Method for combining paths and laser processing planning based on machine vision

Abstract

The invention relates to the technical field of path control, in particular to a combined path and laser processing planning method based on machine vision, which comprises the following steps: acquiring an image by an industrial camera to extract contour measurement parameters to identify defective materials, setting path start-stop optimization connection smooth obstacle avoidance based on a feature table, and detecting a dynamic adjustment range of a hot area. And according to the adaptive group setting threshold evaluation capability, the delay prediction adjustment power speed is calibrated, an instruction set is called to compensate, predict and correct error dynamic matching, and an optimal processing path and a synchronous processing setting table are output. According to the method, the workpiece image is obtained in real time, the machining features are analyzed, the machining precision and efficiency are improved, fine control in the machining process is achieved, effective cooperation of path planning and machining parameters is ensured, the problem of mismatching of path planning is avoided, the response precision and stability in the machining process are improved, and the machining precision and efficiency are improved. The processing quality and time management are remarkably improved, and low efficiency and quality fluctuation caused by manual adjustment in the prior art are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path control, in particular to a method for combined path and laser processing planning based on machine vision. BACKGROUND

[0002] The technical field of laser processing involves the interaction of laser beams with materials to achieve high-precision processing. This field mainly includes laser cutting, laser marking, laser welding, and other processes. Path control is one of the core technologies, focusing on how to optimize and plan laser paths according to processing tasks to ensure efficient and accurate processing. The goal of path planning is to maximize processing precision and minimize processing time while addressing the complexity of different materials. This field also includes laser power regulation and processing speed control to ensure stability and efficiency during processing. Traditional laser processing path control methods involve controlling the laser processing process through pre-set fixed paths. However, in practical applications, there are often problems such as poor coordination between path planning and processing parameters, and poor adaptability to complex situations. Traditional techniques rely on simple path planning algorithms and manual adjustment of processing parameters, ignoring changes in actual working conditions during processing, resulting in low efficiency or unstable processing quality.

[0003] Existing path control methods rely on pre-set paths and ignore the dynamic changes in actual working conditions during processing, leading to insufficient coordination between path planning and processing parameters. In practical applications, traditional techniques cannot adjust in real-time according to the actual shape, surface defects, or material properties of the workpiece, which can lead to low processing precision or long processing time. Due to the use of fixed path planning algorithms and manual adjustment of processing parameters, complex working conditions cannot be addressed, resulting in low efficiency or unstable quality. For example, in the processing of complex-shaped workpieces, the rigidity of path planning and the lack of real-time data feedback adjustment mechanism make the path unable to adapt to the curvature or surface changes of the workpiece, resulting in wasted processing time or poor product surface quality. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to provide a method for combined path and laser processing planning based on machine vision.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a method for combined path and laser processing planning based on machine vision, comprising the following steps: S1: Real-time acquisition of processing area images through an industrial camera, contour extraction and geometric parameter measurement of the workpiece shape, type classification and spatial positioning of workpiece surface defects, physicochemical property identification and component composition analysis of workpiece materials, and generation of a processing feature distribution table; S2: Based on the machining feature distribution table, the starting point and the ending point of the multi-objective machining path are set, the connection order of the multi-path segment is optimized, the smoothing processing of the path inflection point and the path of the obstacle avoidance area are adjusted, and a path planning priority list is generated; S3: According to the path planning priority list, the real-time measurement of the workpiece surface curvature and the change trend are captured, the boundary dynamic detection and diffusion evaluation of the machining area and the heat affected zone are carried out, the range demarcation and condition matching of the machining interval are dynamically adjusted, and the machining adaptation relationship group is established. S4: According to the machining adaptation relationship group, the parameter threshold setting and execution capability evaluation of the current machining requirement are carried out, the delay time and stability prediction of the response capability are calibrated, the continuous stepless adjustment of the laser power and the synchronous adjustment of the scanning speed are carried out, and the machining parameter collaborative control instruction set is output.

[0006] As a further scheme of the present application, the machining feature distribution table includes workpiece number, shape feature identification, defect distribution mark, material property label, the path planning priority list includes path weight value, machining sequence identification, priority level, the machining adaptation relationship group includes curvature change interval, heat affected range identification, machining feasibility determination value, and the machining parameter collaborative control instruction set includes power level code, speed adjustment direction, and periodical adjustment identification.

[0007] As a further scheme of the present application, the generation step of the machining feature distribution table is specifically: S111: Real-time image of the machining area is acquired by an industrial camera, the image is input into a deep learning model for processing, pixel-level features are extracted and mapped, workpiece contour boundaries are identified, topological structures are analyzed, surface defect categories are identified and spatial position is located, material spectral response characteristics are analyzed and physical properties are inferred, and a machining feature sequence is generated. S112: The machining feature sequence is called to perform matching analysis of workpiece geometric shape and defect distribution pattern, risk defect areas are identified and machining priority is labeled, and a key machining area index set is generated. S113: According to the key machining area index set, geometric shape consistency areas are identified, material uniformity areas are verified, non-critical defect areas are marked and machining stability is evaluated, and a machining feature distribution table is generated.

[0008] As a further scheme of the present application, the generation step of the path planning priority list is specifically: S211: Call the machining feature distribution table, extract the geometric feature and boundary information of the key machining area, identify the defect type, size, position information and carry out quantization, preliminarily construct multiple alternative paths and set key nodes, evaluate and convert the path length, bending degree, obstacle avoidance, surface roughness requirement factors, and generate a path weight set; S212: According to the path weight set, compare and analyze the path weight values of each path and judge the size, arrange the paths, determine the priority level of the paths, and generate a path priority sequence; S213: Based on the path priority sequence, the compliance of each path with the preset machining parameters is verified, and the paths that do not meet the machining precision requirements, have material damage risk or low machining efficiency are identified and filtered, and a path planning priority list is generated.

[0009] As a further scheme of the present application, the generating step of the machining adaptation relationship group is specifically: S311: Call the path planning priority list to perform real-time scanning and three-dimensional reconstruction of the workpiece surface curvature, identify the curvature gradient and mutation point, dynamically monitor the machining area temperature field and construct a heat diffusion model, and track and evaluate the morphology of the heat affected area boundary in real time, and obtain the machining environment state parameters by summarizing the curvature change and heat affected data; S312: According to the machining environment state parameters, set the upper and lower limits of the curvature change range, define the allowable threshold of the heat affected range and the safety boundary, compare and analyze the region parameter data point by point, identify and remove the regions that do not meet the machining precision or material integrity requirements, select the intervals that meet the machining requirements, and obtain a machining available interval set; S313: Call the machining available interval set to perform spatial positioning and geometric matching of the path segments in the machining path planning priority list, calculate the overlap degree and compatibility of the path segments and available intervals, analyze the corresponding relationship between the path and the interval characteristics, identify the machining parameter adjustment potential, map the adaptation relationship between the machining interval and the path, calculate the evaluation index value of the machining adaptation relationship, and generate a machining adaptation relationship group.

