492 results about "3d vision" patented technology

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention relates to a non-standard special-shaped part high-precision measurement and verification method based on three-dimensional vision, and belongs to the field of three-dimensional reconstruction and computer vision. Point cloud data of a non-standard special-shaped part are obtained through multi-angle scanning by means of multi-camera-projector collaborative layout in combination with a structured light coding technology and a phase shift code technology; the method comprises the following steps of: acquiring point cloud data, ensuring comprehensiveness and accuracy of the data, realizing high-precision alignment of the point cloud data through matching an underivative optimization algorithm with image feature points, and performing fusion, denoising and registration processing on the acquired point cloud data to ensure that point clouds acquired from a plurality of angles can be effectively fused so as to reconstruct a high-precision three-dimensional model. In the measurement and verification stage, the deviation between the point cloud data and the CAD model is measured through a standard coordinate system, multi-stage registration and a high-precision algorithm, rapid detection and size verification of the geometric deviation of the industrial workpiece are achieved, and reliable technical support is provided for quality control.

The invention provides a massage robot multi-mode fusion treatment system and method based on 3D vision, and relates to the technical field of massage robots. The region recognition module is used for establishing a human body recognition model and recognizing human body features in the processed image; the massage terminal is used for carrying out massage operation according to parts needing to be massaged and human body characteristics and monitoring skin pressure of the massaged parts in real time; the pain sense recognition module is used for human body pain sense recognition; the force adjusting module is used for adjusting skin pressure. According to the method, pertinence and safety of massage operation are remarkably improved through multi-modal image preprocessing and an initialized human body recognition model of a fusion segmentation model and a key point detection model; a global path strategy is generated and optimized through a hybrid ant colony algorithm, efficient and accurate motion control of the mechanical arm is achieved, through multi-modal data fusion analysis, the pain feeling of a user is sensed in real time, the massage strength is dynamically adjusted, and the comfort and the safety coefficient are improved.

The invention discloses a full-automatic grinding method and system for steering knuckle flash based on 3D vision, and relates to the technical field of casting post-processing automation. A trimming-free direct polishing process is innovatively provided, a casting is scanned in real time through a high-precision 3D visual system, a flash space molded line is accurately captured, and one-key calibration of a camera, automatic generation and dynamic simulation of a flash path, multi-tool intelligent switching and self-adaptive adjustment of residual height are achieved in combination with self-developed off-line programming software. According to the system, a robot is adopted for grabbing workpieces to cooperate with a fixed type multi-cutter work station, a disordered feeding technology is integrated for 2D / 3D visual guiding grabbing, workpiece deformation and die difference are effectively dealt with, and flash residues are thoroughly eliminated. The method is characterized in that visual scanning, path planning and execution control are seamlessly fused, the dependence of traditional automatic grinding on the trimming process is broken through, the yield and efficiency are remarkably improved, and the industrial problem of full-process automation of casting flash is solved.

The invention discloses a multi-mode sensing multi-mechanical-arm cooperative control method and system and a robot. Task types are judged according to power consumption and path requirements of a target task; performing module selection according to the task type; data such as material positions, material shapes, obstacle positions and obstacle shapes are collected in real time; the real-time data collected by the sensing module is fused, and the motion path, the motion speed, the motion acceleration, the grabbing posture and the grabbing force of the mechanical arm are determined; storing the execution data and task feedback information, analyzing an operation effect, and optimizing modal selection and parameter setting of a next task; through 3D vision, 2D vision and instruction set multi-modal perception and data fusion, the motion path of the robot is continuously optimized, multi-source data are integrated in real time, and the accuracy and robustness of material recognition and state description are improved.

In estimating a position of a vehicle utilizing a global navigation satellite system (GNSS), it is desirable to exclude outliner GNSS measurements due to navigation signals via non-light-of-sight paths from the GNSS to the vehicle, but it leads to a distorted satellite geometry distribution. Complementariness between low-lying visual landmarks and healthy but high-elevation satellite measurements is explored to improve the geometry constraint. Measurements of an inertial measurement unit, low-lying visual landmarks captured by a forward-looking camera onboard the vehicle, and healthy but high-elevation satellite measurements are tightly-coupled integrated via sliding window optimization of system states used in a factor graph. To improve estimation performance, good initial guesses of system states are important. As such, initial guesses of velocity set and position set inside a sliding window are estimated simultaneously based on data of Doppler measurement, double-differenced (DD) pseudorange measurement and DD carrier-phase measurement as obtained in GNSS measurements.

