1356results about "Image feed-back" patented technology

The invention relates to the technical field of photovoltaic panel cleanliness detection, in particular to a photovoltaic panel cleanliness detection method which comprises the steps of data acquisition, image preprocessing, pollution feature extraction, cleanliness index calculation, cleanliness judgment, data output and result recording and feedback. According to the invention, pollution data is acquired through imaging equipment, and adaptive filtering and morphological analysis are combined, so that precise pollution feature extraction, detection precision improvement and system real-time calculation of a cleanliness index are realized, pollution conditions are fed back through a cloud platform or an Internet of Things interface, and the power generation efficiency of a photovoltaic panel is ensured. The system can also automatically trigger a cleaning device and control the cleaning process according to a cleanliness threshold value, full-automatic management is achieved, the labor burden is relieved, in addition, a machine learning algorithm automatically adjusts the threshold value according to environment monitoring data and seasonal changes, and parallel detection further optimizes the efficiency, shortens the detection time, improves the operation and maintenance effect and prolongs the service life of the photovoltaic panel.

The invention relates to the technical field of chip mounting processing, and discloses an optical alignment system and method for chip mounting processing, and the method comprises the steps: collecting a target chip image and a target substrate image in a mounting process through an upper visual field camera disposed above a suction nozzle assembly and a lower visual field camera disposed above a substrate carrying platform; chip edge accurate coordinates in the target chip image are extracted, and reference mark accurate coordinates in the target substrate image are extracted; solving a chip mounting process compensation parameter matrix of an upper and lower camera-suction nozzle-substrate four-coordinate system in the chip mounting process based on the chip edge accurate coordinate and the reference mark accurate coordinate; calculating an X-Y-theta three-dimensional alignment compensation vector in the chip mounting process according to the mounting process compensation parameter matrix; on the basis of the X-Y-theta three-dimensional alignment compensation vector, the optimal mounting track parameter is calculated, an execution instruction sequence is generated, the problem of error accumulation caused by traditional independent calibration is effectively solved, and the alignment precision is remarkably improved.

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 provides an industrial robot automatic welding method and control equipment, and relates to the technical field of machine control. The method comprises the following steps: acquiring three-dimensional point cloud data of a workpiece, and adjusting a pre-constructed BIM model of the workpiece according to the three-dimensional point cloud data, so that the adjusted BIM model is aligned with an actual workpiece position; obtaining a theoretical welding seam path from the adjusted BIM model; acquiring an actual welding seam image of the workpiece; according to the actual welding seam image and the theoretical welding seam path, calibration parameters of the robot are determined, and a final welding path is determined according to the calibration parameters; and according to the final welding path and the material and thickness of the workpiece, a final control instruction set is determined, and the robot is controlled to conduct automatic welding according to the final control instruction set. The welding precision can be improved, meanwhile, manual intervention is not needed, the welding efficiency can be greatly improved, and the method can adapt to complex and changeable welding scenes.

The invention relates to the technical field of robot target object grabbing, in particular to a simple semantic point cloud-based grabbing and sampling method, which comprises the following steps of: constructing a semantic point cloud taking a target as a center according to an RGB image, a depth image and a mask image; solving a normal vector and a principal axis vector of the point cloud at each sampling point by using a PCA algorithm, and establishing a grabbing coordinate system; performing local grid search on the preliminarily screened grabbing coordinate system to generate candidate grabbing postures, and generating a plurality of candidate grabbing postures through translation and rotation; and a grabbing confidence coefficient function is established, candidate grabbing postures are evaluated and sorted, and the candidate posture with the highest score is selected as the optimal grabbing posture. According to the method, the target information provided by the RGB image and the depth image is utilized, the high-quality grabbing sampling posture of the specified target object can be generated in an unstructured complex environment, and the stability of visual grabbing of the mechanical arm can be improved.

The invention provides a control method and system for a surface shape of an optical device, and the method comprises the steps: carrying out the initial grinding and polishing of the optical device, carrying out the real-time detection through a multi-frequency-band stress detector, obtaining the stress distribution and surface wave front data, and obtaining an initial detection result; a phase shift interferometer and a composite laser confocal probe are used to carry out depth surface shape measurement based on the initial detection result, and adaptive finishing is carried out on an abnormal area to obtain a surface shape finishing log; according to the initial detection result and the finishing log, executing step-by-step load irradiation and stress tracking, and collecting surface shape change under photo-thermal coupling to obtain surface shape evolution information; and according to the surface shape evolution information, performing partition dynamic compensation and real-time detection until a surface shape control result meeting requirements is obtained. According to the method, secondary deformation caused by internal stress release is recognized and corrected through combination of multi-frequency-band stress detection and high-precision interference measurement, high-precision control over the surface shape of the optical device is achieved, and the method is suitable for scenes with high requirements for the surface shape of the optical element.

