557results about "Image feed-back" patented technology

The invention relates to the technical field of digital twinning control, and discloses a composite structure prefabricated segment digital twinning-adaptive assembly correction method and system, and the method comprises the steps: reading BIM geometric model data, and constructing an assembly reference coordinate system and a digital twinning geometry of a prefabricated segment; composite data are collected in real time, and end tooth groove boundary feature point cloud is extracted; matching the actually measured point cloud with the digital twinborn geometry through a dynamic point cloud registration technology, calculating a six-degree-of-freedom pose error vector, and generating a predicted total pose error vector in combination with a pre-constructed pose drift prediction model; based on the error vector, a mechanical fine adjustment jack is driven to execute position and posture adjustment until the position and posture are converged, and then tooth groove precise meshing and mechanical locking are completed; according to the method, through the synergistic effect of the dynamic mapping of the digital twinborn model and the self-adaptive correction algorithm, high-precision dynamic correction of the multi-combination structure segment assembly process is achieved on the premise that an original mechanical connection structure is not changed.

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 belongs to the technical field of laser processing, and particularly relates to a special-shaped curved glass laser cutting track planning method. According to the method, the instantaneous thermal effect and the historical thermal accumulation effect are subjected to coupling calculation, high-precision prediction and compensation of thermal deformation in the laser cutting process are achieved, the accuracy of the cutting track is improved, contour deviation caused by thermal deformation is avoided, and the geometric precision is improved; according to the local geometric complexity of the special-shaped curved glass, regions are divided, and differential virtual scanning layers and scanning modes are matched, so that the dynamic balance of efficiency and precision is guaranteed, the processing quality of regions with high curvature, thin walls and the like is guaranteed, the cutting speed of flat regions is increased, and the processing period is shortened; by monitoring the temperature and feeding back actual thermal historical parameters during cutting, model parameters are reversely optimized by using contour precision data after cutting is completed, so that robustness and stability are enhanced, and the yield and process reliability of laser cutting of the special-shaped curved glass are improved.

The invention relates to the field of industrial automation, in particular to a multi-camera vision stacking pose planning method and system for an intelligent loading and unloading robot. The method comprises the following steps: constructing a collaborative sensing system through a plurality of industrial cameras deployed in a target space, and collecting dynamic environment data of a stacking area in real time; generating a self-adaptive stacking pose based on a hierarchical pose planning mechanism: analyzing an initial pose parameter through a theme control layer and generating a preliminary placement instruction, and verifying the feasibility of the preliminary placement instruction based on environmental data of the collaborative awareness system through a visual verification layer, and a final placement coordinate is optimized by combining the accurate positioning layer with a virtual box matching algorithm. The problems that in the prior art, real-time environment sensing capacity is lacked, pose planning is disjointed with the actual environment, and dynamic change scenes cannot be processed are solved.

A robot in accordance with at least some embodiments of the present technology includes a neck region through which the robot is configured to transmit and receive light. The robot defines a robot height and includes a body, a head carried by the body, and a neck extending between the head and the body along the robot height. The robot further includes a structural neck frame at a periphery of the neck in a plane perpendicular to the robot height. The structural neck frame defines windows distributed around the periphery of the neck. The robot further includes a sensor disposed at least partially within the structural neck frame. The sensor includes a laser configured to transmit light via the windows and a detector configured to receive light via the windows. A field of view of the sensor extends at least 320 degrees around the neck.

The invention discloses a heading equipment cutting head position calibration method based on image acquisition, which comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, extracting cutting head feature points, and constructing a two-dimensional feature point set; s3, calculating a three-dimensional coordinate by using perspective transformation, and generating a point cloud set; s4, performing rigid registration on the point cloud set and a standard structure model of the tunneling equipment, and iteratively solving a rotation matrix and a translation vector to obtain pose parameters; s5, simulating change tracks of the feature points in continuous frames, constructing a time consistency constraint, and optimizing pose parameters; s6, the optimized three-dimensional coordinates are converted to a reference coordinate system, space alignment is completed, and a position calibration result is generated; and S7, outputting a calibration result for control instruction generation and path planning. According to the invention, high-precision and non-contact three-dimensional position calibration of the cutting head of the tunneling equipment is realized, and operation automation and control precision are improved.

