245 results about "Spatial extent" patented technology

The invention discloses a 3DTiles data storage method and a map data query method.The 3DTiles data storage method comprises the following steps that (1) an LOD hierarchy pyramid type hierarchy table is established, and an LOD range index is created; 2) generating a three-dimensional object into an agent point, and segmenting a tile according to a KD-tree structure to construct a three-dimensional KD-tree index tree; and 3) storing the data of the KD-tree leaf node into a GridFS of MongoDB multi-mode storage, mapping a file path into a unique hash value, writing the unique hash value into a corresponding node according to a KD-tree node storage structure, and creating a geographic spatial index. The method creatively solves the problems that file system management is difficult due to the fact that the number of files is large, a hierarchical structure based on a file directory is difficult to support efficient spatial range retrieval, and the spatial query response time is reduced to the millisecond level in combination with a three-dimensional KD-tree index and a MongoDB geographic spatial index.

The invention discloses a coastal sound tomography data assimilation method based on stream function fitting. The method comprises the following steps: arranging a coastal sound tomography system, obtaining underwater sound data containing observation domain hydrological information, carrying out correlation peak extraction, and calculating to obtain a sound ray mutual return transmission time difference; scanning topographic information of an observation domain and temperature and profile data between stations, inputting the topographic information and the temperature and profile data into a sound velocity model for sound ray simulation, determining an actual propagation path of sound rays, and inverting path average flow; constructing a three-dimensional unstructured grid of the observation domain, and determining a projection relation between an actual propagation path of sound rays and the grid; establishing a state transition matrix by taking a sound ray path average flow as an observation vector of extended Kalman filtering and taking a coefficient matrix of a flow function least square fitting ocean numerical pattern calculation result as a state vector; and assimilating and updating by using an extended Kalman filtering algorithm, and obtaining an optimized flow field. According to the invention, the observation precision, the resolution and the spatial range of the coastal flow field can be obviously improved.

The invention provides an unmanned aerial vehicle inspection defect sample generation method and system based on a diffusion model, and relates to the technical field of data processing, and the method comprises the steps: collecting an image of equipment, carrying out the feature compression and mapping operation of the image, carrying out texture gradient analysis and region density calculation, recognizing a local region with strong texture change in the image, and carrying out the feature compression and mapping operation; the method comprises the following steps: performing continuous aggregation on a local area, simulating a space range of defect growth, calculating a gradient difference value, determining consistent distribution of an image on a physical structure, determining a diffusion direction and a boundary position of a defect generation process, performing center feature extraction on the image, and determining a defect starting point and a growth speed of the defect generation process. And performing back diffusion to construct defect features, performing feature fusion on the defect features and the potential image data to obtain mixed image data, and performing feature reduction and image reconstruction to obtain defect image sample data. The defect sample can be reversely generated according to the equipment image.

The invention relates to a spatio-temporal data prediction method based on space grid coding and a large language model, and belongs to the field of artificial intelligence and spatio-temporal data analysis. The method comprises the following steps: according to a spatial range of a geographic area, dividing the area into grid units, processing the grid units by adopting a coding method, and identifying each unit through a unique identifier; constructing a space-time dependency model based on the stay record of the user, representing each stay as a tuple, and predicting the next position of the user by analyzing a historical stay sequence and a context stay sequence; and designing a space-time instruction optimization prompt template in combination with a large language model, guiding the model to analyze historical data and context data by introducing grid coding and context perception reasoning, generating next position prediction, and providing explanation for each prediction. According to the invention, through grid coding and hierarchical modeling, the generalization ability of the model is enhanced, and accurate spatio-temporal behavior prediction is realized.

The invention relates to the technical field of ocean engineering digital twinning and three-dimensional visualization, and provides a method and system for generating dredging terrain twinning based on a right-angle triangular mesh, and the method comprises the steps: obtaining regular grid elevation unified to an engineering plane coordinate system or preprocessed sampling height data, determining a covered space range, reading the space range and density parameters, and calculating the total row number and the total column number of the grids based on the space range and the density parameters; generating regularly arranged vertexes in the covered space, and constructing a topological structure of an isosceles right triangle grid; dividing the global grid into a plurality of Chunk blocks according to a fixed size by taking a vertex as a unit, establishing a triangular index mapping relationship between global rows and columns and the Chunk blocks, filling three vertexes of an isosceles right triangle and elevation / height data in each Chunk block in parallel, and generating local VBO, IBO and AABB; and performing batch rendering and coloring according to a height color table on all Chunk blocks to realize visualization of the terrain.