The invention provides a first bending position deviation correction method and device for a pipe bending machine, and relates to the technical field of pipe bending machines. The first bending position deviation correction method comprises the steps that pipe bending control parameters are set according to bending parameters and pipe fitting data; generating a springback prediction result; springback compensation optimization is carried out, bend pipe control parameters are updated according to a springback compensation optimization result, and a time sequence mapping fitting result is established; configuring monitoring key points, configuring a camera array according to the monitoring key points, and establishing a time sequence image data set; establishing an error identification result; and according to the error identification result and the updated pipe bending control parameters, carrying out first bending position deviation correction. The technical problem that in the prior art, due to the fact that the identification accuracy of the first bending position deviation is insufficient, the first bending deviation correction effect is poor can be solved, and the technical effects that the deviation of the first bending position is corrected, the accuracy of pipe bending operation is ensured, and therefore the quality and consistency of products are improved are achieved.

The invention relates to the technical field of pressure control, in particular to a dynamic pressure control system and method of a strapping machine. The method comprises the following steps: acquiring information data of an object to be bundled; performing three-dimensional modeling on the to-be-bundled object information data to generate three-dimensional modeling data of the to-be-bundled object; binding mode confirmation is conducted on the binding machine according to the three-dimensional modeling data of the to-be-bound object, binding mode data are obtained, and the binding mode data comprise a single-layer binding mode and a multi-layer binding mode; initial strapping pressure setting is carried out on the information data of the to-be-strapped object based on the single-layer strapping mode, and single-layer strapping initial pressure data is obtained; and carrying out object deformation binding pressure adjustment on the single-layer binding initial pressure data to generate single-layer binding pressure adjustment data. Through three-dimensional modeling, dynamic pressure adjustment and a feedback control mechanism, the intelligence and precision of dynamic pressure control of the strapping machine 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.

The invention discloses an intelligent hardware grinding system based on machine vision, which comprises a machine vision module used for carrying out image acquisition on grinding of hardware and generating surface feature information and grinding state image feature information of the hardware; the robot grinding module is used for grinding the hardware according to the surface feature information and the grinding state image feature information; the control module is connected with the machine vision module and the robot grinding module and used for adjusting grinding equipment control instruction features and grinding parameters in real time according to the surface feature information and the grinding state image information; according to the method, the target hardware grinding information is subjected to image recognition, the image description set is mined, and accurate path planning is generated and covers shape, size, defect position and other information. According to the method, deviation judgment is carried out on the multiple grinding task events, so that the deviation correction execution strategy is determined, the hardware grinding precision is effectively improved, the hardware rejection rate is reduced, and the hardware production efficiency is improved.

The invention discloses an unmanned aerial vehicle cluster distributed cooperative positioning method assisted by ground laser radar point cloud. The method comprises the following steps: an unmanned aerial vehicle performs initial pose estimation through a monocular camera and an IMU (Inertial Measurement Unit); the unmanned vehicle constructs a dense point cloud map by adopting a Fast-LIO algorithm through laser radar and IMU data; constructing a factor graph optimization problem by combining sparse and dense point cloud data; a factor graph is formed, each unmanned aerial vehicle receives environment information, and six-degree-of-freedom pose estimation is achieved; the unmanned aerial vehicle independent operation pose estimation module uses a BA algorithm to accurately solve the pose; and information synchronization between the unmanned aerial vehicle and the unmanned vehicle is realized. According to the invention, unmanned aerial vehicle cluster-unmanned vehicle distributed non-prior real-time pose estimation in an unstructured environment is realized, the distributed architecture enables multiple unmanned aerial vehicles to respectively solve respective real-time poses on the basis of sharing the same laser radar map, and construction of a visual odometer factor graph fused with laser radar point cloud information is realized. The drifting problem of the vision-inertia odometer in a long track can be reduced.