[0010] As a further scheme of the present application, the generating step of the machining parameter cooperative control instruction set is specifically: S411: According to the machining adaptation relationship group, extract the surface roughness requirement and morphology accuracy index in the current machining interval, set the material removal rate target and heat damage control threshold, quantitatively evaluate the laser power adjustment range and response speed, detect the scanning galvanometer speed and acceleration performance, match the machining demand and response ability, and generate a machining demand deviation data group; S412: call the processing requirement deviation data set, compare the parameter deviation value with the preset allowable range one by one, identify and classify the deviation items exceeding the allowable range, divide the matching degree between the processing requirement and the response ability into levels and state representation, mark the key parameter matching degree insufficient area, generate a matching state identification set; S413: according to the matching state identification set, verify the retrieval of the processing interval number and the spatial position, calculate the increment or decrement of the required laser power in the current interval, set the acceleration or deceleration amplitude of the scanning speed, identify the power and speed adjustment instructions, and generate a processing parameter cooperative control instruction set.

[0011] As a further scheme of the application, the quantitative evaluation of the laser power adjustment range and the response speed refers to detecting and quantifying the dynamic adjustment ability of the laser power and the response speed of the laser to the adjustment instructions; The step of comparing the parameter deviation value with the preset allowable range one by one refers to respectively comparing the laser power adjustment range deviation, the response speed deviation, the scanning galvanometer speed deviation and the scanning galvanometer acceleration deviation included in the processing requirement deviation data set with the preset allowable range.

[0012] As a further scheme of the application, the method further comprises a step S5: S5: call the processing parameter cooperative control instruction set, perform real-time delay compensation and prediction of the processing path, analyze the fluctuation characteristics and error correction of the response time, perform dynamic correction of the path and corresponding processing parameters, and output an optimal processing path and a synchronous processing setting table; The optimal processing path and the synchronous processing setting table include path delay characteristics, synchronous processing time points and response characteristics.

[0013] As a further scheme of the application, the step of generating the optimal processing path and the synchronous processing setting table is specifically: S511: call the processing parameter cooperative control instruction set, analyze the difference between the laser scanning head motion instruction and the real-time response time sequence, monitor the power adjustment and scanning galvanometer cooperative action lag amount, determine the fluctuation range of the response time under differentiated load conditions, calculate path delay fluctuation characteristic values, and generate path delay fluctuation correlation coefficients; S512: according to the path delay fluctuation correlation coefficients, analyze the complexity of the path geometry, perform parameter mapping of the material thickness and hardness changes, set the accuracy requirement and stability margin of the synchronous processing conditions, evaluate the continuity and smoothness constraints between path segments, screen a path set meeting the synchronous processing conditions, and generate a synchronous processing judgment value interval. S513: Call the synchronous processing decision value interval, match the response speed and accuracy of laser power adjustment, control the acceleration limit and smoothness of scanning galvanometer speed change, optimize the transition time and seamless connection of path segment parameter switching, determine the optimal processing path and corresponding processing parameters, and output the optimal processing path and synchronous processing setting table.

[0014] Compared with the prior art, the advantages and positive effects of the present application are that: In the present application, by real-time acquisition of workpiece images and processing feature analysis, the laser processing path is dynamically optimized based on the actual situation of the workpiece, improving the accuracy and efficiency of the processing process. Through automatic extraction of the geometric parameters of the workpiece, defect classification and material identification, fine control in the processing process is realized, ensuring effective cooperation of path planning and processing parameters. The range and conditions of the processing area are dynamically adjusted, and according to multiple real-time data such as surface curvature and heat-affected zone, the path and parameters in the processing process are optimized, avoiding the problem of mismatched path planning in processing. The synchronous adjustment of laser power and scanning speed makes the response capability in the processing process more accurate and stable, significantly improving the processing quality and time management, thereby avoiding the low efficiency and quality fluctuations caused by manual adjustment in traditional technology. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a schematic diagram of the main steps of the present application; Figure 2 is a flowchart of the acquisition process of the processing feature distribution table in the present application; Figure 3 is a flowchart of the acquisition process of the path planning priority list in the present application; Figure 4 is a flowchart of the acquisition process of the processing adaptation relationship group in the present application; Figure 5 is a flowchart of the acquisition process of the processing parameter collaborative control instruction set in the present application; Figure 6 is a flowchart of the acquisition process of the optimal processing path and synchronous processing setting table in the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0017] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited. Embodiment one

[0018] Please refer to Figure 1 The present application provides a technical solution: a method for combined path and laser processing planning based on machine vision, comprising the following steps: S1: real-time acquisition of processing area image by industrial camera, contour extraction and geometric parameter measurement of workpiece shape, type classification and spatial positioning of workpiece surface defects, physicochemical property identification and component composition analysis of workpiece material, and generation of processing feature distribution table; S2: based on the processing feature distribution table, setting the starting point and ending point of the multi-target processing path, and optimizing the connection order of the multi-path segment, adjusting the smoothing processing of the path inflection point and the path of the obstacle avoidance area, and generating the path planning priority list; S3: according to the path planning priority list, real-time measurement and change trend capture of workpiece surface curvature, dynamic detection and diffusion evaluation of processing area and heat affected zone boundary, dynamic adjustment of processing interval range and condition matching, and establishment of processing adaptation relationship group; S4: according to the processing adaptation relationship group, parameter threshold setting and execution capability evaluation of current processing demand, calibration of response capability delay time and stability prediction, continuous stepless adjustment of laser power and synchronous adjustment of scanning speed, and output of processing parameter cooperative control instruction set; S5: calling the processing parameter cooperative control instruction set, real-time delay compensation and prediction of processing path, analyzing the fluctuation characteristics and error correction of response time, dynamic correction of path and corresponding of processing parameter, and output of optimal processing path and synchronous processing setting table.

[0019] The processing feature distribution table includes workpiece number, shape feature identifier, defect distribution label, material property label, the path planning priority list includes path weight value, processing sequence identifier, priority level, the processing adaptation relationship group includes curvature change interval, heat affected zone identifier, processing feasibility determination value, the processing parameter coordination control instruction set includes power level code, speed adjustment direction, cycle adjustment identifier, the optimal processing path and synchronous processing setting table includes path delay characteristics, synchronous processing time point, response characteristics.