The invention discloses a real-time flight conflict detection method based on digital gridding. The method comprises the steps of dividing airspace grids, managing a three-dimensional space, dividing nodes according to a spatial range, and storing a specified number of grid units in each node; when the node capacity exceeds a threshold value, the node capacity is automatically subdivided into a plurality of sub-nodes, and recursive division of the space is realized; realizing grid query and insertion through spatial cross judgment; acquiring real-time flight path data of the aircraft, and mapping the real-time flight path data into the corresponding grid units according to the position of the real-time flight path data in the airspace; the track data comprises latitude and longitude, height and speed; and dividing conflict detection tasks of each grid unit and adjacent grids into a plurality of sub-tasks for parallel processing, generating a global conflict result set, and realizing large-scale real-time flight conflict detection. According to the invention, the flight conflict can be rapidly and accurately detected.

The invention discloses an HBase-based spatio-temporal index generation method and system, and the method specifically comprises the following steps: 1, collecting trajectory data, and carrying out the preprocessing of the trajectory data; step 2, generating administrative division codes and geographic grid codes based on the preprocessed trajectory data, and processing timestamps in the data trajectory; 3, splicing the administrative division codes, the geographic grid codes and the processed timestamps to form row key data of the HBase; and step 4, storing the row key data in the HBase to generate a row key index of the HBase, the row key index being used for spatial range, time query and nearest neighbor query.

The invention discloses a mining area fine land identification method based on multi-mode fusion and first inflection point constraint. The method comprises the following steps: step A, downloading a research area satellite remote sensing image, constructing a strip mine area space range sample data set, training a DeepLabv3 + deep learning model, and predicting and delineating the maximum space range of a mining area in a research period; step B, acquiring mining area land utilization change data, constructing a time sequence change mode based on a land utilization type transfer relationship, and dividing the time sequence change mode into five types, namely a damaged area, a reclamation area, a reclamation degradation area, a building pressure occupation area and a water body pressure occupation area; and step C, preparing a mining area fine land utilization sample data set, establishing a multi-mode fusion and first inflection point constraint neural network model (MFFIC-Net), training the model to identify mining area annual fine land utilization types, and accurately tracking first transition time information of each type. Compared with the prior art, the mining area land utilization classification is further refined, the proposed MFFIC-Net model fuses long and short term memory and a feedback mechanism, and accurate identification of the land utilization type and the first change time is realized. A feedback mechanism enables the model to self-adjust a prediction result, the precision and the stability are improved, and multi-mode fusion enhances the adaptability of the model to a complex mining area environment. The mining area land utilization identification method provided by the invention is finer and more practical, can effectively identify key areas such as a damaged area, a reclamation area and an occupation area, and provides important technical support for ecological restoration and land management.

The invention discloses a map database-based land type map spot storage and analysis method and system. The method comprises the following steps of: acquiring and processing data; storing data; monitoring pattern spot type distribution analysis; performing annual change investigation pattern spot and land type change analysis; and reading and displaying data. According to the method, spatial analysis is carried out on land type change monitoring pattern spots and annual change investigation pattern spots, and a method based on a pattern database is designed to store analysis results; the incidence relation and important attributes of the change monitoring pattern spots and the annual change pattern spots can be expressed on a visual platform, and the monitoring type distribution condition and the annual change survey of the change conditions of different land types in a certain space range are analyzed; according to the storage and analysis method, the internal relation between the change monitoring pattern spots and the annual change pattern spots is visually presented, the effectiveness of change monitoring pattern spot analysis is improved, the data value of the monitoring pattern spots is deeply mined, natural resource data analysis and application are facilitated, and planning and decision making of related departments of the natural resource industry are supported.