The invention relates to the field of laser cutting control, in particular to a laser cutting control method, device and equipment for a rock plate and a storage medium. The method comprises the following steps: acquiring a laser cutting reflection signal based on an optical fiber detector; digital processing is conducted on the laser cutting reflection signals, light intensity distribution fitting is conducted, and a light intensity distribution diagram is constructed; light distribution topological structure analysis is carried out on the light intensity distribution diagram, and a real-time cutting morphology point cloud model is constructed; performing real-time morphology deviation comparison on the real-time cutting morphology point cloud model based on a preset theoretical rock plate cutting morphology to obtain a morphology deviation parameter; acquiring real-time operation state parameters of the laser cutter; and cutting deviation compensation calculation is conducted on the real-time operation state parameters of the laser cutter based on the morphology deviation parameters, a dynamic cutting control strategy is obtained, and response data in the laser cutting correction process are continuously monitored. According to the laser cutting control system, efficient and accurate laser cutting control is achieved, and the laser cutting deviation is effectively reduced.

The invention provides a cobblestone stratum bored pile construction and drilling parameter intelligent control method, and relates to the technical field of artificial intelligence and bored pile construction, and the method comprises the following steps: arranging a sound wave emission array at the end part of a drill bit to collect cobblestone distribution information, identifying a weak connecting band as a passable area, calculating an optimal obstacle avoidance track, and setting a posture control point; and drill bit displacement, slurry injection and drill rod deformation parameters are adjusted, so that the drilling tool can intelligently pass along the obstacle avoidance track. Large cobblestones can be effectively avoided, the drilling resistance is reduced, the drilling efficiency is improved, and equipment abrasion is reduced.

The invention discloses a paint spraying path optimization method for an automatic paint spraying robot, and relates to the technical field of automation, and the method comprises the steps: obtaining a three-dimensional geometric model of a ship body through laser scanning equipment, carrying out the curvature analysis, and generating the partition data of the curvature information of each ship body region; according to the partition data, performing preliminary path planning to obtain a preliminary paint spraying path; based on the preliminary paint spraying path, a paint spraying task is executed, and coating thickness data of the paint spraying area is detected in real time; according to the detected coating thickness data, the preliminary paint spraying path is corrected in real time, and a new paint spraying path is generated; the paint spraying coverage rate of the paint spraying areas is detected in real time, the coverage uniformity of the paint spraying areas is judged, and the paint spraying areas with uneven coverage are marked; according to the coverage rate detection data, the paint spraying path is adjusted again, a corrected final paint spraying path is generated, and the paint spraying task is executed on the next ship body through the final paint spraying path; the paint spraying path is adjusted in real time, so that the paint spraying process is more flexible and efficient, and the stability of paint spraying quality is improved.

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 relates to the technical field of intelligent printing and industrial automation control, and discloses an intelligent label printing optimization system, method and device based on image recognition. The method comprises the following steps: constructing an object surface feature matrix according to surface three-dimensional point cloud data and texture information of a to-be-labeled object; performing regional adaptability evaluation on the object surface feature matrix to obtain a thermodynamic map so as to determine a target labeling region; obtaining semantic information corresponding to preset label contents to generate typesetting constraint conditions corresponding to the label contents; generating an optimized typesetting scheme corresponding to the label content according to the typesetting constraint condition, and controlling preset printing equipment to print according to the optimized typesetting scheme to form a target label; and according to the first pose information corresponding to the object to be labeled, the preset second pose information of the printing equipment and the area range corresponding to the target area, a preset movement track corresponding to the mechanical arm is generated, and the mechanical arm is controlled to paste the target label to the target area according to the movement track.

An augmented-reality device is aligned with an environment using a correction source. A position of a reflector (or other device that is part of a surveying system) coupled with a surveying rod is measured in relation to a correction. A position of a visual design, which is coupled with the surveying rod, is measured in relation to an augmented-reality device, based on an image of the visual design acquired by the augmented-reality device. A coordinate system of the augmented-reality device is aligned with the environment based on the position of the reflector in relation to the correction source, the position of the visual design in relation to the augmented-reality device, and an offset between the reflector and visual design.

The invention relates to the technical field of stamping part surface detection, in particular to a stamping part surface detection method and a welding slag cleaning device.The detection method is based on three-dimensional point cloud data processing, is not affected by environment light changes, can more accurately separate a defect area from a background, and is high in detection accuracy. The welding slag can be effectively identified even at a complex geometrical shape or a deep concave position, comprehensive detection of a stamping part with a complex shape can be realized, and the accuracy and the efficiency of detection are remarkably improved; by presetting the basic threshold value and dynamically adjusting according to the reflection characteristics, it can be guaranteed that the stamping part can obtain stable detection and cleaning results under the detection conditions of different light environments.

