1024 results about "Visual feedback" patented technology

The invention discloses a three-degree-of-freedom parallel robot trajectory tracking method and system, and relates to the technical field of industry-university-research, and the method comprises the steps: obtaining motion data related to the execution trajectory of a three-degree-of-freedom parallel robot, and carrying out the data preprocessing; based on historical operation data, an intelligent prediction model is constructed to predict trajectory errors and is used for optimizing feedforward compensation of trajectory tracking control parameters; acquiring actual execution track information of the robot based on a visual sensor, and performing error calculation on the actual execution track information and an expected track to perform real-time track correction; dynamically adjusting control parameters in combination with error prediction data and visual feedback data; in the operation process of the robot, the control strategy is continuously optimized, and the intelligent prediction model is updated by using real-time data. Through the intelligent prediction model, visual feedback and adaptive control optimization, high-precision trajectory tracking, rapid error correction, energy consumption reduction and environmental adaptability enhancement are realized.

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention relates to the technical field of welding automation and robot visual perception, and discloses a robot intelligent welding method and system.The method comprises the steps that a three-dimensional point cloud image is constructed through a laser triangulation method; filtering and enhancing the point cloud image, and extracting a weld point cloud set by adopting a U-Net semantic segmentation model; carrying out space path identification and attitude regression based on the set to obtain a track element of a position-attitude pair; fitting a path by adopting a cubic B-spline curve and interpolating to generate a continuous welding track; and in the welding process, visual feedback is combined, and proportional differential control logic is introduced for closed-loop track error compensation. Compared with the technical problem that in the prior art, weld joint path recognition and accurate tracking cannot be stably achieved under complex structures such as curved surfaces and variable cross sections, a perception control closed loop based on visual servo is constructed, high-precision automatic welding of the complex weld joint structures is achieved, and the track fitting precision is improved.

The invention discloses a code spraying identification optimal path planning method based on an intelligent optimization algorithm, and relates to the technical field of code spraying control, and the method comprises the steps: dispersing a code spraying region into a three-dimensional space, and constructing a dynamic environment model of marking parameter constraint; based on the code spraying task priority queue, a hybrid optimization algorithm is adopted to carry out collaborative optimization of marking parameters and path nodes, and an optimal path candidate set is generated; an obstacle avoidance model is constructed based on the optimal path candidate set, a collision-free path is generated, the pose of a nozzle is adjusted in combination with visual feedback, and an anti-interference code spraying action sequence is output; monitoring the operation feedback of the code spraying equipment, and carrying out the iterative updating of the marking parameter library and the algorithm rule. According to the method, the core advantages of multiple intelligent optimization algorithms are fused, a mixed collaborative optimization architecture is constructed, the limitation of a single algorithm in a complex scene is effectively broken through, and a traditional path planning algorithm is easily troubled by a local optimal solution and is difficult to meet multi-dimensional constraint conditions at the same time.

A system configured to generate dynamic visual feedback by synchronizing smart lights with a rhythm and energy levels of music being output. A device may translate audio samples to luma (brightness), chroma (color), and location data using a low-latency algorithm. For example, the device may divide the full band audio data into separate frequency bands based on the number of output devices and control a brightness for an output device by tracking energy within a corresponding frequency band. The color and location may be controlled based on a number of beats per minute, direction, and / or the like. In addition to controlling external lighting via a smart home hub, the device can control LED light(s) on the device via an LED controller. To further reduce latency, the device can directly communicate between an audio processor, the smart home hub, and / or the LED controller using low level hardware abstraction layers (HALs).

