76 results about "Coordinate projection" patented technology

The invention discloses a multi-source data fusion indoor positioning method and system based on deep learning, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining a wireless signal, inertial sensor data, a timestamp and an indoor map topology; processing the signal through a deep learning model to obtain a preliminary positioning coordinate, and mapping the preliminary positioning coordinate to a map path edge for topological constraint; calculating a displacement and a time interval in combination with the coordinate sequence and the timestamp, fusing the speed and the steering angle of the inertial data, and constructing a spatial-temporal feature vector; calculating a reconstruction error of the vector by using an auto-encoder model to obtain an abnormal confidence coefficient score; if the score exceeds a threshold value, calculating a moving direction according to sensor data, screening a candidate path edge through direction similarity matching, and projecting the coordinate to the edge for correction; otherwise, receiving the original coordinates; and finally outputting the corrected positioning track or the positioning track formed by the original coordinates. The method can ensure that the positioning track better conforms to the actual motion state, and the self-adaptive capability of the positioning system is enhanced.

The invention discloses an agricultural robot laser strike positioning method and device based on a virtual mapping surface, and the method comprises the following steps: S1, constructing a virtual mapping surface for the projection of a coordinate system of a visual system through a calibration board in a to-be-operated scene, establishing a mapping function between projection coordinates on the virtual mapping surface and instruction coordinates of the laser galvanometer through data fitting; s2, dynamically compensating target coordinates acquired in real time according to a built-in attitude sensor of the visual system; and S3, projecting the target coordinate after dynamic compensation to the virtual mapping surface, converting the target coordinate into a preliminary instruction coordinate of the laser galvanometer according to the mapping function, and performing dynamic compensation on the preliminary instruction coordinate to obtain a final instruction coordinate to control the laser galvanometer to act. The calibration process is simple, the coordinate conversion calculation amount is small, and fluctuation generated in the robot movement process can be dynamically compensated to ensure the positioning accuracy.

The invention relates to a virtual power plant resource flexible aggregation method and device based on linear programming projection, and the method comprises the steps: constructing a linear state equation corresponding to each to-be-aggregated resource according to the resource characteristic data of each to-be-aggregated resource, and carrying out the linear transformation; aggregating the energy use flexible feasible region of each to-be-aggregated resource, and solving a coordinate projection problem to obtain a preliminary aggregated energy use feasible region; constructing a network security constraint and embedding a solution process of the preliminary aggregation energy use feasible region to obtain an aggregation energy use feasible region considering the network security constraint; and constructing a multi-parameter planning model, and fusing an aggregation cost function into a solving process of an aggregation energy use feasible region considering network security constraints to obtain a virtual power plant resource flexibility aggregation result. Therefore, the problems that in the prior art, energy use feasible regions and adjustment cost of massive heterogeneous resources cannot be aggregated at the same time, network security constraints cannot be embedded, and high-dimensional aggregation errors are large are solved, and higher calculation efficiency and compatibility are achieved.

The invention relates to the technical field of marine monitoring, in particular to a marine video multi-target tracking and behavior recognition method and device.The method comprises the steps that firstly, a coordinate transformation matrix is constructed based on camera equipment parameters, and frequency domain denoising, illumination compensation and false target filtering are conducted on an original video; super-resolution reconstruction enhancement is carried out on the small-scale target; detecting the optimized video frame by using a deep learning model, and outputting a target bounding box and identity features; a single-view-angle local track is generated in combination with Kalman filtering and a Hungary algorithm; when the target crosses the view field, re-identification is carried out through coordinate projection and cross-view-angle feature invariants, and a cross-view-angle global unique track is generated through fusion; and finally, track motion features are extracted and logically compared with a marine management rule threshold value, and an abnormal behavior judgment result is output. According to the method, the small target detection precision under the complex sea condition is effectively improved, the problem of cross-view target identity breakage is solved, and global continuous tracking and illegal behavior intelligent early warning are realized.

