6354 results about "Weld seam" patented technology

The present application relates to a weld seam surface defect detection method and system. The weld seam surface defect detection method comprises: performing multi-angle and multi-surface acquisition of target surface images; then, reconstructing normal maps of the target surface images by means of a high-performance algorithm, and clarifying normal features of defects; and finally, on the basis of the surface normal features, aiming at transverse texture defects and height mutation defects, respectively designing a transverse texture defect detection algorithm based on two stages and rotated rectangle extraction and a height mutation defect detection algorithm based on adaptive dual-threshold segmentation. The present application overcomes texture noise and pseudo-defect interference of ground surfaces, effectively solves the problems of difficulty in feature extraction and positioning of ground surfaces of steel rail weld seams, and has the system operating efficiency superior to that of existing detection methods, thus satisfying requirements of actual production.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention relates to a multi-product-line-oriented robot welding integration method, device and equipment and a medium, and the method comprises the steps that three-dimensional geometrical characteristics and material attribute data of multiple types of workpieces are collected, and geometric parameters and material mechanical properties of weld joints are extracted; an order demand template is intelligently matched, and initial welding parameters and a clamp configuration scheme are generated; the health state of production equipment is evaluated in real time, and the collision-free movement track of the mechanical arm is planned; dynamically analyzing the joint angle sequence to generate a high-precision servo control signal; the welding process is monitored in real time, and current and molten pool temperature parameters are corrected; and iteratively optimizing the algorithm weight of the process knowledge base to improve the adaptive capacity. Through cooperation of multi-source data fusion and an intelligent algorithm, rapid switching of multiple kinds of workpieces, self-adaptive compensation of the equipment operation state and closed-loop optimization of welding process parameters are achieved, and the equipment utilization rate and the welding quality stability in a flexible production scene are remarkably improved.

The invention provides an aluminum alloy auxiliary frame welding accurate positioning and quality control method and system. The method comprises the steps that S4, welding gun track control parameters are updated through a dynamic adjustment instruction, laser tracking data are fused to optimize the welding path angle in real time, and the accurate movement track of each section of welding seam is determined; s5, acquiring real-time thermal expansion data during welding execution, monitoring temperature field distribution of a welding seam area by adopting an optical measurement technology, and judging whether thermal deformation adjustment exceeds a preset threshold value or not; s7, micron-sized welding spot positioning operation is executed according to the stable positioning state, the dynamic path optimization result is fused to generate a final welding instruction, and consistent welding seam quality data is output; and S8, after weld joint quality data are obtained, weld joint deviation and geometric consistency are detected through a laser tracking technology, whether welding quality meets design requirements or not is judged, and a quality verification report is generated. According to the method, the precision and consistency of intelligent welding of the aluminum alloy auxiliary frame are remarkably improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention discloses a mechanical welding seam defect identification method, and relates to the technical field of welding quality intelligent detection and defect identification, and the method comprises the steps: S1, collecting welding seam surface image data, obtaining a standardized image data set, carrying out the feature extraction of the standardized image data set, and obtaining a feature extraction result; the method comprises the following steps: S1, extracting edge contours, texture distribution and gray change features of defects, and generating a high-dimensional feature vector containing space position coordinates and morphological features of the defects, S2, generating a weld quality evaluation database containing defect distribution uniformity and defect severity scores according to edge continuity parameters and gray uniformity parameters in the high-dimensional feature vector; according to the mechanical welding seam defect identification method, accurate identification, classification and positioning of welding seam defects can be achieved, comprehensive evaluation of the welding seam quality is given, and the intelligent level and reliability of welding quality control are effectively improved.

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention discloses an intelligent welding forming method and system for a green building steel heating radiator, and the method comprises the following steps: carrying out the surface defect recognition of a steel heating radiator base material based on an AI visual inspection system, recognizing a qualified base material, and automatically matching the type of a welding material from a material database according to the material and thickness parameters of the qualified base material. Welding parameters are intelligently matched through AI visual inspection and a neural network model, a laser and friction stir hybrid welding process is combined, traditional manual operation is replaced, the welding efficiency and precision are improved, and the problems of uneven welding seams and the like are solved; welding data are analyzed in real time through a multi-mode AI model, parameters are dynamically adjusted, intelligent defect recognition and repair welding are achieved in cooperation with 3D visual inspection, and the quality stability is improved through whole-process monitoring; smoke dust is treated through an environment-friendly process, acid pickling is replaced with mechanical rust removal, efficient recycling of materials is achieved through waste recycling, a green manufacturing system is constructed, and the sustainable development requirement of green buildings is met.

