Galvanized steel pipe coating process control method and system

Abstract

The invention discloses a galvanized steel pipe coating process control method and system, and the method comprises the steps: obtaining galvanized layer integrity prediction information based on obtained galvanizing process selection information and a preset galvanized layer integrity prediction model; based on the obtained welding process control parameters and a preset welding quality prediction model, welding quality prediction information is obtained; analyzing in combination with the zinc coating integrity prediction information and the welding quality prediction information, judging whether the prediction information meets a preset process control index or not, and obtaining parameter model input information according to a judgment result; inputting the parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information; on the basis of the parameter control information and the coating reference control information, the galvanizing welding process and the coating process of the galvanized steel pipe are controlled; the galvanized steel pipe coating technology is more reliable and efficient, and the quality and performance of the galvanized steel pipe are improved.

Description

Technical Field

[0001] The present application relates to the technical field of production process control, and in particular to a method and system for controlling the coating process of a galvanized steel pipe. Background Art

[0002] The galvanized steel pipe coating process is to coat the steel pipe with zinc so that the zinc layer can form a dense protective film on the surface of the steel pipe. The galvanized layer can effectively isolate air, moisture and corrosive media, thereby significantly improving the corrosion resistance of the steel pipe and extending its service life.

[0003] The production process of galvanized steel pipes involves multiple links, including steel pipe pretreatment, galvanizing and post-treatment, and each link has a vital impact on the quality of the final product; the pretreatment stage requires thorough removal of oil, rust and other impurities on the surface of the steel pipe to ensure the adhesion and uniformity of the galvanized layer; the galvanizing stage requires precise control of the galvanizing process parameters to ensure that the zinc layer thickness, adhesion and corrosion resistance meet the requirements; the post-treatment stage involves cooling, passivation, oiling and other steps to further improve the corrosion resistance and appearance quality of the product; since these links are interrelated and influence each other, mistakes in any link may lead to a decline in the quality of the final product, so the process quality control during the production of galvanized steel pipes is relatively difficult.

[0004] The traditional technology is to adjust the various process parameters of the galvanizing machine through experience, so as to ultimately achieve the purpose of on-site implementation. However, the traditional technology has low working efficiency and there will be certain errors, which will affect the quality of galvanized products. In addition, the existing galvanized steel pipes still have uneven quality of the galvanized layer. Therefore, it is necessary to further research and develop a more reliable and efficient galvanized steel pipe coating process control method to improve the quality and performance of galvanized steel pipes. Summary of the Invention

[0005] In order to provide a more reliable and efficient galvanized steel pipe coating process and improve the quality and performance of galvanized steel pipes, the present application provides a galvanized steel pipe coating process control method and system.

[0006] In the first aspect, the invention objectives of this application are achieved by adopting the following technical solutions: Galvanized steel pipe coating process control method, including: Based on the obtained galvanizing process selection information and the preset galvanizing layer integrity prediction model, obtaining galvanizing layer integrity prediction information; Obtaining welding quality prediction information based on the acquired welding process control parameters and a preset welding quality prediction model; Analyzing the galvanized layer integrity prediction information and the welding quality prediction information to determine whether the prediction information meets the preset process control indicators, and obtaining parameter model input information based on the determination result; Inputting the parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information; Based on the parameter control information and the coating reference control information, the galvanizing welding process and the coating process of the galvanized steel pipe are controlled.

