges, and constructing a collaborative topological graph; the vector construction module is used for extracting collaborative potentials, historical responses, spectrum peak values and fold numbers, assembling multi-dimensional mark vectors and carrying out index storage on the multi-dimensional mark vectors; the monomer selection module is used for clustering according to density, constructing a multi-layer priority structure and sequentially selecting active and redundant monomers; and the collaborative management module is used for mapping environment load grids, fusing partition coefficients, distributing differentiated charging and discharging power and realizing accurate collaborative management. Integrated management of coupling relation revelation, hierarchical scheduling and differentiated power distribution is realized through construction of coupling topology and multi-level priority selection based on environmental load grid mapping and composite priority.

The invention relates to a port water area ship navigation risk distribution calculation and visualization method and device based on AIS and GIS data fusion, and the method comprises the following steps: 110, obtaining ship AIS data, and carrying out the preprocessing of the ship AIS data, thereby obtaining the preprocessed ship AIS data, and the preprocessing comprises the standardization, feature extraction and abnormal point detection; step 120, carrying out port water area grid construction, and carrying out grid mapping and legality screening on the preprocessed ship AIS data; step 130, performing port water area static risk assessment, port water area dynamic risk assessment and port water area propagation risk assessment on the port water area, and performing port water area comprehensive risk assessment on the basis to obtain an assessment result; and step 140, drawing a port water area risk thermodynamic diagram based on the evaluation result to realize risk distribution visualization. According to the invention, efficient and scientific decision support can be provided for port safety management.

The application discloses a curved surface structure zero state form finding method, comprising the following steps: (1) a model is established based on a design configuration, and deformation data is obtained by simulating the whole construction process; (2) a zero state configuration is deduced reversely: key node deformation values are extracted, arches are started, a simulation is re-performed after a linear model is fitted and adjusted, and iteration correction is performed until deformation error meets the requirement, and a zero state installation configuration is obtained; and (3) construction data is generated by extracting component line shapes and node coordinates. The application innovatively adopts an integral curvature adjustment strategy to replace a traditional node coordinate local adjustment mode, and through reverse deduction of 'construction simulation-control point arching-curved surface fitting-grid mapping', the problem of curved surface discontinuity caused by the traditional reverse superposition method is solved, construction precision and efficiency are significantly improved, and the application is suitable for intelligent construction of complex curved surface space grid structures.

The invention provides a distributed MIMO clutter echo simulation method based on GIS information, and the method comprises the steps: obtaining the GIS information of a geographic information system, and dividing a surface scatterer irradiated by an MIMO radar into a plurality of basic scattering units based on the GIS information; calculating slope distances, Doppler frequencies, terrain elevation values and land cover types of the plurality of basic scattering units to form a scene model element set; mapping a plurality of basic scattering units to a radar resolution unit by combining a backscattering coefficient empirical model and a resolution adaptive grid mapping method according to the scene model element set; dividing the radar resolution unit into a permanent scattering area and a dynamic scattering area according to GIS information; performing clutter statistical characteristic simulation processing on the permanent scattering region and the dynamic scattering region to obtain a clutter amplitude sequence; and performing echo analysis processing on the clutter amplitude sequence in a time-frequency domain to obtain clutter echo simulation data. And the simulation precision of clutter echo simulation data is integrally improved.

The invention belongs to the technical field of spatial data processing, and particularly discloses a multi-source remote sensing data gridding construction and processing method and system, a terminal and a medium. Comprising the following steps: acquiring remote sensing image data, vector data, point cloud data and TIN data to form a standardized data set; performing three-dimensional layered mesh generation on the target area to generate an initial multi-level mesh set; performing geometric superposition and coverage judgment on the standardized data set to form an element-grid mapping set; performing double-engine coding and decoding on the grid units in the mapping set, generating a unique grid code, and establishing a code-space range mapping table; constructing a three-dimensional space topological structure; obtaining a space intersection result, a space union result and a space difference result; a multi-scale grid set is generated, and a unified gridding integrated data set is formed. Through gridding fusion and self-adaptive multi-scale processing of multi-source data, integrated expression and efficient organization of multi-source remote sensing data in a three-dimensional space are realized.

