792 results about "Motion prediction" patented technology

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

The invention provides an anti-unmanned aerial vehicle intelligent identification and tracking system based on multi-source data fusion, and relates to the technical field of anti-unmanned aerial vehicle detection, and the system comprises a multi-source data preprocessing module which is used for outputting preprocessed multi-source data; the target detection module is used for carrying out unmanned aerial vehicle target detection on visual data in the preprocessed multi-source data and outputting a detection result containing a bounding box position, confidence and morphological characteristics; the target tracking module is used for performing unmanned aerial vehicle target tracking based on the target detection result and outputting a tracking result; and the fusion decision module is used for confirming the target identity based on the tracking result and the preprocessed multi-source data and outputting a final recognition result. The technical problems of low detection precision of small targets, difficulty in distinguishing similar targets, inaccurate 3D motion prediction, difficulty in re-identification after long-time shielding and the like in the prior art can be solved, and accurate identification, stable tracking and intelligent decision making of the unmanned aerial vehicle target are realized.

The invention relates to the technical field of image recognition, and provides an anti-falling recognition early warning method based on image recognition and related equipment thereof. The method comprises the following steps: carrying out multi-modal preprocessing on a real-time image acquisition data set to obtain an RGB-D data stream, carrying out target detection and three-dimensional attitude modeling on the RGB-D data stream to obtain a target personnel label set and a personnel attitude parameter set, and carrying out trajectory prediction on the personnel attitude parameter set through a physical kinematics model to obtain predicted motion trajectory data. Acquiring an inertial monitoring data set in real time according to the target person label set, performing multi-modal fusion in combination with the predicted motion trajectory data to obtain a confidence evaluation value, and performing risk quantification on the confidence evaluation value and the predicted motion trajectory data to obtain a graded early warning instruction. And performing protocol coding and signal conversion on the graded early warning instruction to obtain a control signal. According to the invention, through image identification, motion prediction, multi-modal data fusion and fine processing, the accuracy of anti-falling monitoring in a complex scene is improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an operation safety distance management method based on laser radar point cloud data, and the method comprises the steps: generating a dangerous point cloud set based on an initial three-dimensional point cloud model of a complete field environment; performing coordinate transformation on the point cloud data currently collected by the three-dimensional laser radar equipment based on the rigid body transformation matrix to obtain current point cloud data; registering the current point cloud data with the initial three-dimensional point cloud model and identifying all independent dynamic targets; determining a danger approaching trend of each independent dynamic target based on the motion prediction trajectory of each independent dynamic target; determining a dynamic safety distance threshold value of each dangerous dynamic target based on the danger approaching trend and the response delay of each independent dynamic target and the additional safety distance of the corresponding dangerous source area; triggering a corresponding alarm mechanism based on the dynamic safety distance threshold and the distance between the point cloud set of each independent dynamic target and the dangerous point cloud set; and the overall safety of a working site is improved.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention discloses a robot obstacle sensing and autonomous obstacle avoidance method based on a convex hull model. The method comprises the following steps: 1, acquiring robot positioning and agricultural scene information by using a laser radar and differential RTK; 2, comprehensively analyzing a land slope condition through a sector-based ground segmentation algorithm, and separating ground point clouds from obstacle point clouds; 3, performing clustering analysis on the obstacle point cloud by using a Euclidean clustering method and a Graham scanning method, and establishing an obstacle convex hull model for representation; using a multi-feature nearest neighbor algorithm and Kalman prediction to determine obstacle inter-frame relation and motion prediction information; 4, establishing a finite-state machine decision-making system, and providing reference path guidance in combination with environment perception prediction features; and 5, based on an MPC-based space-time joint obstacle avoidance path planning algorithm, establishing an obstacle avoidance constraint based on scale transformation, and performing optimization solution to obtain an obstacle avoidance path and optimal control input. According to the invention, the accuracy of obstacle detection and the robustness of an obstacle avoidance system are effectively improved, and the high efficiency and safety of the robot during operation are ensured.