[0007] By adopting the above technical solutions, the galvanized layer integrity prediction model and the welding quality prediction model are artificial intelligence models. The galvanized layer integrity prediction model is used to judge the galvanized layer integrity of galvanized products currently using galvanized steel pipe production line equipment under different galvanizing process parameters; the welding quality prediction model is used to judge the welding quality of welding products currently using galvanized steel pipe production line welding equipment under different welding process control parameters, which will affect the subsequent product coating process and overall product quality. However, different products have different process parameters in the early pretreatment stage and galvanizing treatment stage, which may easily lead to differences in the subsequent product processing requirements to the coating process stage. In order to accurately monitor the galvanized layer quality and weld quality of galvanized steel pipes, maintain the production quality of galvanized steel pipes at a high quality level, and automatically and intelligently control the galvanizing welding process and coating process of galvanized steel pipes, a more reliable and efficient galvanized steel pipe coating process is provided. This application uses a galvanizing layer integrity prediction model and a welding quality prediction model to analyze the early galvanizing and welding respectively, and explores the impact of product quality from more angles, thereby facilitating targeted analysis and control of each production stage of the galvanized steel pipe. In addition, the fusion process control model is used to determine the optimal process control parameters (i.e., parameter control information and coating reference control information) for different product process production stages (galvanizing, welding) under the corresponding process indicator requirements. Based on the parameter control information and coating reference control information, the galvanizing welding process and coating process of the galvanized steel pipe are controlled, which can accurately control the galvanizing process parameters, ensure the stability and consistency of the galvanizing layer, weld quality and coating layer, and at the same time reduce the uncertainty and risk in the production process, and improve the overall production efficiency and product quality; thus, this application achieves the purpose of "providing a more reliable and efficient galvanized steel pipe coating process and improving the quality and performance of galvanized steel pipes."

[0008] In a preferred example of the present application, the galvanizing layer integrity prediction information is obtained based on the obtained galvanizing process selection information and the preset galvanizing layer integrity prediction model, specifically including: Obtaining galvanizing process selection information of the galvanized steel pipe, the galvanizing process selection information including galvanizing time, galvanizing temperature, galvanizing layer thickness and galvanizing solution composition; the galvanized steel pipe is associated with a steel pipe identifier; Based on the galvanizing process selection information and the corresponding steel pipe identification, a preset galvanizing layer integrity prediction model is used to perform analysis to obtain galvanizing layer integrity prediction information; The integrity and uniformity of the galvanized layer of the galvanized steel pipe are judged based on the galvanized layer integrity prediction information.

[0009] By adopting the above technical scheme, the galvanizing process selection information is the actual process selection operation information formulated according to the material characteristics of the galvanized steel pipe and the galvanizing operation standard requirements of the process standards, so as to accurately control the key factors in the galvanizing process. These key factors directly affect the formation quality of the galvanized layer. By using the galvanized layer integrity prediction model for analysis, the galvanized layer completeness of the galvanized steel pipe can be predicted in advance. It can not only determine whether the galvanized layer has defects, but also evaluate the uniformity of the galvanized layer, and can find problems in the galvanizing process, such as insufficient galvanizing time, improper temperature control, inappropriate galvanizing liquid composition, etc., which is conducive to timely optimization of the galvanizing process parameters, scientific adjustment of the galvanizing process parameters, and improvement of the instruction and stability of the galvanized layer. It not only improves the production efficiency of the galvanized steel pipe, but also reduces rework and material waste caused by galvanized layer defects.

[0010] In a preferred example of the present application, the welding quality prediction information is obtained based on the acquired welding process control parameters and the preset welding quality prediction model, specifically including: Acquiring welding process control parameters, wherein the welding process control parameters include welding current, voltage, welding speed, welding wire diameter, and welding trajectory; Based on the welding process control parameters, a preset welding quality prediction model is used to perform analysis to obtain welding quality prediction information; The strength, ductility and crack resistance of the weld joint are evaluated based on the welding quality prediction information.

[0011] By adopting the above technical solution, the welding process control parameters are key elements formulated according to the material characteristics of galvanized steel pipes and the welding operation standard requirements of the process standards, so as to finely and accurately control the key elements in the welding process. These parameters directly affect the formation quality and performance of the welded joints. The present invention analyzes the process parameters by utilizing the welding quality prediction model, which can predict the quality characteristics of the welded structure in advance, including potential defects, and provide data support for subsequent process adjustments, thereby effectively avoiding the uncertainty brought about by reliance on experience or trial and error in traditional methods. Through the comprehensive performance evaluation of the welded joints, its long-term stability in the current operating environment is effectively guaranteed. By predicting the welding quality in advance and adjusting the process parameters accordingly, rework and material waste caused by welding defects can be reduced. High-quality welded joints are conducive to improving the overall mechanical properties of galvanized steel pipes.