The invention provides a picking mechanical arm operation method and system based on visual feedback. The picking mechanical arm operation method comprises the steps that firstly, a multi-dimensional visual perception set including the shape contour, surface physical characteristic visual representation, connection part space distribution, growth environment dynamic interference and the like of a picked object is obtained; a mechanical arm collaborative decision-making model is constructed based on the multi-dimensional visual perception set, stress distribution characteristics of connection parts are analyzed, and optimal contact area parameters and mechanical arm posture collaborative parameters are determined; according to the parameters, generating a sub-time sequence action instruction set containing action information in the contact stage, the clamping stage and the separation stage; when the mechanical arm is controlled to execute the instruction set, a dynamic visual feedback set of the picking scene is obtained in real time; and finally, input parameters of the collaborative decision model are updated based on feedback, and instruction set parameters are adjusted, so that the mechanical arm completes picking operation on the premise of keeping the integrity of the picked object, and the intelligent level and the picking operation quality of the picking mechanical arm are improved.

The invention relates to the technical field of ward air purification systems, and discloses a ward air purification system which comprises an environment sensing module, a purification path planning module, a filter unit configuration module, a purification execution module, a dynamic adjustment module, a state monitoring module and a feedback control module. The environment sensing module obtains ward space information to construct an air flow topology; the purification path planning module calls air quality data to label the topology; the filter unit configuration module sets purification nodes and adjustment intervals; the purification execution module implements basic purification; the state monitoring module analyzes data to determine operation state characteristics, and the feedback control module generates monitoring records and performs visual feedback. The system can accurately perceive the air quality of the ward, scientifically plan the purification path, dynamically adjust the purification strategy, effectively improve the air quality of the ward and guarantee the health of patients and medical staff, and has high practical value.

The invention discloses an edge spraying compensation control method based on intelligent visual feedback. The method comprises the following steps: performing space scanning on a to-be-sprayed workpiece to obtain spraying image data, and establishing a space corresponding relation between a spray gun motion coordinate and a workpiece surface coordinate; registering the real-time spraying image by using the corresponding relation, identifying a geometric boundary line, brightness gradient change and a coating adhesion area of the edge of the workpiece, and generating edge feature data; according to the difference between the edge feature data and the target spraying image, the spraying coverage deviation and the boundary overlapping error are calculated, the coating thickness change trend, the optical density change rate and the boundary direction offset are extracted, and the spraying gun posture adjustment amount, the spraying distance correction amount, the spraying pressure correction amount and the path speed correction amount are generated; the angle, the spraying distance, the pressure and the movement speed of the spray gun are dynamically adjusted according to the parameters; according to the method, real-time visual feedback and self-adaptive compensation control in the spraying process are achieved, and the uniformity and consistency of the coating on the edge of the workpiece are effectively improved.

The invention discloses a seeding unit integrating visual feedback and calibration functions and a control method, and relates to the technical field of agricultural machinery, the seeding unit comprises a control system, a row spacing adjusting mechanism and a seeding depth control mechanism, the seeding depth control mechanism is connected with the row spacing adjusting mechanism, and the row spacing adjusting mechanism is connected with the control system; the control system is composed of an industrial camera and an image recognition module, the image recognition module comprises a broken ridge prediction control mechanism and a ridge center line fitting and sowing offset correction mechanism, and the ridge center line fitting mechanism recognizes ridge line boundaries and fits center line tracks. The broken ridge prediction control mechanism detects a ridge line interruption area and delays ridges according to the advancing speed and response of the seeding monomers. According to the seeding monomer integrated with the visual feedback and calibration functions and the control method, the visual identification technology is integrated to monitor the earth surface condition in real time, the response is quick, the control is accurate, and the seeding monomer is suitable for high-speed seeding operation under different terrain and soil conditions.