The invention discloses an eVTOL multi-camera cooperative video compression coding method based on multi-source perception and intelligent partitioning. The method comprises the following steps: constructing a scene three-dimensional perception model by fusing multi-source data of visible light, infrared and depth sensors; the method comprises the following steps: realizing dynamic video partitioning based on motion vectors and semantic analysis, and dividing a picture into a core region, a secondary region and a background region; establishing a parallax compensation motion prediction model by adopting a cross-camera reference frame sharing mechanism; and high-fidelity compression of the key area is realized through layered entropy coding and a dynamic quantization parameter distribution strategy. And the decoding end reversely executes multi-source data fusion and partition reconstruction according to the coding metadata. The method is suitable for eVTOL multi-camera video real-time transmission scenes such as polling, surveying and mapping.

The invention discloses an unmanned aerial vehicle intrusion real-time alarm and countering guidance system based on RID information. The system comprises a multi-source information acquisition and fusion module, an intrusion detection and threat evaluation module, a real-time alarm and countering strategy generation module, a feedback evaluation and strategy iteration module, a signal loss prediction and compensation module, a comprehensive processing terminal module and the like. The system establishes a three-dimensional geofence model by analyzing remote identification information of the unmanned aerial vehicle and fusing radar, photoelectric, electromagnetic and other multi-source data, and performs dynamic judgment and multi-factor threat grading on a target intrusion state. Multi-mode alarm is automatically triggered according to the threat level, and a hierarchical countering scheme from broadcast alarm and electromagnetic interference to physical capture is generated; and when the RID signal is lost, the system realizes continuous tracking and state recovery of the target by using a motion prediction and probability association algorithm.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention relates to the technical field of numerical control lathe control, discloses a high-precision numerical control lathe Y-axis dynamic adjustment drilling reamer holder control method, and aims to improve the precision and efficiency of Y-axis motion control. According to the method, Y-axis motion data, machining parameters, tool abrasion and workpiece material information in the historical machining process are collected, and a motion prediction model is constructed. And acquiring current processing state data in real time, and inputting the model to predict a Y-axis movement track. Meanwhile, Y-axis real-time motion data are obtained and compared with a predicted track, and deviation points are marked. And then, a Y-axis dynamic adjustment decision model is constructed, and the adjustment process is modeled as a Markov decision process. And performing real-time control based on the trained decision model, selecting an optimal adjustment action according to the current processing state and strategy, and automatically executing the optimal adjustment action. According to the method, multiple factors are comprehensively considered, the prediction accuracy and the adjustment flexibility are improved, and the machining quality and the production efficiency are guaranteed.

The present disclosure provides an ice floe motion prediction method, a device, a storage medium, and an electronic device. In the method, the electronic device is configured to acquire a historical motion information sequence of ice floe in a to-be-navigated region, wherein the historical motion information sequence includes multiple pieces of historical motion information of the ice floe at different time points; invoke a motion prediction model to analyze a spatiotemporal relationship of the multiple pieces of historical motion information; and obtain motion prediction information of the ice floe in the to-be-navigated region.

The invention discloses a target detection method and device based on an aerial view, equipment, a medium and a product. The method comprises the following steps: extracting multi-scale features for an input image of a multi-view-angle surround-view camera; converting the point cloud data of the laser radar into a sparse voxel structure and generating BEV features for voxel features; fusing the multi-scale features and the BEV features to obtain fused BEV features; road topology and semantic information are extracted according to the fused BEV features, and a map is generated; and determining a three-dimensional bounding box and a motion state of the traffic participant according to the fused BEV features and a prediction result of the position of the traffic participant in the map. According to the technical scheme, multi-scale geometric features, BEV features and semantic information are fused, time sequence features of dynamic target detection and static map segmentation are synergetic, the reliability of target detection based on aerial view is improved, and the method is suitable for multi-task BEV perception of three-dimensional object detection, high-precision map segmentation and motion prediction.

The invention provides a class-independent motion prediction network system and method combining space-time and frequency domain features, equipment and a medium, and belongs to the field of automatic driving. The problem that the space-time dependency relationship and the dynamic characteristics cannot be fully captured in a complex dynamic scene in the moving target prediction is solved; comprising construction of a feature coding module, a hierarchical space-time integration module, a frequency-space fusion module and a feature decoding module, and the HTSIM fuses space-time information step by step through a multi-level space-time feature interactive learning mechanism, and can capture a long-term space-time dependency relationship and local dynamic change, so that a complex motion mode is modeled more accurately; fSFM combines the characteristics of a frequency domain and a space domain, multiband decomposition is carried out on input characteristics through two-dimensional discrete wavelet transform, and the capturing capability of dynamic characteristics is enhanced through deep fusion of the characteristics of the frequency domain and the space domain; the method is suitable for real-time track prediction tasks in intelligent traffic systems such as automatic driving and the like.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability are improved.

