21059results about "Sorting" patented technology

The invention provides a defect detection method and system for integrated circuit manufacturing. Wherein in the integrated circuit packaging stage, packaged chips are divided into a first type of chips meeting a preset physical characteristic threshold value and a second type of chips exceeding the physical characteristic threshold value through a pneumatic sorting device; performing scanning electron microscope imaging on the surface of the second type of chip, generating a three-dimensional topological structure by using an image reconstruction technology, and positioning a micron-sized particle defect area by comparing reference type differences; synchronously applying high-frequency electric signal excitation in the sorting process, detecting impedance response of the second type of chips, and recording an abnormal signal sequence exceeding a preset frequency domain range; performing space-time correlation matching on the micron-sized particle defect area and the abnormal signal sequence, and judging that the defect is a multi-layer composite defect when a preset coupling condition is met; and dynamically adjusting the mesh diameter and vibration frequency parameters of the pneumatic sorting device. According to the technical scheme provided by the invention, accurate identification of the multi-layer composite defect of the chip is realized, and the defect interception efficiency is remarkably improved.

The invention relates to the technical field of recoverable material sorting, and discloses an efficient intelligent sorting system for life source low-value recoverable materials, which comprises an acquisition unit used for monitoring environmental parameters of an urban low-value recoverable material sorting center and the speed of a conveyor belt in real time, synchronously acquiring voiceprint data, three-dimensional point cloud data and hyperspectral data of the recoverable materials, and sending the voiceprint data, the three-dimensional point cloud data and the hyperspectral data to a cloud server; through close cooperation and data interaction among all the functional units, rapid and comprehensive analysis and accurate classification of low-value recoverable materials are achieved, under the complex working condition of a large-scale solid waste treatment scene, the system can recognize and judge the recoverable materials conveyed at a high speed and then classify and sort the recoverable materials, and in the process, the recoverable materials are classified and sorted, so that the recovery efficiency of the recoverable materials is improved. According to the method, the interference of external conveyor belt speed change and environmental factor fluctuation on identification can be reduced, so that the accuracy of overall classification is ensured, and the overall identification effect can completely meet the requirements of mixing and sorting of recyclable materials under complex working conditions.

The invention provides a visual inspection system and an application method thereof. The system comprises an image acquisition module used for acquiring a multi-angle optical image and laser three-dimensional point cloud data; the data preprocessing module is responsible for denoising, geometric correction and multi-modal data alignment; the feature extraction module extracts texture, edge and defect features through a convolutional neural network; the defect detection module identifies cracks, scratches and foreign matters based on feature fusion; the adaptive optimization module dynamically adjusts a detection threshold value and classifier parameters; the result output module generates a detection report and marks defect positions; the feedback calibration module corrects the weight of the detection model according to an artificial rechecking result; the equipment control module triggers the sorting device to remove defective products; and the performance monitoring module counts the detection accuracy and the system response delay. According to the invention, the detection accuracy and the system stability can be improved.

The invention relates to the technical field of intelligent sorting, and discloses a multi-mode defect feature fusion intelligent sorting method and system, and the method comprises the steps: obtaining and preprocessing a surface image, infrared thermal imaging and voiceprint vibration data of an object; analyzing and generating multi-modal defect feature parameters, fusing to form a fused feature vector set, and constructing a defect detection reference set; performing dynamic matching verification on the multi-modal data based on the reference set, and analyzing the mismatching state of the surface texture and the thermal distribution by using a space alignment technology; and generating a defect form deviation degree index according to a verification result, and judging whether a sorting action is triggered or not. The system comprises a multi-modal acquisition module, a feature fusion modeling module, a form association verification module and a sorting judgment module which are used for respectively realizing data acquisition preprocessing, feature fusion modeling, cross-modal association analysis and sorting decision. Through multi-modal data fusion and cross-modal quantitative analysis, the comprehensiveness of defect detection and the sorting accuracy are improved.

The invention relates to the technical field of medical related data processing, in particular to a medical fabric cleaning and disinfecting inspection method and equipment. VMF, LeNet-5 and an edge learning framework are combined, spectral analysis and chip identification technologies are combined, and the method is used for automatic inspection and sorting of medical fabric cleaning and disinfection. Results show that the method is obviously superior to the existing method in the aspects of detection accuracy, speed and efficiency, and a new technical approach is provided for standardized supervision and intelligent management of medical fabric cleaning and disinfection quality.