The invention discloses a robot unstacking and stacking support system based on a 3D visual system, and belongs to the technical field of 3D visual systems. Comprising a 3D visual data acquisition preprocessing module, a target positioning priority analysis module, a box type intelligent identification matching module, a dynamic path planning obstacle avoidance module, a self-adaptive stacking strategy generation module, an information binding tracing management module and a feedback optimization module. The problems of low efficiency and conflict caused by a single grabbing sequence in traditional stacking and unstacking operation are solved, the priority weight is dynamically adjusted by combining the inclination angle of the carton, weight distribution and reachability verification, and the optimality and safety of the grabbing action of the robot are ensured.

The invention relates to the technical field of 3D vision, and discloses a manipulator dispensing path optimization method and device based on 3D vision, and the method comprises the steps: carrying out the multi-angle three-dimensional vision scanning of a dispensing workpiece, and obtaining the point cloud data of the dispensing workpiece; performing semantic segmentation on the point cloud data, and decomposing the dispensing workpiece into a plurality of functional parts; according to the semantic identifier and the surface feature of the functional component, identifying a target area to be dispensed and a dispensing path constraint condition; generating an initial dispensing path based on the target area to be dispensed and the dispensing path constraint condition; and performing iterative linear secondary adjustment control on the initial dispensing path to generate a motion control instruction. According to the invention, the self-adaptive optimization adjustment of the dispensing path is realized, the problem that the traditional dispensing system cannot cope with the actual state change of the workpiece is solved, and the adaptability and stability of the dispensing system are greatly improved.

The invention discloses a stacked workpiece disordered grabbing method and system based on a 3D visual system, and relates to the technical field of computer vision, and the method comprises the steps: obtaining an RGB image and a depth image of a stacked workpiece through a depth camera, and generating point cloud data; the improved Mask R-CNN model integrated with the MSDA module is used for performing instance segmentation on RGB and depth images, and workpiece pose estimation is realized by combining an ICP algorithm and template point cloud registration; establishing a coordinate relation between the camera and the mechanical arm through hand-eye calibration, and obtaining a 6D pose of the workpiece under a mechanical arm base coordinate system; and according to the point cloud centroid distance and the point number, the grabbing priority is calculated, the optimal target is selected for grabbing, and disordered grabbing of all the workpieces is completed circularly. According to the method, the improved Mask R-CNN model integrating the MSDA module and the ICP algorithm are combined with the 3D visual system, so that accurate instance segmentation and pose estimation of stacked workpieces are realized, and therefore, an unordered grabbing task is accurately completed.

The invention belongs to the technical field of 3D visual reconstruction, and particularly provides a 3DGS segmentation method and system based on boundary adaptive Gaussian splitting, and aims to solve the problem of incomplete structure caused by directly deleting boundary Gaussian in the prior art by accurately positioning and segmenting the boundary Gaussian through a gradient consistency index through a boundary adaptive Gaussian segmentation technology. The boundary mBIoT is improved by 1.3%; meanwhile, a semantic-vision joint alternating optimization mechanism is innovatively proposed, continuous mask labels and alpha are utilized for synthetic rendering, and semantic loss and vision loss are combined for alternating optimization, so that compared with a traditional scheme singly depending on mask or feature optimization, the PSNR is improved by 0.29 dB, the texture definition is improved by 15%, and the segmentation precision and the vision quality are both considered; besides, through small-scale fuzzy Gaussian recognition and a targeted optimization strategy, mIoU is only reduced by 1.1% when the mask error rate reaches 20%, robustness to error masks is remarkably enhanced, and the bottleneck that an existing method is sensitive to pre-training mask errors is broken through.

The invention discloses an inverted coplanarity detection method based on 3D vision and refraction compensation, and the method comprises the following steps: 1, selecting an optical measuring head of which the measuring range can cover the whole bottom surface of a measured object according to the size of the bottom surface of the measured object, installing the optical measuring head below glass, and enabling light to penetrate through the glass from bottom to top; 2, establishing a glass refraction compensation model; 3, correcting the glass refraction compensation model; 4, collecting three-dimensional point cloud data of the bottom surface of the measured object; 5, point cloud extraction and processing; step 6, calculating and evaluating the coplanarity; and 7, repeating the steps 4-6 to realize coplanarity analysis under different conditions. Through fusion of a theoretical model and experimental calibration, nonlinear error measurement caused by factors such as glass refraction and non-ideal installation is solved, the measurement precision is improved, the reference plane is established by adopting a minimum area method, the coplanarity evaluation reasonability is improved, and the method can be expanded and applied to all occasions needing to measure the coplanarity of the bottom structure from bottom to top.