The invention discloses an automatic obstacle avoidance method and system for an unmanned ship based on Beidou artificial intelligence, and relates to the field of computer vision, and the method comprises the steps: collecting a visible light image and a near-infrared image of a water surface environment, and carrying out the reflection suppression processing; performing feature fusion on the image to generate an environment perception graph; obstacle features are extracted through a cascade cavity convolution structure, and an obstacle pixel-level semantic segmentation result is obtained; constructing an obstacle motion prediction model, and obtaining a predicted obstacle motion track; acquiring motion track data of the unmanned ship based on Beidou positioning and electronic chart data; establishing a space-time collision detection model, calculating collision parameters, and generating a collision data set; an obstacle avoidance path is generated through a dynamic situation field method, and a rudder angle instruction and a propeller rotating speed instruction are obtained. The unmanned ship has the advantages that accurate sensing, motion prediction and intelligent obstacle avoidance of the unmanned ship on water surface obstacles are realized through Beidou positioning and technology, and the sailing safety and intelligent level are improved.

The invention belongs to the technical field of ship control motion prediction, and discloses a ship control motion real-time prediction method and system based on a fusion technology. According to the method, standardization processing is carried out on ship motion data and marine environment data, the marine environment data and the ship motion data are enabled to be input into a constructed ship motion prediction model based on a Transform architecture in the same numerical range, a sliding time window technology is used, online gradient updating is carried out, and the method adapts to new data features; a physical loss item is introduced, and an improved ship motion prediction model based on a Transform architecture is obtained; and carrying out optimization verification to finish nonlinear ship control motion prediction. According to the method, the defects of a traditional model in the aspect of nonlinear processing can be overcome, the reliability of a data driving model can be improved through physical constraints, and powerful technical support is provided for operation control and safe navigation of intelligent ships in the future.

The invention discloses a driving track prediction method and device based on sparse BEV features, equipment and a medium, and relates to the technical field of vehicle simulation, and the method comprises the steps: obtaining driving scene information, carrying out the mapping according to the driving scene information, obtaining sparse vehicle features, carrying out the interaction prediction according to the driving scene information, and obtaining motion prediction features, according to the action prediction features and the sparse automobile features, enhanced automobile features are obtained, and according to the enhanced automobile features, a self-automobile prediction trajectory is obtained. Through obtaining the scene information, generating the sparse features through mapping and obtaining the predicted action features, the two features are fused to obtain the enhanced automobile features, the self-vehicle trajectory prediction is realized according to the enhanced automobile features, the driving trajectory prediction efficiency and accuracy are improved, and the collision risk is reduced.

The invention provides a real-time energy-saving control method and device for a central air conditioner and a readable storage medium. The method comprises the steps that real-time position information of personnel in an area where the central air conditioner acts is obtained; according to the real-time position information of the personnel, the number of the personnel in each sub-area in the area where the central air conditioner acts is determined, and each sub-area correspondingly has at least one central air conditioner air outlet; central air conditioner control parameters of the corresponding sub-areas are determined according to the number of people in each sub-area; and the movement information of the personnel is obtained in real time, and the central air conditioner control parameters of all the sub-areas are adjusted in real time based on the movement information of the personnel. An intelligent air conditioner control strategy is adopted, the air speed of the air conditioner is intelligently adjusted according to the position of the person and motion prediction, the dynamic control method can optimize energy consumption, and meanwhile the comfort degree of the environment is kept.

