A fully automatic laser vision coding machine and a coding path optimization method

Abstract

The present application provides a fully automatic laser vision coding machine and a coding path optimization method. The method includes obtaining three-dimensional point cloud data of a target object; performing spatial calibration on the three-dimensional point cloud data by using a point cloud distribution map of a stereo calibration block, and then determining a curvature field distribution required for beam path compensation of the target object; determining absorption response curves of laser beams in all coding regions within a regional boundary range when coding the target object based on the curvature field distribution and the surface material distribution of the target object; determining energy compensation factors corresponding to each coding region according to the energy attenuation rate of the laser energy in the absorption response curves and the optical reflection coefficient of each coding region in the regional boundary range during coding; and when detecting that the absorption response curve of the target object deviates from a preset standard curve, generating a dynamic coding path of the target object based on all the energy compensation factors. By adopting the solution of the present application, the laser coding path can be optimized to compensate for energy attenuation during the coding process.

Description

Technical Field

[0001] The present application relates to the technical field of laser coding, and more specifically, to a fully automatic laser vision coding machine and a coding path optimization method. Background Art

[0002] Laser coding technology is a processing technology that uses high-energy laser beams to perform melting, ablation, or oxidation treatment on the surface of target objects, thereby forming permanent markings. It has the advantages of non-contact, high precision, wear resistance, environmental protection, etc. With the continuous improvement of industrial manufacturing and product informatization requirements, laser coding technology has been widely used in fields such as product identification, anti-counterfeiting, and traceability.

[0003] Existing laser coding technologies usually use fixed coding parameters and preset paths for laser coding. However, when facing target objects with complex surfaces or heterogeneous materials, due to the difference in optical reflection characteristics of target objects with complex surfaces in different regions, energy attenuation of laser energy occurs during the coding process. Fixed coding parameters and preset paths are likely to cause uneven and blurred coding phenomena, thus affecting the clarity of the coded products. Therefore, how to optimize the laser coding path to compensate for energy attenuation during the coding process has become a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides a fully automatic laser vision coding machine and a coding path optimization method, which can optimize the laser coding path to compensate for energy attenuation during the coding process.

[0005] In a first aspect, the present application provides a coding path optimization method for a fully automatic laser vision coding machine, which is used for precisely coding a target object with a complex surface. The method includes:

[0006] Performing multi-view scanning on the target object to be coded to obtain three-dimensional point cloud data of the target object;

[0007] Performing spatial calibration on the three-dimensional point cloud data by using the point cloud distribution map of the stereo calibration block in the fully automatic laser vision coding machine to obtain a three-dimensional feature model of the target object, and further extracting the curvature field distribution required for beam path compensation of the target object from the three-dimensional feature model;

[0008] Obtaining the regional boundary range when coding the target object, and further determining the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the curvature field distribution and the surface material distribution of the target object;

[0009] Determining the energy compensation factor for each coding region according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding region within the regional boundary range during coding;

[0010] When the absorption response curve of the target object in the coding area deviates from the preset standard curve, a dynamic coding path for the target object is generated based on all the energy compensation factors.

[0011] In some embodiments, spatially calibrating the three-dimensional point cloud data by using the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain the three-dimensional feature model of the target object specifically includes:

[0012] Obtaining the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine;

[0013] Constructing a co-visual space domain between the stereo calibration block and the target object based on the point cloud distribution map;

[0014] Calibrating the three-dimensional point cloud data through the co-visual space domain to obtain the three-dimensional feature model of the target object.

[0015] In some embodiments, extracting the curvature field distribution required for beam path compensation of the target object from the three-dimensional feature model specifically includes:

[0016] Performing neighborhood topology analysis on the feature points in the three-dimensional feature model to obtain the unit normal vectors of all the feature points in the three-dimensional feature model;

[0017] Determining the curvature values of each feature point in the three-dimensional feature model through all the unit normal vectors;

[0018] Performing spatial interpolation on all the curvature values to obtain the curvature field distribution required for beam path compensation of the target object.

[0019] In some embodiments, the region boundary range includes multiple independent coding areas.

[0020] In some embodiments, determining the absorption response curves of the laser beams in all the coding areas within the region boundary range based on the curvature field distribution and the surface material distribution of the target object specifically includes:

[0021] Determining the surface material distribution of the target object;

[0022] Establishing an absorption response model of the target object surface to the laser beam based on the curvature field distribution and the surface material distribution;

[0023] Determining the absorption response curves of the laser beams in all the coding areas within the region boundary range based on the absorption response model.