The invention belongs to the technical field of geographic information technology and remote sensing image processing and service, and discloses a slice-free image service system and method based on dynamic projection and real-time mosaic, which abandons the traditional pre-slicing mode, directly provides online service based on original image data, and improves the service efficiency. The problem of redundant storage caused by pre-generation and storage of massive tiles is fundamentally eliminated, and storage space is saved by up to 90%. Meanwhile, when the original data is updated, the system does not need to carry out a time-consuming re-slicing process, real-time updating and publishing of services can be realized, and the core pain points of long data updating period and high delay in the traditional technology are thoroughly solved. Through the integrated dynamic projection engine and the real-time mosaic module, the on-demand service request of the client for any coordinate system, any spatial range and any resolution can be responded.

The invention discloses a track query method and device based on adaptive space division, relates to the technical field of track data indexing, and mainly aims to solve the problem of low track data query efficiency. The method mainly comprises the following steps: constructing an index space layer of target trajectory data based on a data-driven adaptive hierarchical division strategy, and respectively constructing index time layers based on a multilayer piecewise linear approximation model for each space partition in the index space layer; in response to the trajectory data query instruction, obtaining a space range and a time range of a to-be-queried trajectory; identifying a target hierarchy matched with the spatial range and a target spatial partition under the target hierarchy from the index spatial layer, and identifying a track data storage position matched with the time range by utilizing an index time layer of the target spatial partition, and querying according to the track data storage position to obtain a track query result. The method is mainly used for constructing track indexes and querying track data.

The invention discloses a grid-coding-based video space-time retrieval method and device, the grid-coding-based video space-time retrieval method is executed in computing equipment, and the method comprises the following steps: mapping a coordinate range of a target area into a corresponding grid code set according to a Beidou grid standard on the basis of the coordinate range of the target area; acquiring video data with an intersection or inclusion relationship between the spatial range of the shot object and the target area; combining the shooting time of any video data with each grid code to generate a space-time grid code set; constructing an index structure corresponding to the video data by taking each time-space grid code in the time-space grid code set as a main key; and in response to query information of the user, analyzing a space-time range in the query of the user, mapping the space-time range into a corresponding space-time grid code range, executing retrieval based on the range, and returning hit video data. According to the method, rapid positioning and accurate retrieval of large-scale aerial photography video data are realized.

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 a relaxation time evaluation method and system applied to a radiation band modeling process, and solves the technical problem that the distribution and relaxation time of high-energy particles in a radiation band cannot be accurately reflected in the prior art. Comprising the following steps: firstly, dividing a space into three-dimensional grids; secondly, establishing a local coordinate system; then, high-energy particles with different angles and different energies are put into the grid points, and the motion state of the high-energy particles is tracked; then, counting particle distribution states at different time intervals, and generating a particle distribution image; establishing a function of relaxation time of the high-energy particles by comparing particle images at different times; and finally, calculating relaxation time under different space weather conditions, and establishing a database containing the function relationship between the input parameters and the relaxation time. The spatial range of the method can obviously cross a radiation band and a near-earth space, and the calculated relaxation time can provide necessary technical support for communication, navigation and the like.

The invention provides a robot dog intelligent inspection control method and system for an open environment, and relates to the technical field of data processing.The method comprises the steps that the space range of inspection environment data is divided into a plurality of space units, each space unit is marked as a region paragraph, and each region paragraph is marked as an area paragraph; the method comprises the following steps of: obtaining a plurality of regional paragraphs, establishing cross-segment marks among the regional paragraphs to form a recursive chain, calculating the route change transmission degree of each regional paragraph in the recursive chain to obtain recursive feature intensity, layering each regional paragraph in recursive chain data, generating regional paragraph sets with different intensity levels, and obtaining a recursive feature set; and calculating the periodic aggregation degree of each area paragraph in each inspection period to obtain periodic paragraph intensity, correspondingly binding each area paragraph in the rearrangement sequence data with a preset action set to obtain action control data, and generating a control instruction according to the action control data. According to the method, superposition and amplification of tiny offset in control circulation can be avoided, and the problem of interruption of the inspection process caused by frequent correction is solved.