The invention relates to the technical field of medical image processing, in particular to a DICOM sequence intelligent normalization and sorting method and system, and the method comprises the steps: obtaining an original DICOM file; analyzing a key DICOM tag of the original DICOM file to obtain a direction tag and body position information, and based on the direction tag, realizing normalization based on a patient coordinate system and performing direction correction; projecting the coordinates of all the slices to a normal vector direction to reconstruct a layer sequence, and counting a slice interlayer spacing sequence to estimate a real interlayer spacing based on adjacent projection difference values; identifying and rejecting abnormal slices based on the abnormal comparison target label and the slice interlayer spacing sequence; and for a multi-sequence set of the same check, extracting a sorting feature vector corresponding to each sequence, and selecting a sorting rule in combination with scene complexity to obtain a final sorting key for representing a sorting result of the phase dimension and / or the time dimension. The method has the effect of solving the problems that existing DICOM sequences are inconsistent in direction, disordered in layer sequence, difficult in time phase recognition and the like.

The invention discloses an industrial safety detection method combining time alignment and motion consistency, and belongs to the technical field of industrial safety monitoring. According to the method, through seven steps of multi-camera data acquisition and preprocessing, target feature extraction, coordinate projection, instantaneous speed estimation, multi-dimensional similarity fusion, soft matching optimization and model training and target trajectory generation, the problem of space-time inconsistency during multi-camera cooperative detection in an industrial scene is solved. A camera clock offset parameter is innovatively introduced to correct a cross-camera time difference, appearance, geometry and time prior multi-dimensional similarity are fused, a multi-loss function optimization model is constructed, and cross-camera target accurate matching and long-time trajectory tracking are realized. High-precision technical support is provided for personnel safety monitoring and equipment abnormal behavior detection in an industrial scene, and the industrial production safety protection capability is improved.

The invention discloses a six-degree-of-freedom object pose estimation system and method in an industrial scene and a medium, and the system comprises a multi-modal data preprocessing module, a shared encoder feature extraction module, a comparative learning and attention fusion module, a pose regression and rotation decoupling module, and a simulation driving-based data enhancement module. The multi-modal data preprocessing module is used for receiving and processing an original RGB image and a depth map, and outputting a pixel-level space aligned RGB-D data pair through filtering and denoising and coordinate projection transformation based on camera internal reference; and the shared encoder feature extraction module is used for extracting features from the aligned RGB-D data pairs and carrying out adaptive weighted fusion through a channel attention mechanism so as to output a shared feature fused with appearance and geometric information. According to the method, the estimation precision can be improved, the ADD index is improved by 12.8% and the rotation error is reduced by 21.4% in a 70% shielding or light reflection scene, and the method has strong robustness for depth noise, shielding and light reflection and has strong practicability.

The invention relates to the technical field of robot control, in particular to a motion control method and device of a parallel 3RRS robot, the parallel 3RRS robot and a medium, and the robot comprises a base platform, a movable platform and three RRS branch chains connecting the base platform and the movable platform. The method comprises the following steps: acquiring target attitude information and target position information of a mobile platform; based on the structure parameters of the RRS branch chain in the zero position state, the target attitude information and the target position information, calculating the center coordinate of a first plane figure formed by the center of the spherical hinge joint of the movable platform; calculating the center coordinate of the spherical hinge joint; projecting the central coordinate of the spherical hinge joint into a second plane figure determined by the first connecting rod of the corresponding branch chain and the Z axis of the coordinate system of the base; solving a target rotation angle of each first rotation joint based on the projection point coordinates; and finally, all joints are driven to enable the movable platform to move to the target pose. Complex space problems are converted into plane geometry solution, so that calculation is simple, and high-precision and high-efficiency real-time control is facilitated.

The invention discloses a spherical domain magnetotelluric non-structural vector finite element inversion method capable of automatically modeling, which comprises the following steps of: automatically establishing a spherical domain initial model through global topographic data, and performing spatial discretization by using a non-structural tetrahedral mesh to realize three-dimensional magnetotelluric non-structural finite element solution of two polarization modes in a spherical domain; constructing a spherical domain magnetotelluric three-dimensional regularization inversion objective function according to the actual observation data and the prediction data; adopting adjoint forward modeling to solve a Jacobian matrix and a product of transpose and a vector of the Jacobian matrix, and utilizing a nonlinear conjugate gradient method to realize iterative inversion of spherical domain magnetotelluric, so as to obtain a final inversion model and complete spherical domain magnetotelluric non-structural vector finite element inversion. According to the method, spherical domain magnetotelluric non-structural vector finite element inversion is realized, errors caused by coordinate projection can be avoided, and a large-scale inversion result is more accurate and more conforms to a real earth model.