[0012] In a preferred example of the present application, the galvanized layer integrity prediction model is obtained according to the following method: Acquire galvanized layer characteristic reference data, wherein the galvanized layer characteristic reference data includes galvanized layer thickness, zinc layer distribution data, environmental condition data, steel pipe material data, and galvanizing process parameter data; Performing model training based on the galvanized layer characteristic reference data to generate a galvanized layer integrity prediction model; The welding quality prediction model is obtained according to the following method: Acquiring welding quality reference data, wherein the welding quality reference data includes historical welding quality data, welding environment data, welding process parameter data, and welding equipment performance data; Model training is performed based on the welding quality reference data to generate a welding quality prediction model.

[0013] By adopting the above technical scheme, the galvanized layer integrity prediction model and the welding quality prediction model predict and judge the production performance of the galvanized steel pipe from different production stages in the galvanized steel pipe production steps. Therefore, the training objects, training methods and training samples of the galvanized layer integrity prediction model and the welding quality prediction model are different. The galvanized layer integrity prediction model can accurately predict the production quality and performance of the galvanized layer of the galvanized steel pipe, and the welding quality prediction model can predict the welding quality of the galvanized steel pipe. The diversified reference data enables the model to learn the galvanizing and welding characteristics under different conditions during the training process, thereby enhancing the generalization ability of the model.

[0014] In a preferred example of the present application, the fusion process control model is obtained by: Acquire physical process data and steel pipe production equipment data, and construct a process physical equipment model based on the physical process data and steel pipe production equipment data; Obtaining historical process load data of steel pipe production equipment at different production stages, determining process load differences based on the historical process load data, and obtaining load stratification information; Establishing a load model based on the load stratification information; The adjustable process parameter information of each steel pipe production equipment is obtained, the process physical equipment model and the load model are integrated, and the adjustable process parameter information is used as a model input to generate the integrated process control model.

[0015] By adopting the above technical solution, the present invention constructs a process physical equipment model to conduct comprehensive process parameter collection and change law analysis of multiple steel pipe production equipment in the entire galvanized steel pipe production line. By monitoring the key information in the entire production process, it can accurately monitor various process parameters and their change laws in the production process, and the subsequent process physical equipment model constructed can intuitively and accurately display the production process flow, help relevant technical personnel understand the interaction between various production environments, and provide data theoretical support for the subsequent load model establishment and optimization of process control parameters; at the same time, by analyzing the change characteristics of historical process load data, the corresponding load levels are divided; the process load gap refers to the load difference of production equipment in different time periods during the production process, that is, the degree of difference between the actual production load and the design load; since the hot-dip galvanizing equipment needs to be maintained at Within a certain temperature range, long-term operation of plating solution composition detection equipment, heat consumption monitoring equipment, etc. will cause unnecessary equipment energy consumption, which is not conducive to energy-saving production. Therefore, in order to find the balance point between the energy consumption of each production equipment and process control requirements, and reduce the energy consumption and production cost of galvanized steel pipe production, this application obtains load stratification information (stratified according to load mode and load characteristics, and the stratification standard is set to multiple layers according to load height and duration) and establishes a compliance model to predict the load change trend of each steel pipe production equipment, so that the model is more in line with the actual production situation and improves the control accuracy. The fusion process control model can adjust the relevant process parameters according to actual production needs to achieve dynamic optimization of the production process, so that the galvanized steel pipe process control system can respond to challenges by adjusting the process parameters in a timely manner even in the face of complex and changing production environments, thereby ensuring production efficiency and product quality.