[0020] Referring to Figure 2 The generation step of the processing feature distribution table is specifically: S111: Real-time acquisition of processing area image through industrial camera, input image into deep learning model for processing, pixel-level feature extraction and mapping, identification of workpiece contour boundary, analysis of topological structure, classification and spatial location of surface defects, analysis of material spectral response characteristics and inference of physical properties, generation of processing feature sequence; Real-time capture of workpiece surface image stream through industrial camera, resolution 2560x2048, frame rate 30 frames / second, vertical shooting, image data high-speed transmission to image processing unit, input deep learning model, model based on U-Net architecture, encoder extracts multi-scale features such as edge and texture, decoder fuses high-level semantics and low-level spatial details, generates pixel-level feature vector, encodes pixel region type. Identify the workpiece contour boundary, classify the pixels as "workpiece contour" or "background" through a binary classifier, connect the contour pixels through morphological operations, obtain the closed contour, analyze the topological structure, calculate the Euler number, identify the number of holes and boundaries. Perform surface defect classification and spatial positioning, the model outputs defect category, mask and bounding box coordinates, a multispectral imaging system acquires reflectance spectral data to form a spectral vector, which is input into a neural network model, the model infers physical properties such as material hardness and thermal conductivity, and generates a processing feature sequence.

[0021] S112: Call the processing feature sequence, perform matching analysis of workpiece geometric shape and defect distribution pattern, identify risk defect areas and mark processing priority, generate key processing area index set; Call the machining feature sequence, extract the workpiece contour point set, defect type and position list, material physical property data, and perform matching analysis of workpiece geometric shape and defect distribution pattern. Discretize the workpiece contour point set into a series of geometric features, such as calculating the area, perimeter, aspect ratio, and barycenter coordinates of the workpiece, and comparing them with the preset standard geometric shape template, such as comparing the aspect ratio of the workpiece with the ratio of the standard rectangular template. At the same time, analyze the defect distribution pattern, such as calculating the defect density (the number of defects per unit area), the average distance between defects, and the degree of defect clustering, identify risk defect areas and mark the machining priority, and comprehensively evaluate the matching analysis results of defect type, size, position information and workpiece geometric shape. Identify the defect area with a large impact on subsequent machining quality as a risk defect area, for example, a crack near the workpiece boundary (an area within 2 mm of the boundary) is identified as a high-risk defect, and its priority is marked as "1". A single small pore (diameter less than 0.2 mm) inside is marked as low risk, and the priority is "3". The priority classification rules are as follows: priority 1 indicates "high" when the defect type is crack, and the defect size (length or diameter) is greater than 1% of the workpiece feature size, or the defect is located in the key geometric feature area; priority 2 indicates "medium" when the defect type is scratch or surface pit, and the defect size is between 0.1% and 1% of the workpiece feature size; priority 3 indicates "low" when the defect type is non-metallic inclusion or slight oxidation layer, and the defect size is less than 0.1% of the workpiece feature size. Generate a key machining area index set.

[0022] S113: According to the key machining area index set, identify the geometric shape consistency area, verify the material uniformity area, mark the non-critical defect area and perform machining stability evaluation, and generate a machining feature distribution table. According to the key machining area index set, identify the geometric shape consistency area, analyze the local curvature of the workpiece contour point set, evaluate the local smoothness and consistency, verify the material uniformity area, analyze the hardness value standard deviation using the material physical property distribution data, determine the material uniformity area, mark the non-critical defect area, evaluate the machining stability, identify the defects with priority 3, mark the defects as non-critical, analyze the material uniformity, geometric consistency and preset machining parameters around the defects, evaluate the regional machining stability, and calculate the stability evaluation value by weighting the geometric consistency score, material uniformity score and defect severity. The evaluation value is high, medium or low, and a machining feature distribution table is generated.

[0023] Please refer to Figure 3 , the generation of the path planning priority list is specifically as follows: S211: Call the machining feature distribution table, extract the geometric feature and boundary information of the key machining area, identify the defect type, size, position information and perform quantization, perform preliminary construction of multiple alternative paths and key node setting, evaluate and convert the numerical values of path length, bending degree, obstacle avoidance, surface roughness requirement factors, and generate a path weight set; Call the machining feature distribution table, including key machining area index, defect type, size, position information, and machining stability evaluation result structured data, extract key machining area geometric shape features and boundary information, for each key machining area, extract the accurate boundary coordinate point set, and calculate the geometric shape features, for example, calculate the minimum circumscribed rectangle, center point coordinates, area, perimeter, and its relative position to the overall geometric center of the workpiece, for example, a high-risk crack area is marked, the boundary information is represented by a closed polygon point set, the area is 1.2 square millimeters, the defect type, size, and position information are identified and quantified, all defect information in the key machining area is arranged, for example, the defect types include cracks, scratches, and pores, the defect sizes include crack length, scratch depth, and pore diameter, the defect position is given by the (x, y) coordinates relative to the workpiece coordinate system origin (0, 0), and the information is quantified, for example, crack length 5.2 mm, scratch depth 0.05 mm, and pore diameter 0.3 mm, perform preliminary construction of multiple alternative paths and key node setting, based on the geometric shape features of the key machining area and the defect position, generate multiple laser scanning paths, the paths can be straight lines, arcs, or compound curves, each path contains multiple key nodes, for example, path 1 is [(10, 10), (20, 20), (30, 30)], path 2 is [(10, 10), (15, 15), (25, 25), (30, 30)], and path 3 is [(10, 10), (20, 15), (30, 10)], the nodes define the path direction and turning points, evaluate and convert the path length, curvature, obstacle avoidance, and surface roughness requirement factors to numerical values, each alternative path is evaluated in multiple dimensions, the path length is directly calculated as the sum of the lengths of all line segments on the path, for example, the length of path 1 is 28.28 mm, the curvature is measured by calculating the sum of the deviations of the angles at each turning point from 180 degrees, the obstacle avoidance evaluation assesses whether the path interferes with non-machining areas or already identified non-critical defect areas on the workpiece, if interference occurs, the obstacle avoidance score is 0, otherwise it is 1, the surface roughness requirement is assigned according to the specific requirements of the target machining area, for example, for a high-precision surface, the roughness requirement is Ra less than 0.8 microns, and the assignment is 10, and a path weight set is generated. The weight values in the path weight set are obtained by weighted summation of the above evaluation factors, for example, the path weight is calculated by the normalized values of length, curvature, obstacle avoidance, and surface roughness requirement and their corresponding weight coefficients, the weight coefficients are set according to actual machining requirements, for example, length weight 0.3, curvature weight 0.2, obstacle avoidance weight 0.4, and surface roughness requirement weight 0.1.