The invention discloses a control method applied to self-piercing riveting, and relates to the technical field of self-piercing riveting control. According to the method, riveting material data are collected in real time through a multi-source data fusion interface, the high-dynamic deformation process of a riveting head is captured, deformation rate and rebound value characteristic values are extracted, and a multi-dimensional process parameter set is constructed. And comprehensively analyzing the hardness, thickness and plastic dissipation characteristics of the material, and establishing an anti-deformation capability evaluation function. By introducing the fluctuation characteristics of the punching force, the nonlinear relation between the punching force and the elastic deformation of the riveting rod is analyzed, a hybrid compensation model is constructed, and a riveting dynamic coupling compensation vector is output. And generating a compensation weight matrix through an adaptive optimization algorithm, and dynamically adjusting the stamping force and the displacement of the riveting rod in combination with an anti-saturation control strategy. And dynamically adjusting a gain parameter through multi-scale filtering and a Lyapunov stability criterion, and triggering iterative updating of a compensation weight parameter during out-of-tolerance so as to optimize a control effect. Riveting precision and stability are improved, and the service life of equipment is prolonged.

The invention discloses a terminal crimping quality detection method and system, and relates to the technical field of terminal crimping image detection. Comprising the steps that space coordinates of a standard datum point in a crimping image are determined, and the standard datum point is used for representing the space coordinate position, meeting the standard terminal crimping inspection standard, between a corresponding wire and a terminal notch. According to the method, the crimping samples which do not meet the standard terminal crimping inspection standard are rapidly removed in a spatial positioning mode, and then the crimping sample thermal imaging images which do not meet the standard terminal crimping inspection standard are obtained on the crimping surfaces of the crimping samples by means of trigger logic. According to the method, the crimping samples are detected, the invisible faults of the crimping samples are judged based on the set standard terminal crimping inspection standard, and finally different commands are input to the crimping equipment according to the types and severity levels of the invisible faults, so that the rapid detection of the crimping samples and the dynamic adjustment of the crimping equipment are realized, and the stability of the monitoring dimension of the crimping quality is ensured.

The invention discloses a grooving machine rigidity dynamic adjusting system and method based on material image recognition, and relates to the technical field of grooving machine rigidity dynamic adjusting, and the method comprises the following steps: obtaining a material surface and subsurface image sequence of a grooving machine processing area; inputting the image sequence into a pre-trained depth vision analysis model, and outputting a material characteristic parameter set in real time; converting the material characteristic parameter set into a target rigidity value based on a rigidity demand mapping model; within a preset regulation and control window period before the cutter makes contact with the target machining area, the initial rigidity adjustment amount is obtained according to the target rigidity value, and a rigidity adjustment instruction is generated and sent to a rigidity adjustment execution mechanism for dynamic adjustment; in the cutting process, fusion of feedback data and an image analysis result is obtained, and the initial rigidity adjustment amount is corrected; dynamic, accurate and predictive active adjustment of the rigidity of the grooving machine is achieved, and therefore the machining adaptability, intelligence and stability of the grooving machine are effectively improved.

The invention relates to the technical field of spraying control, in particular to a control system of an intelligent robot for spraying automobile clothing and a control method and application of the control system, the control method and the application are applied to the intelligent robot, and the intelligent robot comprises a robot body, a scanning assembly and a spraying assembly; the control system comprises a scanning analysis unit which is used for controlling the scanning assembly to carry out omnibearing scanning and analysis on a target vehicle to obtain vehicle three-dimensional shape parameters, vehicle model information and damaged area characteristics; and the spraying planning unit is used for calling a preset vehicle type spraying template according to the vehicle three-dimensional shape parameters and the vehicle type information to determine an initial spraying path. The deviation in the spraying process is monitored in real time and optimized and adjusted, the situation of uneven thickness or incomplete coverage generated in the spraying process can be effectively avoided, and it is ensured that the spraying quality reaches the expected standard.

