135 results about "Spatial calibration" patented technology

The invention discloses a robot grabbing posture generation method and a related device, and the method comprises the steps: obtaining an RGB image, a depth image and a position coordinate of a target object, and generating a spatial calibration matrix through data preprocessing; target object segmentation is carried out on the calibrated RGB matrix according to the target point coordinate sequence, a segmentation mask is generated, and object mass center coordinates are calculated; generating a three-dimensional point cloud by using the calibrated depth matrix, the segmentation mask and the camera parameters, extracting a plane point set and a non-plane point set through an RANSAC algorithm, and analyzing to obtain an axial feature vector and a plane normal vector; finally, grabbing parameters are calculated according to the feature vectors and the centroid coordinates, and a grabbing posture transformation matrix is generated through vector operation. According to the technical scheme, under the condition of not depending on a preset model library, the grabbing posture of an unknown object is generated by analyzing the geometrical characteristics of the object, the accuracy problem in the process of converting two-dimensional image information into three-dimensional grabbing parameters is solved, and the adaptability and reliability of a robot grabbing task are improved.

The invention relates to the field of navigation ship identification, in particular to a ship identity multi-modal verification method and system based on computer vision and deep learning, and the method comprises the steps: obtaining multi-modal data of a ship, the multi-modal data comprising visible light image data, laser radar point cloud data and ship identity information; extracting visual features of the ship according to the visible light image data; performing dynamic space calibration on the laser radar point cloud data; according to a calibration result, extracting three-dimensional structural features of the ship; the visual features, the three-dimensional structure features and the ship identity information are fused and input into a preset verification model, verification results are output through the verification model, and the verification results comprise normal ships and abnormal ships. Different types of data provide multi-dimensional understanding of the ship, and the precision and robustness of ship identification are enhanced.

The invention relates to the field of urban road modeling, in particular to an aerial photography data processing method and system for urban road modeling. The invention discloses an aerial photography data processing system for urban road modeling. The aerial photography data processing system comprises an aerial photography video acquisition module, a road modeling module, a photogrammetry module, a spatial registration module and a rendering synthesis module. According to the invention, on the basis of a spatial calibration mechanism of the original aerial image and the BIM model, redundant processes such as point cloud acquisition, data fusion and manual correction are eliminated, and linear processing from data acquisition to dynamic result output is realized; according to the architecture, the hardware resource dependency and the operation complexity are greatly reduced, and designers are endowed with the capability of completing large-scale scene visualization processing in a conventional engineering environment; according to the method, a transmission chain from original data to decision support is remarkably shortened, and important engineering scheme deduction and dynamic review have unprecedented timeliness.

The invention provides a target cooperative control method and system based on deep learning, and the method comprises the steps: obtaining the real-time visual data of a target region through a multi-view image collection device, carrying out the recognition and classification of object features through a deep learning model, and generating feature distribution data containing a class identifier and boundary coordinates, a space calibration algorithm is adopted to map the two-dimensional features into three-dimensional coordinate system data, a space position and posture angle parameters are output in combination with posture calculation, a control instruction including path planning, tail end executor posture adjustment and self-adaptive clamping force is generated according to physical attribute parameters of an object, an operation device is driven to complete the precise grabbing action, and the grabbing precision is improved. Therefore, through dynamic multi-view perception, three-dimensional space modeling and physical attribute driven closed-loop control, the problems of feature deficiency, positioning errors, operation rigidity and the like of a traditional visual system in a complex scene are effectively solved, and the precision and adaptability of target operation in an unstructured environment are remarkably improved.

The invention relates to the technical field of production safety management and artificial intelligence, and discloses a safety factor intelligent early warning method based on a hybrid model. The method specifically comprises the following steps: S1, constructing a multi-modal data acquisition layer, an edge reasoning and local early warning layer, a cloud large model training and depth reasoning layer, a visualization and decision-making auxiliary layer and edge equipment; S2, acquiring multi-modal data through the multi-modal data acquisition layer in the S1, and performing time synchronization, space calibration, denoising and formatting processing. According to the invention, a multi-modal data acquisition layer is constructed, an NTP or Beidou clock module is adopted to realize millisecond-level time synchronization, a space definition label is utilized to perform unified compilation on sensing data in the same space, space calibration is completed, and meanwhile, targeted denoising and formatting processing is performed on data such as images, sensors, audios, texts and the like. The problem that the multi-modal data protocol and format are not uniform is solved.