[0024] S212: According to the path weight set, compare and analyze the weight values of each path, and determine the size, arrange the paths, determine the priority level of the paths, and generate a path priority sequence; According to the path weight set, S calculates the path weight value for each candidate path, for example, path A weight 0.75, path B weight 0.82, path C weight 0.69, compare and analyze the path weight value of each path, and determine the size, compare the weight values of all candidate paths in pairs to determine the relative size relationship, for example, path B weight 0.82 is greater than path A weight 0.75, path A weight 0.75 is greater than path C weight 0.69, arrange the path weight values from large to small through the sorting algorithm, arrange the paths, arrange the paths according to the weight value size relationship in descending order, form an ordered path list, for example, path B (0.82), path A (0.75), path C (0.69), determine the path priority level, assign a priority level to each path according to the arrangement order, for example, the first path B obtains priority 1, followed by path A which obtains priority 2, and path C which obtains priority 3, and generate a path priority sequence.

[0025] S213: Based on the path priority sequence, the compliance of each path with the preset machining parameters is verified, and the paths that do not meet the machining precision requirements, have material damage risk or low machining efficiency are identified and filtered to generate a path planning priority list; Based on the path priority sequence, according to the ordered path list and its corresponding priority level, the compliance of each path with the preset machining parameters is verified, and the paths that do not meet the machining precision requirements, have material damage risk or low machining efficiency are identified and filtered to generate a path planning priority list;

[0026] Please refer to Figure 4 , the generation step of the machining adaptation relationship group is specifically: S311: Call the path planning priority list, perform real-time scanning and three-dimensional reconstruction of the workpiece surface curvature, identify curvature gradient and mutation points, dynamically monitor the processing area temperature field and construct a thermal diffusion model, track and evaluate the morphology of the thermal influence area boundary in real time, and obtain the processing environment state parameters by summarizing the curvature change and thermal influence data; Call the path planning priority list, including screening and priority sorting of the candidate path list, perform real-time scanning and three-dimensional reconstruction of the workpiece surface curvature, use a confocal laser scanner or a structured light scanner to perform real-time three-dimensional morphology scanning on the workpiece processing area, for example, by laser triangulation principle, collect surface point cloud data at a speed of 20,000 points per second, and use the iterative closest point algorithm to register and fuse the continuously collected point cloud data to construct a three-dimensional model of the processing area, for example, reconstruct a three-dimensional grid model containing 1 million vertices, identify curvature gradient and mutation points, calculate the surface normal vector at each grid vertex, and calculate the included angle between adjacent vertex normal vectors to obtain the local curvature value of the surface, then calculate the rate of change of the curvature value along the surface direction, for example, if the regional curvature gradient exceeds 0.5, the region is identified as a curvature mutation point, and the mutation point corresponds to an edge, a sharp corner or a hole edge, dynamically monitor the processing area temperature field and construct a thermal diffusion model, use an infrared thermal imager to perform real-time temperature monitoring on the laser processing area, for example, obtain a real-time temperature distribution map of the processing point at a frame rate of 30Hz, with a data resolution of 640x480 pixels, then, based on the real-time temperature data and the thermal physical parameters of the material, construct a transient thermal diffusion model, solve the heat conduction equation in the model, for example, the thermal conductivity of the material is 15 W / (m·K), the specific heat capacity is 450 J / (kg·K), and the density is 7900 , the model predicts the evolution of the internal and surface temperature field of the material under the action of the laser, tracks and evaluates the morphology of the thermal influence area boundary in real time, identifies and tracks the region where the temperature of the material exceeds the recrystallization temperature, for example, 850 degrees Celsius for 304 stainless steel, which is the heat-affected zone, extracts the boundary of the heat-affected zone through image processing algorithm, and evaluates the morphology characteristics such as the width, depth and whether it causes surface bulging or depression in combination with the three-dimensional model, summarizes the curvature change and thermal influence data, integrates the curvature gradient, curvature mutation point information and the thermal influence area boundary, and generates the processing environment state parameters.

[0027] S312: According to the processing environment state parameters, set the upper and lower limits of the curvature change range, define the allowable threshold of the thermal influence range and demarcate the safety boundary, compare and analyze the region parameter data point by point, identify and exclude the regions that do not meet the processing precision or material integrity requirements, screen the intervals that meet the processing requirements, and obtain the processing available interval set. According to the processing environment state parameters, the curvature gradient, the curvature abrupt change point, the heat affected zone boundary and the topography data are summarized, the upper and lower limits of the curvature change range are set, based on the mechanical properties of the workpiece material and the expected processing precision requirements, the allowable range of surface curvature change is set, for example, for brittle materials or high-precision processing, the upper limit of the curvature change rate is set to 0.2, and the lower limit is set to 0.01, the curvature change outside this range will cause processing difficulty or precision decline, and the heat affected range tolerance threshold definition and safety boundary demarcation are carried out, according to the material melting point, phase transition temperature and the requirement of avoiding thermal damage, the maximum allowable width and depth of the heat affected zone are set, for example, for laser welding of 304 stainless steel, the heat affected zone width tolerance threshold is defined as 0.5mm, and the depth tolerance threshold is 0.3mm, exceeding the threshold value will have the risk of thermal damage, at the same time, a safety boundary is demarcated at least 1mm away from the sensitive area on the workpiece to prevent secondary damage caused by heat diffusion, the region parameter data is compared and analyzed point by point, the real-time curvature change rate of each point in the processing area is compared with the set curvature change range, and the real-time width of the heat affected zone is compared with the tolerance threshold, for example, the real-time curvature change rate of a certain point is 0.25, which exceeds the set upper limit of 0.2, the point is marked as a curvature unqualified point, and the real-time width of the heat affected zone is 0.6mm, which exceeds the 0.5mm tolerance threshold, the point is marked as a heat affected unqualified point, the areas that do not meet the processing precision or material integrity requirements are identified and removed, according to the comparison result, all areas that are not suitable for processing due to curvature change exceeding the range or heat affected exceeding the tolerance threshold are identified and removed from the areas to be processed, for example, all areas where the curvature unqualified points and the heat affected unqualified points are located are removed, the intervals that meet the processing requirements are selected, after removing all unqualified areas, the remaining continuous areas that meet all the set conditions are selected, the intervals represent that the workpiece part can be safely processed, and the processing available interval set is obtained.