The invention belongs to the technical field of image processing, and relates to an image video rain removal method based on local perception modeling and multi-stage span fusion, which adopts parallel branch networks with three level scales to respectively and sequentially carry out feature extraction, feature extraction and multi-stage span fusion on raining images with corresponding resolution sizes, performing high-level semantic information mining on the input raining image to obtain a high-dimensional feature map; high-resolution image reconstruction: adopting a local set perception implicit representation module, mapping the high-dimensional feature map to a continuous RGB color space through linear coordinate projection, and compensating continuous textures and edges sheltered by rain in the image to obtain a reconstructed image; and cross-scale feature fusion: carrying out information fusion between stages and scales on the image through double branches, and aligning and enhancing multi-scale semantics to obtain a high-resolution rain-removed image. According to the method, artifacts of a raining image can be suppressed in a multi-domain and multi-scale level, and sharpness and consistency are enhanced.

This invention discloses a quantum entangled light spatial correlation imaging method based on iterative deconvolution, belonging to the field of quantum information. The method includes the following steps: illuminating the target sample with a position-momentum entangled light source, and acquiring multiple frames of entangled light carrying sample information using a single-photon camera; performing statistical operations on the acquired multi-frame images to remove irrelevant background noise and obtain a joint probability distribution image of the sample; extracting the correlation width using the spatial distribution image of the joint probability distribution through differential coordinate projection to construct the point spread function of the optical system; using the point spread function for forward convolution projection and backward error projection, updating the image estimate through iterative calculation until the convergence condition is met to obtain the target sample image. This invention utilizes a camera to image with a position-momentum entangled light source under low illumination conditions, which can obtain high-contrast target imaging and has potential advantages in imaging light-sensitive samples.

The invention provides a course rapid initial alignment method and device for vehicle-mounted integrated navigation, and relates to the technical field of navigation calibration, and the method comprises the following steps: based on three-axis acceleration data, eliminating a component of gravitational acceleration in a horizontal direction through attitude compensation, and obtaining a horizontal longitudinal acceleration measurement value; carrying out plane coordinate projection based on the GNSS positioning data to obtain a track point sequence; determining the horizontal displacement length of the current track point and other track points in the track point sequence; on the basis of the horizontal longitudinal acceleration measurement value and the track point sequence, the motion direction is judged through the speed change trend and the horizontal acceleration direction, the current track angle is corrected, and a corrected course angle is obtained; and based on the corrected course angle and the Z-axis angular velocity of the gyroscope measured by the IMU module, estimating the course angle error and the zero offset of the gyroscope by adopting an adaptive Kalman filtering algorithm, and outputting the corrected course angle. According to the invention, fast and accurate course initialization of combined navigation in a low-speed equipment operation scene can be realized.

This application provides a method, device, electronic device, and storage medium for displaying a target plane. The method includes: acquiring a first coordinate group of P markers attached to the patient's body surface in a CT coordinate system based on CT images; tracking a second coordinate group of the P markers in an optical coordinate system using an optical tracking device; registering the first and second coordinate groups to obtain a first real-time transformation matrix from the CT coordinate system to the optical coordinate system; calculating second real-time coordinates of the needle tip and needle tail in the CT coordinate system based on the first real-time transformation matrix, the first real-time needle tip coordinates, and the first real-time needle tail coordinates in the optical coordinate system; and displaying the positions of the needle tip, needle tail, and target point in the target plane based on the coordinates projected onto the target plane from the second real-time needle tip coordinates, the coordinates projected onto the target plane from the second real-time needle tail coordinates, and the target point coordinates in the CT coordinate system. This method can avoid harm to the patient caused by puncture surgery.

The present application relates to motor drive control and hardware ring simulation test technical field, specifically to a kind of motor operating state simulation test system.It includes: signal acquisition module, for collecting the control instruction issued by measured controller, and obtains the environmental parameter and load parameter of test setting;Manifold space construction module, for reflecting the high-dimensional riemann manifold space of motor energy state, and change into the metric tensor of manifold space;Dynamic curvature evolution unit, for mapping external load disturbance and multi-physical field coupling effect into the dynamic change of metric tensor, geodesic trajectory represents the evolution path of motor operating state, to solve the operating state of motor next time;Physical quantity reconstruction module, for projecting the geodesic trajectory coordinates on manifold space back to Euclidean space, restore as electric signal physical quantity, and feedback to measured controller, form closed loop test.The present application significantly improves the physical authenticity and confidence of motor running simulation under high temperature or magnetic saturation state.