The invention discloses an AI data scheduling distribution system and method based on cloud computing, and relates to the technical field of intelligent cloud computing, and the system comprises an AI data collection module, a feature extraction module, a decision parameter generation module, a scheduling distribution module and a visual feedback module. The AI data acquisition module acquires equipment video data and temperature data, the feature extraction module extracts video temperature features, the decision parameter generation module outputs equipment vibration intensity and temperature rise rate, and the scheduling distribution module calculates equipment shutdown probability and node load score and triggers a shutdown instruction and a task migration decision. And the visual feedback module maps the equipment health state and node load distribution, and optimizes the feature weight. Through multi-modal data fusion and a closed-loop feedback mechanism, accurate early warning of equipment abnormity and dynamic optimal allocation of resources are realized, visual decision assistance and adaptive weight adjustment are combined, equipment safety and cluster load balancing efficiency in a cloud computing environment are improved, and manual intervention requirements and operation and maintenance cost are reduced.

The invention discloses a robot-oriented article recognition and grabbing system and method, and belongs to the technical field of artificial intelligence, and the robot-oriented article recognition and grabbing system comprises a multi-source data input module, an image recognition module, a semantic generation module, a task guiding evaluation module and a visual feedback module, the multi-source data input module comprises an RGB-D camera for obtaining target 3D point cloud data and a natural language instruction interface for receiving a natural language instruction FI; the image recognition module comprises a double-flow convolutional neural network and an improved Transform network; the semantic generation module comprises a large language model LLM; the task-oriented evaluation module comprises a task-oriented capture evaluator TGE and a point cloud encoder; and the visual feedback module is used for judging whether grabbing succeeds or not after the grabbing task is finished. The target object can be accurately recognized, and the grabbing performance of the mechanical arm on the target object is optimized.

The invention relates to the technical field of dispensing machines, and discloses an intelligent dispensing machine based on visual feedback and a dispensing path optimization system.The intelligent dispensing machine comprises an equipment rack, a main control computer is installed in the equipment rack, and the dispensing path optimization system is erected on the equipment rack and electrically connected with the main control computer; the inner bottom wall of the equipment rack is fixedly connected with a base, the top of the base is provided with a three-axis movement mechanism, the three-axis movement mechanism comprises an X-axis linear guide rail, a Y-axis linear guide rail and a Z-axis linear guide rail which are all driven by a stepping motor, and the outer side of the three-axis movement mechanism is provided with a servo driver. Through cooperative work of a laser positioning unit and an image recognition positioning unit in the high-precision positioning module, the positioning precision is controlled within + / -0.01 mm, the dispensing position is accurately determined, the problems of dispensing deviation, point leakage and the like caused by positioning deviation are avoided, the dispensing reliability is greatly improved, and the product quality is remarkably improved.

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

The invention relates to the technical field of navigation and positioning, and discloses an arthroscopic surgery navigation and positioning system based on artificial intelligence, which comprises a preoperative image processing and lesion analysis module for analyzing preoperative images, a multi-modal data fusion module for integrating image data from different sources in the surgery, and a navigation and positioning module for performing navigation and positioning on the integrated image data. The intra-operative real-time joint tracking and posture estimation module is used for tracking the joint position and soft tissue deformation in real time, the AI dynamic operation path planning module is used for intelligently optimizing the operation path, the robot auxiliary operation and automatic adjustment module is used for automatically adjusting the position and angle of an operation instrument, and the intra-operative AI voice and visual feedback module is used for providing real-time feedback. The operation data storage and intelligent learning module is used for storing and analyzing operation data. By means of the multi-modal data fusion technology, arthroscopic images, ultrasonic images and infrared images are combined, accurate visualization of an operation area is achieved, and the effect of improving operation accuracy and safety is achieved.

The invention relates to the technical field of robot control, in particular to a robot object grabbing adaptive learning system based on visual and tactile feedback. The robot object grabbing adaptive learning system based on visual and tactile feedback comprises a data acquisition module used for acquiring adaptive control data of a robot; the joint adjusting module is used for obtaining the joint compensation amount of the robot according to the adaptive control data of the robot and adjusting the grabbing motion of the robot to obtain the final joint adjustment degree; the visual feedback module is used for obtaining a rough grabbing force estimation value of the visual information by using a visual feedback formula according to the visual data of the robot; and the tactile feedback module is used for obtaining correction force of tactile feedback by using a tactile feedback formula according to the tactile data of the robot. Through multi-modal information fusion and modular design, high-precision and self-adaptive grabbing of the thermochromic object by the robot is realized.

