Cold-rolled strip steel welding control method and device, electronic equipment and storage medium

Abstract

The invention provides a cold-rolled strip steel welding control method and device, electronic equipment and a storage medium, and belongs to the technical field of cold rolling equipment.The method comprises the steps that weld seam image information of a first welding joint is obtained based on an X-ray image of the first welding joint; determining defect data corresponding to the first welding joint according to the welding seam image information; and first control parameters of the welding equipment are adjusted according to the defect data, wherein the first control parameters at least comprise parameters for controlling the second welding joint to be welded to be welded. According to the cold-rolled strip steel welding control method and device, the electronic equipment and the storage medium, the defect data in the welding process can be efficiently and accurately detected, the control parameters are adjusted, and welding defects are reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of cold rolling equipment, and more specifically, relates to a cold-rolled strip welding control method and device, electronic equipment, and storage medium. Background Art

[0002] Cold-rolled strip welding is a critical process in steel manufacturing, and welding quality directly impacts the stability of subsequent rolling processes and product quality. Traditional methods for adjusting cold-rolled strip welding parameters rely heavily on operator experience, making precise control difficult and prone to weld defects such as porosity, cracks, and lack of fusion.

[0003] In the existing technology, the adjustment of welding speed is mostly based on temperature, pressure or visual feedback, but these methods can only detect surface defects and are difficult to accurately reflect the internal quality of the weld joint. They cannot detect internal defects of the weld joint, which leads to reduced welding quality. Summary of the Invention

[0004] The purpose of this application is to provide a cold-rolled strip welding control method and device, electronic equipment, and storage medium to accurately detect internal defects of welded joints, adjust the control parameters of welding equipment based on defect data, and improve welding quality.

[0005] A first aspect of an embodiment of the present application provides a cold-rolled strip welding control method, comprising: acquiring weld image information of the first weld joint based on the X-ray image of the first weld joint; determining defect data corresponding to the first weld joint according to the weld image information; The first control parameters of the welding equipment are adjusted according to the defect data, wherein the first control parameters at least include parameters for controlling the welding of the second welding joint to be welded.

[0006] A second aspect of the embodiments of the present application provides a cold-rolled strip welding control device, comprising: an acquisition module, configured to acquire weld image information of the first weld joint based on an X-ray image of the first weld joint; a determination module, configured to determine defect data corresponding to the first weld joint based on the weld image information; An adjustment module is used to adjust first control parameters of the welding equipment according to the defect data, where the first control parameters at least include parameters for controlling the welding of the second welding joint to be welded.

[0007] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned cold-rolled strip welding control method when executing the computer program.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned cold-rolled strip welding control method are implemented.

[0009] The beneficial effects of the cold-rolled strip welding control method and device, electronic device, and storage medium provided in the embodiments of the present application are: X-rays have the ability to penetrate objects and can clearly present the welding conditions inside the first welding joint. The present application can quantify and classify the defects in the weld image information by obtaining the weld image information in the X-ray image and conducting an in-depth analysis of the weld image information to obtain the corresponding defect data. The present application can also effectively avoid defects similar to the first welding joint during the welding process by adjusting the welding parameters according to the defect data, thereby improving production quality. The method provided in the present application is a non-contact detection method, which not only does not cause any damage to the welding joint, but also can effectively avoid additional quality problems that may be caused by the detection process. At the same time, it can accurately and efficiently detect defect data in the welding process, and adjust the control parameters of the welding equipment according to the defect data to reduce welding defects and improve welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A schematic flow chart of a cold-rolled strip welding control method provided in one embodiment of the present application; Figure 2 This is a structural block diagram of a cold-rolled strip welding control device provided in one embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 This is a flow chart of a cold-rolled strip welding control method provided in one embodiment of the present application, the method comprising: S101: Acquire weld image information of the first weld joint based on an X-ray image of the first weld joint.

[0015] In this embodiment, during the cold-rolled steel welding process, an X-ray image corresponding to the first completed weld joint can be acquired in real time. The X-ray image can then be preprocessed, including operations such as noise removal and contrast enhancement, to improve the clarity of the X-ray image. The first weld joint is any completed weld joint. Because X-rays have the ability to penetrate objects, the weld quality inside the first weld joint can be accurately determined through non-contact detection using X-ray images.

