3387 results about "Visual recognition" patented technology

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention relates to the technical field of fabric detection, and discloses a fabric defect intelligent detection method and system based on AI visual identification. According to the method, motion blur is quantized through motion state data, optical blur caused by fabric motion is eliminated through deconvolution solution, so that motion interference in the fabric transmission process is processed in a targeted mode, self-adaptive balance of the deblurring capacity and the feature retention capacity is achieved, and then based on the optical interference principle, the deblurring capacity and the feature retention capacity are improved. Through a dynamic calibration system combining hardware-level real-time compensation and multi-dimensional optical parameter calibration, dynamic optical parameter calibration of primary correction data is realized, then fabric defect characterization data is extracted to accurately obtain defect features, and finally, a detection-production line control closed loop is constructed through a quality quantitative index and a comprehensive risk value, so that fabric defect detection is realized. The fabric defect detection precision can be improved, so that the problem of high defect missing detection and false detection rate caused by optical data distortion due to movement and environment interference in a traditional method is effectively solved.

The invention discloses a multi-modal collaborative security monitoring method, device and equipment and a storage medium, and relates to the technical field of computer vision and sensor fusion, the method comprises the following steps: collecting multi-modal data of a monitoring area, the multi-modal data comprising image data, infrared temperature data and environmental parameter data; based on the target detection model, detecting personnel security features and environment security features in the image data, and outputting a visual identification result and visual identification confidence; and when the visual identification confidence coefficient is lower than a preset threshold value and the consistency of the multi-modal data meets a synchronization condition, performing association verification of the multi-modal data by adopting weighted fusion in combination with the infrared temperature data and the environmental parameter data to perform secondary identification of a target so as to output the multi-modal fusion confidence coefficient. Through combination of visual identification and a multi-sensor fusion technology, accuracy and robustness of personnel and environment safety identification in a complex industrial environment are improved.

The invention discloses an automatic feeding control system based on industrial vision, and belongs to the technical field of feeding control systems.The automatic feeding control system comprises a visual recognition module used for collecting image information such as the appearance, the size and the spatial position of a workpiece; the mechanical clamping module is used for grabbing, carrying and placing the workpiece according to the control instruction; the control operation module undertakes a core control task of the system and is used for coordinating cooperative work among the modules; the data processing and analyzing module is used for deeply processing and analyzing the image data acquired by the visual identification module and the data fed back by other sensors; the environment perception and compensation module is used for assisting the system to dynamically adjust working parameters of equipment, counteracting the influence of environment interference on the operation precision and automatically realizing compensation adjustment; the system can quickly and accurately identify the position and the posture of the workpiece in an industrial production environment with a high real-time requirement, and stably clamp the workpiece to carry out feeding operation.

The invention discloses a manipulator grabbing control system based on visual identification, and the system comprises a visual identification module which is used for collecting the three-dimensional form data and surface feature information of a target object, and eliminating the noise interference in an original image through preprocessing; the multi-modal fusion processor is used for receiving the three-dimensional form data and the surface feature information output by the visual recognition module, executing point cloud structured conversion on the three-dimensional form data, extracting geometric features of an object through a neural network, and meanwhile, performing weight distribution on the surface feature information by adopting a feature attention mechanism to complete multi-feature fusion processing; according to the multi-axis cooperative manipulator provided with the end executor, the end executor integrates a touch sensing assembly used for touch sensing and an adsorption execution device used for assisting grabbing. By integrating the technologies of visual identification, multi-modal fusion, intelligent optimization, dynamic feedback and the like, the sensing precision, the environmental adaptability and the grabbing reliability of the system to object characteristics are improved.

The invention provides a robot navigation method and system based on visual identification, and relates to the technical field of computer vision, firstly, a continuous visual information set of the surrounding environment of a robot is obtained, the continuous visual information set comprises environment visual images of different directions of the advancing direction of the robot, and the object appearance features and the spatial arrangement relation are recorded; then establishing association mapping between the continuous visual information set and a preset navigation area to obtain a double-association mapping result; generating a semantic anchor point navigation path based on a double-correlation mapping result, wherein the semantic anchor point navigation path comprises a semantic anchor point sequence from the current position to the target position; real-time visual semantic features in the navigation process are obtained through a real-time visual collection module, and a semantic anchor navigation path is dynamically updated; finally, a final navigation execution path is output according to the updated path, the robot is driven to complete navigation operation, and the navigation capability and the intelligent level of the robot in a complex dynamic environment are improved.