The invention relates to the technical field of remote sensing recognition, in particular to a garlic recognition system and method based on remote sensing deep learning. According to the method, visible light and red edge wave band information in a multi-temporal remote sensing image for the phenological period of garlic are combined, the alignment precision of image data in the time sequence is improved through spectrum normalization and spatial calibration, and a multi-source classification sample set is constructed by superposing vegetation spectrum indexes and spatial sample positioning labels; a deep learning structure of semantic segmentation and an attention mechanism is introduced to enhance fine expression of spatial coherence and feature response of region recognition, the discrimination definition of a garlic region in an image is improved through channel spatial feature fusion, and a garlic pattern spot spatial boundary is subjected to continuous recombination and comparison verification with a reference sample. And the pattern spot precision and the sample consistency of the final identification area are ensured, so that the classification accuracy and the identification stability of the garlic planting area are remarkably improved under the complex background.

The invention relates to the technical field of battery detection, in particular to a battery multi-physical-quantity performance test and intelligent diagnosis method, system and device and a storage medium, and the method comprises the steps: building a virtual battery model containing an electrode, electrolyte and diaphragm structure, and marking initial mechanical strength parameters; synchronously acquiring real-time current and surface infrared thermal imaging temperature data of a real battery; inputting the current data into the virtual model to execute electrochemical and thermal coupling simulation, and comparing differences to locate a physical abnormal area; disassembling an abnormal area, measuring structural characteristic parameters of the material, recording space coordinates, and extracting electrochemical state variables at corresponding positions of the virtual model through space calibration; and establishing a mapping relationship between the material structure parameters and the electrochemical variables, outputting a dominant failure mechanism, and updating the failure criterion of the virtual model. Abnormality is accurately positioned by combining infrared thermal imaging and simulation difference analysis, multiple physical quantities are fused to reveal correlation between structural degradation and performance attenuation, and data are dynamically updated to improve diagnosis reliability.

The invention discloses a multi-scale frequency-space fusion camouflage target detection method and system, and belongs to the technical field of camouflage target detection, an image and wavelet transform of the image are input into a WaveCamoNet model for camouflage target detection, and the model comprises a double-flow feature extraction module, a cross-domain dynamic fusion module, an edge texture enhancement module and a hierarchical decoder; inputting the image and the wavelet transform of the image into a double-flow feature extraction module, and extracting multi-scale spatial domain features and frequency domain features; inputting the multi-scale spatial domain features and the frequency domain features into a cross-domain dynamic fusion module, and performing multi-scale fusion and cross-domain dynamic fusion to obtain multi-scale fusion features and cross-domain dynamic fusion features; inputting the cross-domain dynamic fusion features into an edge texture enhancement module, and performing spatial calibration to obtain edge enhancement features; and inputting the multi-scale fusion features, the cross-domain dynamic fusion features and the edge enhancement features into a hierarchical decoder for decoding to obtain a binary detection image, and completing pixel-level positioning of the camouflage target.

The invention discloses a multi-source 5G message video cooperative monitoring method and system, particularly relates to the technical field of video monitoring, and is used for solving the problems of multi-camera calibration parameter misalignment and video fusion dislocation caused by thermal deformation asynchronism in the prior art. A temperature sensor is arranged on an industrial camera supporting structure, a thermal deformation three-dimensional displacement compensation vector is calculated, a preferential transmission object is marked according to the displacement gradient of an adjacent camera and a material heat conductivity coefficient threshold value, and the displacement vector is transmitted to an edge calculation node according to the priority through a 5G message. All displacement data are fused at a node end to dynamically correct space calibration parameters, a collaborative calibration matrix is generated, and finally, the matrix is utilized to perform real-time registration on multiple paths of video streams and perform workpiece defect detection, so that the geometric consistency of a multi-camera system in a thermal deformation environment is guaranteed, and the visual detection precision and stability are improved.

The invention belongs to the technical field of intelligent agriculture, and particularly relates to a melon seedling feature recognition and analysis method based on image multi-dimensional features, which comprises the following steps of: firstly, synchronously acquiring two-dimensional image information and three-dimensional point cloud information of a melon crop seedling raising area to form multi-modal data, and performing spatial calibration and preprocessing; the method comprises the following steps of: extracting two-dimensional semantic features and three-dimensional geometric features of a target seedling, identifying a current growth stage of the target seedling based on the extracted features, dynamically adjusting a fusion weight of the two-dimensional semantic features and the three-dimensional geometric features, and performing feature fusion by adopting a cross-modal attention mechanism to generate a dynamic fusion feature vector; and inputting the dynamic fusion feature vector into a task identification model, and outputting the health state and quantitative growth parameters of the target seedling, thereby realizing high-precision, self-adaptive and multi-dimensional perception of the morphology and growth health state of the melon seedling, and playing an important role in promoting high-quality development of modern intelligent agriculture.