[0016] In this embodiment, weld image information of the first weld joint can be obtained based on the X-ray image. The position of the first weld joint in the X-ray image can be identified using an image positioning algorithm or other algorithm, and then the weld joint region can be cropped from the X-ray image according to a preset rule. For example, the weld image information corresponding to the weld region can be obtained by expanding the region where the first weld joint is located by a preset number of pixels.

[0017] S102: Determine defect data corresponding to the first weld joint based on the weld image information.

[0018] In this embodiment, the pixel distribution can be determined based on the number and position of pixels corresponding to different grayscale values ​​in the weld image information. If the grayscale value of a first local area in the pixel distribution is lower than a preset grayscale threshold, and the number of pixels in the local area is greater than the preset number threshold, it is determined that a defective area exists in the weld image information; if the grayscale value of the first local area in the pixel distribution is lower than the preset grayscale threshold, it is determined that a defective area exists in the weld image information; if the number of pixels in the first local area in the pixel distribution is greater than the preset number threshold, it is determined that a defective area exists in the first local area in the weld image information.

[0019] In this embodiment, defect data may be determined based on pixel distribution in the first local area, where the defect type includes at least one of lack of fusion, pores, cracks, slag inclusions, pits, and weld bumps.

[0020] In this embodiment, the analysis method based on local features can provide a quantitative analysis basis for the extraction of weld image information, and at the same time can better adapt to the grayscale changes in different areas of the X-ray image, thereby more accurately extracting defect data in the weld image information.

[0021] S103: adjusting first control parameters of the welding equipment according to the defect data, where the first control parameters at least include parameters for controlling welding of the second welding joint to be welded.

[0022] In this embodiment, the defect level of the first welding joint can be determined based on the defect data. If the defect level is greater than or equal to the first preset level, the first control parameter of the welding equipment is adjusted. The first control parameter includes at least parameters for controlling the welding of the second welding joint to be welded, which may include parameters such as welding speed.

[0023] By adjusting the first control parameter of the welding equipment based on the defect data, the quality of welding can be further improved.

[0024] In one embodiment of the present application, weld image information may be obtained based on an X-ray image corresponding to the first weld joint, including: dividing the X-ray image into a first number of sub-regions; For each sub-region, determining a first segmentation threshold of the sub-region according to the grayscale mean value of all pixels in the sub-region and the grayscale standard deviation of all pixels in the sub-region; Determine a first pixel point set in the sub-region based on a first segmentation threshold; The weld image information in the sub-region is determined according to the first pixel point set.

[0025] In this embodiment, the X-ray image can be divided into a first number of sub-regions. As an example, the X-ray image can be divided into a first number of non-overlapping sub-regions of equal or unequal sizes based on the size and characteristics of the X-ray image. For example, the X-ray image can be divided into an M×N grid, with each grid unit serving as a sub-region. This application does not impose specific limitations on this.

[0026] In this embodiment, for each sub-region, the grayscale mean of all pixels in each sub-region and the grayscale standard deviation of all pixels in each sub-region can be calculated; for each pixel in the sub-region, the first grayscale value of the pixel is determined. The grayscale mean of the sub-region can represent the average value of the grayscale values ​​of all pixels in the sub-region, and the grayscale standard deviation of the sub-region can represent the degree of discreteness of the grayscale values ​​of the pixels in the sub-region. As an example, a first weight and a second weight can be set, the first weight can correspond to the weight of the grayscale mean, and the second weight can correspond to the weight of the grayscale standard deviation. The first segmentation threshold can be determined by the following steps: determining a first value of the product between the first weight and the grayscale mean, determining a second value of the product between the second weight and the grayscale standard deviation, and determining the sum of the first value and the second value as the value of the first segmentation threshold. The size of the first weight and the second weight can be determined according to actual conditions, and this application does not limit it.

[0027] After determining the first segmentation threshold for each subregion, a first set of pixels corresponding to the subregion may be determined based on the first segmentation threshold. A grayscale value of a pixel in each subregion may be determined, and if the grayscale value of the pixel is greater than the first segmentation threshold corresponding to the subregion, the pixel is determined as an element of the first set of pixels.

