154 results about "Data projection" patented technology

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

The invention relates to the technical field of low-altitude unmanned aerial vehicle detection, in particular to an unmanned aerial vehicle detection method based on radar infrared visible light multi-mode fusion, which comprises the following steps: S1, respectively inputting an obtained unmanned aerial vehicle infrared image and an obtained visible light image into two visual branches of a double-flow feature extraction network, and extracting scale features by adopting a lightweight convolutional backbone network; s2, acquiring three-dimensional coordinate data of the unmanned aerial vehicle after radar detection and clutter removal processing in real time, and projecting the three-dimensional coordinate data into two-dimensional image coordinates through a camera external parameter matrix; s3, generating a two-dimensional Gaussian distribution probability graph based on the real-time two-dimensional image coordinates, and inputting the two-dimensional Gaussian distribution probability graph as a mask into the radar feature extraction network to extract a radar feature graph; and S4, under the same scale, performing dynamic weighted fusion on the scale features of the infrared branches and the visible light branches to generate a visual fusion feature map. The method greatly improves the capability of detecting the low-altitude unmanned aerial vehicle.

The invention discloses a virtual projection 3D oral and maxillofacial key point detection method based on deep learning, and aims to solve the problem of insufficient 3D image key point detection precision and efficiency in the prior art. The method comprises the following steps: projecting 3D image data into a plurality of 2D projection images by using a virtual projection technology; then key point detection and marking are carried out on the 2D image by using a deep learning network model, and 2D mark point coordinates are obtained; and finally, performing 3D reconstruction by adopting a virtual back projection method, and back-projecting the 2D mark point information to a 3D space to obtain 3D mark point coordinates. According to the method, 2D key point detection and 3D projection and reconstruction technologies are combined, rapid, high-precision and automatic detection of 3D image mark key points is realized, and the method can be widely applied to the fields of oral medical image analysis and the like.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention relates to the technical field of wind power plant power control, in particular to a wind power plant dynamic power distribution optimization method based on projection dimensionality reduction. The method comprises the following steps: collecting a high-dimensional original operation data stream of a wind power plant, and preprocessing to output a high-dimensional state vector; extracting a dominant mode matrix through an intrinsic orthogonal decomposition algorithm, and projecting a high-dimensional state vector to a low-dimensional space; future modal coefficient evolution is predicted through an autoregression prediction model, and a low-dimensional rolling optimization problem is constructed and solved through a sequential quadratic programming algorithm; reconstructing the optimal low-dimensional modal coefficient vector into a target power instruction of each fan, and issuing and executing the target power instruction; and updating the dominant mode matrix through an incremental singular value decomposition algorithm and correcting parameters of the autoregressive prediction model. According to the method, projection dimensionality reduction from high-dimensional data to a low-dimensional space is realized through intrinsic orthogonal decomposition, and the problem of low optimization solution efficiency caused by high data dimensionality of a traditional method is solved.

The invention relates to an intelligent state monitoring method and system for a high-voltage isolation switch operating mechanism, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: enabling a collected real-time sequence to update a standard operation response potential energy surface, obtaining a real-time operation response potential energy surface, generating a phase response difference tensor through data projection, and obtaining a real-time operation response potential energy surface; and depicting deviation characteristics of actual and standard operation states. And then, in the constructed graph attention network, on the basis of a real-time sequence, monitoring parameters and video motion characteristics, constructing a multi-modal sequence state graph, and determining a state typing result through fine-grained recognition and interpretable analysis of a fault. And finally, introducing a cyclic fingerprint residual model, performing clustering optimization based on a real-time sequence, determining an abnormal deviation index, and combining the abnormal deviation index with a state typing result to determine a fault diagnosis result. Therefore, the accuracy of intelligent state monitoring of the high-voltage isolation switch operating mechanism can be effectively improved, and the method can be suitable for non-ideal environments, low dominant anomalies and other conditions.

The invention relates to the technical field of electrical detection, in particular to a multi-stress coupling environment reliability testing method for an electricity consumption information acquisition terminal. Comprising the steps of applying a combined stress field to an electricity utilization information acquisition terminal through an environment test box; acquiring power utilization information acquisition terminal parameters; the method comprises the following steps of: capturing metering pulse response and carrier communication interaction signals of an electricity utilization information acquisition terminal under the excitation of a combined stress field by utilizing an electrical sensor, extracting carrier signal-to-noise ratio data and metering reference drift data, and generating a reference voltage compensation coefficient and an impedance matching threshold value; projecting the carrier signal-to-noise ratio data to an impedance matching threshold value, and constructing a stress failure envelope surface according to a hardware overload protection threshold value; and mapping the communication quality distortion residual vector to a stress failure envelope surface, and calculating a reliability life prediction value. According to the method, a uniform excitation environment is constructed, and performance degradation of different dimensions is uniformly quantified into accumulative stress damage integral, so that the comprehensive anti-interference capability of the terminal in a multi-stress coupling environment can be truly reflected.