The invention relates to a multi-vehicle cooperative sensing method based on an attention mechanism, and belongs to the technical field of mobile communication. A multi-vehicle collaborative perception scene lacks selective attention to perception information of different values, so that perception characteristics of each vehicle are difficult to effectively utilize. At the same time, in a high mobility scene, it is difficult to align sensing information time delay and asynchronous features, resulting in low dynamic target sensing precision. In order to solve the problem, the method comprises the following steps: firstly, filtering redundant sensing information of cooperative vehicles, and constructing a screening model based on aerial view feature intersection-to-union ratio so as to select high-quality cooperative vehicles; spatial calibration is completed in combination with sparse consensus foreground features and geometric consistency verification, and feature fusion is realized through an attention mechanism; and finally, finishing time calibration of the dynamic target by adopting a time alignment strategy based on distillation learning. The method is suitable for multi-vehicle cooperative perception in an Internet of Vehicles scene, the focus on high-quality perception information is improved, and meanwhile, the real-time perception precision and robustness are enhanced.

The invention discloses a fusion detection and iron tower photoelectric tracking system and a fusion detection and iron tower photoelectric tracking method for a low-speed small target. The system comprises a multi-source detection module, a data fusion module and a photoelectric tracking module, and various heterogeneous data of a target are obtained through a radar detection unit, a frequency spectrum detection unit and a protocol cracking unit; after data preprocessing, performing multi-source data fusion by adopting a dynamic weight fusion algorithm and Kalman filtering to generate comprehensive target information; and based on the equipment space calibration parameters, calculating an optoelectronic equipment pointing instruction through a coordinate conversion model, and driving the optoelectronic equipment to accurately track a target. The system is further provided with a feedback optimization module which dynamically corrects the pointing instruction according to the tracking deviation. According to the invention, deep fusion and intelligent cooperation of multi-source detection data are realized, and the detection precision, tracking stability and system response speed of a low-speed small target are effectively improved.

The invention provides a full-automatic laser vision coding machine and a coding path optimization method. The method comprises the following steps: acquiring three-dimensional point cloud data of a target object; performing spatial calibration on the three-dimensional point cloud data according to the point cloud distribution diagram of the three-dimensional calibration block so as to determine curvature field distribution required for performing light beam path compensation on the target object; based on the curvature field distribution and the surface material distribution of the target object, determining an absorption response curve of laser beams in all coding regions in a region boundary range when the target object is coded; determining an energy compensation factor corresponding to each coding area according to an energy attenuation rate of laser energy in the absorption response curve and an optical reflection coefficient of each coding area in an area boundary range during coding; and when it is detected that the absorption response curve of the target object deviates from a preset standard curve, generating a dynamic coding path of the target object based on all the energy compensation factors. According to the scheme, the laser coding path can be optimized to compensate energy attenuation in the coding process.

The invention provides a power grid composite chain disaster multi-source monitoring damage assessment method and system, and relates to the technical field of data processing, and the method comprises the steps: dividing a reference geographic coverage area, generating a plurality of independent analysis units, enabling each three-dimensional discrete point to belong to the corresponding independent analysis unit, and carrying out the calculation to obtain a unit space calibration coefficient; performing regional correction on the fused disaster monitoring image data set by using the unit space calibration coefficient to obtain calibrated disaster monitoring image data; based on the calibrated disaster monitoring image data, disaster damage features are extracted through a deep learning semantic segmentation network; and based on the disaster damage characteristics, carrying out spatial diffusion path simulation and time evolution process analysis of disaster influence to obtain disaster spatio-temporal evolution characteristics, and carrying out comprehensive quantitative evaluation of damage degree and risk level on power grid facilities to generate a comprehensive damage evaluation result. According to the invention, the accuracy of power grid disaster damage monitoring and the timeliness of evaluation are improved, and the risk early warning and coping capability of power grid facilities is enhanced.