[0028] In this embodiment, through an analysis method based on local features, the first segmentation threshold is determined based on parameters such as the grayscale mean and standard deviation of each sub-region, which can provide a quantitative analysis basis for the extraction of weld image information. At the same time, it can better adapt to the grayscale changes in different regions in the X-ray image, thereby more accurately extracting the weld image information.

[0029] In one embodiment of the present application, the weld image information in the sub-region may be determined based on the first pixel point set, including: Obtaining a first area where a first pixel point set is located; Performing a sliding traversal on the first area based on the first preset matrix, if all first candidate pixel points in the coverage area of ​​the first preset matrix are pixel points in the first pixel point set, determining the pixel points in the coverage area of ​​the first preset matrix as pixel points in the second area; A sliding traversal is performed on the second area based on the second preset matrix. If there is at least one second candidate pixel point in the coverage area of ​​the second preset matrix that is a pixel point in the first pixel point set, the pixel point in the coverage area of ​​the second preset matrix is ​​determined as a pixel point in the third area.

[0030] The image information corresponding to the third area is determined as weld image information.

[0031] In this embodiment, the first area corresponding to the first pixel set in the sub-area can be obtained, and a sliding traversal can be performed on the first area based on the first preset matrix. If the first candidate pixel points of the first preset matrix in the coverage area are all pixels in the first pixel set, the pixel points in the coverage area of ​​the first preset matrix are determined as pixel points in the second area. After the sliding traversal is completed, all pixel points corresponding to the second area can be obtained; a sliding traversal can be performed on the second area based on the second preset matrix. If there is at least one second candidate pixel point in the coverage area of ​​the second preset matrix that is a pixel point in the first pixel set, the pixel points in the coverage area of ​​the second preset matrix are determined as pixel points in the third area. After the sliding traversal is completed, all pixel points corresponding to the third area can be determined based on the second area. After obtaining the third area, the image information corresponding to the third area can be determined as weld image information. Among them, the size of the first preset matrix and the size of the second preset matrix can be determined according to actual conditions, and this application does not limit it.

[0032] After the first sliding pass, the present application can remove pixels irrelevant to the weld area, reduce interference information, and lower the risk of misjudgment. After the second sliding pass, the core area of ​​the weld area can be ensured to be accurate while further refining the boundaries of the weld area, avoiding the transition shrinkage after the first sliding pass that could cause loss of important information. After two sliding passes, the present application can more accurately screen out the weld area corresponding to the weld image information.

[0033] In one embodiment of the present application, a method for determining a defective area in the first local area may be: Determine the gradient magnitude and gradient direction of each pixel point in the first local area, and determine the pixel points whose gradient magnitude exceeds a preset gradient threshold as candidate edge points; For each pixel, determine the gradient magnitude between the pixel and its adjacent pixels in the gradient direction, and determine the pixel with the largest gradient magnitude as a candidate edge point; For all candidate edge points, determining the candidate edge points that are greater than a preset first threshold as target edge points; Get the defect area based on the target edge point.

[0034] In this embodiment, the gradient amplitude and gradient direction of each pixel point in the weld image information can be determined, and the pixel point whose gradient amplitude exceeds the preset gradient threshold is determined as a candidate edge point; for each pixel point, the gradient amplitude between the pixel point and the pixel points adjacent to the pixel point is determined in the gradient direction corresponding to the pixel point, and the pixel point with the largest gradient amplitude between the pixel point and the pixel points adjacent to the pixel point is determined as the candidate edge point corresponding to the pixel point; for all candidate edge points, the candidate edge points greater than the preset first threshold are determined as target edge points; and the defect area is obtained based on the target edge point. As an example, the horizontal gradient of the image in the horizontal direction and the vertical gradient in the vertical direction can be calculated based on the Sobel operator, and the gradient amplitude of each pixel point can be calculated based on the horizontal gradient and the vertical gradient, and the gradient direction, such as 45°, can be determined. A preset gradient threshold is set, and the pixel points whose gradient amplitude exceeds the preset gradient threshold are determined as candidate edge points. Then, for each pixel point, the gradient amplitude between the pixel point and the pixel points adjacent to the pixel point is compared in the gradient direction, and the pixel point with the largest gradient amplitude among the pixel points adjacent to the pixel point is determined as a candidate edge point. After the traversal is completed, multiple candidate edge points will be obtained. For all candidate edge points, the candidate edge points greater than the preset first threshold can be determined as target edge points, and then the defect area is obtained based on the target edge point. Among them, the target edge point can be contour extracted, and the target edge points can be connected into a continuous contour to obtain the defect area corresponding to the contour.