[0024] In some embodiments, determining the energy compensation factor corresponding to each coding area according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding area in the area boundary range during coding specifically includes:

[0025] Determine the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve;

[0026] For each coding area in the area boundary range, determine the optical reflection coefficient of the coding area when coding the target item;

[0027] Determine the reflectivity surface of the coding area according to the optical reflection coefficient and the curvature field distribution of the coding area;

[0028] Determine the energy compensation factor of the coding area according to the reflectivity surface and the energy attenuation rate, and then obtain the energy compensation factors of each coding area in the area boundary range.

[0029] In some embodiments, when it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve, generating a dynamic coding path for the target item based on all the energy compensation factors specifically includes:

[0030] Determine the preset standard curve for laser coding;

[0031] When the deviation value between the absorption response curve of the target item and the preset standard curve is greater than the preset deviation threshold, generate a dynamic coding path for the target item according to all the energy compensation factors.

[0032] In a second aspect, the present application provides a full-automatic laser vision coding machine, and the full-automatic laser vision coding machine includes a coding path optimization unit, and the coding path optimization unit includes:

[0033] An acquisition module, configured to perform multi-view scanning on a target item to be coded, and obtain three-dimensional point cloud data of the target item;

[0034] A processing module, configured to perform spatial calibration on the three-dimensional point cloud data according to the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine, obtain a three-dimensional feature model of the target item, and then extract the curvature field distribution required for beam path compensation for the target item from the three-dimensional feature model;

[0035] The processing module is configured to obtain the area boundary range when coding the target item, and then determine the absorption response curve of the laser beam in all coding areas in the area boundary range based on the curvature field distribution and the surface material distribution of the target item;

[0036] The processing module is configured to determine an energy compensation factor corresponding to each coding area according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding area in the area boundary range during coding;

[0037] The execution module is configured to generate a dynamic coding path of the target item based on all the energy compensation factors when it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve.

[0038] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the coding path optimization method of the full-automatic laser vision coding machine described above.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the coding path optimization method of the full-automatic laser vision coding machine described above.

[0040] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects:

[0041] In the full-automatic laser vision coding machine and the coding path optimization method provided by the present application, first, a target item to be coded is scanned from multiple perspectives to obtain three-dimensional point cloud data of the target item; the three-dimensional point cloud data is spatially calibrated by the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain a three-dimensional feature model of the target item, and then the curvature field distribution required for beam path compensation of the target item is extracted from the three-dimensional feature model; the area boundary range during coding of the target item is obtained, and then the absorption response curve of the laser beam in all coding areas within the area boundary range is determined based on the curvature field distribution and the surface material distribution of the target item; an energy compensation factor corresponding to each coding area is determined according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding area in the area boundary range during coding; when it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve, a dynamic coding path of the target item is generated based on all the energy compensation factors.

[0042] It can be seen that the present application generates a dynamic coding path for the target item based on all energy compensation factors. First, by determining the curvature field distribution, a graph can be obtained that describes the continuous distribution of the curvature values of each point on the surface of the target item in space. The determination of the curvature field distribution can accurately capture the minute geometric changes on the surface of the target item. By analyzing the curvature field distribution, key feature regions (such as protrusions, depressions, and edges) on the surface of the target item can be identified. During the laser coding process, these key feature regions may affect the focus, energy absorption, and reflection of the laser beam. Therefore, the curvature field distribution helps to adjust the parameters of laser coding (such as the power and scanning speed of the laser) to adapt to different surface characteristics for beam path compensation. Then, by determining the absorption response curve, a curve can be obtained that describes the degree of laser energy absorption in different regions of the complex surface of the target item. The determination of the absorption response curve helps to specifically adjust the parameters of laser coding (such as the power and scanning speed of the laser) to ensure that the laser energy is effectively absorbed by the target item and avoid unclear coding problems caused by over-etching or insufficient energy. Finally, by determining the energy compensation factor, a laser output power adjustment ratio can be obtained to compensate for the laser energy attenuation caused by the surface reflectivity and geometric shape of the coding region. The determination of the energy compensation factor helps to accurately adjust the laser output according to the laser energy attenuation rate and optical reflection coefficient of each coding region, realizing dynamic compensation for the uneven energy distribution on the complex surface, thereby significantly improving the consistency, clarity, and stability of the coding effect. In summary, based on the above solution, the laser coding path can be optimized to compensate for the energy attenuation during the coding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is an exemplary flowchart of a method for optimizing a coding path of a full-automatic laser vision coding machine according to some embodiments of the present application;

[0044] Figure 2 is an operation flowchart for determining a three-dimensional feature model according to some embodiments of the present application;

[0045] Figure 3 is an exemplary flowchart for determining an energy compensation factor according to some embodiments of the present application;

[0046] Figure 4 is a schematic structural diagram of a coding path optimization unit according to some embodiments of the present application;

[0047] Figure 5 is an internal structural diagram of a computer device for implementing a method for optimizing a coding path of a full-automatic laser vision coding machine according to some embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0049] Reference Figure 1 , which is an exemplary flowchart of a method for optimizing the coding path of a full-automatic laser vision coding machine according to some embodiments of the present application. The method 100 for optimizing the coding path of the full-automatic laser vision coding machine mainly includes the following steps:

[0050] In step 101, multi-view scanning is performed on the target item to be coded to obtain three-dimensional point cloud data of the target item.