The invention relates to the technical field of material nondestructive testing, and discloses a membrane structure welding seam defect detection method based on image recognition, which is used for solving the problems of low defect segmentation accuracy and incapability of effectively recognizing internal defects caused by continuous image gray change of lap welding seams of unequal-thickness flexible materials in a traditional method. The method comprises the following steps: firstly, collecting an original image of the lap weld of the unequal-thickness flexible material, carrying out gray conversion, analyzing thickness gradient distribution, adjusting a gray value, carrying out region segmentation to lock a weld range, extracting potential defect edge features to form a candidate region, and carrying out classified verification to confirm internal defects. Aiming at the problem of low defect segmentation accuracy caused by continuous change of image gray in the prior art, the method improves the defect identification precision through segmented mapping and boundary tracking logic, and is suitable for membrane structure engineering quality control.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the field of welding, and discloses a non-standard intelligent customized welding system based on vision and surface gradient, which comprises a visual perception module used for iteratively approaching a workpiece from a preset initial height through a self-adaptive high angle shooting exploration mechanism, and combining layered grid division based on a camera view and a progressive multi-angle expansion scanning mode, collecting multi-view three-dimensional point cloud data of the workpiece; and the point cloud processing module is used for performing regional progressive registration on the multi-view three-dimensional point cloud data, implementing threshold constraint based on point cloud curvature characteristics by dynamically adjusting registration step length, and eliminating accumulative errors in combination with pose map optimization. Through self-adaptive high-angle shooting and layered grid scanning, multi-view point cloud can be automatically collected without manually presetting a workpiece model, regional registration and curvature constraint are combined, the workpiece model is constructed, a weld joint structure is automatically extracted based on surface gradient features, traditional manual labeling is replaced, a welding track is generated according to weld joint topology, and the precision is dynamically corrected.

A metal-part welding apparatus based on an angular direction change, comprising a base (1) and a welding torch body (2). The apparatus does not require the cooperation between two operators for welding, and by means of the welding torch body, circumferential weld seams can be automatically welded, thereby improving the work efficiency and welding precision. The present disclosure also relates to a metal-part welding method based on an angular direction change.

The invention relates to the technical field of artificial intelligence welding, and discloses an automatic welding robot based on artificial intelligence and a welding system thereof. The system comprises a welding feature acquisition module, a process parameter generation module, a real-time regulation and control module and a motion planning module. The welding characteristic acquisition module is used for acquiring three-dimensional contour data, material component information and welding seam geometric parameters of a workpiece to be welded, and generating a welding characteristic spectrum through characteristic fusion; a process parameter generation module retrieves a matching template from a welding knowledge base according to the parameters, and outputs a reference welding process parameter combination in combination with an environment temperature and humidity compensation coefficient; the real-time regulation and control module dynamically corrects the reference parameters to generate an optimization instruction according to the molten pool form, thermal radiation distribution and electric arc voiceprint characteristics in the welding process; and the motion planning module calculates a motion track, attitude parameters and a speed curve of the welding execution mechanism according to the optimization instruction to form a robot control instruction set. The system can improve the intelligent level of welding, guarantees stable welding quality, and is suitable for welding scenes of multiple manufacturing industries.

A weld seam detection method and apparatus, an electronic device, and a storage medium, relating to the technical field of battery production. The method comprises: on the basis of a first detection image acquired by an image acquisition device for a weld seam to be detected, determining defects and defect parameters of said weld seam; obtaining scores of the defects on the basis of the defect parameters; and determining a detection result of said weld seam on the basis of the scores of the defects.