The invention provides a humanoid robot hand-eye coordination intelligent calibration control system and a humanoid robot hand-eye coordination intelligent calibration control method, and the humanoid robot hand-eye coordination intelligent calibration control system integrates high-precision contour extraction, visual feedback closed-loop control, multi-sensor fusion and dynamic path planning. The robot hand-eye calibration method has the beneficial effects that the calibration efficiency and precision can be remarkably improved, the problem of calibration parameter drift caused by system time-varying characteristics and environmental interference is effectively solved through continuous tracking of a target contour and real-time online compensation of pose deviation, and the calibration accuracy is improved. Therefore, the reliability and the absolute positioning precision of the robot in long-term complex operation tasks are guaranteed, and the self-adaptive capability of a robot system is enhanced.

The invention discloses a CAD drawing instant positioning method and device based on spatial index analysis, and relates to the related technical field of CAD drawing positioning, and the method comprises the steps: carrying out the multi-stage splitting identification of a multi-version target CAD drawing; performing associated indexing on the drawing version information, the drawing layer information and the drawing component set; semantic analysis and spatial association are carried out on the drawing text annotations; embedding and associating the drawing component space semantic tag set into a component-layer-drawing three-level association graph; performing matching indexing on the drawing semantic query request based on the drawing spatial index mapping table; and inquiring a topology path based on the component to carry out instant positioning and visual feedback. The technical problems that in the prior art, complex spatial relations and semantic information in the drawings are difficult to effectively process, and multi-version CAD drawings are low in retrieval efficiency, insufficient in instantaneity and inaccurate in positioning are solved, and the technical effect of improving the retrieval efficiency, query instantaneity and positioning accuracy of the multi-version drawings is achieved.

The invention, which relates to the technical field of agricultural intelligent equipment, discloses an agricultural plant protection unmanned aerial vehicle sowing control system comprising a sowing flow field dynamic modeling and prediction module, an attitude regulation and control module, a speed matching module, a path compensation module, a visual monitoring module and a control strategy module. A flow field dynamic mapping model based on the flight height, the downward pressing airflow speed and the particle size and density of the scattered particles is constructed, particle drop point deviation is predicted in combination with flight real-time data, and a pneumatic coupling risk area is identified. Through flow field modeling prediction and attitude adjustment, trajectory compensation, speed control and visual feedback, adaptive regulation and control in the sowing process are realized, air scattering and nonlinear collapse are effectively prevented, seedling burning, germination failure and pesticide residue risks caused by over-dense sowing are avoided, the operation uniformity and the agricultural effect are improved, and the method has practical value and popularization prospects.

The invention provides a multi-view video reward mechanism learning framework and method, and the framework comprises a multi-view video evaluation subsystem which employs a multi-view video learning framework MVR to evaluate robot behaviors according to multi-view video learning; the visual feedback reward feedback strategy subsystem is used for generating visual feedback according to task text description through a visual language large model; accurate reward feedback is obtained according to the latest state correlation evaluation, so that the strategy is adjusted more effectively; the visual feedback task reward balancing subsystem analyzes the importance degree of task reward feedback according to the degree that robot behaviors approach to an expected target through a task reward model, dynamically adjusts the relative size between a task reward and a visual language model reward according to state correlation, and carries out visual feedback and task reward balancing; and the multi-view video reward combination subsystem is used for providing more accurate visual feedback and learning effect in a complex robot motion task in combination with multi-view videos and task rewards.

