Welding control method and device for signboard

Abstract

The invention relates to the technical field of welding control, in particular to a welding control method and device for a signboard. The method comprises the following steps: acquiring a high-definition monitoring image and a welding work log of a signboard to be welded; performing signboard texture visual identification on the to-be-welded signboard high-definition monitoring image, and performing welding visual effect simulation to obtain a signboard welding visual effect; welding points are accurately recognized based on the signboard welding visual effect, and a dynamic welding path is planned, so that the dynamic welding path is constructed; performing real-time welding control on the signboard to be welded based on the dynamic welding path, and then performing smooth optimization on the motion track of the welding gun so as to construct an optimal smooth motion track of the welding gun; current real-time working parameters of the welding gun are obtained; and self-adaptive welding gun parameter adjustment is conducted on the current welding gun real-time working parameters based on the optimal welding gun movement smooth track, and therefore self-adaptive welding gun adjustment parameters are obtained. The signboard welding quality is improved, and welding errors are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of welding control, and in particular to a welding control method and device for identification plates. Background Art

[0002] As an important part of the sign production process, sign welding directly affects the quality of the finished product and production efficiency. With the rapid development of industrial automation technology, traditional manual welding methods have gradually exposed problems such as low precision, low efficiency, and easy errors in manual operation, making it difficult to meet the needs of large-scale, high-precision production. Especially in the sign production process, the control of welding position, angle and welding force plays a vital role in the appearance and function of the final product.

[0003] Traditional sign welding control methods rely on manual operation and mechanical control, usually using manual or simple mechanical devices for positioning and welding. However, this control method often faces problems such as unstable welding accuracy, position deviation, and uneven welding. Especially in scenarios with complex shapes and small batch production, the limitations of traditional control methods are more prominent. In order to improve production efficiency and product quality, automated welding technology has gradually become an important trend in the development of the industry. Therefore, it is necessary to develop a more intelligent sign welding control method. Summary of the invention In order to solve the above technical problems, the present invention proposes a welding control method and device for identification plates to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a welding control method for a sign, comprising the following steps: Step S1: obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; performing visual recognition of the identification plate texture on the high-definition monitoring image of the identification plate to be welded, and simulating the welding visual effect, thereby obtaining the identification plate welding visual effect; Step S2: accurately identifying welding points based on the visual effect of the welding of the identification plate, and performing dynamic welding path planning, thereby constructing a dynamic welding path; Step S3: Based on the dynamic welding path, the welding identification plate is subjected to real-time welding control, and then the welding gun motion trajectory is smoothly optimized to construct an optimal welding gun motion smooth trajectory; Step S4: obtaining the current real-time working parameters of the welding gun; performing adaptive welding gun parameter adjustment on the current real-time working parameters of the welding gun based on the optimal welding gun motion smooth trajectory, thereby obtaining adaptive welding gun adjustment parameters; Step S5: analyzing the trend of the temperature change of the welding pool according to the real-time welding monitoring image, and dynamically regulating the welding parameters of the adaptive welding gun adjustment parameters, thereby constructing a dynamic welding parameter regulation strategy; Step S6: Perform local welding defect analysis based on the real-time welding monitoring image, and optimize the welding defect control of the dynamic welding parameter control strategy to build a dynamic welding control optimization model.

[0005] The present invention uses high-definition monitoring image acquisition, and the system can accurately obtain the texture, shape and surface state of the identification plate, provide detailed basic data for subsequent analysis, and simulate the welding visual effect based on the image, which can not only predict the defects that may occur in the welding process in advance, but also evaluate the impact of different welding parameters on the final effect. This provides data support for the subsequent welding path planning and parameter adjustment, ensuring that the welding process can proceed as expected. Simulating the welding visual effect in advance helps to identify potential quality problems and avoid unexpected defects in the actual welding process, thereby improving the controllability of welding quality. Through the analysis of the visual effect, the system can accurately identify each welding point on the identification plate and judge the priority and difficulty of each welding point. Ensure the optimization of welding sequence and method. The dynamic planning of welding path ensures the real-time and pertinence of path selection. By processing the visual information of the welding area, the welding path can not only be flexibly adjusted according to the complexity of different parts, but also effectively avoid potential obstacles or influences, reducing the probability of rework during welding. Dynamic path planning makes the welding process more efficient. The welding gun does not need to go through unnecessary repeated paths, thereby saving welding time and improving production efficiency. Combined with the real-time dynamic path, the welding system can be controlled in real time to ensure that the welding gun is always in the best working state during the welding process. Real-time adjustment can avoid the unstable factors caused by path changes, thereby improving the consistency and stability of welding. The smooth optimization of the motion trajectory not only reduces the welding The vibration and oscillation during the welding process can also reduce the welding defects caused by uneven acceleration and deceleration, improve the quality of the welding joints, and the smoothness of the trajectory also reduces the mechanical wear on the equipment and extends the service life of the equipment. The optimized welding gun motion trajectory can ensure that the precision of the welding process is more refined, so that each welding point meets high standards and avoids quality problems caused by too much or too little welding. By obtaining real-time working parameters, the system can accurately grasp the state of the welding gun (such as temperature, current, airflow, etc.) to ensure that the welding gun is always in ideal working conditions during the welding process. By adaptively adjusting the parameters according to the optimal welding gun motion trajectory, the welding system can respond to different working environments and actual welding conditions in real time, and automatically adjust key parameters such as welding current and speed. Ensure that the heat distribution and material fusion during the welding process are in the optimal state. Adaptive adjustment ensures the stability of the welding process, especially in complex or high-precision welding scenarios, avoiding the errors caused by manually setting parameters and improving the quality consistency of each welding. By analyzing the temperature change trend of the real-time welding monitoring image, the stability of the molten pool during the welding process can be accurately judged. If the temperature is found to be too high or too low, the system can respond quickly and adjust the welding parameters to avoid overheating or insufficient heat to ensure welding quality. Based on the temperature change trend, the system can realize dynamic regulation of welding parameters to avoid the limitations that may be caused by fixed parameter settings in traditional welding methods. Dynamic adjustment enables the welding process to respond more accurately to different welding conditions.By continuously adjusting welding parameters, optimizing welding effects, and avoiding defects in the welding process, the overall production efficiency and welding quality are improved. Through real-time image monitoring and welding defect analysis, the system can quickly detect defects such as pores, cracks, and lack of fusion that may occur during the welding process, and provide timely feedback and corrections. Combined with the defect analysis results, the system can finely optimize the dynamic welding parameter control strategy to avoid the recurrence of the same defects and improve welding quality. By continuously accumulating data and optimizing defect control strategies, an efficient dynamic welding control optimization model is established, which can be applied in different production batches to continuously improve welding quality and stability and achieve higher production standards.

[0006] Preferably, step S1 comprises the following steps: Step S11: Obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; Step S12: performing visual recognition of the texture of the identification plate to be welded on the high-definition monitoring image of the identification plate to generate visual features of the texture of the identification plate; Step S13: performing detail sharpening and enhancement on the texture visual features of the signboard, thereby obtaining a sharpened and enhanced signboard image; Step S14: performing multi-region contrast optimization on the sharpened and enhanced sign image to obtain a contrast-optimized sign image; Step S15: Based on the welding work log, the contrast-optimized sign image is subjected to welding visual effect simulation to obtain the sign welding visual effect.

[0007] Through the collection of high-definition monitoring images, the system of the present invention can obtain detailed texture information, morphology and status of the surface of the sign. The high-definition images provide clear and accurate raw data for subsequent image processing, texture recognition and analysis. The welding work log contains various parameters and settings in the welding process (such as current, voltage, speed, etc.), which provides necessary background data for subsequent welding simulation and optimization, making welding control more accurate and targeted. Through texture visual recognition technology, the system can effectively identify various texture features on the surface of the sign, such as surface roughness, texture direction and shape, etc. These features are crucial for heat conduction, material fusion and other aspects in the welding process, and can provide guidance for subsequent analysis. Provide a basis. The extracted texture features can provide detailed basis for subsequent welding path planning, parameter adjustment, etc., ensure that the welding process is highly adapted to the surface features of the material, and avoid welding problems caused by uneven or irregular surfaces. Detail sharpening and enhancement technology can highlight the subtle features in the image, making each texture, defect and tiny detail of the sign more clearly visible, which is very important for refined welding control, especially in complex sign structures. It can help identify potential small defects. The image after sharpening and enhancement is clearer and more detailed, which is convenient for subsequent contrast optimization, defect analysis and other processing steps, which makes the entire image processing chain more efficient, and the monitoring, analysis and optimization of the welding process It can achieve higher accuracy. Through multi-area contrast optimization, the system can make personalized adjustments according to different areas of the sign image to improve the visual clarity of different areas. For the welding area, surrounding area, and other key locations, the system can specifically enhance or optimize the contrast to ensure that the details and differences of the image in each area can be accurately captured. The optimized contrast image can show the details of the welding area more clearly, which is helpful for welding quality analysis, accurate identification of welding points, early detection of defects, etc. It improves the sensitivity and accuracy of abnormal detection in the welding process. Combined with the welding work log to simulate the welding visual effect, the system can simulate before welding. The visual effects of welding can be displayed, including the smoothness of the weld, color changes, molten pool formation, etc. This provides an intuitive preview of the welding effect for the operator and the system. The simulation can help predict possible problems, such as overheating and uneven welding. Through the simulated welding visual effects, the system can evaluate the impact of different welding parameters on the welding effect, and make optimization adjustments. The simulation results provide an accurate basis for the parameter selection of the subsequent welding process, reducing possible errors in actual operation. Through visual simulation and parameter optimization, potential problems in the welding process can be discovered and corrected before actual welding, avoiding the waste of rework and repair in actual welding, and improving the efficiency and quality of the welding process.

[0008] Preferably, step S15 specifically comprises the following steps: Conduct welding process requirement analysis on the welding work log to generate welding process requirement characteristics of the identification plate to be welded; Identify the welding process flow in the welding work log and extract the multi-stage welding process flow; Based on the welding work log, the contrast-optimized identification plate image is used to locate key identification nodes, and multiple key identification nodes are obtained; Performing identification point spatial distribution analysis on multiple key identification nodes to generate identification node spatial distribution data; Based on the welding process requirement characteristics of the identification plate to be welded, the welding process parameters of the identification nodes are calculated one by one for the identification node spatial distribution data, so as to generate the welding process parameters of each identification node; Based on the multi-stage welding process flow, the welding process parameters of each identification node are simulated to obtain the welding visual effect of the identification plate.