The invention relates to the technical field of fault prediction, and discloses a construction waste intelligent sorting and cyclic regeneration system which comprises an acquisition subsystem used for installing sensor arrays on the working face of a crusher and the edge of a vibrating screen of a construction waste sorting device and acquiring a feature data set; the simulation subsystem is used for establishing a simulation model of the construction waste sorting device and generating a simulation data set; the actual measurement subsystem is used for collecting an actual measurement abrasion data set with labels under the actual construction waste sorting working condition; the training subsystem is used for inputting the simulation data set and the actually measured wear data set with the label into an adaptive domain adversarial neural network for training to obtain an equipment wear degree prediction model; and the output subsystem is used for identifying wear fault characteristics through the equipment wear degree prediction model and outputting a fault closed-loop response scheme, so that the feeding rate, the crushing rotating speed, the screening frequency and the screening amplitude of the sorting device are dynamically adjusted.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention provides a logistics robot control method and system based on artificial intelligence. After the surface material type, the three-dimensional geometric contour and the surface stress distribution data of a to-be-sorted package are obtained through the logistics robot, the hardness grade and the friction coefficient interval of the package are determined, the geometric model of the package is reconstructed, the minimum external cube size is calculated, and the fragile area is recognized. The adsorption or clamping mode and the unfolding angle are adjusted according to the information, the contact point and track of the grabbing action are optimized, and meanwhile a sorting strategy is generated by considering dynamic factors such as the flow speed of a sorting line. In the execution process, action parameters and decision weights are updated through an artificial intelligence algorithm according to the actual effect, and the sorting strategy is continuously optimized. According to the technical scheme provided by the invention, the control efficiency and precision of the logistics robot can be improved.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

The invention discloses a package defect elimination method based on visual inspection, and relates to the technical field of image processing and computer vision, and the method comprises the following steps: setting a polarized light source with a fixed angle on a package production line, and uniformly illuminating a target package area to enable the incident light polarization direction and the package surface reflection direction to form a deviation angle; an industrial camera provided with a polarization filter is used for carrying out image collection on a packaging target area, and the polarization direction of the filter is perpendicular to the polarization direction of a light source so as to restrain reflection interference to the maximum extent. Reflection interference is eliminated through polarized light, and the image quality is improved; by combining a neural network and feature fusion scoring, the defect recognition accuracy and adaptability are improved; a closed-loop elimination control and data tracing mechanism is introduced, the elimination precision is guaranteed, quality responsibility investigation is supported, and the intelligence and reliability of the packaging defect detection and elimination system are integrally improved.

The invention relates to an intelligent garbage sorting method based on multi-modal recognition. The method comprises the following steps that S1, three-dimensional shape information and material information of objects on a conveying belt are captured; s2, preprocessing the acquired image data and sensor data; s3, based on the preprocessed image data and sensor data, generating a unified bounding box and a classification result based on a Hybrid Net model; s4, based on a bounding box and a classification result output by the Hybrid Net model, a discrete PID algorithm is adopted to calculate a motion instruction, a depth sensor is utilized to calculate three-dimensional point cloud data of an object, the stacking layer number of the position where the object is located is judged, a surface layer object is grabbed preferentially, and an initial grabbing path based on a position error and a stacking priority is generated; and S5, on the basis of the initial grabbing path, DQN deep reinforcement learning is used to optimize the initial grabbing path into an optimal grabbing path. According to the method, the grabbing optimization problem of the robot in a dynamic environment (such as stacking and obstacle interference) is effectively solved.

The invention provides an industrial robot multi-station intelligent sorting and stacking method based on visual guidance, which relates to the technical field of robot control, and comprises the following steps: determining a target station by acquiring multi-station real-time state information, calculating an optimal grabbing time window by using a graph neural network state predictor, and obtaining a target grabbing time window; and the placement strategy is dynamically adjusted based on the visual image and the force sensing data in the article stacking process. According to the multi-station intelligent stacking system, the sorting efficiency in a multi-station scene can be improved, the grabbing precision of dynamic objects is enhanced, and stable and reliable intelligent stacking operation is achieved.

The invention belongs to the technical field of lens calibration, and particularly discloses and provides an optical lens production intelligent calibration system which comprises an environment coupling module, an optical detection module, a self-adaptive calibration decision module and an automatic focusing adjustment module. According to the invention, various environmental data are collected in real time through the environmental coupling module to form a time-space correlation parameter set, so that a basis is provided for suppressing environmental interference; the optical detection module generates a dynamic target sequence and synchronously acquires various parameters of the lens, so that a calibration path can be dynamically optimized; the adaptive calibration decision module not only can establish a mapping relation between environment and optical distortion, but also can calculate a focusing adjustment parameter based on multiple parameters when an error exceeds a threshold value; and the automatic focusing adjustment module controls the focusing device in real time according to the adjustment parameters and verifies the adjustment parameters, so that sorting and recalibration of qualified and unqualified lenses are realized, the calibration accuracy is comprehensively improved, and the scene adaptability of an AF algorithm is enhanced.