The invention relates to the technical field of 3D vision measurement, and discloses a 3D binocular line laser weld contour data acquisition method and system. Two core steps of obtaining surface reflection characteristic information of a to-be-scanned area and adjusting output intensity of laser rays projected by a line laser device at different positions of the to-be-scanned area according to the surface reflection characteristic information are introduced, so that the problem that when a high-reflection welding seam is processed by a traditional method, the laser rays cannot be projected by the line laser device is effectively solved; the problem of image overexposure or underexposure caused by dramatic change of illumination characteristics is solved.

The invention relates to the field of three-dimensional vision measurement, in particular to an aluminum alloy CNC product defect detection method and system based on structured light scanning. The aluminum alloy CNC product defect detection system based on structured light scanning comprises an image acquisition and analysis module, a self-adaptive illumination control module, a point cloud reconstruction module, a defect detection module and a report generation module. According to the method, through texture direction perception and matrix type light source dynamic angle modulation, and in combination with a light intensity-wavelength self-adaptive scheme of saturated pixel grading, mirror surface glare of the aluminum alloy high-reflection surface is greatly reduced; in cooperation with multi-exposure gray scale weighted fusion, a stripe image which is uniform in contrast and free of overexposure can be obtained in single scanning; in the same scanning time, compared with a fixed illumination scheme, the point cloud integrity rate can be improved, and a more reliable three-dimensional data basis is provided for subsequent defect detection.

The invention provides a welding seam identification and trajectory optimization method and system based on three-dimensional vision, and the method specifically comprises the following steps: firstly, collecting the three-dimensional point cloud data of a welding seam, extracting the local geometric features of the welding seam, and carrying out the feature enhancement of the local geometric features, so as to screen out representative effective edge points; and then, collecting multi-source point cloud data of the welding seam area, constructing a multi-scale welding seam feature recognition network, inputting effective edge points obtained by screening into the network, realizing accurate recognition of the welding seam area, and further extracting a linear welding seam area. According to the recognition result, a fitting algorithm is adopted for conducting curve fitting on the weld joint area, and a preliminary welding track is generated; and for the condition that multiple sections of welding seams are intersected, an optimal fitting point searching mechanism is further introduced, the preliminary track is optimized, and the continuity and integrity of a welding path are ensured. And finally, based on the center line of the welding seam, the posture of the welding gun is precisely planned in combination with the dihedral structure model, and therefore high-precision track control and posture adjustment are achieved.

The invention discloses a numerical control machine tool autonomous tool setting method based on 3D visual guidance. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutter and a workpiece by using a 3D visual sensor and preprocessing; then, segmenting the point clouds through a region growing algorithm, realizing the separation of the tool point clouds and the workpiece point clouds, and extracting the point clouds of each cylindrical section of the workpiece; thirdly, based on a cylinder fitting algorithm, cylinder model fitting is conducted on the segmented tool and workpiece point cloud, and geometric parameters such as the axis direction, the radius and the center coordinate are obtained; calculating a relative position relation between the tool and the workpiece according to the geometric parameters, generating a tool setting path and converting the tool setting path into an NC code; and finally, the NC code is transmitted to a grinding machine control system through a grinding machine communication module, and tool setting operation is completed. The automatic tool setting device has the advantages that the relative position relation between the workpiece and the tool can be automatically measured in the machining process, automatic and rapid tool setting is completed, and the machining quality and efficiency are guaranteed.

The invention discloses a badminton tracking method and system based on three-dimensional vision, and relates to computer vision. Badminton motion video data of a left view and a right view are synchronously collected through a binocular camera, a left view frame sequence and a right view frame sequence which are aligned in time are obtained, and the collected video data are preprocessed; obtaining standardized left and right view input frame data; according to input frame data, constructing a 2D key frame detection model based on an improved YOLOv8 network, and extracting the sum of 2D key frame coordinates of left and right views; according to the 2D key frame coordinate sum of the left view and the right view, 3D space positioning is carried out on the badminton through stereo matching and track prediction, 3D track data are obtained, according to the 3D track data, a broken track is repaired through a frame leakage compensation mechanism, and a final 3D track is obtained. In order to solve the problem that in the prior art, key frames are prone to being lost and track breakage is caused during high-speed movement such as smash, the breakage track of the shuttlecock is repaired.