[0009] Through the analysis of the welding work log, the system of the present invention can identify and extract the welding process requirements of the identification plate, such as welding current, welding speed, heat input and other key factors. According to the material, thickness, shape and other characteristics of the identification plate, the corresponding welding process requirement characteristics are generated to provide an accurate basis for subsequent welding control. The welding process requirement analysis helps the system understand the welding requirements of different identification plates, so that each welding task can be customized according to actual needs, avoiding the welding inadaptability caused by the unified setting of parameters, and ensuring the efficiency and accuracy of each welding task. Through in-depth analysis of the process requirements, potential process problems can be identified early, overheating or uneven welding can be avoided during welding, and the stability and final quality of welding can be improved. By identifying and extracting the welding process flow, the system can divide the welding process into multiple stages (such as preheating, welding, cooling, etc.) and set different control parameters for each stage. This allows each stage to be optimized according to specific needs, improving the overall efficiency of the welding process. Multi-stage process flow identification can help the system evaluate the operating requirements of different stages and set the most appropriate process parameters at each stage, thereby reducing unnecessary energy waste and improving welding stability. The accurate identification of multi-stage processes provides a clear basis for controlling the temperature, time, speed, etc. of each stage, which helps to supervise the entire welding process and ensure that each step in the process execution strictly complies with the standards. Using contrast-optimized images, the system can accurately locate key welding nodes on the identification plate, such as joint points, corners, or special shape areas. These nodes are usually the most important parts in the welding process, and accurately identifying them helps to finely control the welding quality. By accurately locating key nodes, the welding system can concentrate resources and energy to focus on welding control of these key parts, avoid quality fluctuations caused by unclear nodes, and improve the overall welding accuracy. By performing spatial distribution analysis on key identification nodes, the system can understand the spatial position and relative relationship of these nodes in the identification plate. This analysis helps to determine the welding sequence, control the accuracy of the welding position, and optimize the welding path planning. Through the generated spatial distribution data, the system can calculate the optimal welding path, avoid repeated movements or excessive heat input areas, and reduce errors in the welding process and improve efficiency. Based on the spatial distribution data of each identification node and the characteristics of the welding process requirements, the system can calculate the welding process parameters (such as welding current, welding speed, welding time, etc.) of each node one by one. This precise calculation ensures that each welding node can be welded with the most suitable parameters, avoiding fluctuations in welding quality. Welding nodes with different positions and characteristics may have different requirements for parameters such as heat input and welding speed. Calculating and adjusting welding process parameters one by one can ensure that each welding point can achieve the best welding effect. The accurately calculated process parameters ensure the welding quality of each welding node, avoid defects caused by improper parameter settings, and improve the controllability of the welding process and the quality of the final product.By inputting the welding process parameters of each identified node into the multi-stage welding process flow, the system can simulate the welding process and predict the welding effect of each node. This prediction helps to identify welding defects that may occur in the welding process in advance, such as overheating, cracks, pores, etc. By simulating the welding effect, the system can adjust the welding process parameters in real time to further optimize the welding process. For example, if the welding effect is found to be unsatisfactory, the current or welding speed can be adjusted according to the simulation results to achieve the best welding effect.

[0010] Preferably, the specific steps of step S2 are: Step S21: accurately identifying welding points based on the welding visual effect of the identification plate to obtain multiple welding points; Step S22: performing image visual positioning marking on multiple welding points according to the contrast-optimized identification plate image to obtain the positioning coordinates of each welding point in the image; Step S23: performing a three-dimensional structure analysis of the signboard according to the contrast-optimized signboard image to generate three-dimensional geometric information of the signboard; Step S24: Perform dynamic welding path planning on the positioning coordinates of each welding point in the image based on the three-dimensional geometric information of the identification plate, thereby constructing a dynamic welding path.

[0011] The present invention can accurately identify each point on the identification plate that needs to be welded through welding visual effect analysis. This step extracts the position information of the welding point through image analysis, and ensures that each welding point has a clear and clear mark in the visual effect, thereby ensuring the accuracy of the welding quality. After accurately identifying the welding point, the welding path, welding parameters, etc. can be optimized and adjusted according to each welding point. For complex identification plates, it can ensure that even tiny welding points can be accurately welded. With the accurate identification of welding points, manual identification errors or omissions are avoided, thereby reducing the risk of miswelding or leaking welding, and improving the overall consistency of the welding effect. Through the image visual positioning mark, the system can obtain the precise coordinate information of each welding point in the image. Through the contrast-optimized image, the boundary of the welding point is clearer, making the positioning more accurate. The acquisition of positioning coordinates is not only visual recognition, but also can convert image data into real spatial coordinates, which is crucial for the welding control system and can accurately guide the welding operation. Through three-dimensional structure analysis, the system not only obtains the two-dimensional image information of the identification plate, but also accurately understands the three-dimensional geometric form of the identification plate, including its thickness, surface shape, angle and other important information. This provides the necessary spatial information support for subsequent welding path planning. The three-dimensional geometric information of the sign helps the welding system better adapt to complex geometric shapes and avoid welding defects caused by irregular or curved areas. The system can optimize and adjust the welding process parameters based on these geometric features. By combining the three-dimensional geometric information of the sign with the positioning coordinates of the welding point, the system can generate a dynamic welding path. Path planning not only considers the exact location of the welding point, but also dynamically adjusts the welding path according to the overall shape of the sign, avoiding the limitations of traditional static path planning. Dynamic path planning enables the welding process to adjust the welding path in real time, avoiding unnecessary multiple operations or path overlaps, and maximizing welding efficiency and accuracy. Especially for signboards with complex shapes, dynamic path planning can effectively cope with the challenges of various geometric features.

[0012] Preferably, step S3 specifically comprises the following steps: Step S31: performing real-time welding control on the identification plate to be welded based on the dynamic welding path, and collecting real-time welding monitoring images; Step S32: identifying actual welding contact points on the real-time welding monitoring image and marking the actual welding contact points; Step S33: performing welding point surface change analysis on the actual welding contact point, thereby generating high temperature welding surface change characteristics of the welding point; Step S34: calculating the curvature change of the identification plate welding based on the high-temperature welding surface change characteristics of the welding point, thereby generating identification plate welding curvature change data; Step S35: performing welding path mutation analysis on the dynamic welding path based on the brand recognition welding curvature change data, and marking the welding path mutation points; Step S36: performing smooth optimization of the welding gun motion trajectory based on the welding path mutation points, thereby constructing an optimal welding gun motion smooth trajectory.

[0013] The present invention uses dynamic welding paths and real-time welding control, and the system can respond in time according to the changes in welding points and the adjustment of paths. This real-time control can ensure that the welding path and parameters always match the actual conditions of the identification plate, ensuring that the welding process is stable and efficient. The acquisition of real-time welding monitoring images provides accurate image data for subsequent welding point analysis, surface change analysis, etc. These image data help the system to continuously monitor the welding quality and promptly discover potential problems, such as welding contact point deviation, pores, cracks and other welding defects. The system accurately identifies the actual welding contact points through image analysis algorithms. These points are usually the key areas with the largest heat input and possible defects in the welding process. Accurate identification of contact points can ensure that subsequent control is more targeted. After marking the welding contact points, the system can track and monitor the status of these points, including welding quality, thermal changes, etc. Ensure that each welding point is effectively managed to reduce the risk of omission or miswelding. During the welding process, the surface change in the high temperature zone is an important indicator of welding quality. Analyzing the surface change of the welding point can reveal the influence of heat input on the welding zone and avoid overheating or uneven temperature distribution. By analyzing the surface change of the welding point, the system can evaluate the heat affected zone of the welding in real time. Through the surface change characteristics of the welding point, the system can better predict the problems that may occur during the welding process, such as thermal cracks, deformation, etc. Ensure that the heat input of each welding point is appropriate to avoid welding defects. Curvature change calculation can help analyze the changes in the surface during welding, especially in the area around the welding point. During the welding process, the surface change is closely related to the thermal expansion and contraction of the material. The curvature change can reveal the temperature changes and physical reactions around the welding point. Through the change of welding curvature, the system can monitor the temperature, stress distribution and thermal deformation during the welding process in real time, and identify potential welding defects such as warping and deformation in time. By calculating the curvature change, the system can adjust the welding process to ensure that each welding point in the welding process meets the required geometric standards, avoid quality problems caused by excessive curvature changes, and improve the stability and reliability of welding quality. By analyzing the welding curvature change data, the system can identify the mutation points on the welding path. Path mutations are usually hot spots where errors occur during welding, which may cause welding defects such as thermal stress concentration and material deformation. Identifying these mutation points helps avoid the formation of wrong paths. After marking the mutation points of the welding path, the system can adjust the welding path in real time to avoid quality problems caused by the transition area and optimize the stability and efficiency of the welding path. By analyzing and adjusting the mutation points of the welding path, the path smoothness during the welding process is ensured, unnecessary process fluctuations are avoided, and the stability of welding is further improved. The motion trajectory optimization of the welding gun ensures a smooth transition of the welding gun movement near the mutation points of the welding path. By avoiding sudden changes in movement, not only welding defects are reduced during the welding process, but also stress concentration problems in the transition area are avoided.Smooth optimization of the welding gun's motion trajectory can minimize welding defects caused by uneven trajectory changes, maintain the consistency of the welding process, and thus improve welding quality and production efficiency.

[0014] Preferably, the specific steps of step S4 are: Step S41: obtaining the current real-time working parameters of the welding gun; Step S42: performing multi-stage trajectory segmentation on the optimal welding gun motion smooth trajectory, thereby obtaining a multi-stage welding trajectory; Step S43: calculating the welding process parameters of the multi-stage welding trajectory segment by segment according to the welding visual effect of the identification plate, so as to obtain the welding process parameters of each segment of the trajectory; Step S44: Calculate the welding current based on the current real-time working parameters of the welding gun to obtain the current real-time welding current of the welding gun; Step S45: performing welding gun welding speed identification on the current welding gun real-time working parameters to extract the current welding gun welding speed; Step S46: Adaptively adjust the welding gun parameters of the current welding gun real-time welding current and the current welding gun welding speed based on the welding process parameters of each trajectory, so as to obtain adaptive welding gun adjustment parameters.