The invention provides a surgical instrument pose analysis method and system, and relates to the field of pose analysis, and the method comprises the steps: selecting a plurality of feature points on a surgical instrument as a plurality of infrared reflective mark points, and carrying out the coding processing of the infrared reflective mark points; carrying out synchronous processing on the infrared camera and the color camera and carrying out space calibration; capturing information of the surgical instrument and generating an infrared image and a color image; and constructing a target prediction network and a loss function based on the infrared image and the color image, and solving the pose by adopting a perspective n-point PnP algorithm based on a random sample consensus RANSAC framework to obtain the refined pose of the surgical instrument. According to the method, a strategy combining global shape information and local mark point features is matched with a perspective n-point PnP algorithm based on a random sample consensus RANSAC framework, the instrument shape mask information is used as additional geometric constraint and verification information to correct errors, and high-precision and high-robustness surgical instrument real-time pose tracking is achieved.

The invention provides a display screen dynamic control method and system, and relates to the technical field of data processing, the method comprises the following steps: constructing a space coordinate system comprising a plurality of entity nodes, the entity nodes comprising a display screen preset angular point, a user binocular vision center and a handheld interaction device; analyzing a relative pose relationship among the nodes by calculating a projection dot product of a space vector corresponding to each node; constructing a dynamic topology network based on the relative pose relationship, and synchronously collecting environment illumination intensity, user fixation point coordinates and user pose data; performing time sequence synchronization and Kalman filtering preprocessing on the acquired multi-modal data to obtain an initial feature set; and generating a spatial calibration coefficient according to the distribution characteristics of the dynamic topology network, and performing normalization compensation on the initial feature set through the calibration coefficient to obtain a calibrated multi-modal sensing feature vector. According to the invention, accurate and intelligent dynamic control is realized.

The invention discloses an artificial leather quality rapid detection method based on spectral analysis, and particularly relates to the field of quality detection.The method comprises the steps that component composition characteristics, surface and microcosmic characteristics and dynamic response characteristics of artificial leather are synchronously obtained through multi-dimensional original data acquisition, and a time-space correlation data set is formed through timestamp alignment and space calibration; constructing component analysis, surface association and dynamic response models, and eliminating space-time migration through a synchronous optimization model; after multi-dimensional features are extracted, optimized features are generated through cross-direction tensor fusion, attention enhancement and gating cycle unit time sequence fusion; and finally, quantizing the quality index based on a multi-task model, and outputting a quality grade and a traceable report in combination with a dynamic threshold value through hierarchical coding, graph neural network association enhancement and evidence fusion. The single sample collection duration is less than 3 minutes, the response time is less than 1 second, and full-dimension, rapid and accurate detection and quality traceability are realized.

In a virtual camera calibration method, a target object model of a physical camera device is obtained. The physical camera device includes a camera and a contact plane. The target object model with an operation controller is output for display through a head-mounted display device. The contact plane of the target object model and a contact plane of the operation controller are in an attached state. A camera position of the target object model corresponds to a camera position of the physical camera device. An indication that a camera of the target object model and the camera of the physical camera device are in an overlapping state is output for display. A virtual camera is calibrated based on spatial information of the operation controller and a spatial calibration operation for the virtual camera. The spatial information includes target position information and a target orientation.

The embodiment of the invention discloses an RTK-fused laser inertial navigation odometer mapping method and system, and the method comprises the steps: carrying out the time synchronization and space calibration of a laser radar, an inertial measurement unit and a double-mushroom-head antenna RTK system, and achieving the coordinate system alignment through lever arm compensation; multi-source constraints are fused in pose map optimization, and point cloud ghosting is eliminated by associating poses of a current frame and multiple historical frames; designing a dynamic fusion strategy for RTK positioning data, detecting the height of an environmental obstacle by using a laser radar, dynamically adjusting an RTK constraint weight, adding constraint factors in stages, carrying out intensive accumulation in an initialization stage to realize rapid alignment of a global coordinate system, and adding a suppression accumulative error in a subsequent low frequency; and a GTSAM optimizer is adopted to execute pose map optimization, and the optimized key frame point cloud is converted to a global coordinate system to generate a down-sampling map, so that the map consistency, precision and real-time performance of long-distance mapping are remarkably improved.

The invention relates to the technical field of video monitoring, in particular to a method for quickly searching a linkage monitoring camera in a panoramic video, which comprises the following steps of: obtaining monitoring equipment information and panoramic video picture information through a panoramic monitoring system; constructing a panoramic video picture correction mechanism to adjust the panoramic video picture information to obtain corrected panoramic video coordinate information; introducing a geographic coordinate and pixel rectangular coordinate transformation expression based on the basic imaging information, and analyzing a corresponding relation between a panoramic video picture and a physical space coordinate based on the geographic coordinate and pixel rectangular coordinate transformation expression and the corrected panoramic video coordinate information; combining monitoring equipment information, corrected panoramic video coordinate information and the corresponding relation to build a panoramic area and monitoring camera mapping library; and carrying out panoramic target area frame selection and panoramic video picture display according to the panoramic area and the monitoring camera mapping library. According to the invention, linkage display of a panoramic video and a camera local picture is realized through space calibration and an intelligent algorithm.