The invention discloses a welding robot path planning system cooperating with industrial vision and collision detection, and relates to the technical field of robot automatic welding, the welding robot path planning system comprises an upper computer, the upper computer is in communication connection with the following modules: an environment sensing module used for collecting welding environment information in real time, comprising three-dimensional point cloud data of a workpiece and obstacle information; and the data processing and fusion module is used for fusing the visual identification result and the obstacle detection data. By integrating industrial visual recognition and collision detection technologies, the dynamic change of the welding environment can be sensed in real time, an accurate environment model is constructed based on three-dimensional point cloud data, compared with a traditional welding robot with a preset path, the welding track can be dynamically adjusted, welding defects caused by environment interference are avoided, and meanwhile the welding efficiency is improved. In combination with an assembly error compensation mechanism, the deviation between a theoretical model and an actual workpiece can be corrected, the welding position precision is ensured, and the high-precision ship manufacturing requirement is met.

The invention provides a high-speed lamination production line product quality detection system based on visual identification, and belongs to the technical field of visual detection. Comprising a multispectral image acquisition module (visible light, near infrared and ultraviolet band synchronous imaging), a motion compensation module (encoder synchronous correction displacement deviation), a preprocessing module (denoising, enhancement and ROI coarse positioning), a multi-scale feature fusion module (geometric, texture and spectral feature fusion) and a multi-task deep learning model. And a production line is linked to realize hierarchical control (sudden stop, early warning and parameter adjustment). The system eliminates image dislocation and blurring under high-speed motion through a multispectral information complementation and motion compensation technology; the recognition capability of tiny and complex defects is improved by utilizing multi-scale feature fusion; detection and decision-making integration is realized through a multi-task model, a high-precision and low-delay quality detection effect in a high-speed scene is finally achieved, and the quality control level of a lamination production line is effectively improved.

The invention relates to the technical field of foamed silicone rubber surface detection, and discloses a foamed silicone rubber surface detection method based on image visual identification, which comprises the following steps: acquiring a color image of a foamed silicone rubber surface, carrying out graying and normalization processing, and generating a diffuse reflection effective detection area by using a polarization filter difference technology; color abnormity is detected based on an image gridding and gray statistical method, meanwhile, pore and microbubble defects are detected in combination with minimum value seed point extraction and an eight-neighborhood region growing algorithm, finally, various masks are fused, the defect area and number are counted, and a quality inspection report is generated; the method can effectively inhibit highlight interference and accurately identify uneven colors and pore microbubbles, the detection process does not need external training, calculation is efficient, and the method is suitable for rapid detection and quality control of surface defects of foamed silicone rubber.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention relates to a tin ring automatic focusing laser soldering tin fixing and 3D welding spot detecting system, and belongs to the technical field of industrial automatic laser processing. The system is characterized in that a tin ring preparation module identifies pins in real time and dynamically optimizes winding parameters through visual positioning and a self-adaptive winding head, and performs defect identification and pre-repair analysis through multispectral imaging; the soldering tin positioning module is used for switching stations through a rotary table, and precise positioning and clamping of a product are realized by combining laser contour sensing and a focusing compensation algorithm; the laser tin soldering module can visually identify tin materials and automatically call welding parameters, laser power and action time are regulated and controlled in real time by establishing a regional heat conduction model, and gradient cooling is implemented after welding; and the 3D detection module adopts dual-mode scanning, constructs a welding spot three-dimensional model based on fusion data, recognizes microcracks through super-resolution processing, and discriminates the welding spot quality. And automation and intellectualization of the whole process from tin ring preparation to welding spot quality detection are achieved.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention discloses a rapid demolding device and method for a small prefabricated part, and relates to the technical field of prefabricated part production. The mold bearing table is mounted on the substrate of the demolding rack and is integrated with a multi-point temperature sensor and an acoustic vibration signal collector; the ejection demolding assembly is installed on the stand columns of the demolding rack in a liftable mode and comprises an ejection plate assembly, and an ejection force sensor and a high-frequency vibrator are arranged on the ejection plate assembly; the visual identification module is mounted on the demolding rack; and the controller is electrically connected with the multi-point temperature sensor, the sound vibration signal collector, the ejection force sensor, the high-frequency vibrator, the visual recognition module and a driving mechanism of the ejection demolding assembly. Multi-source sensing information is fused, the component condensation state and the demolding time are accurately judged, the ejection force and vibration in the demolding process are adaptively controlled, accurate and flexible control over the demolding time and process is achieved, and the demolding success rate and the component yield are remarkably increased.