The invention belongs to the technical field of spatial perception positioning, and discloses a spatial perception positioning method and system for a multi-interval complex environment, and the method comprises the steps: obtaining a multi-interval environment video stream from a transformer substation monitoring camera, and synchronously collecting an environment scanning point cloud model; establishing a space coordinate transformation relation between the 2D image and the 3D environment model; acquiring a local area updating point cloud, splicing the local point cloud and the initial point cloud under the condition of no initial pose, identifying an overlapping area, and realizing dynamic map updating of equipment state change; projecting two-dimensional image data of a visible light / infrared / ultraviolet camera video stream to a digital twin three-dimensional space through perspective transformation based on a space coordinate transformation relation to obtain a three-dimensional perception result of multispectral information; and based on a three-dimensional perception result, after multi-camera target tracks are fused, space coordinates are optimized and output. Rapid updating of the local three-dimensional point cloud data and high-precision registration updating of the monitoring video stream and the digital twin space are realized, and the virtual-real consistency is ensured.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

The invention relates to the technical field of optimization of pneumatic clamps, in particular to a simulation optimization design method for structural size parameters of a pneumatic clamp. The method comprises the following steps: acquiring standard cylinder data and target fixture workpiece data, extracting geometric boundary data to reconstruct a three-dimensional cylinder structure, identifying fixture workpiece interface data and analyzing installation constraints, thereby projecting the workpiece data into a three-dimensional cylinder to form a projection combination structure, and simulating the working process of a pneumatic fixture. A guide limiting structure in the clamp is determined based on the simulation result, a clamp guide rail is adjusted to generate an optimized structure, a clamping force path is captured, workpiece deviation is predicted, clamping stability adjustment is conducted, and finally the optimized pneumatic clamp structure is obtained by combining the optimized guide rail and a clamping structure. High-precision and high-efficiency pneumatic clamp design optimization is achieved, and the clamping speed and the machining efficiency are improved.

This invention belongs to the field of airport public transportation allocation technology, and discloses an airport public transportation pre-allocation processing system and method based on a big data model. The method includes: acquiring historical data; using big data processing technology and machine learning and deep learning models to infer passenger demand and public transportation usage from historical data, generating a data projection model; inputting real-time weather, time, and passenger data into the data projection model, employing intelligent optimization algorithms, and dynamically adjusting public transportation resource allocation in conjunction with real-time traffic conditions; monitoring the public transportation resource allocation results in real time, continuously optimizing and dynamically adjusting the results using a feedback mechanism, and pushing the latest adjusted results to downstream third-party systems. This invention utilizes real-time data analysis and intelligent optimization algorithms to achieve a more efficient traffic scheduling scheme, and has significant practical implications and broad application prospects.

The invention discloses an image-based sorted object pollution dynamic tracking method, which belongs to the field of sorted object pollution monitoring, and is characterized in that a pollution identification area is arranged on a sorted object conveying line, and image acquisition, edge detection and 3D point cloud data projection technologies are combined, so that the actual contour of a sorted object is accurately identified; by extracting color, texture and shape characteristic parameters of an appearance image and internal spectral characteristic parameters and density distribution parameters, the appearance and internal pollution conditions of the sorted objects are quantitatively evaluated; the pollution diffusion risk is comprehensively evaluated by calculating the pollution intensity value, the self-expansion evaluation value and the external expansion evaluation value of the polluted sorted objects; and finally, the positions of the polluted sorted objects are tracked in real time, task processing queues of the sorting stations are sorted according to pollution evaluation values, the optimal sorting station is intelligently distributed, the sorting efficiency is improved, and the pollution diffusion risk is reduced. The problems that the sorting object contour recognition precision is insufficient, the pollution degree judgment is not specific enough, and sorting station distribution is rigid are effectively solved.

This device for generating crystal structure information on the basis of an artificial intelligence model comprises: a data preprocessing unit that constructs an interleaved dataset by arranging text and images to cross each other; a learning unit that includes a visual encoder for generating image feature data from the images, a language model for generating text-encoded data from the text, and a projector for converting the image feature data and the text-encoded data into data in a shareable form, learns a relationship between the images and the text on the basis of the converted data, initializes the projector, and then performs, on the basis of the interleaved dataset, fine tuning for simultaneously training the language model and the projector to perform condition learning for generating information, which satisfies a specific condition, and filling learning in a method for filling a masked portion; and an information generation unit for generating at least one piece of crystal structure information on the basis of a prompt input.