A line-field optical coherence tomography (OCT) system and method provide enhanced imaging accuracy through k-linearization of interference data. The system includes a swept laser source, a line-field sensor, and a single board computer (SBC) with processing capabilities. The system uses a frequency reference to produce a periodic reference pattern, which is detected by a subset of pixels in the line-field sensor. The SBC determines a resampling curve based on the reference pattern to correct non-linearities in the wavelength tuning of the laser. The resampling curve is applied to k-linearize the interference data, enabling the generation of high resolution transform-limited depth profiles through inverse Fourier transform. Methods are disclosed for recalculating the resampling curve periodically, continuously, or adaptively based on a linearity threshold. The system can further update the laser's tuning function dynamically to ensure consistent performance. These advancements enable precise and efficient OCT imaging for applications such as ophthalmology, angiography, and other diagnostic uses.

The invention discloses a holographic fan, a linkage imaging system and a linkage imaging method, which are characterized in that: a networked cooperative control architecture: a router is used as a center, time synchronization, data segmentation and real-time feedback calibration are integrated, and the problem of multi-device heterogeneous communication is solved; according to the cross-medium optical fusion algorithm, based on the actual imaging effect captured by the camera, the transparency of the holographic fan and the contrast ratio of the special-shaped screen are dynamically adjusted, and light field natural superposition is achieved; and automatic space calibration: automatically aligning a virtual content coordinate system with the position of the physical equipment by using affine transformation and a machine learning algorithm, and reducing manual intervention. According to the technology, the flexibility, the synchronization precision and the visual effect of a multi-modal display system are remarkably improved, and a new implementation path is provided for an immersive interaction scene.

The invention relates to the technical field of laser measurement, in particular to a laser interference surface shape measurement method based on a calibration plate, a medium, equipment and a system. Comprising the following steps: attaching a reflecting mirror to a reference mirror, and obtaining reflecting surface shape data of the reflecting mirror; the calibration glass plate is tightly clamped between the reflector and the reference mirror, and light facing surface reflection surface shape data and mixed transmission surface shape data of the calibration glass plate are obtained; removing the reflector, moving the calibration glass plate to a preset measurement distance along a laser optical axis, carrying out multi-surface synchronous measurement through a laser interferometer, and respectively obtaining light facing surface mixed reflection surface shape data and to-be-measured surface shape mixed data; and obtaining target surface shape data of the surface to be measured. According to the invention, the movable calibration glass plate is introduced to establish a double-reference measurement system, so that the space calibration of the airflow disturbance error can be realized. The device is simple in structure and low in measurement environment requirement. Interference of air turbulent flow can be eliminated only through single-time measurement, and the measurement time is saved.

Example techniques may involve performing aspects of a spatial calibration. An example implementation may include detecting a trigger condition that initiates calibration of a media playback system including multiple audio drivers that form multiple sound axes, each sound axis corresponding to a respective channel of multi-channel audio content The implementation may also include causing the multiple audio drivers to emit calibration audio that is divided into constituent frames, the multiple sound axes emitting calibration audio during respective slots of each constituent frame. The implementation may further include recording the emitted calibration audio. The implementation may include causing delays for each sound axis of the multiple sound axes to be determined, the determined delay for each sound axis based on the slots of recorded calibration audio corresponding to the sound axes and causing the multiple sound axes to be calibrated.

The invention belongs to the technical field of information technology, and discloses a moxibustion temperature control method and system based on multi-sensor fusion, and the method comprises the steps: obtaining a biopotential signal and position offset data from an acupoint region through a sensor, processing the biopotential signal through a classification algorithm, and obtaining a classified potential distribution mode; on the basis of the classified potential distribution modes, heat conduction influence data between adjacent acupoints are obtained, the position offset data are integrated through an information fusion algorithm, fused physiological state indexes are determined, and the problem of measurement distortion caused by acupoint movement and equipment offset is solved through multi-source data fusion and dynamic error compensation; and meanwhile, the system constructs an accurate potential distribution model by utilizing a spatial calibration and classification algorithm, and temperature control accuracy reaches a millimeter level in combination with real-time position tracking, so that the integrity and reliability of data are remarkably improved.