[0051] It should be noted that in the present application, the three-dimensional point cloud data is a set of discrete information points describing the spatial coordinate information of the target object in the full-automatic laser vision coding machine. The three-dimensional point cloud data can provide key data support for the automatic path planning of the coding area and the laser adaptive adjustment, thereby improving the accuracy and efficiency of coding; in specific implementation, multi-view scanning is performed on the target item to be coded through a structured light industrial camera, and the three-dimensional point cloud data of the target item can be obtained in the following manner, that is: First, a high-precision structured light camera (such as a binocular structured light industrial camera) can be selected and the position of the structured light camera can be changed through a rotary turntable to capture images of the target item from different perspectives to obtain image data of the target item under multiple views. Then, the position information on the surface of the object is extracted from the image data through an existing position extraction algorithm (such as the Steger algorithm) and stereo matching is performed on the images under each view using the position information to obtain the three-dimensional point cloud data of the target item.

[0052] In step 102, the three-dimensional point cloud data is spatially calibrated by the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain a three-dimensional feature model of the target item, and then the curvature field distribution required for beam path compensation of the target item is extracted from the three-dimensional feature model.

[0053] In some embodiments, reference Figure 2 , which is an operation flowchart for determining the three-dimensional feature model according to some embodiments of the present application. In the present application, the three-dimensional point cloud data is spatially calibrated by the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain a three-dimensional feature model of the target item, which can be implemented in the following steps:

[0054] Obtain the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine;

[0055] Construct a co-visual space domain of the stereo calibration block and the target object based on the point cloud distribution map;

[0056] Calibrate the three-dimensional point cloud data through the co-visual space domain to obtain a three-dimensional feature model of the target object.

[0057] It should be noted that in this application, the point cloud distribution map is a spatial density distribution map that describes the surface morphology and point density distribution of the stereo calibration block. This point cloud distribution map provides accurate geometric reference information by reflecting the point cloud density of each area on the surface of the stereo calibration block, thereby ensuring the accuracy of subsequent spatial calibration and laser marking path planning. Among them, the stereo calibration block refers to a three-dimensional stereo geometric object used for precise positioning and position calibration of the target object in the vision system, such as a checkerboard calibration block or a prism-shaped calibration block. Specifically, when implementing, obtaining the point cloud distribution map of the stereo calibration block in the full-automatic laser vision marking machine can be achieved by the following method, that is: First, a high-precision stereo calibration block (such as a checkerboard calibration block or a prism-shaped calibration block) can be selected according to the geometric size of the target object and its surface can be coated to ensure good laser reflection effect. Then, the structure light camera is used to collect images of the stereo calibration block from multiple angles. Next, the existing position extraction algorithm (such as the Steger algorithm) is used to extract the position information of the stereo calibration block in all images and use this position information to perform stereo matching on the images under each perspective to obtain the point cloud data of the stereo calibration block. Finally, the existing visualization tool (such as the cloud visualization tool CloudCompare) is used to convert this point cloud data into a visualization image, and this visualization image is used as the point cloud distribution map of the stereo calibration block.

[0058] Specifically, when implementing, constructing the co-visual space domain between the stereo calibration block and the target object based on the point cloud distribution map can be achieved by the following method, that is: First, the point cloud data in which the stereo calibration block and the target object are both within the scanning range of the laser vision marking machine can be obtained from the point cloud distribution map and the three-dimensional point cloud data. Then, the existing feature extraction method (such as the normal estimation method in the point cloud processing library) can be used to identify all the corner points by calculating the normal and curvature values of each point in the point cloud data, and the corner points of the stereo calibration block and the target object are paired using the feature matching algorithm (such as the matching method based on the Manhattan distance) and the relative position relationship between the stereo calibration block and the target object is calculated. Finally, the relative position relationship between the stereo calibration block and the target object is fitted into a spatial transformation matrix through the fitting algorithm (such as the least squares method), and this spatial transformation matrix is used as the co-visual space domain between the stereo calibration block and the target object. Among them, the co-visual space domain refers to the spatial range in which the stereo calibration block and the target object are simultaneously captured by the scanning device under the same coordinate system. This co-visual space domain helps to unify the point cloud data of the stereo calibration block and the target object into the same coordinate system to ensure the accuracy of the spatial relationship between the two, thereby improving the accuracy of the three-dimensional feature model.