[0028] S313: Call the processing available interval set, perform spatial positioning and geometric matching of the path segments in the processing path planning priority list, calculate the overlap degree and compatibility of the path segments and the available intervals, analyze the corresponding relationship between the path and the interval characteristics, identify the potential for processing parameter adjustment, map the adaptation relationship between the processing interval and the path, and use the formula: ; Calculate the evaluation index value of the processing adaptation relationship, and generate a processing adaptation relationship group; wherein, represents the evaluation index value of the processing adaptation relationship, represents the spatial positioning parameters of the path segment and the available interval i, represents the spatial positioning parameters of the path segment and the available interval i, represents the total number of path segments and available intervals, represents the calculation adjustment constant; The processing available interval set, for example, the set of multiple continuous regions that meet the processing requirements, is called to perform spatial positioning and geometric matching of path segments in the path planning priority list, each path in the path planning priority list is decomposed into multiple path segments, for example, a path is composed of 10 line segments, each line segment is regarded as a path segment, then each path segment is compared with all available intervals in the processing available interval set for spatial positioning and geometric matching, for example, by calculating whether the endpoints of the path segment fall within the available interval and whether the overall shape of the path segment is compatible with the shape of the available interval, the path segment and the available interval are calculated for overlap and compatibility, for each path segment and available interval, the overlap area and overlap rate, for example, the ratio of the overlap area to the path segment area, are calculated, and the geometric compatibility is evaluated, for example, if the curvature of the path segment is too large and the available interval is linear, the compatibility is low, the corresponding relationship between the path and interval characteristics is analyzed, for example, the relationship between the length and curvature of the path segment and the size and shape of the available interval, and the relationship between the defect type of the region where the path segment is located and the material uniformity and geometric consistency of the available interval, the processing parameter adjustment potential is identified, for example, if a path segment is in a highly compatible available interval and the material uniformity of the available interval is high and the curvature change is gentle, the processing parameters such as laser power and scanning speed of the path segment have greater adjustment space, for example, the laser power can be appropriately increased to improve the processing efficiency, the adaptation relationship between the processing interval and the path is mapped, according to the overlap, compatibility and parameter adjustment potential, the adaptation relationship between each path segment and the corresponding available interval is established, and the formula is used: ; Calculate the evaluation index value of the processing adaptation relationship; wherein, represents the evaluation index value of the processing adaptation relationship, indicating the comprehensive degree of matching between the path segment and the available interval, the higher the value, the better the matching degree, represents the spatial positioning parameter of the path segment and the available interval i, for example, the center point coordinate of the path segment , or the average curvature of the path segment, represents the spatial positioning parameter of the path segment and the available interval i, for example, the center point coordinate of the available interval i , or the average curvature of the available interval i, wherein, and represent the feature vectors of the path segment and the available interval, not single numerical values, in order to simplify the calculation example, and represent the feature values of the path segment and the available interval in a certain dimension, for example, the average curvature value or the center point X coordinate, in actual application, and may be multi-dimensional vectors, for example, , where is the center coordinate of the path segment, is the average curvature of the path segment, is the corresponding value of the available interval, at this time the difference and square sum operation in the formula needs to be expanded to vector norm calculation, but for the sake of example clarity, it is still taken as a single-dimensional feature, represents the total number of path segments and available intervals, in the calculation, represents the number of feature dimensions considered when evaluating the adaptation relationship between a specific path segment and an available interval, here indicates the three feature dimensions of the center point X coordinate, the center point Y coordinate, and the average curvature, represents the calculation adjustment constant, used to adjust the sensitivity of the exponential term, its set value is determined by experiment or experience, for example, when and the difference is small, the exponential term contributes less to , when the difference is large, the contribution increases rapidly, experimental verification shows that when takes a value between 3 and 10, it can effectively distinguish between adaptation quality, here ; The benefit of the formula is that by introducing the exponential term, it can punish those spatial positioning parameters with large differences, so that path segments and available intervals with high matching degree obtain higher evaluation values, and the formula considers the matching degree of spatial position and shape features, avoiding the one-sidedness of a single indicator, now an example is given, assuming that a path segment and an available interval need to be evaluated for their adaptation relationship, considering three feature dimensions, namely : dimension 1: X coordinate ( ); dimension 2: Y coordinate ( ); dimension 3: average curvature ( ); set parameter values: path segment center point X coordinate , available interval center point X coordinate , path segment center point Y coordinate , available interval center point Y coordinate , path segment average curvature , available interval average curvature , adjustment constant ; calculation process: for (X coordinate): ; ; ; term 1 contribution ; For (Y coordinate): ; ; ; Item 2 contribution ; For (Average curvature): ; ; ; Item 3 contribution ; Total fitting index value ; The results show that the fitting index value of the path segment and the available interval on the selected three characteristic dimensions is 0.278621, this value will be compared with the fitting index value of the path segment and the available interval, used to quantify the matching degree, the higher the fitting index value, the better the adaptability of the path segment on the available interval, this value will be used as an important basis for generating the machining adaptation relationship group, guiding the subsequent machining parameter collaborative control.

[0029] Please refer to Figure 5 , the generation step of the machining parameter collaborative control instruction set is specifically: S411: According to the machining adaptation relationship group, extract the surface roughness requirement and topography precision index in the current machining interval, set the material removal rate target and thermal damage control threshold, perform quantitative evaluation of laser power adjustment range and response speed, detect the scanning galvanometer speed and acceleration performance, match the machining demand and response ability, generate machining demand deviation data group; The quantitative evaluation of the laser power adjustment range and the response speed refers to detecting and quantifying the dynamic adjustment ability of the laser power and the response speed of the laser to the adjustment instruction; According to the machining adaptation relationship group, the surface roughness requirement and the topography precision index in the current machining interval are extracted, the surface roughness target value and the topography precision index corresponding to the interval are obtained from the machining feature distribution table according to the machining interval indicated by the machining adaptation relationship group, for example, the current interval is a high-precision machining area, the surface roughness requirement is Ra less than 0.8 microns, and the topography precision index is a maximum deviation less than 0.02 millimeters, the material removal rate target and the heat damage control threshold are set, the material removal rate target in the laser machining process is set according to the material type, thickness and machining requirement, for example, for a 2 millimeter thick 304 stainless steel, the target removal rate is set to 0.5 cubic millimeters per second, at the same time, the heat damage control threshold is set according to the material heat damage sensitivity and the demand to avoid micro-crack generation, for example, the highest temperature of the machining area is not allowed to exceed 800 degrees Celsius, and the heat affected zone width is not more than 0.3 millimeters, the laser power adjustment range and response speed quantitative evaluation are carried out, the laser power output range is quantified through actual test and calibration, for example, the laser power can be continuously adjusted between 100W and 1000W, and the response time required for the laser to change from receiving an adjustment instruction to actual power output stabilization is evaluated, for example, the response time for the power to change from 500W to 600W is 10 milliseconds, the scanning galvanometer speed and acceleration performance are detected, the maximum scanning speed of the scanning galvanometer is determined through experimental measurement, for example, the maximum scanning speed is 5000 millimeters per second, and the acceleration performance required from static acceleration to maximum speed or from maximum speed to static deceleration is determined, for example, the maximum acceleration is 200000 millimeters per second square, the set material removal rate target, heat damage control threshold and laser power adjustment range, response speed, scanning galvanometer speed and acceleration performance are compared, for example, the current machining area requires high removal rate and strict heat damage control, but the laser response speed is slow, indicating that the matching degree is low, for example, to achieve a removal rate of 0.5 cubic millimeters per second, theoretically 800W laser power is required, and the laser needs 10 milliseconds to increase from the current power of 500W to 800W, while the path segment only needs 5 milliseconds to complete, the response capability is insufficient, and the machining requirement deviation data group is generated.