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.

The invention discloses a distribution line unmanned aerial vehicle edge end real-time visual identification and target detection method and system based on lightweight AI. The method is based on a YOLOv8 architecture, and constructs a complete lightweight detection framework comprising a feature extractor, an enhancement module and a simplified detection head by introducing a space structure maintaining assembly, a separated large kernel convolution and a weighted reconstruction feature pyramid. A cross-dimension semantic relation model is constructed by utilizing a combined attention structure, multi-level feature fusion is realized by reconstructing a feature pyramid network, model training is performed by adopting a hybrid optimization function and a dynamic sample adjustment mechanism, and the model is deployed on a mobile computing platform after being optimized by a hierarchical knowledge transfer method. According to the method, the detection speed is remarkably increased while high precision is kept, and real-time identification and anomaly analysis of the power line element are effectively realized.

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

The invention discloses an unmanned ship measurement and control system and method based on Beidou artificial intelligence, and relates to the technical field of unmanned ship measurement and control. According to the Beidou-based artificial intelligence unmanned ship measurement and control method, Beidou positioning information, navigation state parameters and water area image data are obtained in real time in the cruising process of an unmanned ship; inputting the image data into an AI visual recognition model to judge whether an obstacle exists or not; if there is an obstacle, extracting the features of the obstacle and constructing an obstacle spatial distribution map; generating a plurality of avoidance paths based on the distribution map and the navigation state parameters, and selecting an optimal path; according to the method, the image of the water area in front of the ship body is input into the AI visual model, feature extraction, target detection and depth estimation are combined, the spatial position and boundary information of the obstacle are extracted, and the spatial distribution diagram is constructed under the ship body coordinate system; and the obstacle identification precision and the positioning modeling capability are improved.

The invention provides an unmanned aerial vehicle inspection accurate positioning method and device integrating vision and satellite navigation, and relates to the technical field of aerospace information. According to the method, satellite signal quality features reflecting aerospace information reliability and multi-dimensional features such as visual geometric textures reflecting ground environment features are jointly extracted, and the environment state is deeply perceived; based on real-time dynamic generation of a fusion strategy adaptive to the current environment, a data weight and an algorithm model are intelligently adjusted, and the limitation of a traditional fixed parameter fusion mode is broken through; a specific inspection stage and a target object where the unmanned aerial vehicle is located are understood through visual identification, and a corresponding priori task constraint model is loaded, so that the positioning process closely meets the precision and safety requirements of actual operation. And finally, fusion calculation is carried out under the common guidance of a dynamic strategy and task constraints, so that the problem of inaccurate positioning of the unmanned aerial vehicle in a complex environment is solved, and the environmental adaptability, robustness and task guidance accuracy of a positioning system are improved.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

The invention discloses a tunnel lining crack identification method and system based on panoramic space image splicing, and relates to the technical field of tunnel visual identification. The method comprises the following steps: acquiring a tunnel lining image array, and performing local splicing on tunnel lining images by using a splicing model based on cylindrical surface projection to obtain a local spliced image; fusing and splicing the local spliced images by using the global image space model to obtain a panoramic spliced data graph; and abnormal region detection is carried out on the panoramic splicing data graph according to the spatial local difference, and a tunnel lining crack identification result is obtained. According to the method, the lining panoramic space model based on image two-stage fusion is used for splicing, so that the visual coherence and naturalness of a global spliced image are improved.