[0015] The present invention obtains the real-time working parameters (such as current, welding speed, temperature, etc.) of the welding gun to provide accurate data for subsequent adaptive adjustment. This enables the entire welding process to dynamically adjust parameters according to the actual working conditions to adapt to different welding requirements. By acquiring parameters in real time, possible deviations or problems in the welding process can be discovered in time and adjusted to ensure that the welding process is stable and accurate. Dividing the welding gun movement trajectory into multiple stages helps to optimize the process parameters separately at each stage. By refining each section of the trajectory, the welding accuracy can be improved and the large-scale trajectory changes can be avoided from affecting the welding quality. After the welding trajectory is segmented, the system can dynamically adjust the welding parameters according to the specific requirements of each stage, thereby improving the accuracy and flexibility of welding control. Through multi-stage trajectory segmentation, the welding process can be adjusted according to the characteristics of each section of the trajectory, further reducing welding defects and thermal stress effects, and ensuring that each section of welding can achieve the best effect. For each section of the welding trajectory, the system can perform refined welding process parameter calculations to ensure that each stage meets specific process requirements. This helps to avoid parameter mismatches in the entire welding process and improve the accuracy of welding. Combining each trajectory with the visual welding effect of the identification plate helps the system predict the welding effect at each stage and make corresponding adjustments to ensure that the welding quality is not affected by changes between stages. By accurately calculating the parameters of each trajectory, the process consistency of the entire welding process can be ensured, and the stability and reliability of the welding quality can be improved. By calculating the welding current of the welding gun in real time, the changes in current during welding and its impact on welding quality can be accurately judged. Current is a key factor affecting heat input and weld joint quality during welding. Real-time monitoring helps avoid welding problems caused by current fluctuations. By accurately calculating the current value, welding defects caused by excessive or insufficient current (such as weak welding, uneven welding pool, etc.) can be avoided. According to the real-time welding current value, the system can quickly feedback and dynamically adjust the welding parameters to optimize the welding process and improve the welding quality. Welding speed is an important factor affecting the welding effect. Real-time identification and extraction of welding speed can effectively judge the heat input and cooling rate during welding, and avoid the negative effects of too fast or too slow welding speed (such as insufficient welding or uneven welds). By accurately identifying the welding speed of the welding gun, the system can make timely adjustments to ensure the optimization of welding speed in each welding process. According to the welding process requirements of each trajectory, the system will adaptively adjust key parameters such as welding current and welding speed. This automatic adjustment ensures that the welding process always meets the optimal process requirements, thereby avoiding errors caused by manual adjustments. Adaptive adjustment can accurately control welding parameters according to the characteristics of different trajectory stages, effectively avoid welding defects such as incomplete welding and overheating, and ensure the quality of each welding section. By dynamically adjusting the parameters of the welding gun, the system can maintain an efficient and stable working state in a complex welding environment, further improving welding quality and efficiency.

[0016] Preferably, the specific steps of step S5 are: Step S51: performing heat distribution analysis of the welding area on the real-time welding monitoring image to generate heat distribution characteristics of the welding area; Step S52: evaluating the state of the welding molten pool based on the heat distribution characteristics of the welding area to obtain the state characteristics of the welding molten pool; Step S53: performing temperature change trend analysis on the welding molten pool state characteristics to generate a temperature change trend of the welding molten pool; Step S54: performing abnormal temperature prediction evolution according to the temperature change trend of the welding molten pool, thereby generating abnormal temperature prediction data of the welding molten pool; Step S55: dynamically adjust the welding parameters of the adaptive welding gun adjustment parameters based on the abnormal temperature prediction data of the welding molten pool, thereby constructing a dynamic welding parameter control strategy.

[0017] The present invention uses real-time welding monitoring image analysis, and the system can accurately understand the heat distribution of the welding area, which provides key data for subsequent welding parameter adjustments. By analyzing the heat distribution of the welding area, it can avoid overheating or uneven heat distribution, ensure that the heat input during the welding process meets expectations, and thus improve the welding quality. Accurately grasping the heat distribution of the welding area helps to predict possible thermal stresses and prevent welding defects (such as cracks, pores, etc.) caused by uneven temperature. By analyzing the heat distribution characteristics of the welding area, the system can evaluate the state of the welding molten pool in real time, which is crucial for controlling the quality and uniformity of welding. The state of the welding molten pool directly affects the welding quality. The system can avoid unstable or incomplete welding caused by an excessively large or small molten pool by evaluating the molten pool state. The system monitors and evaluates the molten pool state in real time to ensure that each link of the welding process is in the best state, thereby enhancing the controllability and stability of the entire welding process. By analyzing the characteristics of the welding molten pool state, the system can predict the changing trend of the molten pool temperature, which helps to adjust the heat input during the welding process in real time to avoid excessively high or low temperatures. Temperature is a key factor affecting welding quality. Accurate prediction of temperature change trends can help Optimize the welding process and avoid welding defects (such as overburning, insufficient melting, etc.) caused by inaccurate temperature control. The temperature change trend analysis helps to accurately control the heat input during the welding process, maintain the uniformity and stability of the welding, and avoid quality problems caused by excessive fluctuations. By predicting the temperature change trend of the welding molten pool, the system can timely detect abnormal temperature fluctuations and issue early warnings, which provides effective preventive measures for adjusting welding parameters and avoiding quality problems. Overheating may lead to overburning or welding defects, and too cold temperature may lead to loose welding. Through the prediction of abnormal temperature, intervention can be made before the problem occurs to ensure the stability of the welding process. According to the abnormal temperature prediction data of the welding molten pool, the system can automatically dynamically adjust the working parameters of the welding gun (such as current, welding speed, welding voltage, etc.) to ensure that each stage of the welding process is in the best temperature control state. By dynamically adjusting the welding parameters, it is possible to respond to temperature anomalies in the welding process in real time, optimize heat input, and ensure that the welded joint is uniform and firm. The dynamic control strategy enables the parameter adjustment in the welding process to respond quickly to temperature changes, avoiding excessive temperature changes from having an adverse effect on welding quality, thereby ensuring the stability of the welding process and high-quality output.

[0018] Preferably, the specific steps of step S6 are: Step S61: performing multi-stage welding result recognition based on the real-time welding monitoring image, and extracting welding results of multiple process stages; Step S62: evaluating the welding quality of each welding point for the welding results of multiple process stages, and generating a welding quality evaluation value for each welding point; Step S63: performing local welding defect analysis based on the welding quality evaluation value of each welding point, thereby generating local welding defect data; Step S64: Optimizing the dynamic welding parameter control strategy for welding defect control based on the local welding defect data to construct a dynamic welding control optimization model.

[0019] The present invention uses real-time welding monitoring images to perform multi-stage analysis of the welding process, and can accurately identify the welding results of each process stage, so that the process progress and welding effect of each stage can be fully grasped, and the results of each welding stage are independently extracted and identified, thereby ensuring the quality control of each link of the welding process, and the welding effects of different stages can be evaluated separately, avoiding the impact of problems in a certain link on the overall welding quality. By evaluating the quality of each welding point, the quality of different welding points can be accurately judged, avoiding potential errors caused by the overall evaluation, which can more carefully discover possible quality problems. The generation of welding quality evaluation values ​​provides quantitative quality indicators for each welding point, which helps to clarify which welding points meet the standards and which may have defects, so as to make targeted adjustments. The welding point quality evaluation value provides strong data support for subsequent defect analysis and optimization control, making the positioning of welding defects more accurate. The quality assessment of each welding point can detect local defects (such as pores, cracks, irregular penetration, etc.) that may occur during the welding process as early as possible. This allows defect analysis to be performed immediately after welding is completed, avoiding large-scale quality problems. Local defect analysis can accurately identify which welding points or areas have problems, thereby providing clear guidance for subsequent repairs and adjustments. Based on the generation of defect data, it can effectively guide the adjustment of welding process, improve parameters such as temperature control and current, so as to reduce the occurrence of similar defects and improve the overall welding quality. Through the analysis of local welding defect data, the links where problems occur in the welding process, such as welding current, welding speed, heat input, etc., can be adjusted, so as to achieve targeted defect control and optimize welding quality. Based on the defect control optimization model, the system can automatically adjust the welding parameters during the welding process to cope with different types of defects. This dynamic adjustment can adapt to changes in the welding environment and process in real time, thereby ensuring the stability of welding quality.

[0020] In this specification, a welding control device for a identification plate is provided, which is used to execute the welding control method for the identification plate as described above, comprising: The welding visual simulation module is used to obtain the high-definition monitoring image of the identification plate to be welded and the welding work log; the identification plate texture is visually recognized on the high-definition monitoring image of the identification plate to be welded, and the welding visual effect is simulated to obtain the identification plate welding visual effect; The welding path planning module is used to accurately identify welding points based on the visual effect of the identification plate welding, and to perform dynamic welding path planning, thereby constructing a dynamic welding path; The welding gun motion trajectory module is used to perform real-time welding control on the welding identification plate based on the dynamic welding path, and then perform smooth optimization of the welding gun motion trajectory to construct the optimal welding gun motion smooth trajectory; An adaptive parameter adjustment module is used to obtain the current real-time working parameters of the welding gun; based on the optimal welding gun motion smooth trajectory, the current real-time working parameters of the welding gun are adaptively adjusted to obtain adaptive welding gun adjustment parameters; Dynamic welding control module, which is used to analyze the temperature change trend of the welding pool according to the real-time welding monitoring image, and dynamically control the welding parameters of the adaptive welding gun adjustment parameters, so as to build a dynamic welding parameter control strategy; The welding control optimization module is used to perform local welding defect analysis based on real-time welding monitoring images and optimize the welding defect control of dynamic welding parameter control strategies to build a dynamic welding control optimization model.

[0021] The present invention can provide accurate visual data for subsequent welding through high-definition monitoring images and precise texture recognition. The welding work log is combined with the welding record to ensure that all welding parameters required in the simulation process are fully considered. Through the simulation of welding visual effects, potential visual defects such as uneven penetration depth, excessive welding heat affected zone, etc. can be discovered in advance before welding, reducing the possibility of rework after welding. Through precise visual simulation and welding effect analysis, the position of the welding point can be accurately identified to avoid welding inaccuracies caused by human errors in traditional methods. Based on the precise positioning of the welding point, the dynamic path planning module can flexibly adjust the welding path according to different welding requirements (such as identification plate size, material properties, etc.) to ensure that each welding point can be accurately welded. The dynamic planning path avoids unnecessary repeated operations, reduces welding time, and improves welding quality. The smooth optimization of the welding gun movement trajectory can avoid vibration or oscillation during welding and reduce local defects (such as cracks, pores, etc.) that occur during welding. Through precise trajectory control, the welding gun movement The movement is optimized, thereby improving the stability of the welding process and reducing human operation errors. During the welding process, the real-time working parameters of the welding gun (such as current, voltage, welding speed, etc.) will be dynamically adjusted according to the real-time monitoring data and movement trajectory. As the welding environment and materials change, the system can adaptively adjust the parameters to ensure that the quality of each welding can reach the best level. Through temperature change trend analysis, the system can timely capture abnormal changes in the temperature of the welding molten pool, and make early warnings and adjustments. Accurate temperature control is crucial to welding quality. The dynamic control module can adjust the welding parameters according to the temperature change trend, optimize the shape and depth of the welding molten pool, and ensure welding quality. Through real-time image monitoring, welding defects (such as pores, cracks, incomplete penetration, etc.) can be locally detected and timely feedback is given. Based on defect detection, the system automatically adjusts welding parameters, optimizes welding technology, eliminates or reduces defects, and improves the final welding quality. By optimizing welding control, welding defects are effectively reduced, the rework rate is reduced, and production efficiency and the stability of the welding process are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of the steps of a welding control method for a signboard according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0024] The example of this application provides a method and device. The execution subject of the welding control method and device for identification plates includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0025] See also Figures 1 to 3 The present invention provides a welding control method for a sign, the method comprising the following steps: Step S1: obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; performing visual recognition of the identification plate texture on the high-definition monitoring image of the identification plate to be welded, and simulating the welding visual effect, thereby obtaining the identification plate welding visual effect; Step S2: accurately identifying welding points based on the visual effect of the welding of the identification plate, and performing dynamic welding path planning, thereby constructing a dynamic welding path; Step S3: Based on the dynamic welding path, the welding identification plate is subjected to real-time welding control, and then the welding gun motion trajectory is smoothly optimized to construct an optimal welding gun motion smooth trajectory; Step S4: obtaining the current real-time working parameters of the welding gun; performing adaptive welding gun parameter adjustment on the current real-time working parameters of the welding gun based on the optimal welding gun motion smooth trajectory, thereby obtaining adaptive welding gun adjustment parameters; Step S5: analyzing the trend of the temperature change of the welding pool according to the real-time welding monitoring image, and dynamically regulating the welding parameters of the adaptive welding gun adjustment parameters, thereby constructing a dynamic welding parameter regulation strategy; Step S6: Perform local welding defect analysis based on the real-time welding monitoring image, and optimize the welding defect control of the dynamic welding parameter control strategy to build a dynamic welding control optimization model.