The invention discloses an intelligent welding quality evaluation method and system based on vision, and relates to the technical field of welding quality evaluation.The method comprises the steps that the welding process is monitored in real time through multi-mode imaging equipment, weld joint surface texture, molten pool heat distribution and three-dimensional shape information are synchronously obtained, and a three-dimensional voxel model is constructed; establishing a two-stage multi-channel architecture for executing dynamic resource scheduling, and extracting geometric features and deep semantic features in the three-dimensional voxel model; and on the basis of the defect confidence coefficient vector, in combination with the real-time process parameters and the molten pool state, a dynamic evaluation model of the welding quality index is constructed, and time-phased quality scores are calculated and output through stage weighting. According to the method, the welding seam surface texture, molten pool heat distribution and three-dimensional shape information are synchronously collected through multi-mode imaging equipment, a texture-temperature-space coordinate bound three-dimensional voxel model is constructed, and multi-source heterogeneous data fusion analysis of the welding quality is achieved.

The invention provides an industrial robot welding track real-time optimization method and system based on deep learning, and relates to the technical field of industrial robots, and the method comprises the steps: collecting three-dimensional point cloud data, carrying out the multi-scale processing to generate a high-precision curved surface, constructing a welding seam feature descriptor, and carrying out the real-time optimization of the welding track of an industrial robot; and the mapping relation between the technological parameters and the welding posture is optimized on the basis of a multi-target reward function, the mapping relation is converted into an initial movement track, the track is dynamically optimized through a segmented self-adaptive optimization model and a variable structure filtering algorithm, and self-adaptive adjustment of the welding process is achieved. The welding precision and quality of the complex curved surface are improved, and the adaptability and stability of robot welding are enhanced.

The invention discloses a body-in-white welding seam recognition and automatic welding method based on a machine vision technology, particularly relates to the technical field of computer vision and image processing, and is used for solving the problem of welding seam track recognition accuracy caused by insufficient processing capability of an existing three-dimensional vision recognition method on incomplete and uncertain point cloud data. Through the steps of multi-view point cloud acquisition and registration, probabilistic confidence evaluation, region growth of track continuity constraint, multi-track fusion optimization and the like, accurate identification of a body-in-white welding seam track under a complex working condition is realized; firstly, multi-view point cloud data are obtained, probabilistic registration is carried out to generate a confidence evaluation result, then candidate tracks are generated based on confidence weighting and semantic constraint, finally, an optimal track is generated through intelligent optimization and converted into a welding instruction which can be executed by a robot, and the accuracy and robustness of weld joint recognition are effectively improved.

The invention relates to the technical field of mechanical engineering, in particular to a three-dimensional simulation design method and system based on an elevator structure. The method comprises the following steps that an elevator structure drawing is obtained; a three-dimensional elevator assembly is constructed according to the elevator structure drawing; lift car static load simulation is conducted according to the three-dimensional elevator assembly, and lift car static load data are obtained; detecting the structural strength of the lift car based on the lift car static load data; evaluating welding defects based on the structural strength of the lift car, and generating welding defect data; identifying a weld weak area according to the welding defect data; determining the abrasion loss of the car structure based on the weld weak area; according to the lift car structure abrasion loss, the lift car bottom plate fracture risk is predicted; carrying out welding optimization design based on the fracture risk of the bottom plate of the lift car to obtain welding optimization design data; and traction and traction simulation is conducted according to the three-dimensional elevator assembly, and traction and traction data are obtained. Based on the mechanical engineering technology, the safety and stability of the elevator structure are improved, and the design precision and efficiency are remarkably improved.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention discloses an automatic welding device for a rotary alloy furnace tube, and belongs to the technical field of arc welding equipment.The automatic welding device for the rotary alloy furnace tube comprises a base and a U-shaped bin located at the top of the base, and the two ends of the interior of the U-shaped bin are jointly provided with a centering clamping mechanism used for clamping the outer side of the furnace tube; a driving mechanism for providing power for the centering clamping mechanism to clamp the furnace tube is arranged on the outer side of the centering clamping mechanism, and a welding mechanism is arranged on the inner top of the U-shaped bin. A local sealing design is adopted for the inflation mechanism, the annular air bag is attached to the inner wall of the furnace tube after being inflated, an independent cavity communicated with the outside only through a welding seam gap is formed near the welding seam, the protection requirement can be met only by filling the local space with argon, the whole furnace tube or a large-range external space does not need to be filled, and compared with traditional overall argon filling, the argon filling efficiency is greatly improved. The argon consumption is greatly reduced, the industrial gas purchase cost is remarkably reduced, and the production cost investment is reduced.