The invention relates to visual identification line drawing assembly welding equipment which comprises a welding conveying assembly, the welding conveying assembly comprises a platform frame and a conveying roller installed on the platform frame and used for conveying a packing machine plate, the conveying direction of the conveying roller is defined as the first direction, and the other horizontal direction perpendicular to the first direction is defined as the second direction; a self-walking line drawing assembly, a self-walking spot welding assembly and an auxiliary positioning assembly which move in a reciprocating mode in the first direction are arranged on the platform frame. According to the automatic line drawing and welding device, line drawing and welding of the packing machine plate are achieved through joint cooperation of the welding conveying assembly, the self-walking line drawing assembly, the self-walking spot welding assembly and the auxiliary positioning assembly, spraying drawing of visual recognition color lines of different colors can be achieved only through one-time walking, the line drawing efficiency is greatly improved, and the labor intensity of workers is lowered. And an automatic accurate assembling and positioning function and visual intelligent welding are realized.

The invention discloses microminiature needle body high-speed visual screening equipment and a screening method thereof, and belongs to the technical field of industrial automatic detection and sorting. The equipment comprises an equipment frame, a feeding module, a visual identification module, a grabbing module, a control module and a material receiving module. The feeding module is used for conveying to-be-screened needle bodies. The visual identification module performs image acquisition and model identification on the needle body; the grabbing module is provided with a plurality of grabbing clamping jaws, can move in the horizontal direction and the vertical direction under the driving of the control module, and grabs a plurality of needle bodies of the same model at the same time at a time. The material receiving module comprises a plurality of material receiving boxes and a transposition driving mechanism for driving the material receiving boxes to move so as to align the target material receiving box to the grabbing module. The equipment disclosed by the invention can be widely applied to high-efficiency sorting of micro-miniature needle bodies with the diameters ranging from 0.3 mm to 3mm and the lengths ranging from 3mm to 77mm, has high precision, high speed and high reliability, and is suitable for large-scale production and multi-variety and small-batch production requirements.

The invention relates to the technical field of image data processing, and discloses a multi-modal large model drawing checking optimization method and system for a power grid drawing, and the method comprises the steps: constructing a power grid logic graph; performing visual identification on the target drawing through the multi-modal large language model, positioning and identifying target equipment, and generating a visual identification result; associating the target equipment with logic nodes in the power grid logic map to generate a logic anchoring result; querying a power grid logic map based on the logic anchoring result, and obtaining equipment information of associated equipment having logic association with the target equipment; predicting and generating a next visual focus area to be examined according to the equipment information of the associated equipment; and identifying and verifying the visual focus area to be examined through the multi-modal large language model to obtain a corresponding examination result. According to the method, dynamic focus transfer based on the equipment logic relationship can be realized, the power grid drawing examination path is optimized, and the intelligence degree and the defect identification capability of drawing examination are improved.

The invention discloses a real-time back break control method of a heading machine based on visual association and digital twinning, which relates to the technical field of intelligent control of coal mining equipment, and comprises the following four steps: processing binocular visual data through a double-domain double-branch Transformer model, extracting coal rock and roadway structure characteristics and outputting cutting head offset; a GCN-LSTM model is adopted to fuse visual, laser radar and fiber-optic gyroscope data, and dynamic calibration and output of a three-dimensional pose are carried out; a roadway three-dimensional model is constructed based on the SLAM technology, and real-time updating of a digital twin model is achieved through edge-cloud collaboration; and in combination with a reinforcement learning strategy, generating a cutting parameter adjustment instruction according to the deviation of the digital twin model, and correcting model parameters through error feedback. The method solves the problems of low visual recognition precision, sensor data drift, digital twinning update lag and the like in an underground complex environment, improves the back break control precision, tunneling efficiency and equipment stability, and is suitable for tunneling operation under complex geological conditions.