Systems and methods for bone surface geometry generation include a computing device. The computing device projects first geometry data indicative of a first bone contact surface of a first bone of a patient to a coordinate space of a second bone of the patient to generate a projected geometry. The second bone includes a second bone contact surface engaged with the first bone contact surface. The computing device also maps the projected geometry to a plurality of poses within a range of motion of the first bone relative to the second bone using position data indicative of relative positions of the first bone and the second bone at the plurality of poses within the range of motion. The computing device also selects an intersecting geometry from the mapped projected geometries to generate second geometry data indicative of a second bone contact surface.

The invention provides a mobile robot capable of realizing automatic calibration of a camera radar. The mobile robot comprises a mobile base; a robot arm; the plurality of laser radars are used for collecting radar point cloud data around the mobile base; the plurality of cameras are used for collecting camera images around the movable base; the control system is used for obtaining radar point cloud data and camera images, the radar point cloud data comprises calibration plate point cloud data collected by a laser radar, the camera images comprise calibration plate images collected by a 2D camera, first edge data of the upper side edge of the calibration plate are extracted from the radar point cloud data, and the first edge data are sent to the calibration plate; extracting second edge data of the upper side edge of the calibration plate from the camera image; and projecting the first edge data to correspond to the second edge data, and optimizing the corresponding relation to determine an external parameter coefficient between the camera and the radar. The method can achieve the convenient calculation of the external parameter coefficient between the camera and the radar, and improves the calibration stability and the calibration precision of the camera and the radar.

The invention discloses a cloud exhibition hall multimedia component cross-terminal adaptive rendering control method, relates to the technical field of digital graphic processing, and is used for solving the problem of cross-terminal rendering mismatch. According to the invention, a cross-terminal adaptive rendering control mechanism oriented to a cloud exhibition hall multimedia component is constructed, and a terminal rendering state is generated according to a terminal display parameter, a viewport parameter, a graphic processing parameter and an interaction state; a target model node, a target texture node, a target viewport parameter and a target display component display sequence are dynamically determined from the layered rendering resource chain based on a preset rendering state machine, so that collaborative rendering and linkage display of a three-dimensional exhibition item component, an image and video display component and an instruction interaction component under different terminals are realized; and a target selection area is generated through projection correction of the hotspot labeling data, and local adjustment and update output are performed in combination with a picture continuity result, a selection consistency result and a component superposition result, so that the rendering consistency and display coordination of the cloud exhibition hall are improved.

The application provides a spectral submanifold-based fast and accurate control method for a cable-driven tensegrity robot, and belongs to the field of control of the cable-driven tensegrity robot. First, end position data is taken as sampling data, data in the direction of the z coordinate axis is removed from the truncated data to obtain training data after truncation. Second, according to the Takens delay embedding theorem, the training data is projected onto a spectral subspace, a spectral submanifold is parameterized, a reduced-order dynamics is obtained, and an optimal solution is found. Finally, the position data and control input of the sampling end center point are open-loop controlled, the data of the end center point is projected onto the spectral subspace, is converted into an optimal problem to learn a control matrix, and then an optimal control problem is constructed. The application has low dependence on high-dimensional data, weak noise sensitivity, strong robustness of the reduced-order model, can efficiently realize fast and accurate control of the end pose of the cable-driven tensegrity robot, and has strong adaptability and expansibility.

Methods and systems for displaying image data in a vehicle are disclosed herein. A method for displaying image data include providing initial and final projection surfaces; receiving a video stream and is buffered in a buffer storage, which is updated at a frame rate. The method includes reading out image data at a readout rate from the buffer at an initial readout instance, intermediate readout instances and a final readout instance. The method includes determining respective sets of initial and final sampling points on the projection surfaces. For each of the intermediate readout instances, the method includes determining intermediate sampling points, which interpolate between the final sampling points and the respective initial sampling points, and an intermediate projection surface containing the intermediate sampling points. The method includes projecting the read out image data to the respective intermediate projection surface and a corresponding image is displayed on a display device.

Provided herein are methods, apparatuses, computer program products, and systems for generating textured surfaces using computed tomography. One method can include generating a computer data structure representing three-dimensional surfaces of a physical object from (i) segmentation of volumetric data reconstructed from radiographs, (ii) position information and orientation information of the physical object, and (iii) size information of the physical object; adding texture data to the three-dimensional surfaces represented in the computer data structure using images captured by at least one optical camera, the images being of the physical object at different rotational orientations, by projecting data of the images onto the three-dimensional surfaces using the size information, the position information, and the orientation information of the physical object and predetermined position, location, and parameters of the at least one optical camera; and providing the computer data structure for processing of the three-dimensional surfaces.