The invention provides an intelligent sorting control method and system for renewable resources based on a YOLO framework, and relates to the technical field of control, and the method comprises the steps: collecting a renewable resource image through a camera arranged above a conveyor belt, carrying out the size normalization processing, inputting an improved YOLOv5 deep neural network, carrying out the target detection, and obtaining the position and category information of the renewable resources. The YOLOv5 network adopts an improved CSPDarknet53 backbone network, and a double attention module is introduced into the YOLOv5 network. And according to the detection result, the predicted time for the targets to arrive at the sorting mechanism is calculated in combination with the speed parameters of the conveying belt, multi-target parallel sorting tasks are optimized and scheduled based on a deep reinforcement learning algorithm, a sorting execution time sequence is generated, and the pneumatic sorting mechanism is controlled to achieve accurate classified putting. According to the invention, the sorting efficiency and accuracy of renewable resources are improved, and the sorting cost is reduced.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

Sorting biogenic material from a stream of heterogeneous materials is disclosed, including: detecting biogenic material within an input stream of heterogeneous material; sorting the biogenic material based at least in part on a desired biochar formulation; and tracking a composition of a sorted mixture of biogenic material.

The invention discloses a detection system and method for packaging box production and processing, and belongs to the technical field of packaging production quality detection. The invention discloses a detection system and method for packaging box production and processing. The system comprises a dual-polarization-angle imaging module, a structured light three-dimensional reconstruction module, a temperature compensation module, a defect judgment module and a sorting execution module. According to the method, dual-polarization images and three-dimensional point clouds are synchronously collected, qualified standard values are dynamically calibrated in combination with temperature, accurate recognition and grading judgment of surface and internal defects of the transparent plastic packaging boxes are achieved, and finally unqualified products are removed through a high-speed sorting mechanism. The method solves the problem of misjudgment caused by reflective interference and temperature drift of the transparent material, and is suitable for online detection of a high-speed production line.

The invention relates to the technical field of automation equipment, and discloses a three-temperature test sorting machine, which is characterized in that a heat preservation cover is arranged on a workbench, and a test space is formed by the heat preservation cover and the workbench, so that the consistency of temperature in the test space can be ensured, a chip is uniformly heated in the test space, and automation of three-temperature test of the chip is realized. The temperature of the chip is adjusted in advance by additionally arranging the pre-heating device, so that the whole chip is heated more uniformly, and meanwhile, the waiting time is fully utilized to adjust the temperature in advance, so that the test time is saved, the accuracy of a chip test result and the stability of a test yield can be ensured, and the test efficiency is improved. Moreover, the test efficiency is improved, and large-scale popularization and application are facilitated.

The invention discloses an automatic shock absorber assembling device and method. The automatic shock absorber assembling device comprises a rack, a rotating disc, a graduator arranged on a shell and used for driving the rotating disc to rotate and a plurality of jigs circumferentially arranged on the rotating disc. The shell and lining feeding and press-fitting mechanism, the transferring mechanism, the sealing cover feeding mechanism, the spin riveting mechanism, the torque detection mechanism and the discharging mechanism are arranged on the machine frame. The shock absorber assembling device has the advantages that the integration degree of all the mechanisms is high, products can be rapidly assembled, the shock absorber assembling efficiency is effectively improved, the size of the whole device is reduced, and the manufacturing cost is saved. According to the shell and lining feeding press-fitting mechanism, linings and shells can be fed at the same time, multiple performance detection of the linings can be completed, synchronous movement of the linings in all assemblies is achieved through the linkage transfer mechanism, and the press-fitting efficiency between the shells and the linings is effectively improved.

The invention discloses a nondestructive testing device for a steel profile and a testing method of the nondestructive testing device. According to the method disclosed by the invention, the ultrasonic technology is combined with optical imaging, so that synchronous detection of surface and internal defects can be completed at one time. Ultrasonic waves penetrate through the interior of the steel profile, and hidden defects such as cracks and air holes are accurately recognized; the optical camera module captures visible problems such as surface scratches and corrosion in real time. The integrated detection mode avoids the tedious process of step-by-step operation required by a traditional method, for example, magnetic powder detection of surface defects and ultrasonic scanning of an internal structure are not needed, the detection period is greatly shortened, and the method is especially suitable for steel structure buildings, bridge engineering and other scenes with high requirements for detection efficiency and has good application prospects. The full-process automatic design effectively reduces the influence of human intervention, and ensures the scanning continuity of the detection area; the intelligent algorithm automatically analyzes the ultrasonic echo signal and the optical image data, and judges the defect level according to the preset standard, thereby avoiding subjective misjudgment of manual visual inspection.