The application provides a semantic grid map optimization method and system based on multi-task road surface information, and belongs to the technical field of automatic driving navigation. The method comprises the following steps: processing a road environment image to obtain lane line segmentation results and drivable area segmentation results; establishing a coordinate projection relationship from an OCC space to a pixel coordinate system based on a rotation and translation matrix between sensors; projecting surface layer grid center points of a semantic grid map to the pixel coordinate system based on the coordinate projection relationship, and supplementing lane line information according to a projection result; performing semantic correction on a drivable area based on the coordinate projection relationship and the drivable area segmentation results; performing instance extraction and semantic association processing on unknown obstacles; and outputting an optimized three-dimensional semantic grid map. Based on the method, the application further provides a semantic grid map optimization system based on multi-task road surface information. The application improves the quality and practicability of a semantic map without significantly increasing the burden of a system.

The invention relates to the technical field of motor driving control and hardware loop simulation test, in particular to a motor running state simulation test system. Comprising a signal acquisition module used for acquiring a control instruction sent by a tested controller and obtaining environment parameters and load parameters set by testing; the manifold space construction module is used for reflecting a high-dimensional Riemannian manifold space of a motor energy state and converting the high-dimensional Riemannian manifold space into a measurement tensor of the manifold space; the dynamic curvature evolution unit is used for mapping external load disturbance and a multi-physical field coupling effect into dynamic change of a measurement tensor, and a geodesic track represents an evolution path of a motor running state, so that the running state of the motor at the next moment is solved; and the physical quantity reconstruction module is used for projecting the geodesic track coordinates on the manifold space back to the Euclidean space, restoring the geodesic track coordinates into electric signal physical quantities, and feeding the electric signal physical quantities back to the tested controller to form a closed-loop test. According to the invention, the physical trueness and confidence of operation simulation of the motor in a high-temperature or magnetic saturation state are remarkably improved.

The invention relates to the technical field of ship target detection, in particular to a navigation radar aided visual target detection method based on ship motion attitude correction, which comprises the following steps: acquiring at least one candidate target point in a navigation radar image of a ship; correcting the at least one candidate target point, projecting the obtained three-dimensional coordinate of the at least one candidate target point into the camera image, and generating a two-dimensional pixel coordinate of the at least one candidate target point on a camera image plane; and detecting the at least one candidate target point according to a dynamic Query formed by target features and target position codes of the region of interest determined by the two-dimensional pixel coordinates and the distance between the ships so as to generate a 2D bounding box and a target classification result of the at least one candidate target point. Therefore, the problems that the reliability and the accuracy of target detection are reduced and the target detection requirement of an offshore scene cannot be met due to the fact that a navigation radar in the related technology lacks a three-dimensional geometric structure and continuous echo intensity information are solved.

The invention provides a multi-sensor time delay estimation method and device, XR equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: acquiring image data of a visual sensor, and determining parameterized coordinates of three-dimensional space points in a space and a change rate of a direction vector in the parameterized coordinates according to the image data; correcting parameterized coordinates of the three-dimensional space points according to the change rate of the direction vector and multi-sensor time delay parameters; calculating a reprojection residual error of the corrected parameterized coordinate of the three-dimensional space point projected to a target image frame so as to construct an optimization problem; the optimization problem is solved to obtain the multi-sensor time delay parameters, observation data from different sensors can be aligned in time, and the result accuracy of state estimation is improved.

This invention discloses a highly adaptable autonomous navigation method for fruit and tea garden equipment based on low-precision maps, belonging to the field of intelligent navigation technology for agricultural machinery. It deploys an autonomous navigation system, acquiring global images of the fruit and tea garden through drone aerial photography, and manually setting key navigation points. Semantic segmentation algorithms are used to detect roads, fitting straight lines to obtain corner points, and automatically generating secondary navigation points connecting the key points. Latitude and longitude coordinates are obtained through automatic conversion between pixel coordinates, projected coordinates, and global geographic coordinates. This invention acquires global low-precision images through drone aerial photography and requires only a few manually set key navigation points. By combining semantic segmentation, automatically generated secondary navigation points, and global geographic coordinates, a usable navigation network can be constructed. This allows for rapid application to different fruit and tea garden environments, avoiding the need for extensive repetitive mapping work for a single garden, and significantly improving the versatility and deployment efficiency of the navigation system across different fruit and tea gardens.