The invention discloses a flame detection method and device for a pipe gallery scene, and belongs to the technical field of flame detection. Projecting the initial camera data onto the image to obtain a preliminary projection image, and selecting a candidate flame region in the preliminary projection image; determining the position of the region of interest in the initial camera image according to the position of the candidate flame region in the initial projection image; extracting flame features in the region of interest to obtain feature extraction data; and inputting the feature extraction data into a feature classification model, wherein the feature classification model identifies the flame according to the feature extraction data. According to the method, the flame features of the region of interest in the camera data are extracted, so that the response speed and the recognition precision of a fire detection scheme based on image processing are improved.

The application discloses a subspace parameter iterative estimation space-time adaptive detection method for a strong clutter environment. In view of the problem that in a space-time adaptive processing system, a target steering vector falls in a clutter subspace, main and auxiliary data exist power mismatch, and existing methods are difficult to effectively maximize the marginal likelihood, the application projects the main and auxiliary data to a low-dimensional coordinate domain to obtain sufficient statistics, regards the clutter coefficient as a hidden variable, and under two kinds of assumptions, respectively uses EM and ECME algorithms to iteratively maximize the marginal log-likelihood function, and obtains the stationary point estimation of the clutter covariance matrix, the power mismatch factor and the target complex amplitude after convergence, and simultaneously constructs three detection statistics, GLRT, Rao and Wald, according to the stationary point estimation, and compares with a pre-calibrated threshold to complete the judgment. The iterative process of the application has the guarantee of the monotone non-decreasing of the marginal log-likelihood, can effectively compensate for the main and auxiliary power mismatch, and can still maintain good detection performance under the condition of small training samples.

This application provides a disease intelligent diagnostic device based on brain structural connectivity identifiers and its application. The device includes (1) a data acquisition module; (2) a data processing module: reconstructing the spin distribution function of the data and distributing the spin distribution function in a standard space; (3) a data projection module: projecting the spin distribution functions of patients and healthy individuals into the standard space to obtain the Z-value of the maximum direction within the voxel; and (4) a Connectome Identifier extraction module: reducing the dimensionality of the Z-value to form a 1-dimensional feature value. The device of this application can be used for the diagnosis of various brain diseases and the determination of lesion location and severity.

The invention relates to a three-dimensional scene model cutting method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a three-dimensional scene model at a cutting view angle; obtaining a target direction vector of a drawing surface in the three-dimensional scene model, and calculating a distance between the target direction vector and a surface point in the drawing surface to obtain a cutting plane; acquiring contour data of the three-dimensional scene model, and projecting the contour data into the cutting plane to obtain an initial cutting boundary line; based on the distance relationship between the cutting boundary line and the model surface, performing boundary line fitting by using the initial cutting boundary line and the reference point in the cutting plane to obtain a target cutting boundary line; and cutting the three-dimensional scene model according to the target cutting boundary line to obtain a cutting scene model corresponding to the three-dimensional scene model. By adopting the method, the accuracy of model cutting can be improved.

The invention relates to a multi-fine-granularity double-arm robot teaching data acquisition and skill knowledge characterization method, which specifically comprises three part processes: firstly, based on an RGB depth camera integrated system, capturing information such as spatial position and angle of a big limb, and realizing data acquisition of the big limb and a big joint; secondly, millimeter-level tracking is conducted on 26-degree-of-freedom characteristics of the two hands through a VR immersive interactive data acquisition system, and data acquisition of small limbs and small joints is completed; and finally, projecting the acquired data into Riemann space for expression, and based on a DQN reinforcement learning method, taking a trajectory tracking error as a reward function, and guiding the robot to autonomously learn knowledge representation of operation skills. According to the requirements for multiple fine granularity levels in the data acquisition process of the double-arm robot, two data acquisition systems and a reinforcement learning strategy are integrated, and accurate acquisition and knowledge representation of robot operation data are achieved.

This invention discloses a method for detecting multi-channel constant-mode signals without prior electromagnetic interference, belonging to the field of radar signal processing technology. The method includes modeling multi-shot data received by a multi-channel antenna array to obtain high-dimensional multi-channel data; introducing weight vectors to project the high-dimensional multi-channel data onto a single channel for dimensionality reduction, resulting in single-channel weighted data; constructing a generalized likelihood ratio test detector without prior spatial electromagnetic interference information based on the probability density functions under two assumptions: the presence and absence of multi-channel constant-mode signals; and building an optimization model based on the generalized likelihood ratio test detector without prior spatial electromagnetic interference information. A hybrid optimization framework combining alternating optimization and alternating direction multiplier methods is used to solve the optimization model, obtaining the detection parameters and completing the multi-channel constant-mode detection.