The invention discloses a multi-source fusion point cloud data driven three-dimensional streetscape generation and optimization method, which relates to the technical field of three-dimensional streetscape generation, and comprises the following steps of: according to a space range of input large-range point cloud data, combining a three-dimensional coordinate system distribution characteristic of the input large-range point cloud data; dividing the whole registration area into a plurality of uniformly distributed subunit areas with the same area by adopting a structured space division algorithm; for each subunit region, geometric structure features of internal point clouds of the subunit region are obtained, and a geometric feature descriptor containing multiple dimensions is constructed; according to the method, subunit structure complexity identification is realized through a heterogeneity evaluation mechanism based on geometric features and a support vector machine model, the upper limit of the number of matching points is dynamically regulated and controlled, the registration precision of a high-heterogeneous region is improved, and the mismatching risk of a low-heterogeneous region is reduced, so that the structure restoration and continuity of a streetscape model are optimized, and the robustness of the streetscape model is improved. And the stability and intelligence of the three-dimensional modeling system are enhanced.

The invention belongs to the technical field of computer vision, and discloses a semantic edge guided three-dimensional scene reconstruction method based on an SDF diffusion model. The objective of the invention is to solve the problems of high bandwidth occupation of input data, poor structural consistency and insufficient reconstruction quality in traditional three-dimensional reconstruction. A point fine segmentation and gradient semantic edge extraction method is provided, edge information with consistent semantics and rich geometric details is extracted, and lightweight scene representation retaining key geometric information is realized. On the basis, a three-dimensional reconstruction framework of staged diffusion is proposed: firstly, a global occupancy field is generated to determine an object space range; then executing high-resolution SDF diffusion in the local area to recover a fine geometric surface; and finally, carrying out space alignment and fusion to ensure the consistency of a reconstruction result under a global coordinate system. According to the method, the geometric accuracy, semantic consistency and resource overhead of reconstruction are effectively balanced, and a novel solution is provided for high-quality three-dimensional scene reconstruction under a low-bandwidth condition.

The invention discloses a meteorological ocean data prediction processing method and system. The method comprises the following steps: acquiring position data of a geographic position for operation on the sea; acquiring meteorological data in a future preset time range in a space range corresponding to the position data; data related to sea waves and ocean currents are obtained from the meteorological data, and the related data are used for predicting the sea waves and the ocean currents; the sea waves and the ocean currents are predicted according to the related data, and sea wave data and ocean current data are obtained respectively; the presented meteorological data, the ocean wave data and the ocean current data are used as the basis for performing the operation in the spatial range and the preset time range; according to the method and the device, the problem of limited business guidance effect caused by only giving weather data when the business executed on the ocean is predicted in the prior art is solved, so that relatively reliable data support is provided for operation tasks on the ocean.

The invention relates to the technical field of artificial intelligence, and provides an attention calculation optimization method, electronic equipment, a storage medium and a program product, and the method comprises the steps: generating a bounding box list of an attention matrix based on the mask information of attention calculation, the bounding box list comprises at least one effective bounding box, and the effective bounding box is used for generating an attention matrix; the effective bounding box is used for defining a spatial range of a target data block to be subjected to attention calculation in the attention matrix; and based on each effective bounding box in the bounding box list, loading a target data block corresponding to each effective bounding box from a query matrix, a key matrix and a value matrix, and executing attention calculation on the loaded target data block to obtain attention output corresponding to each effective bounding box. According to the method, the bounding box list is generated, and only the data blocks in the effective bounding boxes in the bounding box list are loaded and attention calculation is executed, so that invalid calculation and computing power resource waste are avoided, and the overall calculation performance is improved.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.

The invention discloses a GeoServer-based map tile generation method and device, a GeoServer-based map tile release method and device and electronic equipment. The method comprises the following steps: acquiring source data from a geographic information system; performing reprojection and resampling operation on the source data according to a preset projection coordinate system and resolution to obtain specific resolution data; cutting the specific resolution data to obtain data of a specific area; determining a tile space range of each map tile, segmenting the data of the specific area according to the tile space range of each map tile, and obtaining tile preliminary data of each map tile; performing data format conversion on the tile preliminary data of each map tile to obtain map tile data in a target tile format; and storing the map tile data in the target tile format to a database, and publishing the map tile data in the target tile format in a GeoServer server.