The invention discloses a real-time rendering optimization method based on dynamic rendering and multi-hardware acceleration cooperation, and relates to the technical field of AR and VR real-time rendering, and the method comprises the following specific steps: dividing a user field angle into three-level resolution regions through an eye movement tracking module, and dynamically adjusting the resolution and anti-aliasing strategy of each region; stage processing is realized through an asynchronous time warping hardware acceleration module; the asynchronous time warping hardware acceleration module is combined with the optical flow compensation module to cooperatively optimize anti-aliasing compensation, and dynamic key points are extracted through a sparse optical flow algorithm; a multi-hardware acceleration collaborative architecture is adopted, and GPU rendering pipelines are divided according to a multi-viewport parallel rendering module; and the motion prediction module synchronizes IMU data in real time through a special AXI bus, so that optimization model updating is realized, and a final rendering frame is obtained. Through cooperation of dynamic rendering and multi-hardware acceleration, the bottleneck of a traditional scheme in adaptability, energy efficiency and real-time performance is solved, and the rendering quality and cruising ability of AR / VR equipment are remarkably improved.

The invention discloses a physically-guided critical region fluid nonlinear wave motion prediction method, system and device and a medium. The method comprises the steps that a critical region fluid computational domain and a nonlinear time-varying equation are established; constructing a sample data set; constructing a physical information neural network model; constructing a loss function including nonlinear time-varying equation residual error loss, initial point error loss and boundary point error loss; training the physical information neural network model to obtain a critical region fluid free water surface displacement wave envelope prediction model; weighting internal matching point samples in each iteration round by adopting a gradient-free nonlinear attention mask method based on residual accumulation in the training process; and inputting the time-space coordinates of the to-be-predicted point of the critical region fluid into the prediction model to obtain the free water surface displacement wave envelope of the to-be-predicted point fluid. According to the method, error accumulation is effectively reduced, the model convergence speed is increased, and the prediction precision of the nonlinear wave motion of the fluid in the critical region is improved.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.

The invention relates to the technical field of panoramic video processing, in particular to a panoramic video stability enhancement method based on space-time consistency, which comprises the following steps: acquiring a panoramic video frame sequence; obtaining spherical projection coordinates of each frame in the panoramic video frame sequence; based on the spherical projection coordinates, a deep learning model is constructed, and the deep learning model comprises a feature extraction encoder and a motion prediction compensation decoder; performing feature extraction on the panoramic video frame sequence by using a feature extraction encoder; performing motion prediction and compensation by using a motion prediction compensation decoder based on a feature extraction result; constructing a space-time filter according to the motion prediction and compensation result, and smoothing the motion vector; based on the smoothed motion vector, performing multi-frame motion analysis by using a long time sequence network; according to a multi-frame motion analysis result, designing panoramic video optical flow motion estimation; performing stable enhancement processing on the panoramic video frame sequence based on an optical flow motion estimation result; and outputting the stably enhanced panoramic video.

The invention provides a laser radar odometer and static map construction method and system, and relates to the technical field of positioning and navigation.The method comprises the steps that semantic reasoning is used for obtaining point-by-point semantic tags for subsequent true dynamic point detection, and semantic constraints are provided for ICP registration; distortion removal is carried out based on motion prediction of a constant-speed motion model; performing dynamic point detection according to the shielding relation of the potential dynamic points; according to the method, multi-target tracking is carried out on a potential dynamic object to obtain state estimation, cross validation is carried out on the state estimation and a dynamic point detection result, instance-level dynamic objects based on priori pose estimation are accurately removed, unstable dynamic points are filtered out in the pre-registration stage, and the positioning accuracy and robustness of an odometer are improved. Semantic weights are introduced into ICP data association, pose estimation is obtained through robust optimization, and a static map is constructed. And high-precision and robust laser radar odometer and static map building is realized.

A computer-implemented method for generating a knowledge graph for traffic motion prediction. The method includes: receiving environment sensor data of at least one environment sensor of an ego-vehicle; receiving map data from an electronic road map; extracting the information regarding the at least one traffic participant from the environment sensor data and extracting the information regarding the motion track the traffic participant is positioned on from the map data; and generating a knowledge graph of the road network in the environment of the ego-vehicle including nodes and edges based on the map data and / or the environment sensor data. The knowledge graph includes at least one node representing the traffic participant and at least one node representing the lane the traffic participant is positioned on.