The application discloses a point cloud map change real-time detection method based on light tracking and registration, and belongs to the technical field of three-dimensional space intelligent perception and point cloud intelligent processing. The method constructs a hierarchical detection architecture of "light tracking pre-screening-double-thread registration precision verification". Firstly, a local point cloud map is dynamically constructed through three-dimensional intelligent grid division, cross-modal depth image mapping is realized in combination with spherical coordinate projection, and a suspected change point set is quickly screened based on an adaptive threshold; then, the efficiency of nearest neighbor search is optimized through hierarchical KD tree indexing, a double-thread parallel mechanism of "new point real-time verification-missing point delayed trigger verification" is adopted, and real change points are accurately identified in combination with multi-frame incremental point cloud accumulation. The application can accurately identify new obstacles, structural missing and other three-dimensional space changes, adapt to long-term point cloud high-precision point cloud map demand scenes such as automatic driving, intelligent robots and underground space intelligent operation and maintenance, and greatly reduce the map update cost and operation and maintenance redundancy.

The application discloses a kind of laser radar point cloud prediction method and system based on Transformer, it is related to three-dimensional environment perception technical field.The method first converts distance image by spherical coordinate projection conversion for historical point cloud sequence with semantic label, and utilizes semantic embedding module to map discrete semantic label into high-dimensional continuous feature;Subsequently, the geometric feature and semantic feature are spliced in channel dimension, to form the integrated composite feature tensor;The tensor is input into the improved Transformer network for training, the network adopts encoder-double-branch attention-decoder architecture, and is supervised using the composite loss function combining geometric loss and semantic loss;Finally, the network synchronously outputs future distance image and semantic probability distribution, and generates future three-dimensional point cloud with semantic attributes through inverse projection and color mapping.The application significantly improves the modeling ability of the motion law of different categories of objects and the prediction accuracy in complex dynamic scenarios by explicitly integrating semantic information.

The invention discloses a hanging object two-dimensional swing angle measuring method based on an AHRS, the AHRS is horizontally fixed on a hanging tool plate, the swing angle of the hanging tool plate in the XY direction is the two-dimensional swing angle of a hanging object, and the method specifically comprises the following steps: step 1, initializing a system, establishing a coordinate system, defining the original point of the world coordinate system as a suspension point, the X-axis direction is parallel to the track direction, and the X-axis direction is parallel to the track direction; the Z axis is vertically downward in the positive direction, and the Y axis is determined according to the right-hand rule; 2, when the lifting appliance plate is static, data of a three-axis magnetometer are collected, and the magnetic north initial direction is calculated; step 3, acquiring three-axis acceleration data of the sensor to obtain a normal vector of the lifting appliance plate in the moving coordinate system; 4, calculating the rotation angle of the lifting appliance plate around the Z axis; step 5, calculating a lifting rope direction vector under a world coordinate system; and step 6, obtaining a two-dimensional swing angle of the hanging object in the XY direction under the world coordinate system through coordinate projection.

The invention discloses an experimental method based on crowd disaster video data and virtual scene reconstruction, and the method comprises the steps: carrying out the frame-by-frame analysis of an obtained disaster monitoring video, dividing the disaster monitoring video into a close-shot region and a long-shot region, and extracting the two-dimensional coordinate positions capable of recognizing individual pedestrians through employing a target detection technology, the crowd density estimation technology is adopted to obtain overall density distribution data, and a particle image velocity measurement tool is utilized to extract the velocity and direction of pixel motion frame by frame. Establishing a three-dimensional virtual scene model corresponding to a real accident scene, mapping the processed crowd position and motion information into three-dimensional data matched with a virtual scene through a coordinate projection tool, endowing the three-dimensional data with a unique identity label, and carrying out cross-frame association and trajectory optimization processing according to a time sequence and a spatial proximity relationship; and generating a three-dimensional pedestrian continuous track file for virtual reality simulation, driving the dynamic position and posture change of a virtual pedestrian entity, and realizing credible reproduction of a crowd disaster situation and human in-loop experiment evaluation.