The disclosure provides a multiple superposition detection system and a multiple superposition detection method for capacitor appearance defects based on visual detection, relating to a capacitor detection field. The detection system includes a conveying mechanism, a visual detection mechanism, a loading mechanism, a sorting mechanism, a clamping mechanism and a clamping component; a driving mechanism is further arranged in the conveying mechanism. The capacitor is horizontally placed in the clamping mechanism of the conveying mechanism through the negative pressure pick-and-place mechanism, when the conveying mechanism drives the capacitor to sequentially penetrate through the three detection stations, the clamping mechanism is matched with the driving mechanism, and the front face and the side face of the capacitor are displayed to the visual detection mechanism. After detection, high-quality capacitors and defective capacitors are separately stored through the sorting mechanism.

The invention relates to the technical field of intelligent garbage sorting, and discloses a garbage sorting method based on artificial intelligence. According to the method, heterogeneous data fusion is performed on a multi-source garbage feature data set through a semantic alignment module, feature dimensions are unified, redundant items are eliminated, and a standardized feature set is output. And extracting a spatial position identifier and a physical state identifier of the junk entity, and dynamically dividing time slices by adopting a transfer learning model to generate a periodic junk flow data set. The coordinates of the sorting starting point and the sorting ending point are converted into a three-dimensional space grid to construct a migration track topological graph, flow data and physical state identifiers are combined, sorting paths are clustered and analyzed according to material categories, the material flow intensity in a specific period of a target area is quantified, and a dynamic index matrix is generated. According to the method, space-time correlation analysis of multi-modal junk data is realized, and the adaptability of sorting path planning in a complex environment is improved.

The invention discloses an intelligent PCB sorting process and system based on an unmanned factory, and the process comprises the steps: obtaining surface images and internal structure parameters of PCBs in real time through an image collection module and a scanning module on sorting equipment, and generating a standardized feature data set; the lightweight classification model performs classification and quality grade division on the PCBs in combination with a process grade judgment rule, and dynamically allocates sorting priorities based on MES system order demands to generate a task queue; the path planning algorithm calculates the optimal grabbing path of the mechanical arm and the AGV conveying path according to the priority queue, and a sorting instruction set is formed and issued to the execution unit through the industrial communication network; the mechanical arm completes PCB grabbing and placing operation according to the instruction, and meanwhile, the visual verification module detects a sorting result in real time and outputs a sorting state report and quality data; according to the process, high efficiency, accuracy and unmanned operation of PCB sorting are realized through automatic data acquisition, intelligent classification decision and a closed-loop optimization mechanism.

The bamboo cane defect detection table comprises a frame body, a bamboo strip inlet, a bamboo strip outlet, a detection channel, a front power wheel assembly and a rear power wheel assembly are arranged on the frame body; the detection channel is positioned between the bamboo strip inlet and the bamboo strip outlet and is used for forwards conveying bamboo strips; the front power wheel assembly is used for pulling the bamboo strips into the detection channel from the bamboo strip inlet; the rear power wheel assembly is used for pulling and discharging the bamboo strips in the detection channel from the bamboo strip outlet; the frame body is further provided with a visual detection device used for conducting visual detection on the surfaces of the bamboo strips in the detection channel and a laser sensor assembly used for detecting the thickness of the bamboo strips. According to the bamboo strip defect detection table and the detection method, the structural design is ideal, the design that the visual detection device, the laser sensor assembly and the front power wheel assembly and the rear power wheel assembly are matched is adopted, the intelligent degree of bamboo strip defect detection is high, bamboo strip defect detection is comprehensive, the refining degree is high, and the stability of the frame body is good. And various defects such as pits, chipped edges, split strips and worm holes can be picked out, and high-quality bamboo strip products can be screened out.

The invention discloses an impurity sorting and removing system based on machine vision, and the system comprises a data collection and preprocessing layer which is responsible for obtaining multi-dimensional physical characteristics of tea leaves and impurities, and constructing three-dimensional scene representation; the feature analysis and decision-making layer is used for extracting light field fusion features by utilizing a ResNeSt-50 backbone network, directly outputting a preliminary classification confidence coefficient through a full connection layer, forming a first layer of decision-making candidates, simulating branches by combining rigid body dynamics to predict a blade movement track, performing Bayesian correction on the preliminary classification confidence coefficient after a shielding probability graph is sampled and calculated through Monte Carlo Dropout, and forming a second layer of decision-making candidates; meanwhile, a Cook-Torrent BRDF model is integrated to carry out material reflection correction, a feature map after material correction is output, and a correction value is converted into decision threshold offset; and the knowledge storage and update layer is used for constructing a bimodal memory pool composed of a gradient direction matrix and an LRU elastic cache, and generating a pseudo sample supplement long-tail category in combination with the four-layer full-connection pulse neural network.