[0016] In a preferred example of the present application, the method of obtaining adjustable process parameter information of each steel pipe production equipment, integrating the process physical equipment model and the load model, and using the adjustable process parameter information as a model input to generate the integrated process control model includes: Obtaining adjustable process parameter information and historical process parameter adjustment information of each steel pipe production equipment, and using the historical process parameter adjustment information and the historical process load data as a training data set; Based on a preset model fusion algorithm, the process physical equipment model and the load model are fused to generate a fusion model, wherein the model fusion algorithm includes a linear fitting algorithm, a process control fitting algorithm, and a cross fusion algorithm; The adjustable process parameter information is used as a model input, and the fusion model is trained based on the training data set to generate a fusion process control model.

[0017] By adopting the above technical solution, the adjustable process parameter information refers to the equipment parameters in the galvanized steel pipe process control system that can be adjusted, and the galvanized steel pipe process control system can adjust the parameters automatically. Therefore, by using the adjustable parameter information as the input of the model, it is convenient to learn the adjustment amount of each adjustable parameter under the equipment load requirements of different energy consumption. When the process physical equipment model and the load model are integrated, the model is integrated through a variety of preset model fusion algorithms. For example, when integrating through the linear fitting algorithm, the linear change trend of the load of each node on the production path of the equipment load that meets the model identification is considered, and the control linear relationship between each steel pipe production equipment represented by the process physical equipment model and the controlled object is considered, so that each node in the load model can be better integrated. Therefore, by using a variety of different fusion algorithms, the adaptability of the fusion of the process physical equipment model and the load model can be guaranteed, and the output of the fusion process control model can be guaranteed to be accurate.

[0018] In a preferred example of the present application, after controlling the galvanizing welding process and coating process of the galvanized steel pipe based on the parameter control information and the coating reference control information, the present application further includes: Collect temperature data of heat-affected zone during welding; Acquire an infrared image of the galvanized steel pipe during the welding process in combination with the heat-affected zone temperature data, track the heat conduction path of the galvanized steel pipe during welding based on the infrared image, locate the heat-affected zone, and obtain a plurality of heat-affected analysis units; An integrated element spectrum analyzer is used to compare the zinc layer thickness and distribution changes of the galvanized steel pipe in the corresponding area of ​​the thermal impact analysis unit to obtain steel pipe comparison and tracking information; The steel pipe comparison and tracking information is compared with a preset change threshold value. If the steel pipe comparison and tracking information is outside the preset change threshold range, a parameter adjustment instruction is triggered.

[0019] By adopting the above technical solution, the heat-affected zone is located based on infrared imaging technology, and the area is subdivided into several heat-affected analysis units, which helps to more accurately analyze the impact of heat on galvanized steel pipes during welding, so as to achieve real-time monitoring and precise control of the welding process. By performing real-time comparison and analysis of the zinc layer thickness and distribution changes of the galvanized layer, the flatness of the zinc layer can be quickly and accurately achieved by a non-contact detection method, and problems such as damaged or uneven distribution of the zinc layer can be discovered in time. By comparing the steel pipe comparison tracking information with the preset change threshold, an intelligent adjustment mechanism is provided to quickly respond to abnormal situations in the welding process, which is helpful to assist staff in eliminating adverse effects by adjusting welding parameters or taking other treatment measures.

[0020] In the second aspect, the invention objective of this application is achieved by adopting the following technical solutions: A galvanized steel pipe coating process control system is applied to the galvanized steel pipe coating process control method described above, and the system comprises: A galvanizing layer integrity prediction module is used to obtain galvanizing layer integrity prediction information based on the obtained galvanizing process selection information and a preset galvanizing layer integrity prediction model; A welding quality prediction module is used to obtain welding quality prediction information based on the acquired welding process control parameters and a preset welding quality prediction model; A joint analysis module is used to jointly analyze the galvanized layer integrity prediction information and the welding quality prediction information to determine whether the prediction information meets the preset process control indicators, and obtain parameter model input information based on the judgment result; A model fusion analysis module, configured to input the parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information; The control module is used to control the galvanizing welding process and coating process of the galvanized steel pipe based on the parameter control information and the coating reference control information.