[0059] It should be noted that in this application, the three-dimensional feature model refers to a feature model that describes the key geometric features and topological structure of the target object. This three-dimensional feature model helps to accurately identify the shape and surface characteristics of the target object and provides an accurate reference basis for the subsequent laser coding path planning. When specifically implemented, the three-dimensional point cloud data is calibrated through the co-visual space domain, and the three-dimensional feature model of the target item can be realized by the following method, that is: First, existing noise reduction algorithms (such as statistical filtering) can be used to remove the noise points in the three-dimensional point cloud data. Then, a feature matching algorithm (such as the three-dimensional point cloud stitching and registration method based on feature matching) is applied to perform initial registration on the denoised three-dimensional point cloud data through the co-visual space domain to obtain the calibrated point cloud data. Finally, the calibrated point cloud data is input into a three-dimensional reconstruction algorithm (such as the three-dimensional reconstruction algorithm in the computational geometry algorithm library) to generate the three-dimensional feature model of the target item.

[0060] In some embodiments, the extraction of the curvature field distribution required for beam path compensation of the target item from the three-dimensional feature model can be achieved by the following steps:

[0061] Perform neighborhood topological analysis on the feature points in the three-dimensional feature model to obtain the unit normal vectors of all feature points in the three-dimensional feature model;

[0062] Determine the curvature values of each feature point in the three-dimensional feature model through all the unit normal vectors;

[0063] Perform spatial interpolation on all the curvature values to obtain the curvature field distribution required for beam path compensation of the target item.

[0064] In specific implementation, neighborhood topology analysis is performed on the feature points in the three-dimensional feature model to obtain the unit normal vectors of all feature points in the three-dimensional feature model, which can be achieved in the following way: First, for each feature point in the three-dimensional feature model, K-nearest neighbor search can be used to perform neighborhood topology analysis on the feature points (for example, select the 20 nearest neighborhood points for each feature point), and all neighborhood points within the neighborhood area of the feature point are obtained. Then, the covariance matrix formed by the spatial coordinates of all neighborhood points is calculated, and then the eigenvector corresponding to the smallest eigenvalue in the covariance matrix is extracted using an existing feature extraction method (such as principal component analysis), and this eigenvector is used as the normal vector of the neighborhood area of the feature point. Finally, the normal vector of the feature point is normalized using an existing vector normalization method (such as Euclidean normalization) to obtain the unit normal vector of the feature point. Through the above steps, the normal vectors of all feature points in the three-dimensional feature model can be obtained; where the unit normal vector is a unit vector describing the local surface direction of the feature point in the three-dimensional feature model, and this unit normal vector ensures the consistency and accuracy of the normal information of each feature point in the curvature extraction process by eliminating the scale influence, thereby improving the reliability of subsequent surface feature analysis and process control.

[0065] In specific implementation, the curvature values of each feature point in the three-dimensional feature model are determined through all the unit normal vectors, which can be achieved in the following way: For each feature point in the three-dimensional feature model, first, an existing local modeling model (such as a quadratic surface least squares fitting modeling model) can be loaded, and the unit normal vector of the feature point and the point cloud data of all neighborhood points within the neighborhood area of the feature point are used as the input for this local modeling model. The local modeling model is run, and the average curvature in the output of the local modeling model is used as the curvature value of the feature point. Through the above steps, the curvature values of each feature point in the three-dimensional feature model can be obtained; where the curvature value is a quantitative index measuring the degree of bending of the surface of the target item at the feature point, and this curvature value can accurately describe the change characteristics of the surface shape of the target item, providing accurate information support for subsequent laser marking path planning.

[0066] It should be noted that in this application, the curvature field distribution is a graph that describes the continuous distribution of the curvature values of each point on the surface of the target object in space. This curvature field distribution can intuitively display the degree of curvature of each region on the surface of the target object, providing an accurate basis for subsequent analysis of surface features for beam path compensation and laser coding path planning. When specifically implemented, spatial interpolation is performed on all curvature values, and the curvature field distribution required for beam path compensation of the target object can be achieved in the following manner, that is: existing spatial interpolation techniques (such as inverse distance weighted interpolation method or Kriging interpolation method) can be used to interpolate the curvature values of all feature points and their corresponding three-dimensional spatial coordinates to obtain a continuous curvature field distribution map, and this curvature field distribution map is used as the curvature field distribution required for beam path compensation of the target object.