[0030] S412: Call the machining requirement deviation data group, compare the parameter deviation values with the preset allowable range one by one, identify and classify the deviation items exceeding the allowable range, divide the matching degree between the machining requirement and the response capability into levels and represent the state, mark the area with insufficient matching degree of key parameters, and generate a matching state identification set; The comparison of the parameter deviation values with the preset allowable range one by one means that the laser power adjustment range deviation, response speed deviation, scanning galvanometer speed deviation and scanning galvanometer acceleration deviation included in the machining requirement deviation data group are compared with the preset allowable range in numerical value respectively; The processing requirement deviation data set is called to compare the laser power adjustment range deviation, response speed deviation, scanning galvanometer speed and acceleration deviation with the preset allowable range one by one, such as the power deviation 20W, the allowable range ±50W, the response speed deviation 5ms, the allowable range ±2ms, the speed deviation 50mm / s, the allowable range ±100mm / s, and the acceleration deviation 30000mm / s², the allowable range ±50000mm / s². The items exceeding the range are identified, such as the response speed deviation exceeding the standard, which is marked as "insufficient response speed" and classified as a high-priority adjustment item; the power deviation within the range is not marked. The matching level is divided according to the number and severity of the deviations: "high matching degree" if all are within the range, "medium matching degree" if a small amount of slight deviations, and "low matching degree" if significant deviations. For example, only the response speed is insufficient, which is marked as medium matching degree, and the specific insufficient items are recorded to generate a matching state identification set.

[0031] S413: According to the matching state identification set, the search of the processing interval number and the spatial position are verified, the increment or decrement of the required laser power of the current interval is calculated, the acceleration or deceleration amplitude of the scanning speed is set, the power and speed adjustment instructions are identified, and the processing parameter cooperative control instruction set is generated; Based on the matching state identification set, the spatial information of the processing interval is searched, such as the starting point (100, 50) and the ending point (120, 55), the physical processing area is located, and the required power increment or decrement is calculated, such as the target removal rate 0.5mm³ / s requires 800W, and the current 500W, then the increment +300W; if more precise processing is required, the power can be reduced to 450W. Combined with the performance of the galvanometer and the path geometry, the scanning speed adjustment amplitude is set, such as the galvanometer allows acceleration 200000mm / s², the linear segment speed 1000→1200mm / s, or the complex curve 800→600mm / s. The power and speed adjustments are converted into digital instructions, such as "power +300W" and "speed +200mm / s", and finally the processing parameter cooperative control instruction set is generated to drive the power and galvanometer to adjust synchronously in real time, ensuring the processing precision and efficiency.

[0032] Please refer to Figure 6 , the generation steps of the optimal processing path and synchronous processing setting table are as follows: S511: Call the processing parameter cooperative control instruction set, analyze the difference between the laser scanning head motion instruction and the real-time response time sequence, monitor the power adjustment and scanning galvanometer cooperative action lag, measure the fluctuation range of the response time under different load conditions, and use the formula: ; Calculate the path delay fluctuation characteristic value and generate the path delay fluctuation correlation coefficient; Among them, represents the path delay fluctuation characteristic value, Total number of sampling sequences, Timestamp of the laser scanning head motion instruction in the kth sampling sequence, Real-time response timestamp of the laser scanning head in the kth sampling sequence; Call the processing parameter collaborative control instruction set, for example, a sequence containing instructions such as "laser power + 300W" and "scanning speed + 200 mm / s", analyze the difference between the laser scanning head motion instruction and the real-time response time sequence, compare the motion instruction of the laser scanning head (i.e. the scanning galvanometer) set in the instruction, for example, linear scanning from point A to point B at a speed of 200 mm / s, with the actual feedback motion trajectory and timestamp of the laser scanning head, for example, the time instruction for the laser head to move from point A to point B is 50 ms, but the actual completion time is 53 ms, there is a time sequence difference of 3 ms, monitor the power adjustment and the scanning galvanometer collaborative action lag, monitor the lag between the time required for the actual power output to stabilize after the laser receives the power adjustment instruction (for example, "laser power + 300W") and the time when the scanning galvanometer starts to respond (for example, starts scanning), for example, the laser power stabilizes within 10 ms after the power instruction is issued, and the scanning galvanometer starts to move 2 ms before the power stabilizes, so the lag is -2 ms (indicating that the galvanometer is ahead), if the galvanometer moves 3 ms after the power stabilizes, the lag is 3 ms, determine the fluctuation range of the response time under different load conditions, repeatedly measure the response time of the laser scanning head under different processing load conditions (for example, processing workpieces of different thicknesses and different materials, or high-speed and low-speed scanning), and statistically determine the fluctuation range of the response time, for example, when processing 1 mm thick stainless steel, the laser scanning response time fluctuates between 10 ms and 12 ms, and when processing 2 mm thick stainless steel, the fluctuation range is between 12 ms and 15 ms, using the formula: Calculate the path delay fluctuation characteristic value and generate the path delay fluctuation correlation coefficient; The path delay fluctuation characteristic value is used to quantify the difference between the laser scanning head motion instruction and the real-time response timestamp under a certain sampling sequence, the larger the value, the greater the fluctuation, Total number of sampling sequences, indicating the number of samples for timing difference monitoring, here Indicates that 5 groups of data have been collected, which is the number of discrete samples for evaluating the difference between the laser scanning head motion instruction and the real-time response timestamp, Timestamp of the laser scanning head motion instruction in the kth sampling sequence, for example, the system time when the instruction is issued, Real-time response timestamp of the laser scanning head in the kth sampling sequence, for example, the feedback time when the laser head actually starts to execute the instruction; ​The formula is beneficial in that, by calculating the root mean square error of the instruction timestamp and the actual response timestamp, the stability and consistency of the laser scanning response in the processing process can be accurately reflected, a high fluctuation characteristic value indicates that there is an unstable factor, further optimization and coordination control are needed, and a low fluctuation characteristic value indicates that the system response is stable, which is conducive to ensuring the processing precision; now an example is given, assuming that 5 samplings are performed, the following instruction timestamp and real-time response timestamp data are obtained: Table 1: Path delay fluctuation sampling data table