The invention provides a geographic JSON data generation method and device, equipment, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the steps that geographic semantic keywords are obtained, and the geographic semantic keywords are used for representing the query intention of a user in a geographic space; converting the geographic semantic keywords to obtain a target space range; space objects in the target space range are screened, a candidate space data list related to the target space is obtained, and the candidate space data list comprises identifiers corresponding to one or more candidate space objects; extracting position information and attribute information corresponding to each candidate spatial object in the candidate spatial data list, and constructing a geographic information cue word; and inputting the geographic information cue word and a preset GeoJSON format template into a pre-constructed large language model, and outputting GeoJSON data. According to the method and the device, the GeoJSON data generation efficiency can be improved, and meanwhile, the labor cost involved in the process is reduced.

The invention relates to the technical field of three-dimensional vision and robot perception, in particular to a belt conveying line multi-camera point cloud real-time splicing method and system based on GPU acceleration. The point cloud real-time splicing method comprises the following steps of S1, collecting point cloud data of objects on a belt conveying line through a plurality of depth cameras; s2, performing spatial range filtering of the point clouds in parallel by using a GPU, and removing invalid points; s3, point cloud coordinate transformation is executed in parallel by using a GPU, and the point clouds of all the cameras are uniformly converted into the same world coordinate system; s4, performing triple filtering processing on the transformed point cloud to realize noise suppression, downsampling and density equalization; s5, fusing the filtered point clouds to form a complete scene point cloud; and S6, publishing the fused point cloud data through an ROS mechanism. GPU parallel computing is utilized, multi-camera point cloud high-speed real-time splicing is achieved, denoising equalization is effectively achieved through triple filtering, the processing efficiency and precision are remarkably improved, and the method is suitable for long-distance industrial monitoring.

The invention discloses a method and device for supporting multi-radar high-resolution networking and a storage medium, and relates to the technical field of radar data processing. The method comprises the following steps: determining a space range of a networking field; according to the spatial range and the target resolution, defining a grid structure of a networking field data body; allocating computing resources for high performance computing; aiming at each radar, pre-calculating a mapping area of the detection range of the radar in the grid structure; loading the original polar coordinate observation data of each radar into the computing resource in parallel; solving a networking value of each grid point in the grid structure layer by layer based on the mapping region and original polar coordinate observation data, and generating a networking result; and post-processing and storing the networking result, and releasing the computing resources. According to the method, the solution is directly solved from the original polar coordinate observation data based on the mapping region, so that the generation and conversion of massive intermediate data are avoided, and the technical bottleneck of huge memory and video memory consumption is fundamentally solved.

The invention discloses a multi-drill-boom dynamic task collaborative optimization method based on deep reinforcement learning, and the method comprises the steps: building a drill boom kinematics model of a multi-boom drill jumbo based on a D-H method, solving the tail end position of a drill boom according to the joint motion parameters of the drill boom, and determining the spatial range of the collaborative operation of each drill boom; a depth space-time diagram convolutional network is constructed and used for extracting the tail end pose and the tail end movement track of each drill arm from laser point cloud data and / or image information collected on a construction site; a sensor is used for sensing the working state of the drill boom in real time; a multi-drill-arm deep reinforcement learning model is constructed, states and actions are defined based on a drill arm kinematics model of the multi-drill-arm drill jumbo, and a reward function is established; and a multi-drill-arm deep reinforcement learning model is adopted, and according to the state of each drill arm joint and the pose of the tail end of the drill arm at the current sampling moment, motion parameters of each drill arm joint at the next sampling moment are planned, so that the tail end of each drill arm at the next sampling moment is in the optimal pose. According to the method, dynamic collaborative optimization of multi-drill-arm operation tasks can be realized.