[0067] In step 103, obtain the regional boundary range when coding the target object, and then determine the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the curvature field distribution and the surface material distribution of the target object.

[0068] It should be noted that in this application, the regional boundary range refers to the spatial range of the surface area of the target object that needs to be processed during the laser coding process. This regional boundary range contains multiple independent coding regions, and can perform laser energy compensation for the curvature and surface material of each coding region. When specifically implemented, the regional boundary range when coding the target object can be achieved in the following manner, that is: First, obtain the three-dimensional point cloud data of the target object after denoising, and use existing boundary detection algorithms (such as boundary detection method based on normal vector change) to analyze the change of the normal vector of the data points in the three-dimensional point cloud data to determine whether they are located on the boundary, so as to obtain all boundary points. Then, use curve fitting technology (such as B-spline curve) to smooth all boundary points to obtain a continuous and smooth boundary curve. Finally, the area range enclosed by this boundary curve is used as the regional boundary range when coding the target object.

[0069] In some embodiments, determining the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the curvature field distribution and the surface material distribution of the target object can be achieved by the following steps:

[0070] Determine the surface material distribution of the target object;

[0071] Establish an absorption response model of the target object surface to the laser beam based on the curvature field distribution and the surface material distribution;

[0072] Determine the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the absorption response model.

[0073] In specific implementation, the surface material distribution of the target object can be determined in the following manner: A high-resolution multispectral camera can be used to scan the surface of the target object to obtain reflectivity images of each band of the target object under different bands. Then, existing image processing algorithms (such as the K-means clustering algorithm) are applied to perform material analysis on all the reflectivity images to obtain the spatial distribution information of the surface material of the target object, and this spatial distribution information is used as the surface material distribution of the target object; wherein, the surface material distribution refers to the spatial distribution information of the materials in different regions on the surface of the target object, and this surface material distribution helps to predict the absorption characteristics of each region on the surface of the target object to laser and establish an accurate absorption response model.

[0074] In specific implementation, the absorption response model of the surface of the target object to the laser beam can be established based on the curvature field distribution and the surface material distribution in the following manner: First, existing micro-surface theory modeling methods (such as Trowbridge-Reitz modeling) can be used to model the surface of the target object according to the curvature field distribution and the surface material distribution to obtain the absorption response model of the surface of the target object to the laser beam; wherein, the absorption response model is a model that describes the absorption characteristics of the surface of the target object to the laser beam. This absorption response model regards the seemingly smooth surface of the target object as being composed of a large number of tiny planes with different orientations (i.e., micro-surfaces) according to the micro-surface theory. Each micro-surface has a specific normal direction and material properties, and then the bidirectional reflectance distribution function is used to describe the relationship between the incident light and the reflected light of each micro-surface and calculate the reflected light intensity at different incident angles and exit angles, which helps to predict the absorption degree of different regions of the target object to the laser.

[0075] It should be noted that in this application, the absorption response curve is a curve that describes the absorption degree of laser energy by different regions on the complex surface of the target object, and this absorption response curve helps to optimize the laser coding process and ensure the coding quality and consistency; in specific implementation, the absorption response curve of the laser beam in all the coding regions within the regional boundary range can be determined based on the absorption response model in the following manner: The absorption response model can be used to simulate different incident angles and reflection angles of each coding region within the regional boundary range of the target object under the irradiation of the laser beam and calculate the reflected light intensity at different incident angles and exit angles, and then existing data fitting methods (such as the Fresnel equation) are used to fit all the reflected light intensities to obtain the absorption response curve of the laser beam in all the coding regions within the regional boundary range.

[0076] In step 104, according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding region within the regional boundary range during coding, the energy compensation factor corresponding to each coding region is determined.

[0077] In some embodiments, refer to Figure 3 , which is an exemplary flowchart for determining an energy compensation factor according to some embodiments of the present application. In the present application, the energy compensation factor corresponding to each coding area can be determined according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding area in the area boundary range during coding by the following steps:

[0078] In step 1041, determine the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve;

[0079] In step 1042, for each coding area in the area boundary range, determine the optical reflection coefficient of the coding area when coding the target item;

[0080] In step 1043, determine the reflectivity surface of the coding area according to the optical reflection coefficient and the curvature field distribution of the coding area;

[0081] In step 1044, determine the energy compensation factor of the coding area according to the reflectivity surface and the energy attenuation rate, and then obtain the energy compensation factors of each coding area in the area boundary range.