The invention discloses a rapid sorting method for retired lithium ion batteries based on an electrochemical impedance spectroscopy and a semi-parameter clustering algorithm, which comprises the following steps of: firstly, performing electrochemical impedance spectroscopy test and relaxation time distribution analysis on the retired lithium ion batteries to obtain electrochemical impedance spectroscopy data and relaxation time distribution peak value data; and then, extracting features from the electrochemical impedance spectroscopy data by using a convolutional auto-encoder, extracting features from relaxation time distribution peak data by using a full-connection neural network, carrying out feature splicing on the two features, and inputting the spliced features into a deep neural network to quickly obtain the estimated capacity of the retired battery. And finally, taking the ohmic internal resistance, the impedance characteristic information and the estimated capacity of the retired lithium ion battery extracted from the electrochemical impedance spectrum data and the relaxation time distribution peak value data as the input of a semi-parameter clustering algorithm, and carrying out rapid sorting on the retired battery. According to the method, the capacity of the decommissioned lithium ion battery can be quickly predicted, outliers of the decommissioned battery are eliminated, and the decommissioned battery is quickly sorted.

The invention discloses an intelligent identification and sorting method for waste textiles, and belongs to the technical field of textile recovery. An intelligent identification and sorting method for waste textiles comprises the steps of scanning recycled waste textiles by using a hyperspectral camera, obtaining hyperspectral image data of the waste textiles, preprocessing the obtained hyperspectral image data to improve data quality, constructing a material identification model, inputting dimension-reduced hyperspectral features into the model, and identifying the waste textiles according to the dimension-reduced hyperspectral features. Through training and learning spectral feature differences of different materials, the materials of the waste textiles are accurately identified; recording the surface condition of the waste spinning through a monitoring device, and analyzing the influence of the surface condition on spectrum identification; according to the material recognition result and the surface recognition result, sorting equipment is controlled to carry out sorting operation on polyester textiles in the waste textiles; a sorting result is fed back to a control system, the sorting accuracy rate is recorded, and the performance of the recognition model and the sorting equipment is evaluated and optimized.

The invention relates to the technical field of waste management, in particular to a building solid waste intelligent disposal and reutilization system and method based on the Internet of Things, and the system comprises a solid waste classification optimization module, a solid waste disposal adaptation module, a recycling path planning module, an environmental impact evaluation module and a scheduling optimization module. According to the method, the sorting priority is dynamically adjusted by recognizing the particle size, density and energy consumption change of the solid waste, the recognition precision and separation efficiency of the mixed materials are improved, the path is compared and adjusted in combination with the equipment capacity, the solid waste attributes are matched, resource loss and mistaken sorting are avoided, the trend and concentration degree are extracted in the recycling link, and the path matching degree is improved; according to the method, the proportion of unavailable components is reduced, environmental load and energy consumption change are fused in the processing process, a high-emission section is adjusted, environmental impact is relieved, jump frequency is analyzed in combination with task switching, the adaptability and scheduling continuity of the system to change interference are enhanced, and resource utilization continuity, response sensitivity and environmental adaptation level are integrally improved.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention provides an intelligent sorting method and system for radioactive substances. The method comprises the following steps: synchronously detecting surface element composition and radiation intensity of a radioactive substance through an X-ray fluorescence spectrometer, and constructing a spatial difference matrix of element abundance distribution and a radiation gradient topological graph; the three-dimensional coordinates, the pressure threshold value and the shape adaptation parameters of the target grabbing position are determined on the basis, the curvature radius of the clamping jaw deformation contour is dynamically adjusted, and the error of the dynamic geometric envelope and the contour template is converged to be within the threshold value; finally, when the clamping jaw reaches a target position, the area of the heat shrinkage material is activated to be heated according to the radiation intensity abrupt change boundary coordinates, a sealed isolation layer synchronously expanded with the dynamic geometric envelope is generated, and safe grabbing and sealed isolation of radioactive substances are achieved. According to the technical scheme, accurate grabbing of radioactive substances, error self-adaptive compensation and synchronous isolation of radiation leakage can be achieved, and the sorting safety and efficiency are improved.