The invention discloses a forehead electroencephalogram rhythm-driven sleep state staging method based on multi-dimensional features, and belongs to the field of electroencephalogram signal processing in the field of digital signal processing, and the method comprises the following steps: step 1, preparing a sleep physiological signal data set; 2, preprocessing the forehead electroencephalogram signals; 3, rhythm wave extraction is conducted on the forehead electroencephalogram signals; 4, time domain feature values, feature values based on power spectral density and feature values based on multi-scale entropy of the forehead electroencephalogram signals and the electrooculogram signals are calculated respectively; step 5, sending the characteristic values calculated in the step 4 into an SVM (Support Vector Machine) for automatic staging, and finally optimizing a characteristic value group to obtain an automatically predicted sleep staging result; the method has high accuracy, can provide visual feedback for the user, and helps the user to know the sleep quality and state of the user so as to improve the sleep habit of the user.

The invention discloses a digital twin intelligent rendering method and system based on GPU load optimization and camera visual angle self-adaption, and the method comprises the steps: obtaining the basic information of a GPU, and evaluating the actual rendering capability of the GPU through a performance test; pre-defining a plurality of surface number grades according to the complexity of the digital twin model; performing LOD calculation based on the visual angle of the camera; the system performance is monitored in real time; on the basis of LOD calculation of the view angle of the camera, the GPU load condition is combined, a self-adaptive adjustment or predictive adjustment strategy is adopted, and the rendering complexity of a non-key area is optimized; user manual adjustment options are provided, allowing the user to adjust the rendering quality according to personal preferences, and providing visual feedback at the time of adjustment. According to the method, the GPU load is reduced while high-precision rendering of the key area is guaranteed, LOD switching is optimized in combination with historical inspection data, and the rendering efficiency and the system stability are improved.

The invention relates to a region-limited virtual touch interaction method and related equipment, and the method comprises the following steps: carrying out the boundary scanning of a region-limited projection plane, and obtaining a projection region contour coordinate set; performing region division on a projection plane based on the projection region contour coordinate set to obtain interaction region grid data; performing three-dimensional feature point collection on the hand of the user through a depth camera to obtain a hand key point space coordinate sequence; when the hand key point space coordinate sequence is within a depth interaction threshold range of interaction area grid data, collision detection is carried out on the hand key point space coordinate sequence based on the interaction area grid data, and a touch event judgment result is obtained; and performing visual feedback rendering on the interaction area grid data based on the touch event judgment result to obtain interaction state display data, thereby solving the technical problem of low interaction precision caused by difficulty in accurately positioning a gesture of a user in an open and irregular space in an existing solution.

The invention discloses a rice field aquaculture intelligent management system based on the Internet of Things, and relates to the technical field of intelligent management. During operation of the system, various environmental parameters in a rice field are monitored and collected in real time by deploying an Internet of Things sensor network, and water quality changes and other factors of a farmland environment are monitored; water quality adjusting equipment is controlled to adjust a rice field water body, automatic adjustment is achieved through a feedback control mechanism, water quality changes are responded in real time, a crop growth monitoring sensor and a camera are installed, the growth state of rice is monitored in real time, visual feedback of the growth state is provided through the image recognition technology and the data analysis technology, and the rice field water quality monitoring system is established. The automatic irrigation system is used for controlling the water volume of the paddy field, automatically adjusting the irrigation frequency and the water volume, calculating the comprehensive benefit index TGI of the paddy field by using the data mining and statistical analysis technology, monitoring the environmental parameters of the paddy field and the growth state of crops in real time, identifying abnormal conditions and timely giving out early warning.

The invention relates to an unmanned aerial vehicle simulation training method for video guidance and track deviation evaluation based on deep learning. The method specifically comprises the following steps: S1, data acquisition and input; s2, generating and prompting a path visible area; s3, track deviation evaluation of image and flight control fusion; s4, trajectory behavior modeling and score analysis; s5, training a feedback output mechanism; and S6, carrying out personalized training recommendation and playback analysis. According to the invention, space-level deviation evaluation is realized by fusing the image and the flight control data, and the judgment is more accurate; the real-time visual feedback and voice prompt system can improve the training instantaneity and the interaction experience; a Transform-based trajectory scoring mechanism is close to the nature of an operation behavior, and multi-dimension evaluation is carried out; differentiated task recommendation is supported, and teaching pertinence and training efficiency are improved; the whole process is visual and can be repeated, and the teaching closed-loop design is optimized.