The application discloses a kind of forest scene voxel model construction method and system, the method includes: based on laser radar obtains the point cloud data of reference tree of the region to be measured and the point cloud data of canopy structure;According to the point cloud data of canopy structure, the point cloud data of reference tree is carried out point cloud conversion, and the point cloud conversion data of reference tree is obtained;According to the point cloud conversion data of reference tree, the blank of the point cloud data of canopy structure is filled with reference tree, and high-density point cloud data is generated;High-density point cloud data is voxelized, and forest scene voxel model is constructed.The system includes: data acquisition module, point cloud conversion module, data filling module and construction module.Through using the application, it can be realized in large space range to reconstruct the fine scale three-dimensional structure of forest scene and improve resolution.The application is a kind of forest scene voxel model construction method and system, and can be widely applied to laser radar data three-dimensional modeling technical field.

The invention discloses a multi-dimensional construction safety risk management and control method for a high-altitude highway, particularly relates to the field of construction risk management and control, and is used for solving the problems of slope monitoring lag and passive safety management and control caused by complex terrain in the existing high-altitude highway construction process. The method comprises the following steps: acquiring aerial survey images and laser point cloud data of a construction slope area according to a fixed period, generating a time sequence three-dimensional live-action model sequence, identifying and tracking natural feature points and artificial structure feature points in the model sequence, calculating a feature point displacement field based on precision registration among multiple periods of models, and obtaining a feature point displacement field. The method comprises the following steps: identifying risk deformation data in space-time association with a construction activity in combination with historical construction activity records, performing spatial clustering analysis on the risk deformation data to mark a potential instability region, and generating a prospective construction risk grading control signal according to the space range of the potential instability region and construction information data; and dynamic pre-judgment and graded response of high-altitude highway construction risks are realized.

The invention provides a multi-person posture estimation method and system based on topology perception dynamic association, and relates to the technical field of computer vision, and the method comprises the steps: synchronously obtaining a plurality of initial human body instance sets in an image and a corresponding initial key point set in each initial human body instance set based on a multi-scale feature map, generating a preliminary topological association between the human body instance and the key point; for each key point in the initial key point set, dynamically adjusting the spatial range of the key point based on the global context feature of the human body instance to which the key point belongs and the local semantic feature of the key point, and generating a dynamic key point frame; based on key point features extracted by the dynamic key point frame, key point relation reasoning is carried out based on a topology perception correlation model, key point positioning is optimized, grouping from key points to human body instances is completed, and a final multi-person posture estimation result is output. According to the method, the technical problems of poor robustness and insufficient precision of the existing multi-person attitude estimation method in a complex scene are solved.

The invention belongs to the technical field of economic value evaluation, and discloses an economic value estimation method and system based on a natural resource asset package, and the method comprises the steps: processing natural resource data through employing a natural language processing technology; identifying spatial distribution modes of different natural resource data by applying a geographic information technology, and defining a spatial range of an asset package; integrating the space range of the asset package and the physical quantity data by using geographic information processing software, establishing a physical quantity data set, and constructing an asset package evaluation model in combination with a machine learning algorithm; and evaluating the asset package by using the asset package evaluation model to obtain an asset package score, and establishing an asset package price system based on the natural resource type to obtain the economic value of the asset package. According to the method, the natural resource data of various sources are acquired and input, and the spatial range of the asset package is precisely defined by using the geographic information technology, so that the integrity and accuracy of the data are ensured, and a basis is provided for subsequent resource management and planning.

A phase-difference-based high-resolution azimuth estimation algorithm for a small-aperture array with multiple targets comprises the following steps: 1) according to various array structures, the coordinate vectors of the array elements are subtracted and then combined accordingly to obtain new vectors along each coordinate axis; 2) the phase of each array element is obtained by performing a fast Fourier transform on the array element sampling data, the phase difference between the array elements is obtained by subtracting the phase of each array element, and the phase differences are combined according to the combination of the array element coordinate vector differences to extract the component k of the incident direction along each coordinate axis. x , k y and k z 3) After obtaining the various components of the incident direction, the unambiguous incident angle across the entire spatial range is calculated using inverse trigonometric functions. This invention utilizes a combination of array element sampling data to achieve more accurate target direction estimation, enhances resistance to noise and other interference, and broadens the applicable frequency band, enabling flexible application to various arrays, as well as direction estimation using multi-line spectrum signals and broadband signals.