The invention discloses a ship plane segmented welding multi-robot path planning system based on visual inspection, and relates to the technical field of ship manufacturing, the ship plane segmented welding multi-robot path planning system comprises a multispectral visual inspection module which is mounted on a portal frame and comprises a laser scanning sensor and an infrared thermal imager, the system is used for collecting three-dimensional point cloud and temperature distribution data of ship plane segments in real time. According to the ship plane segmented welding multi-robot path planning system based on visual inspection, the problem of path planning misalignment caused by assembly errors, thermal deformation and complex working conditions in large ship plane segmented welding is fundamentally solved through a multispectral visual real-time perception and material self-adaption dynamic correction mechanism. A composite correction vector and space-time grid collaborative pre-judgment technology is adopted, the welding seam track precision is effectively improved, it is ensured that the weld leg size strictly reaches the standard in the high-difficulty processes such as wrap angle welding and vertical position welding of the maximum-size workpiece, the defects such as air holes and cracks are eliminated, and the rework rate and the quality risk are greatly reduced.

The invention belongs to the technical field of robot welding, and discloses a welding robot motion path optimization planning method based on multi-sensor fusion. Comprising the steps that sensor data are obtained, and the sensor data comprise a welding seam three-dimensional point cloud, a molten pool image, acceleration and angular velocity of the tail end of a welding gun in three axial directions, welding current and welding voltage; converting the sensor data into a unified coordinate system to obtain converted sensor data; performing filtering processing on the converted three-dimensional point cloud of the welding seam to obtain a filtering point cloud, and performing feature extraction on the filtering point cloud based on an RANSAC (Random Sample Consensus) algorithm to obtain a center line feature of the welding seam; in combination with the converted three-dimensional point cloud of the welding seam and the characteristics of the center line of the welding seam, an initial welding path is obtained through planning based on an improved A * algorithm; intelligent and precise adjustment of welding parameters is achieved, the stability of welding quality is guaranteed, and the performance of motion path planning and welding operation of the welding robot is improved in an all-around mode.

The invention discloses a welding robot system based on body sensing and an intelligent route generation method, and belongs to the technical field of robot welding, the welding robot system comprises a body sensing module, a fusion processing module, an intelligent route generation module and an execution feedback module, the body sensing module collects multi-modal data in parallel through multiple sensing units and preprocesses the multi-modal data; the fusion processing module combines a three-dimensional attention mechanism and digital twinning mapping to realize deep feature fusion, the intelligent route generation module outputs an adaptive path through planning-optimization-layered correction, and the execution feedback module constructs a full-closed-loop dynamic adjustment mechanism. By the adoption of the system and method, the problems that physical and virtual scenes are not synchronous, dynamic interference response is insufficient and path adaptability is poor in complex welding seam welding are accurately solved, and the welding path fitting degree and quality stability are remarkably improved.

The invention discloses a welding seam monitoring system based on an electric welding machine, and relates to the technical field of electric welding, an initial setting module is used for setting a normal fluctuation threshold range of each working parameter by collecting working information of the electric welding machine in a fault-free state, and the working information at least comprises welding current, voltage and welding speed; when it is determined that one or more pieces of working parameter information collected in real time exceed the preset normal fluctuation threshold range, whether short-term fluctuation or fault fluctuation exists is judged by judging the change of the parameters, and therefore parameter adjustment caused by misjudgment of the electric welding machine in the short-term fluctuation process can be prevented; and the quality defect of the welding seam is caused.