The invention discloses an industrial smart energy multi-energy evaluation visualization system and method, and relates to the technical field of energy big data visualization crossover, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and generating space-time aligned high-dimensional energy data through a space-time hash coding method; based on the three-dimensional manifold coordinate model, converting equipment energy efficiency parameters into an equivalent energy mass field, and through an energy field reconstruction and geometric optimization method, generating a curved manifold space and identifying an optimal transmission path and a distortion strength cloud picture; and based on the distortion strength cloud picture, generating a thermodynamic color gradation through a thermodynamic color gradation mapping algorithm, rendering three-dimensional gradient field distribution of the equivalent energy mass field by adopting a ray tracing method, and generating a visual energy evaluation interface. According to the method, space and time hash codes are generated through a space-time hash coding method, the equipment-grid mapping relation is established in combination with GeoHash coding, and accurate space-time alignment of multi-source heterogeneous data is achieved.

The invention discloses a three-dimensional model lightweight method, and the method comprises the steps: obtaining a plurality of pieces of point cloud data of a target agricultural scene, building a distribution grid based on the point cloud data, enabling the initial position of the distribution grid to be located above the point cloud data, enabling the distribution grid to comprise a plurality of particles which are connected with each other, and enabling the distribution grid to be mapped with the texture information of the plurality of pieces of point cloud data; performing gravity dynamic simulation on the cloth grid, and obtaining a cloth form when the cloth grid reaches a target stable state; based on the distribution form and the multiple pieces of point cloud data, a target grid is determined, the target grid is a three-dimensional model with multiple ground points in the multiple pieces of point cloud data as a substrate, and the surface of the target grid comprises target textures; and performing high-ratio simplification on the target grid and performing texture optimization processing on the grid after high-ratio simplification to obtain a lightweight three-dimensional model. According to the scheme, the data transmission rate and the loading rendering speed are improved.

The invention discloses an additive manufacturing surface topography prediction method and system based on multi-source molten pool feature fusion. The method comprises the steps that a visible light image and temperature field data of a molten pool in the laser additive manufacturing process are collected; key molten pool features are extracted according to the collected visible light images and temperature field data; fusing the extracted key molten pool feature data through a grid mapping method to generate a feature matrix; scanning the surface of the formed part to obtain surface topography data, and processing the surface topography data through a grid interpolation segmentation method to generate a label matrix with the same size as the feature matrix; and using a deep learning network based on an adaptive multi-source model, taking the feature matrix as an input, taking the label matrix as an output, carrying out training, and constructing a surface topography prediction model. By means of the scheme, the surface appearance in the laser additive manufacturing process is accurately predicted in real time, and an effective monitoring and predicting means is provided for quality control of additive manufacturing.

The invention discloses a generalized energy storage equivalence model construction method based on multi-type micro-grid mapping, and relates to the technical field of energy storage model construction, and the method comprises the steps: dividing an energy storage unit into a plurality of equivalence model candidate structures through a graph neural network model based on an electrical topological graph and a node state of an energy storage system; model parameters are quickly initialized and calibrated online based on meta learning; deep reinforcement learning dynamic decision structure switching and parameter optimization are introduced; and adopting Monte Carlo Dropout to evaluate the parameter confidence and trigger model rollback, and outputting a final energy storage equivalent model. According to the method, the accuracy and robustness of the model under the dynamic working condition are improved, the uncertainty in the modeling process can be sensed in real time, rollback correction is actively carried out, the reliability and engineering availability of model output are ensured, and the method is particularly suitable for energy storage system modeling and control scenes with high requirements for the response speed, the adaptive capacity and the operation stability.

The invention relates to the technical field of physical property testing, and discloses a mobile phone appearance multi-angle automatic detection system and method based on a multi-face reflection structure, and the system comprises a panorama collection unit which obtains an original panorama image formed by a to-be-detected mobile phone through a multi-face panorama reflection mechanism; the reference data storage unit is used for storing standardized three-dimensional model data of the mobile phone to be tested; the central processing unit is used for establishing the three-dimensional model data as a space measurement reference, constructing an optical path mapping matrix according to the distribution parameters, mapping a surface grid of the three-dimensional model data to a camera coordinate system to determine geometric distortion sampling coordinates, extracting pixels to complete space topology reconstruction, and generating a standardized surface expansion view; according to the invention, nonlinear perspective distortion in reflection imaging is eliminated, tight alignment of pixel sites and physical topology is realized, and physical balance with objective scales is provided for quantitative analysis of flaws.