[0026] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a welding control method for a signboard according to the present invention. In this example, the steps of the welding control method for a signboard include: Step S1: obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; performing visual recognition of the identification plate texture on the high-definition monitoring image of the identification plate to be welded, and simulating the welding visual effect, thereby obtaining the identification plate welding visual effect; In this embodiment, a high-resolution camera (such as a SLR camera or an industrial camera) is used to photograph the welding identification plate, and it is ensured that the shooting is carried out under good lighting conditions to avoid the influence of shadows and reflections on the image quality. The camera parameters (such as shutter speed, ISO, aperture, etc.) are set to ensure the clarity of the image and the retention of details. At the same time, a tripod can be used to fix the camera position to ensure the consistency of the shooting angle. Before welding begins, a welding work log is recorded, including welding materials, welding process parameters (such as current, voltage, welding speed, etc.), environmental conditions (temperature, humidity) and expected welding effects. This information will provide a reference for subsequent analysis. The work log can be recorded using a spreadsheet or a special welding recording software to ensure the systematicness and traceability of the information. The collected high-definition monitoring images are preprocessed to improve the accuracy of subsequent texture recognition, including the steps of denoising, color correction and contrast enhancement. Image processing software (such as OpenCV or MATLAB) can be used for image preprocessing, filters are applied to remove background noise, and the brightness and contrast of the image are adjusted to ensure that the texture details are clearly visible. Computer vision technology is used for labeling. Identification of the brand texture, use feature extraction algorithms (such as SIFT or ORB) to extract texture feature points from the image, these feature points will be used for subsequent visual effect simulation, after extracting the features, use matching algorithms (such as FLANN or BFMatcher) to verify the validity of the texture features to ensure that the extracted texture can accurately reflect the actual situation of the brand, based on the extracted texture features and the process parameters in the welding work log, simulate the welding visual effect, which can be achieved through computer graphics technology, use 3D modeling software (such as Blender or Maya) to build a virtual model of the welding process, in the simulation process, the temperature distribution of the welding heat affected zone, the flow of molten metal and the influence of the cooling process after welding should be considered, these factors will directly affect the final welding effect, compare the simulated welding visual effect with the actual brand image, record the differences and analyze the reasons, which can help identify potential welding quality problems and provide a basis for subsequent process adjustments, record the simulation results and analysis conclusions for reference and adjustment in the subsequent welding process, all data should be stored in the database to ensure the traceability of information.

[0027] Step S2: accurately identifying welding points based on the visual effect of the welding of the identification plate, and performing dynamic welding path planning, thereby constructing a dynamic welding path; In this embodiment, an image processing algorithm is used to analyze the image and identify the features of the welding points. An edge detection algorithm (such as Canny edge detection) and a contour extraction technique can be used to clarify the specific location of the welding points. The image of the welding visual effect is analyzed by image processing software (such as OpenCV) to extract the coordinate information and related features of the welding points. These features include the size, shape and position of the welding points, which provide basic data for subsequent path planning. A feature matching algorithm (such as template matching or SURF) is applied to further confirm the location of the welding points. The extracted features are compared with the preset welding point template to ensure the accuracy of the recognition. After confirming the welding points, the coordinates (X, Y, Z) of each welding point are recorded and stored in a data structure for easy call during subsequent path planning. A dynamic welding path is designed based on the identified welding point information. A path planning algorithm (such as an A* algorithm or a Dijkstra algorithm) is used to calculate the optimal path from the starting point to each welding point. In the path planning process, obstacles that may exist in the welding process (such as other equipment or Workpiece), ensure that the planned path can ensure the smooth movement of the welding gun and avoid collision. After the preliminary path planning is completed, the path is optimized to reduce welding time and improve welding quality. For example, a path smoothing algorithm (such as Bezier curve or spline curve) can be used to optimize the welding path to make it smoother. In this process, the adjustment of welding speed and welding sequence should also be considered to ensure the best welding effect and heat input control. During the welding process, the state of the welding point and the changes in the surrounding environment are monitored in real time. Based on sensor feedback and real-time image analysis, the welding path is dynamically adjusted to realize a closed-loop control system. When the position deviation of the welding point or the environmental change is detected, the system automatically calculates the new welding path to ensure that the welding quality is not affected. After completing the dynamic welding path planning, the path verification and testing are carried out. The welding process is simulated by simulation software (such as ROS or Gazebo) to ensure that the planned path is feasible in actual operation and meets the welding process requirements. The test results and welding quality data are recorded to evaluate the effectiveness of the path planning, which provides a basis for the optimization of the subsequent welding process.

[0028] Step S3: Based on the dynamic welding path, the welding identification plate is subjected to real-time welding control, and then the welding gun motion trajectory is smoothly optimized to construct an optimal welding gun motion smooth trajectory; In this embodiment, a welding control system is established according to the dynamic welding path generated in the previous step. The system needs to integrate real-time monitoring data, including the state of the welding point, the position of the welding gun and environmental changes, and use a control algorithm (such as a PID controller) to adjust the welding parameters (such as current, voltage, welding speed, etc.) in real time to ensure steady-state and dynamic response during the welding process. The temperature and molten pool state of the welding point are monitored in real time so that the parameters can be adjusted in time during the welding process to ensure the welding quality. High-precision sensors and cameras are deployed to monitor the welding process in real time. The sensors may include temperature sensors, pressure sensors and displacement sensors to ensure that the key parameters of the welding process can be obtained. The image data of the welding process is analyzed by algorithms (such as image processing technology) to identify welding defects (such as pores, incomplete penetration, etc.) in real time and feedback to the welding control system for adjustment. On the basis of the dynamic welding path, a preliminary welding gun motion trajectory is generated according to the welding process parameters. In this stage, the path planning algorithm is mainly used to determine the motion path of the welding gun from the starting point to the welding point. Combined with the welding speed and welding sequence, the preliminary generated motion trajectory should avoid sharp turns and unnecessary path backtracking to improve welding efficiency and quality. The smoothing algorithm (such as Bezier curve, spline curve or B-spline) is used to optimize the initially generated welding gun motion trajectory. This process aims to eliminate the sharp changes and discontinuities in the trajectory and make the welding gun movement smoother. When performing smoothing optimization, ensure that the movement speed and acceleration of the welding gun are within a reasonable range to avoid affecting the welding quality and welding effect. After the optimization is completed, the smoothed welding gun motion trajectory is verified by simulation software (such as MATLAB Simulink or ROS), the welding process is simulated, whether the welding gun movement is smooth, and the welding quality is evaluated. The smoothing parameters are adjusted according to the simulation results to ensure that the final welding gun motion trajectory can be effectively executed in the actual welding operation and maintain the welding quality. In the actual welding process, a real-time control and feedback mechanism is implemented to ensure the dynamic adjustment of the welding gun motion trajectory. The welding status is monitored in real time through sensor data, and the motion trajectory of the welding gun is adjusted in time. If an abnormality is found in the welding process (such as temperature exceeding the standard or position offset), the system will immediately adjust the motion trajectory of the welding gun to ensure that the welding quality is not affected and maintain the continuity and stability of the welding.

[0029] Step S4: obtaining the current real-time working parameters of the welding gun; performing adaptive welding gun parameter adjustment on the current real-time working parameters of the welding gun based on the optimal welding gun motion smooth trajectory, thereby obtaining adaptive welding gun adjustment parameters; In this embodiment, high-precision sensors (such as current sensors, temperature sensors, and voltage sensors) are deployed around the welding gun to monitor the working state of the welding gun in real time. The sensors should be able to provide accurate and real-time data feedback, including parameters such as welding current, welding voltage, welding speed, and molten pool temperature. The sensor data is transmitted to the control unit in real time through the data acquisition system. The control unit can be based on a PLC (programmable logic controller) or an embedded system, which is responsible for data collection, processing, and analysis. The collected real-time working parameters are preprocessed to remove noise and outliers. A filter (such as a Kalman filter) can be used to smooth the data to ensure the accuracy and reliability of the data. The real-time parameters are analyzed by a data analysis algorithm (such as statistical analysis or a machine learning model) to evaluate whether the current working state of the welding gun is within the expected range. This process will help identify possible problems during welding, such as excessive current or abnormal temperature. The optimal welding gun motion smooth trajectory generated in the previous step is used as a reference to analyze the relationship between the trajectory and the current welding gun working parameters. Ensure that the motion trajectory of the welding gun can match the changes in welding parameters. Evaluate the impact of the welding gun motion trajectory under different welding speeds and current conditions to determine the ideal welding parameter combination. Design an adaptive adjustment algorithm based on real-time working parameters and optimal motion trajectory. Fuzzy control or adaptive control strategies can be used to dynamically adjust welding current, voltage and welding speed based on real-time data. For example, if the welding current is too high, the control system can automatically reduce the current and adjust the welding speed to maintain the stability of the welding pool. Such adjustment can be achieved through a PID controller or other control algorithms. During the welding process, a real-time feedback mechanism is implemented to ensure the effectiveness of adaptive welding gun parameter adjustment. The control system should continuously monitor the working parameters of the welding gun and adjust them based on real-time feedback to cope with changes in the welding process. Through a closed-loop control system, ensure that during the welding process, if an abnormality is detected (such as excessively high molten pool temperature), the system can quickly adjust the welding parameters to ensure that the welding quality is not affected. After each welding is completed, record the adaptive adjustment parameters and welding quality data for subsequent analysis and improvement. A database can be established to store parameter records and quality evaluation results for each welding. Regularly evaluate and optimize the adaptive adjustment algorithm, and adjust the control strategy based on historical data to improve the stability and quality of the welding process.