The invention provides an intervention control method for aided driving and an automobile, and the method comprises the steps: obtaining a panoramic stitching image of a test environment, and recognizing a calibration object set in the panoramic stitching image; generating a semantic object according to the calibration object set; obtaining pose data of the vehicle, and determining a spatial relationship parameter between the vehicle and the semantic object according to the semantic object and the pose data; performing intention classification according to the semantic object and the driving state information of the user to obtain a driving intention identifier; and determining whether the vehicle is in danger according to the semantic object, the space calibration data and the driving intention identifier, and intervening in controlling the vehicle when the vehicle is in danger.

The invention relates to the technical field of space calibration, in particular to a non-overlapping field-of-view dual-camera space calibration method based on an ArUco calibration board, and the method comprises the steps: carrying out the single-target calibration of two cameras; fixing the calibration plate at a plurality of preset postures and spatial positions, carrying out image acquisition, and obtaining an image frame of the calibration plate under the current spatial pose; extracting an ArUco marker ID and a two-dimensional angular point coordinate of the ArUco marker ID in the image frame, and recovering three-dimensional information of the calibration plate in each frame of image under a camera coordinate system; three-dimensional space points corresponding to the same marker ID in the two camera coordinate systems are recovered, and a plurality of space point pairs with the known corresponding relation are constructed; calculating a rotation matrix and a translation vector from the first camera coordinate system to the second camera coordinate system; according to the method, marker data in respective visual fields are collected under a public world coordinate system, and the unique coding characteristic of the marker data is combined, so that cross-visual-field space point pair matching and accurate transformation estimation are realized.

The invention provides a sea target identification method and device based on AIS and SAR image fusion, and the method comprises the steps: obtaining the AIS data of a sea target through an AIS system, obtaining the SAR image of the sea target through an SAR satellite, and carrying out the preprocessing of the data; after time calibration and space calibration are carried out on the preprocessed data, the fusion weight of the AIS data and the SAR image in Kalman filtering is determined according to the confidence score of the AIS data and the SAR image; determining a dynamic measurement noise covariance matrix according to the fusion weight, calculating a Kalman gain according to the dynamic measurement noise covariance matrix and an error covariance matrix, and updating the state of the marine target and the error covariance matrix by using the Kalman gain; and inputting the SAR image and the final state output by the Kalman filtering into the deep learning model to obtain a sea target recognition result output by the deep learning model. According to the invention, the precision, robustness and real-time performance of sea target identification are improved.

The present invention discloses a multi-sensor spatio-temporal collaborative calibration method for camera and millimeter-wave radar fusion perception. First, spatial calibration is performed on the millimeter-wave radar to determine its world coordinates. Then, the internal and external parameters of the roadside camera are determined, and according to the transitivity of continuous coordinate transformation, the conversion relationship between the millimeter-wave radar coordinate system and the pixel coordinate system is determined. Pulse signals are used to correct the time of sensors in the local area network. At the same time, according to the data confidence index, a method based on simple linear interpolation and Bayesian neural network is used to predict the equivalent data frame. This method takes into account the situation when pixel coordinates and image coordinates are irregularly arranged, greatly improving the conversion accuracy of target positions in different coordinate systems. At the same time, according to different data requirements, the reliability of the equivalent data frame is introduced, and the calibration process under different accuracy requirements is completed, providing a decision-making basis for the refined management and control of highways.

The invention belongs to the technical field of building information model and cultural heritage digital protection, and particularly relates to a historical building BIM refined modeling method based on multi-source data fusion, and the method comprises the steps: collecting multi-source heterogeneous data such as three-dimensional laser point cloud, unmanned aerial vehicle oblique photography and surveying and mapping drawings; spatial calibration is carried out under the unified geographic coordinates and time reference, and a structured metadatabase is constructed; based on building component classification and fineness grading standards, parameterized BIM modeling is executed, and details such as a platform base, a bucket arch and a roof are truly restored; parameterized reconstruction and environment integration of the vegetation system are synchronously carried out; and finally, importing an unreal engine, and constructing a high-fidelity and strong-immersion digital humanity scene in combination with physical rendering, dynamic weather, day and night circulation and AI interaction logic. According to the method, total-factor, high-precision and interactive digital twinning of historical buildings is realized, and the protection, display and activation utilization level of cultural heritage is remarkably improved.