The invention discloses an urban traffic signal lamp adaptive control method based on artificial intelligence, and the method comprises the steps: firstly converting vehicle data into a fluid field matrix through employing a space grid mapping technology, calculating a virtual fluid dynamic pressure index combining kinetic energy density and acceleration divergence, so as to represent a congestion evolution direction; the system identifies a fluid infinitesimal state, generates a game weight, monitors back propagation characteristics of a dynamic pressure shock wave, sends a phase pre-wake-up request to an upstream intersection, edge computing nodes construct a game revenue function based on a node reputation value and downstream impedance, and searches an optimal timing strategy through distributed interaction. And the system triggers asynchronous signal switching according to the deviation between the optimal strategy and the current state, and executes phase difference nonlinear compensation. According to the method, through quantification of the traffic flow state and the cooperative game, potential congestion sources are effectively identified, non-cooperative behaviors of nodes are inhibited, and real-time blocking of burst shock waves and dynamic coordination of regional signals are realized.

The application discloses a handheld spectrum analyzer real-time spectrum signal processing system and processing method, the system comprises a pre-processing unit, an ADC unit, an FPGA and an ARM which are connected in sequence, the FPGA comprises a digital down conversion unit, a multi-rate decimation filter unit, a real-time FFT operation unit and a real-time detection unit which are connected in sequence, and a grid mapping unit, a vector connection unit and a fluorescence statistical unit which are connected with the real-time FFT operation unit in sequence, the ARM comprises a real-time spectrum diagram and waterfall diagram unit which is connected with the real-time detection unit, and a fluorescence display unit which is connected with the fluorescence statistical unit. The application adopts parallel frame real-time detection and vector connection-based digital fluorescence statistical method to compress multiple spectrum data, reduces the requirement of a large number of spectrum data on screen refreshing, adopts vector connection to make fluorescence statistics, so that the measured signal is easier to distinguish, and the application meets the test requirement of a handheld instrument.

The invention discloses a territorial space current situation evaluation model method fusing multi-source heterogeneous data, which comprises the following steps of: generating grid units based on topological boundary constraints of an administrative region, and establishing a multi-level index of the grid units of the administrative region through STree; integrating the topological boundary constraint and a point-grid mapping technology to realize that vector space file elements and a table generate a spatial index relationship in the grid unit; according to the grid units and the spatial index relationship, performing label index calculation in the grid by utilizing a labeling calculation tool with natural resource business semantics and fuzzy spatial semantics to obtain a grid label table; and calculating a label index weight based on a CRITIC algorithm fused with the spatial weight matrix, and obtaining a natural resource comprehensive evaluation according to the label index and the label index weight. According to the method, multiple space collaborative evaluation according to the industry field can be supported, and the space calculation scale is effectively unified.

The invention relates to the technical field of blockchain traceability, in particular to a blockchain-based drug storage traceability and search system, which comprises an identification information generation module, an on-chain drug identification module, a lattice mapping reading module, a state field verification module and a chain log right confirmation module. According to the method, the drug approval number, the drug general name code and the GTIN code are spliced into the unified identification reference string and are subjected to code verification, and the drug attributes are extracted and consistent matching is completed in combination with an on-chain data positioning mechanism, so that the accuracy of drug identity tracing is effectively guaranteed; the method comprises the following steps: extracting an inventory grid position state, screening legal storage information to realize real-time state verification and available grid position identification, establishing a current binding relationship through terminal number cross comparison and state field verification, realizing dynamic management of an actual storage state of a medicine, enhancing traceability, verifiability and right verifiability of the medicine in a storage link, and improving the efficiency of the medicine storage. And the transparency and accuracy of the whole medicine storage and search process are improved.

The invention discloses a neural operator network method based on grid parameterization. The method comprises the following steps of: drawing grids of a physical domain by using a grid division tool or software; mapping grids of different physical domains to grids of a general domain, and sampling on the general domain; based on the geometric domain and the parameters, generating an input and output field data set, and sampling the input and output fields at the acquired sampling points; and based on the generated data set and the deep neural operator network, solving PDEs (partial differential equation). According to the method, operator problems on different domains are converted into solution on a general domain at the same time, so that PDEs solution across different domains is realized.

In various embodiments, a scene reconstruction model generates three-dimensional (3D) representations of scenes. The scene reconstruction model computes a first 3D feature grid based on a set of red, blue, green, and depth (RGBD) images associated with a first scene. The scene reconstruction model maps the first 3D feature grid to a first 3D representation of the first scene. The scene reconstruction model computes a first reconstruction loss based on the first 3D representation and the set of RGBD images. The scene reconstruction model modifies at least one of the first 3D feature grid, a first pre-trained geometry decoder, or a first pre-trained texture decoder based on the first reconstruction loss to generate a second 3D representation of the first scene.