[0082] Specifically, when implemented, the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve can be implemented in the following manner, that is: First, for each coding area in the area boundary range, obtain the reflected light intensity data and the corresponding incident light intensity data of the coding area in the absorption response curve, and match each reflected light intensity with the corresponding incident light intensity. Then, divide the difference between each reflected light intensity and its matched incident light intensity by the incident light intensity, and use the obtained ratio as the energy attenuation value corresponding to each reflected light intensity. Then, take the average value of all the reflected light intensity energy attenuation values as the energy attenuation value of the coding area. Through the above steps, the energy attenuation values of each coding area in the area boundary range can be obtained. Finally, take the set composed of the energy attenuation values of all coding areas as the energy attenuation rate of the laser energy on the complex surface of the target object; wherein, the energy attenuation rate is a data set describing the attenuation degree of the laser energy caused by absorption and scattering when the laser propagates in different areas of the complex surface of the target object. This energy attenuation rate helps to optimize the laser parameter settings and ensure uniform and clear coding effects in different surface areas.

[0083] In specific implementation, the optical reflection coefficient of the coding area when coding the target item can be achieved in the following manner, that is: First, obtain the reflected light intensity data and the corresponding incident light intensity data of the coding area in the absorption response curve, and match each reflected light intensity with the corresponding incident light intensity. Then, take the ratio of each reflected light intensity to its matched incident light intensity as the reflectivity of the reflection intensity, and then take the average value of all reflectivities as the optical reflection coefficient of the coding area when coding the target item; wherein, the optical reflection coefficient is an index to measure the reflection characteristics of the surface of the target item to the laser, reflecting the reflection ability of the surface of the target item to the laser.

[0084] In specific implementation, the reflectivity surface of the coding area can be determined according to the optical reflection coefficient and the curvature field distribution of the coding area in the following manner, that is: First, obtain the curvature field distribution of the coding area in the curvature field distribution required for beam path compensation of the target item. Then, the optical reflection coefficient and the curvature field distribution of the coding area can be input into an existing optical simulation software (such as COMSOL Multiphysics) for simulation calculation to generate the reflectivity surface of the coding area; wherein, the reflectivity surface is a three-dimensional graph describing the reflected light intensity distribution of the coding area on the surface of the target object at different positions and angles. This reflectivity surface helps to deeply understand the influence of the surface microstructure on the laser reflection characteristics, thereby optimizing the energy distribution and compensation strategy in the laser coding process to ensure the uniformity and clarity of the coding effect.

[0085] In some embodiments, the energy compensation factor of the coding area can be determined according to the reflectivity surface and the energy attenuation rate by the following steps:

[0086] Determine the optical response parameters of the coding area according to the reflectivity surface;

[0087] Conduct Monte Carlo simulation on the energy attenuation rate to obtain the energy loss distribution of the laser energy transmitted in the coding area;

[0088] Determine the energy compensation factor of the coding area based on the optical response parameters and the energy loss distribution.

[0089] In specific implementation, the optical response parameters of the coding area can be determined according to the reflectivity surface in the following manner, that is: The reflectivity surface can be input into an existing bidirectional reflectance distribution function, and the surface reflectivity, the ratio of diffuse reflection to specular reflection, and the micro-roughness of the coding area can be calculated through curve fitting, and the set of the surface reflectivity, the ratio of diffuse reflection to specular reflection, and the micro-roughness is used as the optical response parameters of the coding area; wherein, the optical response parameters are indexes to describe the optical behaviors (such as absorption, reflection, and scattering) shown by the coding area on the surface of the target object under laser irradiation.

[0090] In specific implementation, Monte Carlo simulation is performed on the energy attenuation rate to obtain the energy loss distribution of laser energy transmission in the coding area. The following method can be used, that is: First, the existing Monte Carlo ray tracing technology (such as Zemax OpticStudio) can be used to randomly generate a large number of laser photon paths according to the energy attenuation value in the coding area of the energy attenuation rate, and the energy loss caused by absorption and scattering during the process of each photon path passing through the coding area is statistically analyzed to obtain the loss data of laser energy transmission in the coding area. Then, finally, according to the existing fitting algorithm (such as the least squares method), the loss data is fitted into an energy loss distribution map, and this energy loss distribution map is used as the energy loss distribution of laser energy transmission in the coding area; where the energy loss distribution refers to the spatial distribution of the energy attenuation caused by absorption and scattering of the laser in the coding area, and this energy loss distribution helps to accurately regulate the laser power and achieve a uniform and high-quality laser coding effect.