[0030] Step S5: analyzing the trend of the temperature change of the welding pool according to the real-time welding monitoring image, and dynamically regulating the welding parameters of the adaptive welding gun adjustment parameters, thereby constructing a dynamic welding parameter regulation strategy; In this embodiment, a high-resolution thermal imaging camera or an infrared sensor is used to monitor the temperature of the welding molten pool in real time to ensure that the equipment can continuously track and record temperature changes during the welding process, provide high-frequency temperature data, and transmit the monitoring image and the corresponding temperature data to the data processing system for subsequent analysis. This process needs to ensure the real-time and accuracy of the data to avoid welding quality problems caused by delays. The collected real-time temperature data is preprocessed, including noise elimination and data smoothing. A filter (such as a moving average filter or a Kalman filter) can be used to process the temperature data to improve the accuracy of subsequent analysis. Key features such as temperature peak value, temperature fluctuation range and change rate are extracted from the processed data. These features will be used to analyze the thermal dynamic behavior of the welding molten pool. Statistical analysis and data mining algorithms (such as time series analysis or regression analysis) are applied to conduct in-depth analysis of the temperature change trend of the welding molten pool. Graphical tools (such as Matplotlib or Tableau) can be used to generate Temperature change graphs help visualize trends and identify patterns and anomalies in temperature changes. For example, a rapid rise in temperature may indicate that welding parameters are improperly set. By analyzing these trends, appropriate adjustment strategies can be determined. According to the temperature change trends and their impact on welding quality, a dynamic welding parameter control strategy is designed. The strategy should include adjustment schemes for welding current, voltage, and welding speed in response to temperature changes. Fuzzy control or adaptive control algorithms are used to ensure that welding parameters can be automatically adjusted based on real-time temperature feedback. For example, when the temperature is too high, the system can automatically reduce the welding current or adjust the welding speed to prevent overheating. During the welding process, a dynamic control mechanism is implemented to ensure that welding parameters can respond to changes in the melt pool temperature in real time. The control system should continuously monitor temperature data and adjust welding parameters according to the set strategy. A closed-loop control system is designed to ensure that if abnormal temperature is detected (such as temperature outside the set range), the system can immediately perform adjustment operations to ensure the stability of the welding process and the welding quality.

[0031] Step S6: Perform local welding defect analysis based on the real-time welding monitoring image, and optimize the welding defect control of the dynamic welding parameter control strategy to build a dynamic welding control optimization model.

[0032] In this embodiment, a high-resolution camera device is used to collect images of the welding process in real time. These images should be able to clearly display the welding area and capture the details of welding defects, such as pores, cracks, and incomplete penetration. It is ensured that the frequency of image acquisition is high enough to capture the rapidly changing situation during the welding process and ensure the comprehensiveness and real-time nature of the data. Image processing and computer vision technology are used to analyze the collected welding images. Edge detection algorithms (such as Canny edge detection) and morphological processing methods (such as opening and closing operations) are used to extract welding defect features. Machine learning algorithms (such as convolutional neural networks (CNNs)) are applied to identify and classify defects. The model is trained to enable it to automatically identify different types of welding defects and output the location and type of the defects. The identified welding defect data is recorded in a database, including defect type, location, severity, and other information. This data will be used for subsequent analysis and model optimization. Statistical analysis is performed on the defect data to identify common defect types and their frequency and conditions of occurrence, to help determine the key factors that need to be paid attention to during the welding process. Based on the analysis results of local welding defects, the existing dynamic welding A parameter control strategy is developed to identify which parameters have a significant impact on defects, such as welding current, welding speed, and welding gas flow. The welding parameters are adjusted using optimization algorithms (such as genetic algorithms or particle swarm optimization) to reduce the occurrence of defects. For example, if it is found that high current leads to increased porosity, the current setting is adjusted. A new control strategy is formed in combination with other welding parameters. A dynamic welding control optimization model is constructed based on the optimized welding parameters and defect analysis results. The model should be able to respond to changes in the welding process in real time and intelligently adjust welding parameters to control defects. Appropriate modeling methods, such as fuzzy logic control, neural networks, or rule-based systems, are selected to ensure that the model can effectively handle complex welding processes and achieve dynamic control. The constructed dynamic welding control optimization model is verified in an experimental environment to evaluate its effect in the actual welding process. The effectiveness of the model is verified by comparing the welding quality and defect incidence. After confirming the effectiveness of the model, it is applied to actual welding production, and the welding quality is continuously tracked through a real-time monitoring system. According to actual welding feedback, the model is continuously adjusted and optimized to ensure its adaptability and stability.

[0033] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; Step S12: performing visual recognition of the texture of the identification plate to be welded on the high-definition monitoring image of the identification plate to generate visual features of the texture of the identification plate; Step S13: performing detail sharpening and enhancement on the texture visual features of the signboard, thereby obtaining a sharpened and enhanced signboard image; Step S14: performing multi-region contrast optimization on the sharpened and enhanced sign image to obtain a contrast-optimized sign image; Step S15: Based on the welding work log, the contrast-optimized sign image is subjected to welding visual effect simulation to obtain the sign welding visual effect.

[0034] In this embodiment, a high-resolution camera (such as a DSLR or an industrial camera) is used to photograph the identification plate to be welded under good lighting conditions to ensure that the image is clear and unblurred, suitable for subsequent visual recognition processing. During the shooting process, attention is paid to selecting a suitable angle and distance to maximize the capture of the details and texture of the identification plate. Work logs related to the welding process are collected, including welding parameters (such as current, voltage, welding speed, etc.), welding material information, welding time, and operator notes, etc., to ensure that the format of the work log is clear and structured for subsequent data processing and analysis. The high-definition monitoring images and welding work logs are stored in a dedicated database or file system to ensure data security and traceability. At the same time, the data collection time and related elements are recorded. Data, pre-process the acquired high-definition monitoring images, including denoising, grayscale and normalization. Gaussian filters can be used to remove image noise to ensure the accuracy of subsequent texture recognition. Texture recognition algorithms (such as LBP (local binary pattern), GLCM (gray level co-occurrence matrix) or SIFT (scale invariant feature transform)) are used to extract image texture features. These algorithms can effectively capture subtle texture changes on the surface of the sign. The generated texture features can be used to describe the surface state of the sign and any potential defects. The extracted texture features are stored in the database for subsequent processing and analysis. Ensure that the feature data is associated with the original image and work log. Select a suitable sharpening algorithm, such as Laplacian operator or Unshar pMask (Unsharp Mask), these algorithms can enhance the edges and details of the image. Apply the selected sharpening algorithm to the sign image to be processed. Adjust the sharpening strength to ensure that the enhancement effect is moderate and the image is not over-sharpened. After processing, carefully check the sharpening effect to ensure that the details of the sign are clearer and the texture features are obvious. Save the sharpened image as a new file and ensure that it is in a format suitable for subsequent processing. You can use PNG or JPEG format. Depending on your specific needs, choose a suitable contrast optimization algorithm, such as histogram equalization or adaptive histogram equalization (CLAHE), which can enhance the local contrast of the image while preserving details. Divide the sharpened sign image into multiple regions ,Each region can be divided according to the complexity and characteristics of the image content to facilitate local contrast optimization. The selected contrast optimization algorithm is applied to each region to ensure that the natural characteristics of the image are preserved during the processing and to avoid unnatural contrast effects. Finally, the processed regions are integrated to generate a complete contrast-optimized sign image. The optimized image is saved in a new file format to ensure that it has good visual performance and is suitable for subsequent welding effect simulation. Welding parameters such as welding temperature, welding time, welding materials, etc. are extracted from the welding work log. These parameters will be used to simulate the visual effects during the welding process. The appropriate welding effect simulation algorithm can be selected. Physical-based simulation methods or image processing algorithms can be considered.For example, image-based welding effect simulation technology combines the extracted welding parameters with the contrast-optimized signboard image to perform visual simulation of the welding effect. This can be achieved by adjusting the brightness, color, and texture of the image to simulate the color changes and surface effects that may occur after welding. Pay attention to maintaining the authenticity of the simulation results to ensure that they are consistent with the actual welding effect. The simulated signboard welding visual effect is saved as a new image file, and a high-quality image format can be used for subsequent display and analysis. The simulation results are evaluated to ensure that the visual performance of the welding effect can truly reflect the expected effect in the actual welding process. If necessary, parameters can be adjusted and re-simulated according to the evaluation results.

[0035] In this embodiment, the specific steps of step S15 are: Conduct welding process requirement analysis on the welding work log to generate welding process requirement characteristics of the identification plate to be welded; Identify the welding process flow in the welding work log and extract the multi-stage welding process flow; Based on the welding work log, the contrast-optimized identification plate image is used to locate key identification nodes, and multiple key identification nodes are obtained; Performing identification point spatial distribution analysis on multiple key identification nodes to generate identification node spatial distribution data; Based on the welding process requirement characteristics of the identification plate to be welded, the welding process parameters of the identification nodes are calculated one by one for the identification node spatial distribution data, so as to generate the welding process parameters of each identification node; Based on the multi-stage welding process flow, the welding process parameters of each identification node are simulated to obtain the welding visual effect of the identification plate.

[0036] In this embodiment, the welding work log is collected and sorted, including welding parameters, material information, environmental conditions, etc., to ensure the integrity and accuracy of the data for in-depth analysis, and the log information is imported into data analysis software (such as Excel, Python's Pandas library) for structured processing. By analyzing the parameters in the welding work log, the key welding process requirement characteristics are identified. These characteristics may include welding material type, welding temperature range, welding speed, welding current and voltage, etc. The mean, variance and other statistical characteristics of each parameter are calculated using statistical analysis methods to identify the factors that have the greatest impact on welding quality. The extracted welding process requirement characteristics are sorted into documents, and the definition of each feature and its corresponding reference value are recorded. This will provide a detailed basis for the subsequent welding process design and optimization. By analyzing the welding work log, the various stages of the welding process can be identified, which may include the preparation stage, welding stage, cooling stage, etc. Combined with the timestamps and parameter changes recorded in the welding work log, a timeline of the welding process is drawn, and the identified welding process stages are displayed in the form of a flow chart. Use flowchart software (such as Visio or Lucidchart) to create a visual process flow chart, and indicate the main operations, parameter settings and time requirements of each stage in the flow chart to ensure that the process is clear and easy to understand. The welding process flow chart and the corresponding detailed description documents are organized into a report to facilitate subsequent process implementation and review. If necessary, communicate with the on-site welding technician. Verify whether the extracted process conforms to the actual operation. Preprocess the contrast-optimized signboard image, including denoising, contrast enhancement and grayscale, to improve the recognizability of key nodes. Use image processing software (such as OpenCV or MATLAB) to import the processed image. Use edge detection algorithm (such as Canny edge detection) or feature detection algorithm (such as SIFT or SURF) to identify the key nodes in the image. By setting appropriate thresholds and parameters, ensure that the detected nodes have good repeatability and accuracy. Record the coordinate information of the detected key nodes in the database to ensure that this information can be combined with the characteristics of the welding process requirements for subsequent analysis. Count the coordinate information of each key node and calculate The spatial distribution characteristics of nodes, including aggregation, distribution range, etc., are analyzed by using statistical analysis software (such as R or Python), and scatter plots or heat maps are drawn to show the spatial distribution of key nodes. The spatial distribution data is visualized to intuitively understand the distribution characteristics of nodes, which can help identify which areas have dense nodes and which areas are relatively sparse. Combined with the characteristics of welding process requirements, the potential impact of node distribution on the welding process is analyzed, and the generated spatial distribution data of the identified nodes is organized into documents or database records for subsequent calculation of welding process parameters. Based on the requirements, the appropriate welding parameter calculation method is selected, which can include empirical formulas, machine learning models, or physics-based simulation methods. For each key node,Combined with its spatial distribution characteristics and welding process requirements, calculate the welding process parameters, which may include welding current, voltage, welding speed, etc. Record and organize the calculation results into a table to ensure that the parameters of each node are clear and accessible. When necessary, verify and adjust the parameters. The rationality and effectiveness of the calculation can be verified by simulating the welding process or small-scale trial welding. Select appropriate welding effect simulation software or tools (such as ANSYS, SolidWorks or custom image processing scripts) to ensure that it can support the simulation of multi-stage welding processes. Input the welding process parameters of each identified node into the simulation tool to ensure that the parameter settings meet the actual welding conditions and avoid deviations in the simulation results. Start the simulation tool and run the visual effect simulation of the welding process. In this process, observe the images generated by the simulation and analyze the details and quality of the welding effect. If the generated welding effect does not meet expectations, adjust the input parameters as needed and simulate again. Integrate the welding visual effects of all key nodes into a report, record the welding effect and parameter settings of each node, and evaluate the simulation results to ensure that they can reflect the expected effects in the actual welding process, providing a basis for subsequent welding process optimization. ,