[0035] This embodiment can capture the edge information of the defect area and achieve preliminary positioning of the edge of the defect area by acquiring the gradient amplitude and direction of the pixel points in the first local area; by comparing the gradient amplitudes of adjacent pixel points in the gradient direction for the candidate edge points, the position of the edge can be further refined, and the candidate edge points are screened according to the first threshold, which can filter out those with small amplitudes and eliminate pseudo edges caused by noise or slight changes in the image, thereby making the edge positioning more accurate and improving the accuracy of defect area positioning.

[0036] In one embodiment of the present application, determining defect data according to the defect area includes: Determine the grayscale features, texture features and shape features based on the pixel distribution of the defect area; Determine the defect type corresponding to the defect area based on grayscale features, texture features, and shape features; The defect type includes at least one of lack of fusion, pores, cracks, slag inclusions, pits or weld bumps.

[0037] In this embodiment, after obtaining the defect area, the pixel distribution of the defect area can be obtained to determine the grayscale features, texture features and shape features. The defect type is determined based on the grayscale features, texture features and shape features. The defect type may include at least one of lack of fusion, pores, cracks, slag inclusions, pits or weld bumps. Among them, the grayscale features may include the average grayscale value, grayscale variance, etc.; the texture features may include the energy, entropy, contrast, correlation, etc. of the defect area. Energy can represent the uniformity of grayscale distribution, entropy can represent the randomness of grayscale distribution, contrast can represent the degree of grayscale difference, and correlation can represent the regularity of grayscale change; shape features may include the area, perimeter, major axis length, minor axis length, shape factor, boundary, etc. of the defect area. The shape factor may include factors such as circularity and rectangularity. Circularity is used to measure the degree of similarity with a circle, and rectangularity is used to measure the degree of similarity between the defect and a rectangle. The boundary may include whether the boundary is regular, the clarity of the boundary, etc. Feature templates corresponding to various defect types can be obtained, and the grayscale features, texture features, and shape features of the defect area can be matched with the feature templates corresponding to each defect type, and the similarity can be calculated. The defect type corresponding to the template with the highest similarity can be determined as the defect type corresponding to the defect area; or a multi-layer neural network can be constructed to determine the defect type, which is not limited in this application.

[0038] This embodiment extracts three types of features: grayscale, texture, and shape. It can comprehensively analyze the features of the defect area from different angles. By matching these features of different dimensions with the preset defect feature template, it can effectively distinguish multiple defect types, avoid misjudgment caused by a single dimension, and improve the accuracy of defect type identification.

[0039] In one embodiment of the present application, adjusting the first control parameter according to the defect data includes: determining a defect level of the first weld joint based on the defect data; If the defect level is greater than or equal to the first preset level, reducing the welding speed in the first control parameter by the first step length; If the defect level is less than the first preset level and greater than or equal to the second preset level, the welding accuracy in the first control parameter is increased by a second step; the welding accuracy is the swing accuracy of the robot of the welding equipment.

[0040] In this embodiment, the defect level of the first weld joint can be determined based on the defect data. For example, the defect level can be determined based on the defect type, the defect size within the defect region, and the number of defects within the defect region. Different weights can be assigned to different defect types. For example, cracks and lack of fusion can be assigned a first weight, pores and slag inclusions can be assigned a second weight, and pits and weld bumps can be assigned a third weight, where the first weight is greater than the second weight, and the second weight is greater than the third weight. For defect sizes corresponding to different defect regions, the first weight is assigned to a crack length exceeding a preset length threshold, a pore diameter exceeding a preset direct threshold, a lack of fusion area ratio exceeding a preset ratio threshold, a maximum slag inclusion size exceeding a preset slag inclusion threshold, a maximum weld bump size exceeding a preset weld bump threshold, or a pit depth exceeding a depth threshold; the other weights are assigned a second weight. For the number of defects corresponding to different defect regions, if the number is greater than a preset number threshold, the first weight is assigned; if the number is less than or equal to the preset number threshold, the second weight is assigned. The weights corresponding to the defect type, defect size, and number of defects can be added together, and the added value is used to determine the defect level.