The invention belongs to the technical field of biological sample sorting, and discloses a visual identification control system for automatic sorting of biological samples, through cooperation of a multi-modal sensing acquisition unit and a knowledge graph multi-dimensional identification unit, condensed water of a frozen sample tube can be penetrated to accurately identify label and molecular characteristics, and'condensed water shielding 'and'label damage' are distinguished; meanwhile, a sample preprocessing state is associated, and samples which do not meet requirements are automatically intercepted. Compared with the prior art, manual secondary verification is not needed, sorting dislocation caused by optical interference of smear glass slides is thoroughly solved, the sorting precision and efficiency of mixed samples are remarkably improved, and diversified sample treatment requirements in clinical detection are met; the full-link twin operation and maintenance unit integrates key data in a sorting process through a block chain technology, and is interconnected and intercommunicated with a hospital LIS system and a sample storage system; and when the subsequent storage environment of the sample is abnormal, the quality state of the sorting stage can be reversely associated, and the compliance auditing vulnerability is eliminated.

The invention discloses a target object targeting agent monitoring method and device based on AI visual identification, belongs to the technical field of termite monitoring, and aims to solve the problems of high manual dependence degree, high misjudgment rate, low data transmission efficiency and the like in traditional target object monitoring. A slow-release targeting agent and a solar hybrid power supply system are arranged in the device, and low-power-consumption endurance is supported. According to the core scheme, vibration and infrared sensor signals are fused, and image acquisition is triggered through feature analysis; recognizing a target object through background difference, a multi-scale image and feature matching, and dynamically optimizing a detection threshold value; after video acquisition, activity intensity is calculated in combination with target tracking and gridding analysis, and transmission is optimized based on intensity and quota; light supplement is automatically adjusted according to ambient light, and image quality is ensured through pixel compensation and brightness adjustment. According to the scheme, a closed-loop system with multi-mode accurate triggering and intelligent identification is constructed, and the intelligence and efficiency of termite monitoring are improved.

The invention relates to a leather material cutting robot and a cutting method thereof, and belongs to the technical field of material cutting, the leather material cutting robot comprises a human-computer interaction interface, a CAD / CAM system, a multi-mode visual perception unit, a central processing system, a dynamic compensation cutting unit and a quality closed-loop system, and the multi-mode visual perception unit comprises a high-resolution multispectral imaging module and a 3D scanning module. According to the method, the problems that a traditional leather material cutting robot visual recognition system cannot recognize leather thickness changes, material waste is easily caused by subcutaneous defects, natural curling deformation of leather is difficult to adapt, and the edge precision error is large are solved, the rejection rate can be further reduced through multispectral and 3D defect recognition, and the production efficiency is improved. Three-dimensional dynamic compensation enables edge errors to be further reduced, and real-time force control further reduces the occurrence rate of crush damage.

The invention discloses an intelligent accompanying robot system, and the system comprises the following modules: a multi-mode interaction module which is composed of a voice recognition unit, a voice synthesis unit, an emotion recognition unit, and a visual recognition unit, and is used for collecting the voice, facial expression, motion, and environment information of a user; the localized AI decision module is used for deploying a lightweight DeepSeekR1 large language model based on an ESP32-S3 edge computing chip, performing real-time processing on the multi-modal data and generating a social guidance strategy and an emotion intervention instruction; the data management module comprises a user behavior database and a privacy protection unit and is used for desensitizing the data and realizing sensitive data isolation through local storage; according to the method, the single-person intervention cost is reduced, and the time consumption of manual scene simulation is reduced.

The invention discloses a video double-gun welding path planning control method based on synchronous visual identification, and particularly relates to the technical field of multi-welding-gun collaborative welding, cross-equipment time alignment is realized through multi-angle image acquisition, a synchronous video frame sequence is constructed, a welding seam boundary is identified, and a change track is generated; predicting path conflicts and planning non-interference paths in combination with the initial posture of the welding gun and the space parameters; a time scheduling model is constructed to execute beat dislocation control, and in the welding process, videos and electric arc feedback signals are collected, track offset is judged, the posture, the position and the compensation speed are dynamically corrected according to the track offset, and closed-loop control and synchronous execution are completed; according to the method, the image time consistency is improved through multi-angle video frame synchronous construction, and the welding seam track recognition precision is improved; introducing double-welding-gun path prediction and conflict analysis, and planning a non-interference path combination; and video and arc signal closed-loop adjustment is combined, dynamic synchronous correction of the path posture and the speed is achieved, and the welding stability and precision are guaranteed.