[0037] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: accurately identifying welding points based on the welding visual effect of the identification plate to obtain multiple welding points; Step S22: performing image visual positioning marking on multiple welding points according to the contrast-optimized identification plate image to obtain the positioning coordinates of each welding point in the image; Step S23: performing a three-dimensional structure analysis of the signboard according to the contrast-optimized signboard image to generate three-dimensional geometric information of the signboard; Step S24: Perform dynamic welding path planning on the positioning coordinates of each welding point in the image based on the three-dimensional geometric information of the identification plate, thereby constructing a dynamic welding path.

[0038] In this embodiment, a welding effect image is obtained to ensure that the image quality is good and the characteristics of the welding point can be clearly displayed. If necessary, image preprocessing is performed, including denoising, contrast enhancement and clarity enhancement, so as to improve the recognizability of the welding point. A suitable welding point recognition algorithm is selected, such as a threshold segmentation algorithm based on image processing, edge detection (such as Canny edge detection) or a machine learning method (such as a convolutional neural network CNN) for automatic recognition of welding points. For welding points under complex backgrounds, the use of a deep learning method can improve the recognition accuracy. The selected recognition algorithm is applied to process the welding effect image and extract the coordinate information of the welding point. For each welding point, a connected domain analysis can be used to confirm its position, and the recognized welding point is recorded in the data. In the library, ensure that the coordinate information of each point is detailed and accurate. Perform manual verification on the identified welding points to ensure the accuracy of the recognition results. If the recognition effect is not ideal, you can optimize it by adjusting the algorithm parameters or changing the algorithm. Import the contrast-optimized sign image to ensure that the image is clear and has good contrast to facilitate accurate visual positioning. Use image processing tools (such as OpenCV) to draw the positioning mark of each welding point on the contrast-optimized image. Different colored dots or cross marks can be used to represent different welding points. For each welding point, record its specific coordinates in the image and ensure that these coordinates correspond to the recognition results of the welding point. The positioning coordinates of all welding points and their corresponding image mark information The information is stored in the database for subsequent processing and query, and the image coordinates of each welding point and its corresponding relationship in 3D space are recorded to prepare for subsequent 3D analysis and path planning. The marking results are checked to ensure that the marking of each welding point is accurate. If an error is found, the marking position needs to be adjusted in time and the coordinates need to be re-recorded. Stereo vision technology is used to optimize the contrast of multiple signboard images at different angles for 3D reconstruction. If there is only one image, a depth estimation algorithm (such as monocular depth estimation) can be used to obtain depth information. Camera calibration technology is used to ensure that the geometric relationship of the captured images is clear, thereby improving the accuracy of 3D reconstruction. By processing multi-view images, 3D reconstruction algorithms (such as structured light, laser scanning or The three-dimensional geometric model of the signboard is generated by the beam method. The three-dimensional model can be generated and processed by software such as MeshLab or Blender. The generated three-dimensional model should contain the surface details, shape and spatial position of the welding points of the signboard. Geometric information is extracted from the generated three-dimensional model, including the three-dimensional coordinates, surface normals, curvature, etc. of the welding points. This information will be helpful for subsequent welding path planning. The extracted geometric information is stored in a structured database for subsequent query and calculation. Select a suitable path planning algorithm, such as the A* algorithm, Dijkstra algorithm or RRT (fast random tree) algorithm. These algorithms can find the optimal path in a complex three-dimensional space. According to the distribution of welding points and the geometric shape of the signboard,Determine the important parameters of path planning, such as the starting point, end point and walkable area, dynamically generate the welding path based on the three-dimensional coordinate information of each welding point and the positioning coordinates in the image, ensure that the path follows the shortest distance principle as much as possible, reduce the moving time during welding, consider the obstacles and environmental factors that may appear during welding, ensure that the generated welding path is safe and efficient, optimize the generated welding path, consider the welding sequence and welding process requirements, ensure that the path planning meets the requirements of the actual welding operation, and when necessary, perform path simulation to verify the effectiveness and feasibility of the welding path. The final generated welding path is recorded and saved for subsequent welding control and execution. The path information can be exported to a specific format (such as G-code or other control instructions) for direct use of welding equipment. ,

[0039] In this embodiment, step S3 includes the following steps: Step S31: performing real-time welding control on the identification plate to be welded based on the dynamic welding path, and collecting real-time welding monitoring images; Step S32: identifying actual welding contact points on the real-time welding monitoring image and marking the actual welding contact points; Step S33: performing welding point surface change analysis on the actual welding contact point, thereby generating high temperature welding surface change characteristics of the welding point; Step S34: calculating the curvature change of the identification plate welding based on the high-temperature welding surface change characteristics of the welding point, thereby generating identification plate welding curvature change data; Step S35: performing welding path mutation analysis on the dynamic welding path based on the brand recognition welding curvature change data, and marking the welding path mutation points; Step S36: performing smooth optimization of the welding gun motion trajectory based on the welding path mutation points, thereby constructing an optimal welding gun motion smooth trajectory.

[0040] In this embodiment, the motion trajectory of the welding equipment is set using the pre-generated dynamic welding path data, which includes the starting position, travel path and end position of the welding gun, to ensure that the welding equipment can automatically adjust the movement according to the path data, integrate the welding control system with the motion control system, and ensure that the welding gun can accurately move along the preset path. A PLC (programmable logic controller) or motion control card can be used to achieve real-time control of the welding equipment, and a high-definition camera or thermal imaging camera is configured to capture welding monitoring images in real time during the welding process. These images can provide visual information of the welding process, help monitor the welding quality, and ensure that the image acquisition system can capture images at a high frequency during the welding process so as to obtain the welding contact points in a timely manner. status, store the collected real-time welding monitoring images in a database or cloud storage for subsequent analysis and processing, use a suitable image format (such as PNG or JPEG) to ensure image quality, pre-process the real-time welding monitoring images, including denoising, contrast enhancement and grayscale, to ensure the visibility of welding contact points, you can use image processing libraries such as OpenCV to perform these operations, apply edge detection algorithms (such as Canny edge detection) or machine learning-based image segmentation methods to automatically identify welding contact points, if conditions permit, use deep learning models (such as YOLO or MaskR-CNN) for more accurate contact point detection, mark the identified welding contact points, and use color markers or symbols to distinguish them, The marking information and the coordinates of the contact points are stored for subsequent data analysis. The identified and marked welding contact points are verified to ensure the accuracy of the marking. If errors are found, the parameters of the recognition algorithm need to be adjusted or manual corrections need to be made. Based on the contact point position of the welding monitoring image, the image data around the welding point is extracted, and the initial model of the high-temperature welding surface is constructed. The welding contact points are analyzed using three-dimensional reconstruction technology and surface fitting algorithms (such as B-spline surface fitting), and the surface change characteristics of the welding points at high temperatures are extracted. The curvature change, radius change and other geometric features of the welding points are calculated. These features can reflect the changes in the heat-affected zone during welding and help analyze the welding quality. The surface change characteristics of the welding points are recorded and stored for subsequent analysis. Analyze and compare, visualize the data, generate surface change diagrams to understand the changes that occur during the welding process, select appropriate curvature calculation methods, commonly used methods include Gaussian curvature and average curvature calculations, and use numerical methods (such as finite difference method) to calculate the curvature change of the surface. Based on the surface change characteristics of the welding contact point, perform curvature calculations, analyze the curvature change of the welding point at high temperature, and record the change data, calculate the local curvature of the welding point, evaluate the influence of the heat affected zone on the welding quality during the welding process, organize the welding curvature change data into structured information for subsequent analysis, and generate curvature change diagrams to visualize the curvature change trend during the welding process. Verify the curvature change data to ensure that it is consistent with the actual welding situation.If necessary, adjust the calculation method or parameters, select a suitable analysis method, such as a threshold-based mutation detection algorithm or a wavelet transform method in signal processing, to identify mutation points in the welding path, apply the selected analysis method, perform mutation analysis on the dynamic welding path, identify mutation points by comparing adjacent curvature change values, and record their positions in the welding path, mark the identified mutation points on the welding path diagram, use different colors or symbols to distinguish them, and record the coordinate information of the mutation points, record the information of the mutation points in the welding path in the database, and perform statistical analysis to understand the potential problems in the welding process and select appropriate trajectory smoothing. Algorithms, such as Bezier curve fitting, spline curve interpolation or Kalman filter, can effectively smooth the motion trajectory of the welding gun and reduce the impact of mutation points. Based on the identified mutation points of the welding path, the selected smoothing algorithm is used to process the motion trajectory of the welding gun to ensure that the trajectory is as smooth as possible during the welding process to avoid the impact of mutations. The smoothed motion trajectory of the welding gun is visualized and a motion trajectory diagram is generated to facilitate observation of the changes in the trajectory. The trajectory before and after smoothing is compared to verify the effectiveness of the smoothing effect. The optimized motion trajectory of the welding gun is saved in the database, and the corresponding control instructions are generated for direct use of the welding equipment.