The invention discloses an intelligent deviation correction control method and device based on 3D vision and a storage medium, and relates to the field of conveying belts. Carrying out edge searching calculation on the belt according to the no-load point cloud data of the belt, and determining a reference detection area and a point cloud reference curve of the fitting belt; screening belt point clouds and material point clouds based on the point cloud reference curve, performing offset detection on the belt point clouds based on the reference detection area, and performing gravity center deviation calculation on the materials based on the material point clouds; and according to the material gravity center deviation and the belt movement speed, the basic deviation correction angle and the flow compensation angle of a deviation correction motor are calculated, and belt deviation correction is conducted. According to the scheme, the reference detection area is established, point cloud screening, offset detection and gravity center deviation calculation in the load state are combined, the angle parameters of the deviation correction motor are dynamically adjusted, and the problems that 2D visual detection is poor in stability, 3D point cloud data processing is insufficient, and multi-motor cooperative control precision is low are solved; the method has the advantages of improving the detection stability, enhancing the data processing capability and optimizing the correction control precision.

The invention provides a 3D point cloud defense method, device and equipment based on CLIP guidance, and a medium, relates to the technical field of point cloud defense, and aims to enhance the robustness and security of 3D point cloud data in the face of adversarial attack by using a contrast language-image pre-training model. According to the method, a new defense framework is provided by combining geometric and semantic features, triggers in backdoor attacks can be effectively identified and inhibited, meanwhile, the geometric structure of point cloud data is repaired, and the classification performance is improved. Specifically, the method includes projecting a 3D point cloud into a multi-view 2D depth map, extracting cross-modal features using a CLIP model, and performing semantically guided 3D reconstruction through a variational auto-encoder (VAE) to generate a repaired point cloud that retains key information and neutralizes perturbations. The method shows excellent performance in various complex scenes, and provides powerful support for the safety of the 3D vision technology in key application.

The invention relates to the technical field of cigarette production, in particular to an automatic auxiliary material tray matching work station suitable for full-specification production. Comprising an industrial robot module, a 3D visual camera module, a code reader module, a tool clamp module, a clamp storage module, a cigarette auxiliary material and a tray. The tool clamp module and the clamp storage module are arranged on the two sides of the industrial robot module respectively, the 3D visual camera module is located above to-be-prepared auxiliary materials to be used for positioning and sending position information, the code reader module is arranged between an industrial robot and the prepared auxiliary materials to be used for scanning codes, and an automatic gun changing disc at the tail end of the industrial robot can switch corresponding clamps. And after the industrial robot receives the position information, the clamp is switched to grab the to-be-prepared auxiliary materials, and the auxiliary materials are placed on the prepared auxiliary material tray after being matched with the code reader to scan codes. Through multi-module cooperation, full-process automation of tray distribution is achieved, manual operation is reduced, production efficiency is improved, meanwhile, by means of accurate positioning and code scanning, the material recognition accuracy is guaranteed, and the workshop automation and intelligence level is remarkably improved.

The invention provides a precise workpiece positioning method and system based on three-dimensional vision, and the method comprises the steps: obtaining a scene three-dimensional vision data set containing a target workpiece, carrying out the multi-modal data fusion processing of the scene three-dimensional vision data set, generating a fusion data set with aligned space coordinates, extracting a workpiece feature identifier set from the fusion data set, and carrying out the recognition of the workpiece feature identifier set. And feature matching association processing is carried out on the workpiece feature identifier set and a pre-stored standard workpiece feature template, a feature matching corresponding relation set is generated, three-dimensional positioning coordinate information of the target workpiece in the scene is calculated based on the feature matching corresponding relation set, and the three-dimensional positioning coordinate information comprises workpiece centroid coordinates and space attitude angle parameters. The precision of precision workpiece machining is improved, and the stability of the positioning process is ensured.