[0091] It should be noted that in this application, the energy compensation factor refers to the laser output power adjustment ratio for compensating the laser energy attenuation caused by the surface reflectivity and geometric shape of the coding area. This energy compensation factor helps to accurately adjust the laser output according to the laser energy attenuation rate and optical reflection coefficient of the coding area, realize the dynamic compensation for the uneven energy distribution on the complex surface, and thus significantly improve the consistency, clarity and stability of the coding effect; in specific implementation, determining the energy compensation factor of the coding area based on the optical response parameter and the energy loss distribution can be achieved by the following method, that is: The optical response parameter and the energy loss distribution can be input into the existing optical simulation software (such as COMSOL Multiphysics) to construct a coupled mathematical model to numerically solve the energy transmission and reflection of the laser in the coding area, so as to calculate the adjustment ratio of the laser output power in the coding area, and this adjustment ratio of the laser output power is used as the energy compensation factor of the coding area.

[0092] In step 105, when it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve, a dynamic coding path of the target item is generated based on all the energy compensation factors.

[0093] In some embodiments, when it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve, generating a dynamic coding path of the target item based on all the energy compensation factors can be achieved by the following steps:

[0094] Determine the preset standard curve for laser coding;

[0095] When the deviation value between the absorption response curve of the target item and the preset standard curve is greater than the preset deviation threshold, a dynamic coding path for the target item is generated based on all the energy compensation factors.

[0096] It should be noted that in this application, the preset standard curve is the reference curve for real-time monitoring of whether there is a deviation in the absorption response curve of the coding area, which helps to promptly detect and correct the deviation of the absorption response curve, thereby ensuring the quality and consistency of the laser coding process. Specifically, when implementing, the preset standard curve for laser coding can be determined in the following way, that is: a representative target item sample (such as the coding accuracy rate is greater than 98%) that has been laser coded can be selected, and the absorption response curve of this target item sample is used as the preset standard curve.

[0097] Specifically, when implementing, when the deviation value between the absorption response curve of the target item and the preset standard curve is greater than the preset deviation threshold, the generation of the dynamic coding path for the target item based on all the energy compensation factors can be achieved in the following way, that is: First, the absorption response curve of the target item can be obtained, and the mean square error between this absorption response curve and the preset standard curve is calculated as the deviation value between the absorption response curve and the preset standard curve. If this deviation is greater than the preset deviation threshold (such as 0.1), then the dynamic programming algorithm is used to re-plan the laser coding path and adjust the power and irradiation speed of the laser beam according to the pre-calculated energy compensation factors to obtain the dynamic coding path of the target item.

[0098] It should be noted that in this application, the dynamic coding path is the path used to adjust the irradiation sequence and position of the laser beam during the coding process in real time. This dynamic coding path can flexibly adjust the coding process according to the surface characteristics of the target item, compensate for the energy attenuation caused by surface non-uniformity, and ensure that the coding effect is clear and accurate.

[0099] In addition, on the other hand of this application, in some embodiments, this application provides a fully automatic laser vision coding machine, which includes a coding path optimization unit. Refer to Figure 4 , this figure is a schematic structural diagram of the coding path optimization unit shown in some embodiments of this application. The coding path optimization unit 400 includes: a collection module 401, a processing module 402, and an execution module 403, which are described as follows:

[0100] The collection module 401, in this application, the collection module 401 is mainly used to perform multi-view scanning on the target item to be coded to obtain the three-dimensional point cloud data of the target item;

[0101] The processing module 402. In this application, the processing module 402 is mainly used to perform spatial calibration on the three-dimensional point cloud data by using the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine, obtain the three-dimensional feature model of the target item, and then extract the curvature field distribution required for beam path compensation of the target item from the three-dimensional feature model;

[0102] It should be noted that in this application, the processing module 402 is also used to obtain the regional boundary range when coding the target item, and then determine the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the curvature field distribution and the surface material distribution of the target item;

[0103] In addition, it should be noted that in this application, the processing module 402 is also used to determine the energy compensation factor corresponding to each coding region according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding region in the regional boundary range during coding;

[0104] The execution module 403. In this application, the execution module 403 is mainly used to generate a dynamic coding path for the target item based on all the energy compensation factors when it is detected that the absorption response curve of the target item in the coding region deviates from the preset standard curve.

[0105] Each module in the above full-automatic laser vision coding machine can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above each module.

[0106] In addition, in one embodiment, this application provides a computer device, which can be a server, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data of the coding path optimization method of the full-automatic laser vision coding machine. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a coding path optimization method of a full-automatic laser vision coding machine.

[0107] Those skilled in the art can understand, Figure 5The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0108] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps in the embodiment of the coding path optimization method of the above-mentioned full-automatic laser vision coding machine are implemented.