The invention provides a point cloud welding seam recognition method combining a 2D segmentation model and spatial features, relates to the field of welding automation, and solves the technical problem of low welding seam recognition precision in the prior art. The method comprises the following steps: acquiring point cloud data of the surface of a to-be-identified workpiece by utilizing three-dimensional laser scanning equipment; preprocessing the point cloud data, and projecting the preprocessed point cloud data to a two-dimensional image to obtain two-dimensional data; performing semantic segmentation on the two-dimensional data by adopting a pre-trained image segmentation model, and identifying a welding seam region; back-projecting the weld joint area to a point cloud space, and extracting spatial features of adjacent planes of the weld joint; calculating an included angle and a distance between the adjacent planes based on the spatial characteristics of the adjacent planes, and judging the welding seam type; and according to the welding seam type and the spatial characteristics of the adjacent planes, calculating to obtain end point coordinates of the welding seam. The device is used in the industrial welding automatic production process.

The invention provides a self-adaptive teaching-free welding robot intelligent gas saving monitoring system, which relates to the technical field of industrial robots, and comprises a three-dimensional sensing module used for identifying the workpiece material category through a multispectral sensor integrated with a welding gun of a welding robot, meanwhile, a stereoscopic vision camera and a line structure light scanning camera are used for obtaining three-dimensional point cloud data of the welding seam in real time; the parameter adjusting module is used for calling a heat conductivity coefficient database based on the three-dimensional point cloud data, and correcting the initial flow of the welding current, the arc voltage and the protective gas in real time; and the matching control module is used for calculating the arc length variable quantity based on the arc voltage, controlling the opening degree of the gas flow valve through a dynamic regulator, realizing dynamic matching of the gas flow and the changed arc length, and generating gas dynamic matching control parameters. Through cooperative operation of self-adaptive attitude compensation and real-time gas consumption monitoring, teaching-free accurate operation in the welding process is achieved, and intelligent control over gas consumption is improved.

The invention discloses a robot system for steel structure welding, relates to the field of steel structure machining equipment, and aims at solving the problem that a steel structure can be welded by collecting robot track data and a welding seam image in the welding process, constructing a track continuity evaluation model, identifying a path change trend and fusing an image structure and track change information. The position and confidence of a track interruption section are recognized, so that the high-precision recognition and automatic recovery capability after path interruption is improved; a smooth compensation track is generated by adopting third-order Bessel interpolation based on starting and ending points of an interruption section, curvature and posture consistency registration with an original track is completed, and continuity and stability of a welding path are ensured; multi-modal data such as current, voltage, temperature, acoustics and welding seam images are collected through multiple channels, welding abnormity is recognized, state feature vectors are formed, and the problems such as splashing abnormity and welding seam deviation can be found in real time; through multi-dimensional analysis and dynamic adjustment of the whole welding process, the stability, intelligence and adaptability of the welding system are remarkably improved.

Disclosed is a robot welding assembly mechanism and an application in annular welding seam detection and postweld heat treatment. The robot welding assembly mechanism comprises a rotatable column. A plurality of transverse clamping portions are disposed at intervals on the column. A height-adjustable first V-shaped support portion is disposed at a periphery of the column. The first V-shaped support portion is mounted on a radial track. An annular track is disposed on an outer side of the radial track. A plurality of height-adjustable second V-shaped support portions are disposed on the annular track. The first V-shaped support portion and the second V-shaped support portions are capable of supporting a non-welded portion of the welded workpiece. A first driving mechanism of the rotation base, a second driving mechanism of the transverse clamping portions, and a third driving mechanism of each of the second V-shaped support portions are connected with a control system.

The invention discloses a welding track planning method and device of a welding robot, the welding robot, a storage medium and a computer program product, and the method comprises the following steps: inputting point cloud data of a workpiece into a point cloud semantic segmentation model to obtain welding seam point cloud data of a linear welding seam of the workpiece; fitting the welding seam point cloud data to obtain a welding seam welding spot of the linear welding seam; welding spot fine adjustment and pose estimation are carried out on the welding spot of the welding seam, and the welding track of the linear welding seam is obtained. According to the scheme, the semantic segmentation model is used for processing the point cloud data, welding seam fitting and welding spot fine adjustment are carried out, the point cloud of the linear welding seam can be accurately recognized and extracted, the efficiency and accuracy of welding seam detection are improved, and the precision and adaptability of welding track planning are further improved.