The invention discloses a collision risk assessment method and device based on lane and surrounding vehicle motion prediction, and belongs to the field of automatic driving. The method comprises the following steps: calculating position information of a vehicle and surrounding vehicles in a road coordinate system; based on a quintic polynomial, generating all local candidate paths of the vehicle; based on the lane state transition matrix and a lane transition model, calculating dynamic real-time probability distribution of each surrounding vehicle driving to each lane; based on the position information of the surrounding vehicles and the dynamic real-time probability distribution, using a cubic polynomial to generate future driving tracks of the surrounding vehicles on different lanes; and based on the dynamic real-time probability distribution and the time required for collision between the local candidate path of the own vehicle and the future driving track of each surrounding vehicle on different lanes, calculating a total collision risk value when the own vehicle runs along each local candidate path. According to the method, probability motion prediction and a lane structure are combined, the motion uncertainty in a real driving environment is effectively reflected, and the risk assessment accuracy is improved.

The invention discloses a method and system for guiding a three-dimensional point cloud robot based on a natural language, and the method comprises the steps: setting an action position t based on a robot of a Transform architecture, converting the visual image data of the action position t into a three-dimensional point cloud, carrying out the standardized input, carrying out the down-sampling, and completing the data preprocessing; on the basis of data preprocessing, encoding the point cloud of the generated preprocessed data, extracting spatial features of the point cloud, and generating visual information; vector embedding is carried out on the natural language instruction, the natural language instruction is expressed as a vector which can be understood and processed by the model, and text information is generated; based on the visual information and the text information, fusing the generated visual information and context information through an attention mechanism; based on contextual information fusion, the ability of the robot to understand and execute complex instructions and the accuracy of future motion prediction of the robot are improved by predicting a heat map and offset and predicting the three-dimensional position of the motion position # imgabs0 # step.

The invention discloses a mobile robot control method and system, and relates to the technical field of mobile robot intelligent motion control, and the method comprises the steps: collecting environment feature data and motion state data; performing preprocessing and feature fusion to generate standardized state data; constructing a reinforcement learning training environment in an offline simulation environment, and obtaining a convergent reinforcement learning strategy model; inputting the current robot standardized state data into the reinforcement learning strategy model, and obtaining and outputting a preliminary motion control instruction and a reference trajectory; a prediction control model MPC is established, current robot standardized state data are input, short-time motion prediction and optimization are carried out, and a final control instruction meeting constraint conditions is obtained through solving; instructions are distributed to the four Mecanum wheel drivers, and the rotating speed and the direction of each wheel are adjusted. According to the invention, high-precision trajectory tracking, adaptive obstacle avoidance and energy efficiency optimization of the mobile robot in a complex dynamic environment are realized, and the system stability and the intelligent level are improved.

The invention discloses a real-time respiratory movement prediction and compensation method for thoracic and abdominal radiotherapy. A respiratory signal acquisition module, a respiratory movement prediction module, a body surface mark point position monitoring module, a body surface mark point displacement prediction module, a body surface mark point coordinate conversion module, a tumor target center point prediction module and a tumor target visualization module are included. The invention discloses a respiratory movement prediction system based on a causal convolutional network, and the system achieves the precise prediction of the position of a tumor target region in the future by collecting a respiratory waveform signal and a body surface mark point movement track of a patient in real time, extracting a periodic movement mode, and predicting the three-dimensional displacement of the tumor target region in 0.5-2 seconds in the future.

The invention discloses a visual labeling mechanism device and an automatic labeling method thereof, and the method comprises the steps: obtaining a workpiece surface image in motion in real time through a visual assembly, extracting a pose parameter of a workpiece in combination with an image processing algorithm, generating a pose data set containing a coordinate parameter and a deflection angle, and inputting a motion prediction model. Calculating a future movement track of the workpiece in combination with the speed parameter of the conveying line, and generating an adjustment instruction set of the labeling head; the movement track of the multi-axis mechanical arm is controlled based on the adjusting instruction set, the attaching pressure of the label contact face is adjusted through the pressure feedback module, and labeling operation of the curved surface of the workpiece is completed; a line scanning camera module is used for scanning the geometric position of the labeled label, a real-time detection result is generated by comparing the geometric position with a preset quality standard, a compensation signal is triggered if position deviation exists, and compensation operation is conducted through a compensation pressure head so as to ensure that the labeling quality meets the preset standard; and through cooperative control of visual identification, track prediction and compensation, the stability and the fitting precision of dynamic labeling are effectively improved.