[0041] In this embodiment, step S4 includes the following steps: Step S41: obtaining the current real-time working parameters of the welding gun; Step S42: performing multi-stage trajectory segmentation on the optimal welding gun motion smooth trajectory, thereby obtaining a multi-stage welding trajectory; Step S43: calculating the welding process parameters of the multi-stage welding trajectory segment by segment according to the welding visual effect of the identification plate, so as to obtain the welding process parameters of each segment of the trajectory; Step S44: Calculate the welding current based on the current real-time working parameters of the welding gun to obtain the current real-time welding current of the welding gun; Step S45: performing welding gun welding speed identification on the current welding gun real-time working parameters to extract the current welding gun welding speed; Step S46: Adaptively adjust the welding gun parameters of the current welding gun real-time welding current and the current welding gun welding speed based on the welding process parameters of each trajectory, so as to obtain adaptive welding gun adjustment parameters.

[0042] In this embodiment, appropriate sensors are installed on the welding gun and welding equipment to obtain key parameters of the welding process in real time. These sensors may include current sensors, voltage sensors and speed sensors to ensure that they can accurately reflect the working status of the welding gun. A data acquisition system is configured to transmit the sensor data to the central control system in real time. An industrial controller (such as PLC) or an embedded system (such as Arduino or RaspberryPi) can be used for data acquisition and processing. A user interface is developed to display the working parameters of the welding gun in real time in a graphical manner. The user interface can display current, voltage, welding speed and other important information for the operator to monitor. The real-time obtained welding gun working parameters are recorded in a database. For subsequent analysis and evaluation, ensure that the data storage format is suitable for subsequent processing and analysis, obtain and analyze the smooth motion trajectory data of the welding gun, use data processing tools (such as MATLAB or Python) to visualize and analyze the trajectory, ensure that the trajectory data is available and easy to process, select a suitable trajectory segmentation algorithm, such as a segmentation method based on speed change or a segmentation method based on distance, and identify the natural segmentation points in the trajectory by setting a threshold. Segment the smooth trajectory according to the selected algorithm and divide it into multiple stages. Each stage should contain the welding process parameters required by the welding gun in this segment of movement. Record the start and end points of each stage in the database and combine it with the subsequent welding process parameter calculation to ensure that each The parameters of each stage can be clearly defined. With the help of the previously generated identification plate welding visual effect, the influence of each trajectory stage on the welding process parameters is analyzed. Image processing tools are used to ensure that the connection between welding effect and process parameters is clear. Based on the requirements of the welding process, a parameter calculation model is established. The model should contain calculation formulas for parameters such as welding current, voltage, welding speed, and welding time to ensure that it can adapt to different welding stages. For each welding trajectory stage, the established model is used to calculate various welding process parameters, and the current, speed, welding time and other parameters of each stage are recorded and stored in the database to verify whether the calculation results meet the actual welding requirements. If necessary, the calculation model or parameters are adjusted to ensure the welding process parameters of each stage. To achieve the best welding effect, apply the welding current calculation formula, which usually includes factors such as material type, welding method, welding rate, etc., to ensure that the formula is suitable for the current welding situation, input the real-time welding gun working parameters (such as voltage, welding speed) into the current calculation model, use the set formula for real-time calculation, output the real-time welding current calculation result, ensure that it can reflect the working status of the welding gun in time, record the results and store them in the database for subsequent analysis and reference, monitor the changes in real-time welding current to ensure that it is within a reasonable range, if the current is abnormal, adjust the welding parameters in time to avoid welding quality problems, configure welding speed sensors to monitor the moving speed of the welding gun in real time, these sensors can be photoelectric sensors or encoders,Ensure that it can accurately reflect the movement state of the welding gun. Calculate the welding speed of the welding gun through the real-time acquired welding gun movement data. Usually, the speed can be calculated by the ratio of the distance the welding gun moves to the time. Record and store the real-time welding speed in the database to ensure that the speed data is combined with the welding process parameters for subsequent analysis. Monitor the real-time changes in the welding speed to ensure that the speed remains within the set range during the welding process. If the speed is abnormal, make timely adjustments to optimize the welding process. Design an adaptive control algorithm that can automatically adjust the working parameters of the welding gun according to the changes in the real-time welding current and welding speed. Common methods include PID control algorithm or fuzzy control algorithm. According to the comparison of the process parameters of each welding section with the real-time current and welding speed, adjust the current and welding speed of the welding gun in real time to ensure that the welding gun can adapt to different process requirements during the welding process. Monitor the adjusted welding current and welding speed to ensure that they meet the set process parameters. The feedback mechanism can promptly detect and adjust the parameters that do not meet the requirements to ensure the stability of the welding process. Record and store the adaptively adjusted welding gun parameters for subsequent analysis and optimization. Based on historical data, optimize the adjustment algorithm to improve the automation and accuracy of future welding processes. ,

[0043] In this embodiment, the specific steps of step S5 are: Step S51: performing heat distribution analysis of the welding area on the real-time welding monitoring image to generate heat distribution characteristics of the welding area; Step S52: evaluating the state of the welding molten pool based on the heat distribution characteristics of the welding area to obtain the state characteristics of the welding molten pool; Step S53: performing temperature change trend analysis on the welding molten pool state characteristics to generate a temperature change trend of the welding molten pool; Step S54: performing abnormal temperature prediction evolution according to the temperature change trend of the welding molten pool, thereby generating abnormal temperature prediction data of the welding molten pool; Step S55: dynamically adjust the welding parameters of the adaptive welding gun adjustment parameters based on the abnormal temperature prediction data of the welding molten pool, thereby constructing a dynamic welding parameter control strategy.

[0044] In this embodiment, a thermal imaging camera or a high temperature sensor is used to collect monitoring images of the welding area in real time to ensure that the image is clear and can effectively capture the temperature changes generated during the welding process. The collected image is preprocessed, including noise removal and contrast enhancement, to improve the visualization effect of the heat distribution. These operations can be performed using image processing software (such as OpenCV). A heat distribution analysis algorithm is applied to calculate the heat distribution of the preprocessed image. This can be done by converting pixel values, mapping the colors in the image to corresponding temperature values, and generating a heat distribution map. An interpolation algorithm (such as bilinear interpolation) is used to improve the accuracy of the heat distribution, ensure that the temperature changes in different areas can be accurately captured, and extract the welding area from the heat distribution map. The heat distribution characteristics of the area, including heat concentration area, temperature gradient and other information, will be used for the subsequent melt pool state assessment. The extracted feature data will be sorted and stored for subsequent analysis and comparison. The heat distribution characteristics will be displayed in a visual way to generate a heat distribution map. Different temperature areas will be displayed by color coding to facilitate the rapid identification of heat concentration areas and affected areas. The state characteristics of the welding melt pool will be defined, including the area, depth, temperature distribution, etc. of the melt pool. These characteristics have an important impact on the welding quality. Based on the heat distribution characteristics, a melt pool state assessment model will be established. Machine learning algorithms, such as support vector machines (SVM) or decision trees, can be used to evaluate the state of the welding melt pool. The heat distribution characteristics of the welding area will be input into the melt pool state assessment model. In the process of real-time evaluation, the model will output the state characteristics of the melt pool, including whether it is within the normal range, and record the evaluation results in the database to ensure that the state characteristics of the melt pool in each welding process are recorded in detail. The evaluation results are analyzed for subsequent optimization of welding parameters. The data of the melt pool state characteristics are collected from real-time monitoring, including the temperature, area and other information at different time points, to ensure the integrity and accuracy of the data, and to establish a temperature change trend analysis model. Time series analysis methods (such as ARIMA model) or machine learning methods (such as linear regression) can be used to predict the temperature change trend of the welding melt pool. The collected melt pool state characteristic data are input into the model for temperature change trend analysis. The model will output the trend of the melt pool temperature changing over time. The trend diagram shows the increase, decrease or steady state of temperature, and visualizes the temperature change trend in the form of a chart, which is convenient for operators to understand the law of temperature change, record the analysis results for subsequent reference and optimization of welding parameters, determine the normal temperature range of the welding molten pool, and define the standard of abnormal temperature. This can be based on historical data, industry standards or laboratory test results, and use machine learning methods (such as random forests or neural networks) or statistical methods (such as Z-score analysis) to establish an abnormal temperature prediction model. The collected temperature change data is used for training, and the real-time welding molten pool temperature change trend data is input into the abnormal prediction model for real-time prediction. The model will output the abnormal temperature prediction data of the welding molten pool, and the prediction results will be recorded in the database.And combined with the real-time monitoring system to ensure that operators can be informed of potential abnormal situations in a timely manner, design control strategies based on the abnormal temperature prediction data of the welding molten pool, which may include real-time adjustment of parameters such as welding current, welding speed and welding interval in response to temperature changes, and develop an adaptive control system that can automatically adjust the working parameters of the welding gun according to the real-time status and prediction data of the welding molten pool. This can be achieved using PID control algorithms or fuzzy control algorithms. The abnormal temperature prediction data is input into the adaptive control system to adjust the welding gun parameters in real time to ensure that when the temperature is abnormal, the welding gun can quickly adjust the current and speed to reduce the temperature and maintain the welding quality. Continuously monitor the adjusted parameters during the welding process to ensure that they meet the welding process requirements. If problems are found, the system should be able to make automatic adjustments and record all adjustment data for subsequent analysis. ,

[0045] In this embodiment, the specific steps of step S6 are: Step S61: performing multi-stage welding result recognition based on the real-time welding monitoring image, and extracting welding results of multiple process stages; Step S62: evaluating the welding quality of each welding point for the welding results of multiple process stages, and generating a welding quality evaluation value for each welding point; Step S63: performing local welding defect analysis based on the welding quality evaluation value of each welding point, thereby generating local welding defect data; Step S64: Optimizing the dynamic welding parameter control strategy for welding defect control based on the local welding defect data to construct a dynamic welding control optimization model.