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.

The invention discloses a railway cargo grabbing method based on improved YOLO and depth map processing, and the method comprises the steps: collecting a color image and a depth image of a target scene through a camera, inputting the color image into an improved YOLOv8 model, and obtaining a mask image; performing registration processing on the mask image by using the depth image to obtain a plurality of alternative target areas; performing three-dimensional statistical analysis on each alternative target area, and screening all the alternative target areas to obtain an effective target area; and selecting a plurality of candidate edge points in the effective target area, performing screening according to the score of each candidate edge point, and obtaining a grabbed area according to the screened candidate edge points. According to the embodiment of the invention, accurate identification and positioning can be carried out on railway goods in a complex scene, so that the robot grabbing efficiency and success rate in a railway freight scene are improved.

Dynamic pick pose recovery is described. The concepts can be relied upon to determine the pose of an item during a process for picking, moving, and placing the item among totes, or other uses. In one example, an item can be picked using a robotic arm, and the item can be repositioned for placement at a second location. Images of the item can be captured as the item moves. The images can be segmented to determine masks of the item. The masks can be projected into a point cloud within a bounding volume, to generate contours. Points of the point cloud that are positioned outside the contours can be discarded, to find a subset of points representative of a pose of the item. The position and orientation of the item can be calculated, for placement of the item at a second location, based on the pose of the item.

The embodiment of the invention relates to the technical field of image processing and positioning, in particular to a processing and positioning method and system for adaptive image recognition, and the method comprises the steps: obtaining a multi-source image data set of a target processing scene; performing image state adaptability analysis processing on the original image sequence to obtain an image recognition feature set of each image region in the original image sequence; performing positioning state mapping processing on the image recognition feature set based on the equipment positioning state data set, and generating a spatial positioning driving feature set corresponding to each image region; and generating a self-adaptive positioning control instruction according to the spatial positioning driving feature set, transmitting the self-adaptive positioning control instruction to an execution end of the processing equipment, and indicating the processing equipment to execute self-adaptive pose positioning control operation for the current image area. Therefore, the flexibility of processing equipment to cope with complex processing scenes is greatly improved, and it is ensured that target processing operation is accurately executed under various observation poses and environmental conditions.

The invention discloses a shoe tree grabbing robot track planning system and method based on multi-modal sensing. The system comprises a shoe tree storage area, a mechanical arm grabbing assembly, a multi-modal sensing device, a posture recognition module, an interference sensing module, a grabbing point optimization module, a stable window generation module, a path planning module, a fine adjustment control module and a self-learning module. The system extracts the posture of the shoe tree by fusing visual images, structural sound waves and infrared contour information, then utilizes interference fringes to analyze and judge the material and friction characteristics, combines stability to judge and screen a grabbable target, and executes posture fine adjustment and fitting at the tail end of a clamping jaw through a touch sensing film. And the grabbing failure information is used for updating the strategy database to realize self-adaptive optimization of the grabbing strategy. The method is suitable for grabbing special-shaped objects and is high in robustness and success rate.