This invention discloses a three-dimensional measurement method for tomato internode length. Addressing the geometric errors easily caused by greenhouse shading, stem bending, and direct two-dimensional pixel ranging, the method acquires a single-frame color image and depth data of the plant. The color image is input into a feature-enhanced multi-task perception model, which outputs an internode instance segmentation mask and two-dimensional coordinates of key points at both ends. Candidate three-dimensional point clouds are extracted based on pixel correspondence, and the three-dimensional coordinates of the internode endpoints are generated from the neighborhood depth of the key points. The candidate point clouds are subjected to voxel downsampling, statistical outlier filtering, depth bandpass filtering, and spatial density clustering. The target main stem point cloud is obtained by combining spatial distance and depth consistency evaluation. Principal component analysis is used to estimate the principal axis, and axial slicing is performed. The centerline is extracted and fitted using a cubic B-spline curve. The three-dimensional coordinates of the endpoints are projected onto the fitted curve, and the outer endpoints of the curve are linearly extrapolated and compensated along the corresponding tangential direction. The arc length of the spatial curve is calculated to obtain the three-dimensional measurement result of the internode length.

The invention discloses a target positioning method and device, and relates to the field of picture mapping, and the method comprises the steps: capturing a picture through a first camera, and determining a first image coordinate of a detection target in the picture; based on the first image coordinate and the parameter of the first camera, acquiring a three-dimensional coordinate of the detection target in a first camera coordinate system as a first target coordinate; acquiring an external parameter of the second camera in the current attitude and an internal parameter of the second camera in the current zoom; projecting the first target coordinate to a current image plane of a second camera according to the internal reference and the external reference of the second camera to obtain a second image coordinate corresponding to the detection target; and according to the offset between the second image coordinate and the center coordinate of the current image of the second camera, generating a control signal for controlling the rotation of the second camera, so that the detection target falls into the view area of the second camera after switching. According to the invention, accurate positioning of the detection target in the image captured by the first camera in the image captured by the second camera under any PTZ attitude can be realized.

This invention relates to the field of marine biological resource survey technology, specifically disclosing a method for predicting Antarctic krill resource distribution based on multi-algorithm integration. The method includes: acquiring krill acoustic survey density data and multi-source environmental variable data; performing coordinate projection transformation, missing value processing, and grid aggregation on the data; constructing a modeling dataset and performing logarithmic transformation to mitigate the bias of zero-inflation features on model training; calculating the Pearson correlation coefficient and variance inflation factor among various environmental variables; and constructing a correlation-collinearity dual screening criterion by combining krill density correlation ranking. This invention constructs a complementary modeling system of multiple heterogeneous algorithms, utilizing the structural dissimilarity between algorithms to maximize the capture of diverse response patterns between krill density and environmental factors. This effectively suppresses the technical defects of single algorithms exhibiting drastic fluctuations in prediction performance under different sea areas and marine environmental conditions, significantly improving the generalization stability and environmental adaptability of Antarctic krill resource distribution prediction.

The application relates to the technical field of radar calibration, and provides a ground-based lunar radar geometric calibration method. The method comprises the following steps: processing radar echo original data into a first radar image, each pixel in the first radar image having a range-Doppler coordinate; performing coordinate projection on each pixel in the first radar image to obtain the geographic coordinates of each pixel; performing homonym matching according to the geographic coordinates of each pixel to obtain a plurality of homonym point pairs; each homonym point pair comprises an oblique range-Doppler coordinate and a lunar surface optical image coordinate; performing geometric calibration according to the plurality of homonym point pairs to determine correction parameters; the correction parameters comprise a radar lower point oblique range correction amount, a radar lower point Doppler frequency correction amount, a range direction sampling frequency correction amount and a pulse repetition frequency correction amount; and obtaining corrected lunar surface geographic longitude and latitude coordinates according to the correction parameters. The application solves the correction amount from the radar parameter level, thereby realizing high-precision geographic positioning error correction that is universal in the full field of view.

The invention discloses a road modeling method and system, terminal equipment and a storage medium, and the method comprises the steps: obtaining the original street map data of a to-be-modeled road region, and determining the original road data based on the original street map data; acquiring original coordinate data and original attribute data of the original road data; obtaining current coordinate data based on a coordinate projection conversion algorithm, the original coordinate data and a reference point of the to-be-modeled road area; obtaining current attribute data based on the road semantic mapping rule base and the original attribute data; obtaining current road data based on the current coordinate data and the current attribute data; selecting a target completion algorithm based on defect data of the current road data; performing defect completion on the current road data based on a target completion algorithm to obtain target road data; and constructing simulation standard road data based on the target road data, so that the simulation model obtains a target road model based on the simulation standard road data. Therefore, the accuracy and reliability of road modeling are improved.