[0033] Calculation process: , ; , ; , ; , ; , ; , ; milliseconds; The results show that, under the current sampling sequence, the path delay fluctuation characteristic value of the laser scanning head motion instruction and the real-time response timestamp is 0.5099 milliseconds, which reflects the average deviation degree of the laser scanning system response, and serves as a key input for generating the path delay fluctuation correlation coefficient. If this value is higher than the preset threshold (for example, 0.4 milliseconds), it indicates that the response has a large fluctuation, and further system calibration or optimization is needed. The fluctuation characteristic value will be used to evaluate the processing stability of the path and determine whether it is suitable for synchronous processing.

[0034] S512: According to the path delay fluctuation correlation coefficient, analyze the complexity of the path geometry, and perform parameter mapping of the material thickness and hardness change, set the precision requirement and stability margin of the synchronous processing condition, evaluate the continuity and smoothness constraint between path segments, select the path set that meets the synchronous processing condition, and generate a synchronous processing judgment value interval; According to the path delay fluctuation correlation coefficient, the geometric characteristic parameters such as path bending degree, turning point number and curvature change rate are obtained, and are divided into "low", "medium", "high" levels, the parameter mapping of material thickness and hardness change is carried out, the material thickness and hardness change is provided, and is mapped to the machining stability influence factor, the synchronous machining condition precision requirement and stability margin are set, the synchronous machining precision threshold value is set according to the product precision level and process requirement, and the stability margin is set to cope with fluctuation and external interference. The continuity and smoothness constraint between path segments is evaluated, the geometric change or material attribute change of adjacent path segment connection point is evaluated, the machining continuity and smoothness are ensured, the path set meeting the synchronous machining condition is screened, the delay fluctuation, the geometric complexity degree, the material change influence, the precision requirement, the stability margin, the continuity constraint are comprehensively considered, the path meeting the synchronous machining condition is screened out, and the synchronous machining judgment value interval is generated.

[0035] S513: Call the synchronous machining judgment value interval, carry out the response speed and precision matching of laser power adjustment, carry out the acceleration limitation and smoothness contrast of scanning galvanometer speed change, carry out the transition time optimization and seamless connection of path segment parameter switching, determine the optimal machining path and corresponding machining parameter, output the optimal machining path and synchronous machining setting table; The synchronous processing judgment value interval is called to filter out all paths meeting the synchronous processing conditions and their corresponding judgment value ranges, and the laser power adjustment response speed and accuracy are matched. In the synchronous processing judgment value interval, the power adjustment instruction in the processing parameter cooperative control instruction set is used for matching analysis with the evaluation of the laser response speed. For example, a certain path segment needs to be quickly switched from 500W power to 800W, and the laser response time is 10 milliseconds. The effective processing time of the path segment is only 8 milliseconds. The power adjustment response speed does not match the processing requirement and needs to be adjusted. The accuracy matching requires that the output power deviation after power adjustment is less than 1%. For example, the power deviation is kept within plus or minus 5W after adjustment, and the scanning galvanometer speed change acceleration limit and smoothness are compared. The scanning speed change requirement on the path segment, for example, from 1000mm / s to 1200mm / s, is compared with the evaluation of the maximum acceleration performance of the scanning galvanometer to ensure that the speed change does not exceed the physical limit of the galvanometer. For example, the required acceleration is 250000mm / s2, and the maximum acceleration of the galvanometer is 200000mm / s2, so it cannot be realized. The smoothness during the speed change process is evaluated to ensure that the acceleration change rate is within the allowed range to avoid vibration. The transition time optimization and seamless connection between path segments are performed for the parameter switching points between adjacent path segments in the same path. For example, the transition time during the switching process is optimized by preloading or early instruction method, and the power is smoothly transitioned from one value to the next value to avoid sudden changes. For example, the power is transitioned by setting a 0.1 second ramp-up / down time to ensure seamless connection during processing. For example, when the laser power and scanning speed reach the new target value, the next path segment starts to execute immediately to avoid processing defects at the junction of the path segments. The optimal processing path and corresponding processing parameters are determined. Based on the above matching and optimization results, the path with excellent performance in power response speed, accuracy, scanning galvanometer acceleration and transition between path segments is selected as the optimal processing path from the synchronous processing judgment value interval, and the corresponding accurate processing parameter combination is determined. For example, path 1 is selected as the optimal path, and the corresponding parameters are laser power 800W and scanning speed 1200mm / s. The optimal processing path and synchronous processing setting table are output.

[0036] The above is only a preferred embodiment of the present application, and does not limit the form of the present application. Any skilled person in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the technical solution of the present application.