The invention discloses an elbow connector forward and reverse intelligent deviation rectification virtual assembly blanking method, which comprises the following steps: carrying out 3d scanning on a new elbow and two old pipes, carrying out space matching on two axes of the new elbow and the axes of the two old pipes, searching an optimal assembly position, calculating boundary data of an annular track and extracting track deviation rectification characteristics; the track deviation correction characteristics are added into the preliminary cutting control data so as to compensate the track installation deviation when the cutting robot cuts; and generating final cutting control data. The device has the beneficial effects that a new bent pipe and an old pipe are scanned in a handheld 3d mode, point cloud data are recognized, a cutting route is simulated, final cutting control data, the walking route for controlling the cutting robot and control data for controlling the cutting torch to move on the sliding rail are obtained, automatic cutting is achieved in cooperation with the cutting robot, and the cutting efficiency is improved. Therefore, data support is provided for efficiently completing pipeline first-aid repair.

The invention provides a robot vision object semantic understanding and posture generation method based on a large model, and belongs to the technical field of image processing. Comprising the following steps: S1, inputting an image and a 3D model; s2, object detection; s3, multi-modal feature alignment is carried out; s4, diffusion model sampling; s5, performing geometric screening; s6, carrying out NeRF (New Random Field) morphological modeling; s7, optimizing the optical flow; s8, joint loss calculation; s9, confidence coefficient analysis; and S10, outputting the attitude junction. According to the invention, through an innovative single-view rendering-optical flow optimization closed loop strategy, the calculation overhead is significantly reduced and the estimation precision is improved. According to the method, the algorithm performance in a complex scene is remarkably improved through multi-scale feature fusion and a confidence decomposition strategy.

The invention provides an aluminum veneer special-shaped cutting track planning method and system based on visual localization, and relates to the technical field of visual localization, and the method comprises the steps: employing an anisotropic guide filter to carry out the preprocessing of an aluminum veneer image, and maintaining the edge features through multi-scale filtering and adaptive weight fusion; carrying out edge enhancement treatment by combining material characteristics; extracting contour features by using a non-uniform rational B-spline contour descriptor, wherein the density of control points and the local curvature are in an exponential relationship; and finally, a cutting track is generated based on the feature processing network, and accurate cutting control over the special-shaped contour of the aluminum veneer is achieved.

The invention provides a curved surface local grinding and polishing trajectory planning method and a grinding and polishing system, and relates to the technical field of grinding and polishing machining. According to the curved surface local grinding and polishing trajectory planning method, workpiece point clouds are collected, simplification and filtering are carried out, the number of effective voxels is reduced, and the operation processing efficiency is improved; the method comprises the following steps: extracting a damaged area, determining a grinding and polishing area according to the damaged area, planning the grinding and polishing area according to surface topography characteristics of a workpiece, constructing a grating path corresponding to the grinding and polishing area based on workpiece point cloud, and converting according to the grating path to obtain a workpiece grinding and polishing path so as to realize accurate and efficient grinding and polishing operation. Besides, the normal pressure of the grinding and polishing tool corresponding to the grinding and polishing path of the workpiece is calculated in combination with coupling of the surface curvature of the workpiece, the normal pressure of the grinding and polishing tool can be adjusted in real time, concave area force increasing and convex area force reducing are achieved, the over-grinding problem of the curved surface workpiece can be effectively avoided, and accurate grinding of the area boundary is achieved.

The invention discloses an intelligent identification system for puffed corn, and relates to the technical field of computer vision and image processing, and the system comprises an image collection module which integrates multispectral imaging and a near-infrared sensor and is used for obtaining surface morphology, internal structure and moisture content data of puffed corn; an environment light intensity sensor and an LED array are arranged in the self-adaptive optical compensation module, the wavelength and illumination intensity of a light source are adjusted in a closed-loop feedback mode, and color feature deviation caused by workshop environment light changes is compensated; the image processing module is used for deploying a lightweight hybrid neural network model and is used for adhesive particle segmentation and defect detection; the real-time sorting control module is used for driving a three-axis mechanical arm and a pneumatic spray valve according to an identification result so as to realize defective product rejection and grade subpackaging; and the digital twinning optimization platform is used for outputting a process adjustment suggestion to the production line PLC. According to the invention, through the holographic sensing network, the hybrid intelligent decision and the cross-domain cooperative control, the problems of inaccurate reading, unquick judgment and inaccurate control in puffed corn identification are solved.