The invention discloses a remote de-trapping method and system and a computer program product, and relates to the technical field of image processing, and the method comprises the steps: collecting data of a plurality of initial images in different directions based on a plurality of fisheye cameras distributed at intervals, and carrying out the time synchronization of the data of the plurality of initial images through time division multiplexing; eliminating lens distortion of the multiple pieces of initial image data, and projecting the multiple pieces of initial image data to a unified virtual aerial view coordinate system to obtain to-be-processed image data; fusing and splicing the to-be-processed image data by using a pre-constructed triangular mesh deformation model to obtain panoramic image data; and performing remote de-trapping based on the panoramic image data. Dependence on complex hardware is reduced through the fisheye camera array, and in addition, in combination with the pre-constructed triangular mesh deformation model, accurate panoramic image data is obtained in a low-hardware-cost mode for remote de-trapping.

The application discloses an industrial information physical system cross-layer anomaly detection method based on information physical feature fusion, acquires a data packet sequence of each communication event; for each data packet sequence, extracts coarse-grained features of a sequence level thereof, analyzes all data packets to obtain fine-grained features of a data packet level, and then fuses the coarse-grained features and the fine-grained features to obtain an overall feature vector of the sequence; taking the overall feature vector of the data packet sequence as observation data, projecting the observation data to a high-dimensional feature space by using a dictionary, learning the dictionary and a linear classifier; in online testing, obtaining an overall feature vector of a data packet sequence corresponding to a current communication event, obtaining sparse coding based on the dictionary, and outputting a current state label of the industrial system by using the linear classifier. The application improves real-time performance and accuracy of anomaly detection by effectively fusing information physical heterogeneous data.

The invention provides a user portrait construction method under an AI platform, and belongs to the technical field of user portray.The method comprises the steps that microscopic interaction data are obtained through a user behavior collector, and a behavior state vector set is constructed according to a time sequence; a probability model and an interest energy function are established based on a Maxwell-Boltzmann distribution principle to automatically identify and filter noise data, and sparse matrix decomposition is adopted to project filtered clean data to a low-dimensional semantic feature space. A semantic enhancement model is utilized to perform clustering analysis on high-quality behavior data to identify a real user interest mode, a user individual graph structure based on semantic association is constructed through a knowledge graph technology to accurately express user preferences, and MCP protocol packaging is adopted to realize fusion processing of clean microscopic behaviors and accurate macroscopic features. The technical problem that the accuracy of the formed user portrait is not enough due to the fact that the user microcosmic behavior data is easily interfered by noise is solved.

The invention provides a digital twinning-oriented campus energy consumption intelligent management method, which comprises the following steps of: projecting data into a three-dimensional campus model by adopting a space mapping algorithm according to an obtained energy consumption data set, and determining a distribution position of a high-energy-consumption area; if the determined high-energy-consumption area exceeds a preset threshold value, real-time data fluctuation is analyzed through an anomaly positioning algorithm, and a potential anomaly source is judged; obtaining the judged abnormal source information, and decomposing the energy consumption level from a macroscopic panorama to a microscopic building by adopting a multi-level analysis method to obtain a layered visual structure; for the obtained structure, rendering a dynamic thermodynamic diagram through a visual expression technology to obtain a visual spatial distribution view; extracting functional characteristic data from the obtained view, generating an energy-saving measure suggestion for the building position by adopting a decision tree algorithm, and determining an optimization path; and through the determined optimization path, the feedback cycle of the energy consumption data set is updated, and a continuous monitoring intelligent management framework is obtained.

The invention relates to an error projection system and method and a storage medium, and the system comprises a tracking unit, a scanning unit, an error projection unit, and a processing unit. The scanning unit is used for scanning a measured object to obtain scanning data of the measured object and transmitting the scanning data of the measured object to the processing unit; the processing unit is used for comparing the scanning data of the measured object with the reference data of the measured object so as to determine the error data of the measured object; the tracking unit is used for tracking the error projection unit to obtain first position information of the error projection unit; the processing unit is further used for generating error display data matched with the first position information according to the error data and transmitting the error display data to the error projection unit; the error projection unit is used for projecting the error display data to the surface of the measured object. According to the technical scheme, free error projection at any angle can be realized, the cost is reduced, the flexibility is improved, and meanwhile, the accuracy of error projection is ensured.