The invention relates to a USB interface quality detection method, system and terminal, and relates to the field of automobile parts, and the method comprises the steps: determining model information from a preset model database according to housing detection information; determining an illumination position, an illumination brightness value and reference transmittance according to the model information; according to the illumination position, a preset illumination device is controlled to be lightened with the illumination brightness value to obtain illumination detection information; obtaining illumination transmittance according to the illumination detection information; if the illumination light transmittance is consistent with the reference light transmittance, light transmittance detection is completed; if the light transmittance is not consistent with the reference light transmittance, an abnormal position is marked; according to the shell detection information corresponding to the abnormal position, material shortage shape information is determined, and a corresponding material supplementing block is manufactured; and repairing the material supplementing block to the corresponding outer cover or base according to the abnormal position and the material supplementing block. According to the invention, the USB interface is automatically detected, the efficiency is improved, and errors are reduced.

The invention discloses a point cloud feature extraction method, device and system based on an inspection humanoid robot. The method comprises the following steps: acquiring point cloud data of an underground space; performing denoising, filtering and normalization processing; local features and global spatial enhancement features of the point cloud data are extracted, the local features comprise geometric structure, curvature and boundary features, curvature feature transformation is carried out, and the global spatial features comprise extracted global spatial features and global features for enhancing spatial attention obtained through convolution; and feature information is extracted and classified based on the local features and the global spatial enhanced features. According to the method, efficient extraction and enhancement of global features are realized in a sparse point cloud semantic segmentation task, and the classification precision and robustness of the model in a complex scene are remarkably improved; according to the method, the semantic segmentation precision is remarkably improved.

The invention relates to the technical field of image processing, and discloses an automobile decorative plate gluing positioning method. The method comprises the following steps: acquiring a decorative plate image, identifying a contour to determine a gluing area, and detecting a material to select an adhesive; setting a reference point to calculate position deviation, and adjusting the position if the deviation is greater than a threshold value; environment temperature and humidity are detected and adjusted, and the surface of the gluing area is activated; generating a track to control coating, and adjusting parameters if the detection uniformity is low; glue distribution is analyzed, and curing parameters are set according to glue types to reach the standard. According to the automobile decorative plates made of different materials, based on material characteristic data and adhesive curing characteristics, self-adaptive accurate gluing positioning and differential curing treatment are achieved, and the quality stability and production efficiency of decorative plate bonding are improved.

The invention discloses a laser-arc hybrid welding interface temperature regulation and control method and system with an external magnetic field. The method comprises the steps that current welding parameters of a welding sample are obtained; an interface temperature distribution feature neural network prediction model is adopted to process the welding parameters, and current temperature distribution features of the welding interface are obtained through prediction; based on the current temperature distribution characteristics of the welding interface and the standard temperature distribution characteristics of the welding interface, the deviation value of the current temperature distribution characteristics of the welding interface is determined; based on the deviation value, determining the adjustment amount of the magnetic field intensity of the induction coil through a fuzzy rule; and based on the adjustment amount of the magnetic field intensity of the induction coil, the magnetic field intensity of the induction coil is adjusted so as to adjust the current temperature of the welding interface.

The invention provides a mechanical arm grabbing prediction method, electronic equipment and a storage medium, and belongs to the technical field of mechanical arm vision. A mechanical arm grabbing posture correction platform is built, and an RGB image of an actuator posture is obtained; an LLaMA-Adapter is adopted to construct a multi-mode large model; visual features of the RGB image are extracted through a CLIP model, a pre-trained LLaMA word segmentation device is used for analyzing visual feature prompts, and text information and image information of RGB image content are obtained; on the basis of a continuous thinking fine-tuning reasoning grabbing strategy, the operation posture, corresponding to the processed text information and image information, of an end effector of the mechanical arm is predicted; and operation posture operation data of an end effector of the mechanical arm are obtained in real time, and the multi-modal large model is verified based on an index moving average method. According to the method, a continuous strategy learning method is provided, the adaptability of the model to current scene configuration is enhanced, the frequency of expert intervention is reduced, and the mechanical arm meets the use requirement.

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.

A method includes: accessing a toolpath and processing parameters—including a target force and feed rate—assigned to a region of a workpiece; and accessing a wear model representing abrasive degradation of a sanding pad arranged on a sanding head. The method also includes, during a processing cycle: accessing force values output by a force sensor 199 coupled to the sanding head; navigating the sanding head across the workpiece region according to the toolpath and, based on the force values deviating the sanding head from the toolpath to maintain forces of the sanding head on the workpiece region proximal the target force; accessing contact characteristics representing contact between the sanding pad and the workpiece; estimating abrasive degradation of the sanding pad based on the wear model and the sequence of contact characteristics; and modifying the set of processing parameters based on the abrasive degradation.

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.