A cabin virtual docking method based on actually measured point cloud data belongs to the technical field of spaceflight docking, and comprises the following steps: acquiring original point cloud data of a cabin structural member; inputting the initial point set into a feature extraction network; the key features comprise decoding features and coding features, the coding features are obtained through the feature processing block, and the decoding features are obtained through the feature recovery block; inputting the extracted key features into a feature segmentation network, segmenting the key features by the feature segmentation network according to categories, and performing parameter fitting on the segmented key features; optimizing matching indexes of the key characteristics after parameter fitting to obtain pose adjustment amounts of the upper cabin section and the lower cabin section so as to realize alignment of butt joint end surfaces and axis superposition of positioning pin holes; and virtual docking between the cabin sections is carried out by using the pose adjustment amount. According to the method, the cabin surface data are directly obtained through measurement, error accumulation is avoided, accurate calculation of the initial relative poses of the two cabins is achieved, and an efficient and reliable solution is provided for high-precision butt joint.

In one aspect, a grid map construction method includes: acquiring a point cloud of a target scene in the current field of view; according to the point cloud in the current field of view, constructing an occupation grid map and a height grid map corresponding to each other in the current field of view; and then updating a target grid map of the target scene according to a point cloud occupation probability value of each grid in the occupation grid map and a point cloud height value of each grid in the height grid map in the current field of view.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The invention belongs to the technical field of industrial control methods and systems, particularly discloses a cold working process parameter optimization and plate rolling control method based on artificial intelligence, and relates to the technical field of cold working forming and intelligent control. The method comprises the steps that historical process parameters and corresponding plate quality data are collected, an evaluation function is constructed, and a process weight value is output; optimal parameters are screened according to the process weight and imported into cold working equipment; the curvature distribution of the plate in the plate rolling process is monitored in real time, and the plate rolling state is recognized; and executing a maintenance strategy or a correction strategy according to the state, and fusing correction feedback to optimize subsequent parameters. According to the method, the plate rolling precision and the forming consistency can be remarkably improved, the abnormal state self-adaptive correction capacity is achieved, dependence on manual experience is reduced, and the method is suitable for various plate rolling devices and plate types and has good universality and popularization value.

The invention discloses a transparent glass pose detection method and system based on multi-modal data and a glass grabbing method, and relates to the technical field of glass pose detection. Geometric distortion characteristics of a glass contour are captured by using a camera, a relative pose relationship between the geometric distortion characteristics and a camera plane is analyzed, and a mechanical arm is driven to be roughly adjusted to a roughly parallel pose; based on distance information collected by an ultrasonic sensor array, a glass plane equation is fitted through space geometric solution, the normal vector direction and distance parameters of glass are accurately obtained, and closed-loop fine adjustment of the pose of the mechanical arm is achieved. The detection bottleneck that a transparent medium is difficult to reflect an optical signal is broken through, high-precision pose detection can be realized without installing auxiliary equipment on the surface of the glass, and the damage of a contact mark to the surface of the glass is avoided. The detection reliability is remarkably improved through multi-modal data complementation, meanwhile, a low-cost sensor is adopted to replace high-precision laser equipment, and industrial-grade precision and economical efficiency are both achieved.

The invention discloses a robot cluster cooperative control method and device based on indoor positioning and virtual-real fusion simulation, equipment and a medium, and relates to the technical field of robot cluster control, and the method comprises the steps: building a unified coordinate system through arranging a plurality of optical dynamic capture cameras indoors, obtaining the three-dimensional space coordinates and attitude information of a target object, and obtaining the three-dimensional space coordinates and attitude information of the target object; based on the data, data fusion display synchronized with the actual environment is realized in the virtual simulation environment. And then, a cluster control algorithm is utilized to generate a cooperative control instruction, and the cooperative control instruction is sent to the robot cluster through wireless communication to execute a specific task, so that high-precision positioning of the robot cluster is realized, and the task execution efficiency and cooperative control capability of the robot cluster in an indoor environment are greatly improved.