[0041] If the defect level is greater than or equal to the first preset level, the welding speed in the first parameter is reduced by the first step length. If the defect level is greater than or equal to the second preset level and less than the first preset level, the welding accuracy in the first control parameter is increased by the second step length. The welding accuracy is the swing accuracy of the robot of the welding equipment.

[0042] In this embodiment, by quantifying and summing the defect data, complex welding defects can be evaluated with a unified standard, thereby improving the efficiency of the evaluation. By adjusting the control parameters based on the defect level, adaptive optimization of the process parameters can be achieved, thereby improving the efficiency and accuracy of welding.

[0043] Corresponding to the cold-rolled strip welding control method of the above embodiment, Figure 2 This is a structural block diagram of a cold-rolled strip welding control device provided in one embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 2 The cold-rolled strip welding control device 20 includes: an acquisition module 21, a determination module 22 and an adjustment module 23.

[0044] The acquisition module 21 is used to acquire weld image information of the first weld joint based on the X-ray image of the first weld joint; a determination module 22, configured to determine defect data corresponding to the first weld joint based on the weld image information; The adjustment module 23 is used to adjust the first control parameters of the welding equipment according to the defect data, where the first control parameters at least include parameters for controlling the welding of the second welding joint to be welded.

[0045] In one embodiment of the present application, the acquisition module 21 is also used to divide the X-ray image into a first number of sub-regions; for each sub-region, determine a first segmentation threshold of the sub-region based on the grayscale mean of all pixels in the sub-region and the grayscale standard deviation of all pixels in the sub-region; determine a first pixel point set in the sub-region based on the first segmentation threshold; and determine the weld image information in the sub-region based on the first pixel point set.

[0046] The acquisition module 21 is also used to obtain the first area where the first pixel point set is located; based on the first preset matrix, a sliding traversal is performed on the first area; if the first candidate pixel points of the first preset matrix in the coverage area are all pixel points in the first pixel point set, then the pixel points in the coverage area of ​​the first preset matrix are determined as pixel points in the second area; based on the second preset matrix, a sliding traversal is performed on the second area; if there is at least one second candidate pixel point in the coverage area of ​​the second preset matrix that is a pixel point in the first pixel point set, then the pixel points in the coverage area of ​​the second preset matrix are determined as pixel points in the third area; and the image information corresponding to the third area is determined as weld image information.

[0047] The determination module 22 is also used to determine the pixel distribution based on the number and position of pixel points corresponding to different grayscale values ​​in the weld image information; if the grayscale value of a first local area in the pixel distribution is lower than a preset grayscale threshold, and / or the number of pixel points in the first local area is greater than a preset number threshold, then the defect area in the weld image information is obtained; and the defect data is determined based on the defect area.

[0048] The determination module 22 is further used to determine the gradient amplitude and gradient direction of each pixel point in the first local area, and determine the pixel points whose gradient amplitude exceeds the preset gradient threshold as candidate edge points; for each pixel point, determine the size of the gradient amplitude between the pixel point and the pixel points adjacent to the pixel point in the gradient direction, and determine the pixel point with the largest gradient amplitude as the candidate edge point; for all candidate edge points, determine the candidate edge points greater than the preset first threshold as target edge points; and obtain the defect area based on the target edge point.

[0049] The determination module 22 is further used to determine the grayscale features, texture features and shape features based on the pixel distribution of the defect area; determine the defect type corresponding to the defect area based on the grayscale features, texture features and shape features; wherein the defect type includes at least one of unfusion, pores, cracks, slag inclusions, pits or weld bumps.

[0050] The adjustment module 23 is also used to determine the defect level of the first welding joint based on the defect data; if the defect level is greater than or equal to the first preset level, the welding speed in the first control parameter is reduced by the first step length; if the defect level is less than the first preset level and greater than or equal to the second preset level, the welding accuracy in the first control parameter is increased by the second step length; the welding accuracy is the swing accuracy of the robotic arm of the welding equipment.

[0051] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of the acquisition module 21, the determination module 22 and the adjustment module 23 are shown.