The invention relates to the technical field of warehouse management, in particular to a material inspection method and system for an emergency warehouse. Comprising the following steps that multi-dimensional data of materials in an emergency warehouse are collected in real time through a multi-modal sensor array, and the multi-modal sensor array comprises an RFID reader-writer, an infrared thermal imager and a 3D visual sensor; dynamically grading the materials in the warehouse based on a material priority classification model, wherein the priority classification model considers the validity period, the use frequency and the emergency response level of the materials; constructing a material three-dimensional digital twinborn model, and comparing the collected multi-dimensional data with preset material standard parameters in real time; when the comparison difference exceeds a preset threshold value, abnormal early warning is triggered, and an inspection task is generated; planning an optimal inspection path for the automated guided vehicle based on an improved conflict avoidance algorithm; according to the invention, material inspection with dynamic standard adaptation and intelligent priority adjustment can be realized.

The intelligent mounting system comprises a mounting module and a ground interaction control module, the mounting module comprises a tool, a working platform, a mechanical arm and a clamping jaw, the tool and the mechanical arm are both mounted on the working platform, the clamping jaw is mounted at the tail end of the mechanical arm, and a 3D visual guiding device is mounted on the clamping jaw; the 3D visual guiding device is used for guiding the clamping jaw to grab the cantilever and the bolt at the same time; the ground interaction control module comprises a main control unit, a data transmission unit, a power supply, a safety unit and a man-machine interaction unit, the main control unit is used for analyzing an instruction of the man-machine interaction unit into a control signal, and the control signal is sent to the mechanical arm control unit through the data transmission unit; the mechanical arm control unit controls the mechanical arm to act and feeds back an execution result and state data to the ground interaction control module. According to the invention, the field construction efficiency is greatly improved, and the intelligence, automation and less manpower of cantilever installation are realized.

According to the intelligent voice broadcast system and method for the elevator, elevator car environment data are collected in real time through a multi-source heterogeneous sensor set, and passenger density distribution and movement tracks are calculated through fusion of a millimeter wave radar and a 3D visual sensor; physiological features of a handrail contact area are detected through a biological impedance sensor so as to judge the health state of a passenger, and the current signal-to-noise ratio is dynamically calculated and the voice frequency band is optimized through an environment noise analysis module. Therefore, by constructing a technical system of'multi-modal perception-edge intelligent decision-cross-modal interaction 'and adopting innovative means such as millimeter-wave radar and biological impedance fusion sensing, deep reinforcement learning of a dynamic strategy and a phased array directional sound field, the problems of broadcast stiffness, poor scene adaptability and privacy potential safety hazards are solved. According to the system and method, accurate passenger feature recognition, dynamic voice content generation and multi-channel collaborative feedback are achieved through two-dimensional innovation, and the intelligent level and service efficiency of elevator voice interaction are improved.

The invention discloses a stereoscopic vision optimization method and system for a naked-eye 3D large screen, and particularly relates to the technical field of naked-eye 3D vision optimizing.The method comprises the steps that environment illumination and audience positions are sensed in real time through multi-sensor fusion, and an environment light field model is established; glare crosstalk noise is predicted based on physical simulation, and self-adaptive suppression and compensation are carried out in combination with human eye visual sensitivity and image content features; a virtual camera is dynamically generated according to the real-time positions of the eyes of the audience, and a lightweight neural radiation field renderer is used for real-time re-rendering, so that motion parallax is realized; and finally, intelligently fusing the glare compensation layer and the perspective correction layer, coding and outputting to a screen. The system correspondingly comprises an environment perception module, a glare compensation module, a perspective rendering module and a fusion coding module. The naked-eye 3D large-screen display method effectively inhibits ambient light interference, improves the quality and immersion of a stereoscopic picture under different visual angles, and is suitable for naked-eye 3D large-screen display under outdoor and complex illumination environments.

The invention belongs to the technical field of industrial automation, and particularly relates to a snake-shaped pipe grabbing point positioning method based on 2D vision and 3D vision fusion, and the method comprises the steps: collecting an original image set of a snake-shaped pipe, the original image set comprises original images of the snake-shaped pipe at a plurality of different visual angles, and the original images comprise depth images and texture images; based on each texture image in the original image set and the trained target detection model, determining a planar clip-free area; based on the original image set and the planar clamping piece-free area, determining a complete point cloud of the coiled pipe and a point cloud of the area to be positioned; based on the complete point cloud of the coiled pipe and the point cloud of the to-be-positioned area, determining a target positioning area; and determining a grabbing point of the coiled pipe based on the target positioning area. According to the technical scheme provided by the invention, the grabbing point is positioned in combination with the 2D image and the 3D image of the serpentine pipe, so that the accuracy and efficiency of positioning the grabbing point of the serpentine pipe are improved.