The invention provides a hub visual positioning method applied to a laser sand opening system, and relates to the technical field of automatic sand opening in a hub machining system.The hub visual positioning method comprises the following steps that a visual image of a to-be-positioned hub is obtained, and the target hub type of the to-be-positioned hub is recognized; calling and traversing a hub template database to obtain hub template data corresponding to the target hub type, detecting an actual calibration point of the to-be-positioned hub from the visual image, and obtaining the to-be-positioned hub based on the image coordinates of the actual calibration point, the first template coordinates of the theoretical calibration point, the second template coordinates of the theoretical center point and the structural feature information. Calculating the current attitude deviation of the hub to be positioned; according to the current posture deviation, the rotating platform is controlled to adjust the posture of the to-be-positioned hub; and repeating the steps until the current attitude deviation is smaller than a preset tolerance threshold, and completing positioning. According to the method, extremely high positioning angle precision is achieved, it is ensured that the laser machining track is perfectly matched with the molded surface of the hub, and the sand opening quality is fundamentally guaranteed.

The invention relates to the technical field of vision, in particular to an industrial intelligent spraying tracing method, which comprises the following steps of: firstly, acquiring an original depth map of a curved surface workpiece by utilizing a depth camera, and obtaining original data containing depth information; next, edge feature enhancement preprocessing is carried out on the original depth image, edge pixels are accurately extracted through morphological gradient operation and a user-defined anisotropic diamond kernel, and an edge mask image is constructed; then, curvature region division and self-adaptive interpolation restoration are carried out, quadratic polynomial curved surface fitting is carried out on a hole pixel neighborhood, a local curvature value is calculated, a curved surface region is divided according to the curvature value, and corresponding interpolation strategies are adopted for different regions; afterwards, edge detail sharpening post-processing is carried out on the repaired depth image, and the edge contrast is enhanced by using a non-sharpening mask algorithm; and the repaired and sharpened depth map is converted into a three-dimensional point cloud, workpiece pose information is determined based on three-dimensional point cloud data, and spraying track planning is carried out.

The invention discloses a cross-linking agent production control system and method based on the Internet of Things, and relates to the technical field of chemical production control, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module for obtaining spectral data in a reaction kettle and obtaining temperature data and pressure data of different positions on the wall of the reaction kettle; the data processing and control module is used for converting the spectral data into reaction data of a cross-linking agent intermediate product based on a meta-calibration model; on the basis of the temperature data and the pressure data of different positions, state data, including a local overheating area and a pressure abnormal area, of a cross-linking agent intermediate product are identified by adopting an image identification and mode analysis algorithm; and the decision execution module is used for establishing a dynamic model fusing the production characteristics of the cross-linking agent based on the reaction data and the state data, training an ML model by using historical data and real-time IoT data based on the dynamic model, and automatically generating and executing an optimal control strategy under security constraints.

The invention relates to a tea leaf picking point positioning method based on virtual and real depth fusion, and belongs to the technical field of intelligent tea leaf picking. According to the method, through combination of an RGB-D camera, deep learning and biomorphological modeling, accurate positioning of a picking point and picking suitability in a complex tea garden environment is realized. According to the method, through multi-sensor information fusion, the growth state, morphological characteristics and environmental data of the tea leaves are obtained, machine vision and a deep learning model are combined, picking key points are accurately positioned, the picking opportunity is judged through a growth model, and therefore a more efficient and more intelligent tea leaf picking solution is provided. Through deep learning and biomorphic modeling technologies, growth characteristics and real-time image data of tea trees are comprehensively utilized, the accuracy and efficiency of tea leaf picking are greatly improved, and intelligent upgrading of the tea industry is further promoted.