[0046] In this embodiment, a high-resolution welding monitoring device (such as a thermal imaging camera or a high-frame-rate video camera) is used to collect real-time images to ensure that the quality of the monitoring image is sufficient to capture the details of the welding process. The collected images are preprocessed, including denoising, contrast enhancement, and image normalization, to improve the accuracy of subsequent analysis. Image processing and computer vision technology are used to identify the welding area in the image using edge detection and contour extraction algorithms (such as Canny edge detection and Hough transform). By setting specific thresholds and conditions, different process stages (such as preheating, welding, and cooling) are segmented to ensure that the results of each stage can be effectively extracted. The welding results of each process stage identified are classified and marked to form a structured Data sets, which include specific features of each stage (such as weld width, depth, etc.), store the extracted results in the database for subsequent analysis and evaluation, visualize the welding results of multiple process stages, generate dynamic charts or image sequences of the welding process, help technicians quickly understand the changes in the welding process, formulate welding quality evaluation standards, including the geometric features of the weld (such as depth, width, shape) and physical properties (such as strength, toughness), determine the qualified range of each feature, so that subsequent evaluation work can have a basis, obtain the key features of each welding point from the extracted welding results, which can be extracted through image processing technology. The geometric parameters of the weld, temperature distribution and other information, apply feature extraction algorithms (such as shape The extracted features are evaluated by machine learning models (such as support vector machines or decision trees), the features of the welds are input into the model, and the welding quality evaluation value is output. The evaluation model is continuously optimized based on historical data and expert experience to improve the prediction accuracy. The evaluation value of each weld is recorded in the database and associated with other data of the welding process for comprehensive analysis. Statistical analysis is performed to identify welds with unqualified quality and further defect analysis is performed to determine the types of defects that may occur in the welding process, such as pores, inclusions, lack of penetration, welding cracks, etc. A defect classification standard is established to facilitate subsequent analysis. Feature analysis and pattern recognition technology are used. , conduct defect analysis on the welding quality assessment value. Statistical methods (such as control charts) or machine learning methods (such as cluster analysis) can be used to identify defects. Local defects can be identified based on the assessment value and defect analysis results, and problematic welding points can be marked. Combined with image processing technology, defect distribution maps can be generated to clarify the location and type of defects. Detailed data of local welding defects (including defect type, location, severity, etc.) can be recorded and stored in the database to ensure that defects can be tracked and analyzed later. According to the local welding defect data, targeted welding parameter optimization strategies can be formulated, including the adjustment direction and amplitude of parameters such as welding current, welding speed, and welding gas flow. A dynamic welding control optimization model can be constructed, and a feedback control mechanism can be used to adjust welding parameters in real time.In response to defect information in the welding process, adaptive control or fuzzy control algorithms can be used to apply optimization models in the actual welding process, monitor welding quality and defects in real time, and continuously adjust model parameters according to feedback results to ensure its adaptability and accuracy. Continuously monitor parameter changes in the welding process, record welding quality data after the optimization strategy is implemented, ensure the effectiveness of optimization measures, and regularly evaluate and update the model to improve the level of welding quality control.

[0047] In this embodiment, a welding control device for a identification plate is provided, which is used to execute the welding control method for the identification plate as described above, including: The welding visual simulation module is used to obtain the high-definition monitoring image of the identification plate to be welded and the welding work log; the identification plate texture is visually recognized on the high-definition monitoring image of the identification plate to be welded, and the welding visual effect is simulated to obtain the identification plate welding visual effect; The welding path planning module is used to accurately identify welding points based on the visual effect of the identification plate welding, and to perform dynamic welding path planning, thereby constructing a dynamic welding path; The welding gun motion trajectory module is used to perform real-time welding control on the welding identification plate based on the dynamic welding path, and then perform smooth optimization of the welding gun motion trajectory to construct the optimal welding gun motion smooth trajectory; An adaptive parameter adjustment module is used to obtain the current real-time working parameters of the welding gun; based on the optimal welding gun motion smooth trajectory, the current real-time working parameters of the welding gun are adaptively adjusted to obtain adaptive welding gun adjustment parameters; Dynamic welding control module, which is used to analyze the temperature change trend of the welding pool according to the real-time welding monitoring image, and dynamically control the welding parameters of the adaptive welding gun adjustment parameters, so as to build a dynamic welding parameter control strategy; The welding control optimization module is used to perform local welding defect analysis based on real-time welding monitoring images and optimize the welding defect control of dynamic welding parameter control strategies to build a dynamic welding control optimization model.

[0048] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0049] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A welding control method for a sign, characterized in that: The following steps are involved: Step S1: obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; performing visual recognition of the identification plate texture on the high-definition monitoring image of the identification plate to be welded, and simulating the welding visual effect, thereby obtaining the identification plate welding visual effect; Step S2: accurately identifying welding points based on the visual effect of the welding of the identification plate, and performing dynamic welding path planning, thereby constructing a dynamic welding path; Step S3: Based on the dynamic welding path, the welding identification plate is subjected to real-time welding control, and then the welding gun motion trajectory is smoothly optimized to construct an optimal welding gun motion smooth trajectory; Step S4: obtaining the current real-time working parameters of the welding gun; performing adaptive welding gun parameter adjustment on the current real-time working parameters of the welding gun based on the optimal welding gun motion smooth trajectory, thereby obtaining adaptive welding gun adjustment parameters; Step S5: analyzing the temperature change trend of the welding pool according to the real-time welding monitoring image, and dynamically regulating the welding parameters of the adaptive welding gun adjustment parameters, thereby constructing a dynamic welding parameter regulation strategy; Step S6: Perform local welding defect analysis based on the real-time welding monitoring image, and optimize the welding defect control of the dynamic welding parameter control strategy to build a dynamic welding control optimization model.

2. The welding control method for identification plates according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Obtaining a high-definition monitoring image of the identification plate to be welded and a welding work log; Step S12: performing visual recognition of the texture of the identification plate to be welded on the high-definition monitoring image of the identification plate to generate visual features of the texture of the identification plate; Step S13: performing detail sharpening and enhancement on the visual features of the signboard texture, thereby obtaining a sharpened and enhanced signboard image; Step S14: performing multi-region contrast optimization on the sharpened and enhanced sign image to obtain a contrast-optimized sign image; Step S15: Based on the welding work log, the contrast-optimized sign image is subjected to welding visual effect simulation to obtain the sign welding visual effect.

3. The welding control method for identification plates according to claim 2, characterized in that: The specific steps of step S15 are: Conduct welding process requirement analysis on the welding work log to generate welding process requirement characteristics of the identification plate to be welded; Identify the welding process flow in the welding work log and extract the multi-stage welding process flow; Based on the welding work log, the contrast-optimized identification plate image is used to locate key identification nodes, and multiple key identification nodes are obtained; Performing identification point spatial distribution analysis on multiple key identification nodes to generate identification node spatial distribution data; Based on the welding process requirement characteristics of the identification plate to be welded, the welding process parameters of the identification nodes are calculated one by one for the identification node spatial distribution data, so as to generate the welding process parameters of each identification node; Based on the multi-stage welding process flow, the welding process parameters of each identification node are simulated to obtain the welding visual effect of the identification plate.

4. The welding control method for identification plates according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: accurately identifying welding points based on the welding visual effect of the identification plate to obtain multiple welding points; Step S22: performing image visual positioning marking on multiple welding points according to the contrast-optimized identification plate image to obtain the positioning coordinates of each welding point in the image; Step S23: performing a three-dimensional structure analysis of the signboard according to the contrast-optimized signboard image to generate three-dimensional geometric information of the signboard; Step S24: Perform dynamic welding path planning on the positioning coordinates of each welding point in the image based on the three-dimensional geometric information of the identification plate, thereby constructing a dynamic welding path.

5. The welding control method for identification plates according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing real-time welding control on the identification plate to be welded based on the dynamic welding path, and collecting real-time welding monitoring images; Step S32: identifying actual welding contact points on the real-time welding monitoring image and marking the actual welding contact points; Step S33: performing welding point surface change analysis on the actual welding contact point, thereby generating high temperature welding surface change characteristics of the welding point; Step S34: calculating the curvature change of the identification plate welding based on the high-temperature welding surface change characteristics of the welding point, thereby generating identification plate welding curvature change data; Step S35: performing welding path mutation analysis on the dynamic welding path based on the brand recognition welding curvature change data, and marking the welding path mutation points; Step S36: performing smooth optimization of the welding gun motion trajectory based on the welding path mutation points, thereby constructing an optimal welding gun motion smooth trajectory.

6. The welding control method for identification plates according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: obtaining the current real-time working parameters of the welding gun; Step S42: performing multi-stage trajectory segmentation on the optimal welding gun motion smooth trajectory, thereby obtaining a multi-stage welding trajectory; Step S43: calculating the welding process parameters of the multi-stage welding trajectory segment by segment according to the welding visual effect of the identification plate, so as to obtain the welding process parameters of each segment of the trajectory; Step S44: Calculate the welding current based on the current real-time working parameters of the welding gun to obtain the current real-time welding current of the welding gun; Step S45: performing welding gun welding speed identification on the current welding gun real-time working parameters to extract the current welding gun welding speed; Step S46: Adaptively adjust the current welding gun real-time welding current and the current welding gun welding speed based on the welding process parameters of each trajectory, so as to obtain adaptive welding gun adjustment parameters.

7. The welding control method for identification plates according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing heat distribution analysis of the welding area on the real-time welding monitoring image to generate heat distribution characteristics of the welding area; Step S52: evaluating the state of the welding molten pool based on the heat distribution characteristics of the welding area to obtain the state characteristics of the welding molten pool; Step S53: performing temperature change trend analysis on the welding molten pool state characteristics to generate a temperature change trend of the welding molten pool; Step S54: performing abnormal temperature prediction evolution according to the temperature change trend of the welding molten pool, thereby generating abnormal temperature prediction data of the welding molten pool; Step S55: dynamically adjust the welding parameters of the adaptive welding gun adjustment parameters based on the abnormal temperature prediction data of the welding molten pool, thereby constructing a dynamic welding parameter control strategy.

8. The welding control method for identification plates according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: performing multi-stage welding result recognition based on the real-time welding monitoring image, and extracting welding results of multiple process stages; Step S62: evaluating the welding quality of each welding point for the welding results of multiple process stages, and generating a welding quality evaluation value for each welding point; Step S63: performing local welding defect analysis based on the welding quality evaluation value of each welding point, thereby generating local welding defect data; Step S64: Optimizing the dynamic welding parameter control strategy for welding defect control based on the local welding defect data to construct a dynamic welding control optimization model.

9. A welding control device for a sign, characterized in that: The method for executing the welding control method for identification plates according to claim 1 comprises: The welding visual simulation module is used to obtain the high-definition monitoring image of the identification plate to be welded and the welding work log; the identification plate texture is visually recognized on the high-definition monitoring image of the identification plate to be welded, and the welding visual effect is simulated to obtain the identification plate welding visual effect; The welding path planning module is used to accurately identify welding points based on the visual effect of the identification plate welding, and to perform dynamic welding path planning, thereby constructing a dynamic welding path; The welding gun motion trajectory module is used to perform real-time welding control on the welding identification plate based on the dynamic welding path, and then perform smooth optimization of the welding gun motion trajectory to construct the optimal welding gun motion smooth trajectory; An adaptive parameter adjustment module is used to obtain the current real-time working parameters of the welding gun; based on the optimal welding gun motion smooth trajectory, the current real-time working parameters of the welding gun are adaptively adjusted to obtain adaptive welding gun adjustment parameters; Dynamic welding control module, which is used to analyze the temperature change trend of the welding pool according to the real-time welding monitoring image, and dynamically control the welding parameters of the adaptive welding gun adjustment parameters, so as to build a dynamic welding parameter control strategy; The welding control optimization module is used to perform local welding defect analysis based on real-time welding monitoring images and optimize the welding defect control of dynamic welding parameter control strategies to build a dynamic welding control optimization model.

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