The invention relates to the field of wood gauging, and discloses an automatic wood bayonet type intelligent gauging system, which comprises a multi-source data acquisition module used for acquiring vehicle parking position deviation, log end surface point cloud and equipment operation state through interaction of a sensor, a hardware interface and a mechanical arm; the flow cooperative control module is used for coordinating movement, code spraying operation and data transfer of the truss type mechanical arm according to a preset operation flow and monitoring interaction logic of a PLC signal and a shaft control instruction issued by the visual module in real time; and the peripheral execution module comprises a truss type mechanical arm for scanning and a license plate recognition camera. Through the truss-type mechanical arm, the 3D visual sensor and a deep learning algorithm, full-process automation from vehicle entrance verification, wood end face scanning to code spraying identification is achieved, manual intervention errors are avoided, and the scale detection efficiency and consistency are improved.

This invention provides a system and method for calibration of a 3D vision system using a multi-layer 3D calibration target that removes the requirement of accurate pre-calibration of the target. The system and method acquires images of the multi-layer 3D calibration target at different spatial locations and at different times, and computes the orientation difference of the 3D calibration target between the two acquisitions. The technique can be used to perform vision-based single-plane orientation repeatability inspection and monitoring. By applying this technique to an assembly working plane, vision-based assembly working plane orientation repeatability, inspection and monitoring can occur. Combined with a moving robot end effector, this technique provides vision-based robot end-effector orientation repeatability inspection and monitoring. Vision-guided adjustment of two planes to achieve parallelism can be achieved. The system and method operates to perform precise vision-guided robot setup to achieve parallelism of the robot's end-effector and the assembly working plane.

The invention discloses an intelligent grabbing system anomaly detection and processing method and device based on 3D vision, and belongs to the technical field of industrial robot 3D vision guide grabbing. The method comprises the steps of collecting 3D point cloud data of stacked flexible plates; carrying out point cloud preprocessing and template matching, and determining a plate pose and a grabbing point; generating a cushion block detection area and a deformation detection area based on the grabbed point pose; counting the number of point clouds in the detection area, comparing the number with a threshold value, and judging whether cushion block residues or plate excessive deformation exists or not; if yes, triggering processing, and if not, outputting a grabbing instruction. The device comprises a truss manipulator, an end effector, a 3D visual sensor, a visual processing unit and a robot control unit. According to the method, the detection area is dynamically generated, point cloud threshold judgment is fused, self-adaptive detection and processing of excessive deformation of the cushion block and the plate are achieved, the robustness, safety and automation level of a grabbing system are remarkably improved, and the method is suitable for automatic unstacking operation of stacked flexible plates.

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and belongs to the technical field of warehouse management. A sensing unit comprises a 3D vision module, an environment sensing module and a cargo positioning module; the edge calculation unit comprises an Internet of Things gateway module, an edge database and an edge calculation module; the data twinning unit comprises a three-dimensional modeling engine module and a virtual deduction module; the intelligent decision-making unit comprises a path optimization module, a demand prediction module and an abnormality diagnosis module; the execution unit comprises an AGV conveying module and a stacking machine storage module. According to the method, closed-loop intelligent management of the whole warehousing process from cargo sensing, intelligent decision making to automatic execution is achieved, accurate prediction, scientific decision making and continuous optimization are driven based on mass multi-source data fusion and AI analysis, and the real-time performance of the whole warehousing process is improved through real-time sensing, edge calculation, digital twinborn deduction and intelligent decision making. The system has strong ability to deal with uncertainties such as order fluctuation, equipment fault and environment change, and improves operation toughness.

The invention discloses a robot disordered grabbing method and system based on 3D vision, and relates to the field of vision grabbing, and the method comprises the steps: configuring a 3D vision collection module to execute incoming material area continuous collection, executing definition of a robot base coordinate system, a camera coordinate system and an incoming material station coordinate system, and setting a tray in-place consistency constraint. Deterministic mapping of a clamp and a strategy is formed under multiple types of incoming materials, the grabbing success rate is improved, deformation and falling risks are reduced, collision safety is improved with minimum safety gap constraint and path cost selection in an unordered environment, and operation stability is maintained through action time accounting and beat conformity judgment on the beat side. And traceability is realized by a sorting accuracy target and a task log write-in mechanism, and finally, shutdown waiting and secondary faults are reduced through alarm and separate disposal rules in a static incoming material and structure separation risk scene.