Claims

1. A method of combined path and laser machining planning based on machine vision, characterized in that, Comprise the following steps: S1: Real-time acquisition of processing area image by industrial camera, contour extraction and geometric parameter measurement of workpiece shape, type classification and spatial positioning of workpiece surface defect, physicochemical property identification and component composition analysis of workpiece material, and generation of processing feature distribution table; S2: Based on the processing feature distribution table, the starting point and ending point of multi-objective processing path are set, and the connection order of multi-path segment is optimized, the smoothing processing of path inflection point and the path of obstacle avoidance area are adjusted, and the path planning priority list is generated; S3: According to the path planning priority list, the real-time measurement and change trend capture of workpiece surface curvature are carried out, and the boundary dynamic detection and diffusion evaluation of processing area and heat affected zone are carried out, the range demarcation and condition matching of processing interval are dynamically adjusted, and the processing adaptation relationship group is established; The generation steps of the processing adaptation relationship group are as follows: S311: Call the path planning priority list, carry out real-time scanning and three-dimensional reconstruction of workpiece surface curvature, identify curvature gradient and mutation point, dynamically monitor processing area temperature field and build heat diffusion model, track and evaluate the morphology of heat affected zone boundary in real time, and obtain processing environment state parameters by summarizing curvature change and heat affected data; S312: According to the processing environment state parameters, the upper and lower limits of curvature change range are set, and the allowable threshold value definition and safety boundary demarcation of heat affected range are carried out, the region parameter data is compared and analyzed point by point, the region that does not meet the requirements of processing precision or material integrity is identified and removed, the interval that meets the processing requirements is screened, and the processing available interval set is obtained; S313: Call the processing available interval set, carry out spatial positioning and geometric matching of path segment in processing path planning priority list, carry out overlap calculation and compatibility evaluation of path segment and available interval, analyze the corresponding relationship of path and interval characteristics, identify the potential of processing parameter adjustment, map the adaptation relationship between processing interval and path, calculate the evaluation index value of processing adaptation relationship, and generate processing adaptation relationship group; S4: According to the processing adaptation relationship group, the parameter threshold setting and execution capability evaluation of current processing demand are carried out, and the delay time and stability prediction of response capability are calibrated, the continuous stepless adjustment of laser power and the synchronous adjustment of scanning speed are carried out, and the processing parameter collaborative control instruction set is output; S5: Call the processing parameter collaborative control instruction set, carry out real-time delay compensation and prediction of processing path, analyze the fluctuation characteristics and error correction of response time, carry out dynamic correction of path and corresponding processing parameter, output optimal processing path and synchronous processing setting table; The optimal processing path and synchronous processing setting table includes path delay characteristics, synchronous processing time point and response characteristics.

2. The method of machine vision based combined path and laser machining planning of claim 1, wherein, The processing feature distribution table comprises a workpiece number, a shape feature identifier, a defect distribution label, and a material property tag; the path planning priority list comprises a path weight value, a processing sequence identifier, and a priority level; the processing adaptation relationship group comprises a curvature variation interval, a heat-affected range identifier, and a processing feasibility determination value; and the processing parameter cooperative control instruction set comprises a power level code, a speed adjustment direction, and a periodical adjustment identifier.

3. The method of machine vision based combined path and laser machining planning of claim 1, wherein, The generation step of the processing feature distribution table is specifically as follows: S111: Real-time acquisition of a processing region image through an industrial camera, input of the image into a deep learning model for processing, extraction and mapping of pixel-level features, identification of a workpiece contour boundary, analysis of a topological structure, category identification and spatial position positioning of surface defects, analysis of material spectral response characteristics and inference of physical properties, and generation of a processing feature sequence; S112: Calling the processing feature sequence, matching analysis of a workpiece geometric shape and a defect distribution pattern, identification of a risk defect area and labeling of a processing priority, and generation of a key processing area index set; S113: According to the key processing area index set, identification of a geometric shape consistency area, verification of a material uniformity area, marking of a non-critical defect area and processing stability evaluation, and generation of a processing feature distribution table.

4. The method of machine vision based combined path and laser machining planning of claim 3, wherein, The generation step of the path planning priority list is specifically as follows: S211: Calling the processing feature distribution table, extraction of geometric shape features and boundary information of a key processing area, identification of defect types, sizes, and position information and quantization, preliminary construction of multiple candidate paths and key node setting, evaluation and numerical conversion of path length, bending degree, obstacle avoidance, and surface roughness requirement factors, and generation of a path weight set; S212: According to the path weight set, comparative analysis of path weight values and judgment of sizes, arrangement of paths, determination of path priority levels, and generation of a path priority sequence; S213: Based on the path priority sequence, compliance verification of each path and a preset processing parameter, identification and filtering of paths that do not meet processing precision requirements, have material damage risks, or have low processing efficiency, and generation of a path planning priority list.

5. The method of machine vision based combined path and laser process planning of claim 1, wherein, The generation step of the processing parameter cooperative control instruction set is specifically as follows: S411: According to the processing adaptation relationship group, extraction of surface roughness requirements and topographic accuracy indicators in a current processing interval, setting of a material removal rate target and a heat damage control threshold, quantitative evaluation of a laser power adjustment range and a response speed, detection of a scanning galvanometer speed and acceleration performance, matching of processing requirements and response capabilities, and generation of a processing requirement deviation data group; S412: Calling the processing requirement deviation data group, one-by-one comparison of parameter deviation values and a preset allowable range, identification and classification of deviation items that exceed the allowable range, hierarchical division and state representation of matching degrees between processing requirements and response capabilities, marking of a key parameter matching degree insufficient area, and generation of a matching state identifier set; S413: According to the matching state identifier set, verify the retrieval of the processing interval number and the spatial position, perform incremental or decrement calculation of the required laser power of the current interval, set the acceleration or deceleration amplitude of the scanning speed, identify the power and speed adjustment instructions, and generate a processing parameter cooperative control instruction set.

6. The method of machine vision based combined path and laser machining planning of claim 5, wherein, The quantitative evaluation of the laser power adjustment range and response speed refers to detecting and quantifying the dynamic adjustment capability of the laser power and the response speed of the laser to the adjustment instructions. The step of comparing the parameter deviation value with the preset allowable range one by one refers to comparing the laser power adjustment range deviation, response speed deviation, scanning galvanometer speed deviation, and scanning galvanometer acceleration deviation included in the processing demand deviation data set with the preset allowable range respectively.

7. The method of machine vision based combined path and laser process planning of claim 1, wherein, The generation steps of the optimal processing path and synchronous processing setting table are as follows: S511: Call the processing parameter cooperative control instruction set, analyze the difference between the laser scanning head motion instruction and the real-time response time sequence, perform power adjustment and scanning galvanometer cooperative action lag monitoring, determine the fluctuation range of the response time under differentiated load conditions, calculate the path delay fluctuation characteristic value, and generate the path delay fluctuation correlation coefficient; S512: According to the path delay fluctuation correlation coefficient, analyze the complexity of the path geometry, and perform parameter mapping of the material thickness and hardness change, set the precision requirement and stability margin of the synchronous processing condition, evaluate the continuity and smoothness constraint between path segments, screen the path set that meets the synchronous processing condition, and generate the synchronous processing judgment value interval; S513: Call the synchronous processing judgment value interval, perform response speed and precision matching of laser power adjustment, and perform acceleration limitation and smoothness comparison of scanning galvanometer speed change, optimize the transition time and seamless connection of the parameter switching between path segments, determine the optimal processing path and corresponding processing parameters, and output the optimal processing path and synchronous processing setting table.

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