[0109] In one embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the embodiment of the coding path optimization method of the above-mentioned full-automatic laser vision coding machine are implemented.

[0110] In one embodiment, a computer program product or a computer program is provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the embodiment of the coding path optimization method of the above-mentioned full-automatic laser vision coding machine.

[0111] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application may include at least one of non-volatile and volatile memories. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0112] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0113] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for optimizing the coding path of a full-automatic laser vision coding machine, characterized in that, The method includes the following steps: Performing multi-view scanning on the target object to be coded to obtain the three-dimensional point cloud data of the target object; Spatially calibrating the three-dimensional point cloud data by the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain the three-dimensional feature model of the target object, and then extracting the curvature field distribution required for beam path compensation of the target object from the three-dimensional feature model; Obtaining the regional boundary range when coding the target object, and then determining the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the curvature field distribution and the surface material distribution of the target object; Determining the energy compensation factor for each coding region according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding region in the regional boundary range during coding; When it is detected that the absorption response curve of the target object in the coding region deviates from the preset standard curve, generating a dynamic coding path for the target object based on all the energy compensation factors.

2. The method according to claim 1, wherein Spatially calibrating the three-dimensional point cloud data by the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain the three-dimensional feature model of the target object specifically includes: Obtaining the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine; Constructing a co-visual space domain between the stereo calibration block and the target object based on the point cloud distribution map; Calibrating the three-dimensional point cloud data through the co-visual space domain to obtain the three-dimensional feature model of the target object.

3. The method according to claim 1, wherein Extracting the curvature field distribution required for beam path compensation of the target object from the three-dimensional feature model specifically includes: Performing neighborhood topology analysis on the feature points in the three-dimensional feature model to obtain the unit normal vectors of all feature points in the three-dimensional feature model; Determining the curvature values of each feature point in the three-dimensional feature model through all the unit normal vectors; Performing spatial interpolation on all the curvature values to obtain the curvature field distribution required for beam path compensation of the target object.

4. The method according to claim 1, wherein The regional boundary range includes multiple independent coding regions.

5. The method according to claim 1, characterized in that, Determining the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the curvature field distribution and the surface material distribution of the target object specifically includes: Determining the surface material distribution of the target object; Establishing an absorption response model of the target object surface to the laser beam based on the curvature field distribution and the surface material distribution; Determining the absorption response curve of the laser beam in all coding regions within the regional boundary range based on the absorption response model.

6. The method according to claim 1, wherein Determining the energy compensation factor for each coding region according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding region in the regional boundary range during coding specifically includes: Determining the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve; For each coding region in the regional boundary range, determining the optical reflection coefficient of the coding region when coding the target object; Determining the reflectivity surface of the coding region according to the optical reflection coefficient and the curvature field distribution of the coding region; Determine the energy compensation factor of the coding area according to the reflectivity surface and the energy attenuation rate, and then obtain the energy compensation factors of each coding area in the area boundary range.

7. The method according to claim 1, characterized in that, When it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve, generating the dynamic coding path of the target item based on all the energy compensation factors specifically includes: Determine the preset standard curve for laser coding. When the deviation value between the absorption response curve of the target item and the preset standard curve is greater than the preset deviation threshold, generate the dynamic coding path of the target item according to all the energy compensation factors.

8. An automatic laser vision coding machine, the automatic laser vision coding machine includes a coding path optimization unit, characterized in that, The coding path optimization unit includes: An acquisition module, configured to perform multi-view scanning on the target item to be coded to obtain the three-dimensional point cloud data of the target item. A processing module, configured to perform spatial calibration on the three-dimensional point cloud data by using the point cloud distribution map of the stereo calibration block in the full-automatic laser vision coding machine to obtain the three-dimensional feature model of the target item, and then extract the curvature field distribution required for beam path compensation of the target item from the three-dimensional feature model. The processing module is configured to obtain the area boundary range when coding the target item, and then determine the absorption response curve of the laser beam in all coding areas within the area boundary range based on the curvature field distribution and the surface material distribution of the target item. The processing module is configured to determine the energy compensation factor corresponding to each coding area according to the energy attenuation rate of the laser energy on the complex surface of the target object in the absorption response curve and the optical reflection coefficient of each coding area in the area boundary range during coding. An execution module, configured to generate the dynamic coding path of the target item based on all the energy compensation factors when it is detected that the absorption response curve of the target item in the coding area deviates from the preset standard curve.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the coding path optimization method of the full-automatic laser vision coding machine according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the coding path optimization method of the full-automatic laser vision coding machine according to any one of claims 1 to 7.

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