[0052] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0053] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0054] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store device type information.

[0055] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present application can execute the implementation methods described in the first and second embodiments of the cold-rolled strip welding control method provided in the embodiments of the present application, and can also execute the implementation methods of the electronic device described in the embodiments of the present application, which will not be repeated here.

[0056] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0057] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0058] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0061] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0062] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0063] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A cold-rolled strip welding control method, characterized in that: include: acquiring weld image information of the first weld joint based on the X-ray image of the first weld joint; determining defect data corresponding to the first weld joint according to the weld image information; The first control parameters of the welding equipment are adjusted according to the defect data, wherein the first control parameters at least include parameters for controlling the welding of the second welding joint to be welded.

2. The cold-rolled strip welding control method according to claim 1, characterized in that: The acquiring weld image information of the first weld joint based on the X-ray image of the first weld joint includes: dividing the X-ray image into a first number of sub-regions; For each sub-region, determining a first segmentation threshold for the sub-region according to a grayscale mean value of all pixels in the sub-region and a grayscale standard deviation of all pixels in the sub-region; Determine a first pixel point set in the sub-region based on the first segmentation threshold; The weld image information in the sub-region is determined based on the first pixel point set.

3. The cold-rolled strip welding control method according to claim 2, characterized in that: The determining the weld image information in the sub-region according to the first pixel point set includes: Obtaining a first area where the first pixel point set is located; Performing a sliding traversal on the first area based on a first preset matrix, if all first candidate pixel points within the coverage area of ​​the first preset matrix are pixel points in the first pixel point set, determining the pixel points within the coverage area of ​​the first preset matrix as pixel points in the second area; Performing a sliding traversal on the second area based on a second preset matrix, if at least one second candidate pixel point in the coverage area of ​​the second preset matrix is ​​a pixel point in the first pixel point set, determining the pixel point in the coverage area of ​​the second preset matrix as a pixel point in the third area; The image information corresponding to the third area is determined as the weld image information.

4. The cold-rolled strip welding control method according to claim 1, characterized in that: The determining defect data corresponding to the first weld joint according to the weld image information includes: Determining pixel distribution according to the number and position of pixels corresponding to different grayscale values ​​in the weld image information; If a grayscale value of a first local area in the pixel distribution is lower than a preset grayscale threshold, and / or the number of pixels in the first local area is greater than a preset number threshold, then obtaining a defective area in the first local area; The defect data is determined according to the defect area.

5. The cold-rolled strip welding control method according to claim 4, characterized in that: The obtaining of the defective area in the first local area includes: Determine the gradient magnitude and gradient direction of each pixel point in the first local area, and determine the pixel points whose gradient magnitude exceeds a preset gradient threshold as candidate edge points; For each pixel point, determine the gradient magnitude between the pixel point and the pixel points adjacent to the pixel point in the gradient direction, and determine the pixel point with the largest gradient magnitude as a candidate edge point; For all candidate edge points, determining the candidate edge points that are greater than a preset first threshold as target edge points; The defect area is acquired according to the target edge point.

6. The method according to claim 4, characterized in that The determining the defect data according to the defect area includes: determining grayscale features, texture features, and shape features based on pixel distribution of the defect area; Determining the defect type corresponding to the defect area according to the grayscale features, texture features, and shape features; The defect type includes at least one of lack of fusion, pores, cracks, slag inclusions, pits or weld bumps.

7. The method according to claim 1, characterized in that The adjusting the first control parameter according to the defect data includes: determining a defect level of the first weld joint according to the defect data; If the defect level is greater than or equal to a first preset level, reducing the welding speed in the first control parameter by a first step; If the defect level is less than the first preset level and greater than or equal to the second preset level, the welding accuracy in the first control parameter is increased by a second step; the welding accuracy is the swing accuracy of the robot of the welding equipment.

8. A cold-rolled strip welding control device, characterized in that: include: an acquisition module, configured to acquire weld image information of the first weld joint based on an X-ray image of the first weld joint; a determination module, configured to determine defect data corresponding to the first weld joint based on the weld image information; An adjustment module is used to adjust first control parameters of the welding equipment according to the defect data, where the first control parameters at least include parameters for controlling the welding of the second welding joint to be welded.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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