This invention discloses a method, system, and medium for data mapping between a solid geometric model and an unstructured mesh, relating to the field of nuclear reactor core coupling programming technology. By converting the CSG geometric entity into a coupling intermediate mesh (unstructured mesh), and using the coupling intermediate mesh as an intermediary between the CSG geometric entity and other professional unstructured meshes, high-precision coupled data transmission and coupled calculation between the CSG geometric entity and other professional unstructured meshes can be achieved through mesh mapping and other methods, based on the geometric correspondence between the CSG geometric entity and the coupling intermediate mesh.

A grid map includes a first-layer map including multiple first grids and a second-layer map including multiple second grids. The number of the multiple second grids is greater than the number of the multiple first grids and multiple second grids are mapped by each first grid. When the multiple second grids mapped by the first grid have a same attribute value, the attribute value is stored in the first grid. The first grid is alternatively configured to store index information, and the index information is configured to point to a data structure of the plurality of second grids mapped by the first grid.

The invention relates to the technical field of ship traffic management and marine monitoring, and discloses a multi-source data-based false AIS signal detection method, which comprises the following steps of: firstly, extracting a historical track to construct a coverage grid mapping table containing radar detection attributes; receiving real-time data and dividing the real-time data into an AIS to-be-detected group and a radar truth value group; non-radar blind area targets are screened according to the mapping table; retrieving an associated target in the radar truth value group to execute existence reverse verification; a dynamic time warping algorithm is adopted to align time sequences and calculate feature differences, and fine comparison verification is executed; constructing a neighborhood flow field model, and executing flow field consistency verification according to the motion vector deviation; and outputting a false AIS signal list in combination with multi-stage verification results. According to the method, through physical coverage modeling and environmental dynamics constraint, high-simulation-degree false signals in a radar blind area can be effectively identified, so that the false alarm rate is reduced.

The application discloses an aircraft structured grid generation method and device based on dipole regulation, equipment and a storage medium, relates to the technical field of aircraft, and comprises the following steps: mapping a non-structured grid of an aircraft to a plane space corresponding to a parameter domain to obtain a parameter domain plane grid, and determining a compression ratio value between the non-structured grid and the parameter domain plane grid; determining a target feature point set based on the angle values of each dihedral angle in the parameter domain plane grid; generating an initial scalar field based on the compression ratio value, and superimposing a dipole field corresponding to a target dipole on the initial scalar field to form a target scalar field; adjusting a feature parameter corresponding to a target contour line in the target scalar field until the distance between the target contour line and each feature point in the target feature point set meets a preset condition, and then determining the adjusted target contour line as a target feature line to generate a target grid line of the aircraft. In this way, the aircraft structured grid with good orthogonality can be obtained.

The invention relates to the field of big data, artificial intelligence and unmanned aerial vehicle remote sensing, and particularly discloses a space-time alignment and collaborative analysis method for air-ground multi-source heterogeneous data fusion, which comprises the following steps: accessing 5G-A data, unmanned aerial vehicle images and positioning meteorological data online, cleaning and enhancing, and unifying UTC time and WGS-84 coordinates to eliminate space-time reference difference; based on reference time delay and cross-correlation fine tuning, PCHIP is adopted to interpolate low-frequency signaling to a high-frequency image sequence, then pre-training rotation / translation parameters are utilized to complete space registration and grid mapping, and a computable real-time space-time diagram is generated to be compatible with different space granularities. On the space-time diagram, firstly, through gating attention fusion signaling and the sensations, forming sensations joint features, then carrying out cross-modal interaction fusion with the image, inputting ST-GCN to extract space-time dependence, and outputting abnormal alarm and trajectory prediction; when it is monitored that the data quality is reduced, an adaptive weight strategy is triggered to adjust the fusion weight online, and reasoning is carried out again.

This application relates to the field of open-pit mining technology, and provides an adaptive open-pit geological spatial grid mapping method and system. In this method, based on the Hough space curve of the geographic coordinates of open-pit geological exploration boreholes, a first kernel density estimation curve for the boreholes is determined. After constructing a linear space for the boreholes based on the first kernel density estimation curve, the geographic coordinates of the boreholes are converted into linear spatial coordinates, and a second kernel density estimation curve for the boreholes is determined based on the linear spatial coordinates. A peak matrix is ​​constructed based on the coordinate axis values ​​corresponding to the peak values ​​of the second kernel density estimation curve, and the adaptive grid position of the boreholes is determined based on the linear spatial coordinates and the peak matrix. This achieves an adaptive open-pit exploration borehole grid and determines the position of each open-pit geological exploration borehole within the grid, providing a new means for three-dimensional geological modeling of open-pit mines and meeting the practical needs of combining open-pit geological exploration with three-dimensional geological modeling.