The invention, which belongs to the technical field of mine safety monitoring, provides a multi-source information fusion-based roadway surrounding rock roof separation tour-inspection early-warning system comprising a data acquisition module, an edge calculation module, a cloud analysis module and an early-warning feedback module. The data acquisition module acquires multi-source data of surrounding rock of a coal mine tunnel in an automatic mode; the edge calculation module performs spatio-temporal data synchronization on the collected multi-source data and extracts data features; the cloud analysis module performs feature weighted fusion based on the multi-source data to construct a risk early warning model; and the early warning feedback module triggers graded early warning according to prediction data of the risk early warning model, and provides visual feedback, so that a set of automatic, full-coverage and multi-source data fused roadway roof separation monitoring system is constructed.

The embodiment of the invention provides a ceramic diaphragm automatic positioning system based on high-precision visual feedback and closed-loop control, and the system comprises a parameter reference setting module which is used for setting a theoretical reference point and a visual detection parameter; the real-time visual detection module is used for collecting an image of the to-be-stacked film and outputting an image coordinate of the mark point; the core operation module is used for converting the image coordinates of the mark points into actual coordinates based on the visual detection parameters, reading a theoretical reference point of the current to-be-stacked diaphragm and calculating a position error vector between the actual coordinates and the theoretical reference point, and the position error vector comprises a translation compensation amount and a rotation compensation amount; and the motion control and closed loop feedback module is used for receiving the position error vector and generating a control instruction to drive the workbench to move. According to the system, through parameterized reference setting, real-time visual detection, intelligent error calculation and real-time motion compensation, the technical limitation is effectively overcome, and the lamination alignment precision and efficiency are remarkably improved.

The invention relates to the field of artificial intelligence, in particular to a reflective interference workpiece visual identification positioning and grabbing method and system based on artificial intelligence, and the method comprises the steps: obtaining two-dimensional image data containing a reflective workpiece, and carrying out the image enhancement and filtering preprocessing; training and reasoning the two-dimensional image by using a deep learning model, and automatically identifying and extracting a non-reflective region in the image to form a region of interest; acquiring three-dimensional point cloud data of the scene through a binocular linear scanning laser vision sensor, and extracting a point cloud area corresponding to the non-reflective area from the three-dimensional point cloud data; on the basis of a point cloud matching algorithm, registering the extracted point cloud of the region of interest with a standard workpiece model, and calculating pose information; planning a robot grabbing path according to the calculated workpiece pose data; feedback and optimization are carried out, the grabbing process is monitored in real time, a grabbing strategy is adjusted by combining a sensor or visual feedback, and the grabbing success rate and stability are improved; according to the invention, accurate identification, positioning and stable grabbing of the reflective workpiece are realized.

The invention relates to the technical field of digital twinning, in particular to a real-time interactive visualization system and method based on digital twinning, and the system comprises a data collection module which is used for configuring a multi-protocol adapter deployed on a physical equipment layer to collect the operation data of physical equipment, and transmitting the operation data of the physical equipment to an edge computing node. According to the method, the general data adapter is used for driving the three-dimensional rendering engine to execute real-time rendering based on physics, so that the virtual scene can dynamically reflect details such as illumination change and material aging of a physical entity, and high-fidelity visual feedback is provided. According to the method, a closed-loop control mechanism is established in combination with a multi-modal interaction framework, pure data monitoring is converted into operable bidirectional interaction, operation of a user on a visual interface can be converted into feedback instructions in real time to reversely adjust physical equipment, and the response efficiency of remote operation and maintenance is improved.