[0021] By adopting the above technical scheme, the early galvanizing and welding are analyzed respectively through the galvanizing layer integrity prediction model and the welding quality prediction model, and the product quality impact is explored from more angles, thereby facilitating targeted analysis and control of each production stage of the galvanized steel pipe. In addition, the fusion process control model is used to judge the optimal process control parameters (i.e. parameter control information and coating reference control information) of different product process production stages (galvanizing, welding) under the corresponding process indicator requirements. Based on the parameter control information and coating reference control information, the galvanizing welding process and coating process of the galvanized steel pipe are controlled, which can accurately control the galvanizing process parameters, ensure the stability and consistency of the galvanizing layer, weld quality and coating layer, and at the same time reduce the uncertainty and risk in the production process, and improve the overall production efficiency and product quality; thus, this application achieves the purpose of "providing a more reliable and efficient galvanized steel pipe coating process and improving the quality and performance of galvanized steel pipes."

[0022] In a preferred embodiment of the present application, the system further includes: Thermal data acquisition module, used to collect temperature data of the heat-affected zone during welding; a heat impact analysis module, configured to obtain an infrared image of the galvanized steel pipe during the welding process in combination with the heat affected zone temperature data, track the heat conduction path of the galvanized steel pipe during welding based on the infrared image, locate the heat affected zone, and obtain a plurality of heat impact analysis units; A spectrum analysis and change tracking module is used to integrate an element spectrum analyzer to compare the zinc layer thickness and distribution changes of the galvanized steel pipe in the corresponding area of ​​the thermal impact analysis unit to obtain steel pipe comparison and tracking information; The parameter adjustment control module is used to compare the steel pipe comparison and tracking information with a preset change threshold, and trigger a parameter adjustment instruction if the steel pipe comparison and tracking information is outside the preset change threshold range.

[0023] By adopting the above technical solution, the heat-affected zone is located based on infrared imaging technology, and the area is subdivided into several heat-affected analysis units, which helps to more accurately analyze the impact of heat on galvanized steel pipes during welding, so as to achieve real-time monitoring and precise control of the welding process. By performing real-time comparison and analysis of the zinc layer thickness and distribution changes of the galvanized layer, the flatness of the zinc layer can be quickly and accurately achieved by a non-contact detection method, and problems such as damaged or uneven distribution of the zinc layer can be discovered in time. By comparing the steel pipe comparison tracking information with the preset change threshold, an intelligent adjustment mechanism is provided to quickly respond to abnormal situations in the welding process, which is helpful to assist staff in eliminating adverse effects by adjusting welding parameters or taking other treatment measures.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. Through the galvanizing layer integrity prediction model and the welding quality prediction model, the early galvanizing and welding are analyzed respectively, and the impact of product quality is explored from more angles, which facilitates targeted analysis and control of each production stage of the galvanized steel pipe. In addition, the fusion process control model is used to determine the optimal process control parameters (i.e., parameter control information and coating reference control information) for different product process production stages (galvanizing, welding) under the corresponding process indicator requirements. Based on the parameter control information and coating reference control information, the galvanizing welding process and coating process of the galvanized steel pipe are controlled, which can accurately control the galvanizing process parameters, ensure the stability and consistency of the galvanizing layer, weld quality and coating layer, and reduce the uncertainty and risk in the production process, thereby improving the overall production efficiency and product quality. Therefore, this application achieves the purpose of "providing a more reliable and efficient galvanized steel pipe coating process and improving the quality and performance of galvanized steel pipes." 2. The present invention conducts comprehensive process parameter collection and change law analysis on multiple steel pipe production equipment of the entire galvanized steel pipe production line through the constructed process physical equipment model. By monitoring the key information in the entire production process, it can accurately monitor various process parameters and their change laws in the production process. The subsequent process physical equipment model can intuitively and accurately display the production process flow, help relevant technical personnel understand the interaction between various production environments, and provide data theoretical support for the subsequent load model establishment and optimization of process control parameters; at the same time, by analyzing the change characteristics of historical process load data, the corresponding load levels are divided; process load difference refers to the load difference of production equipment in different time periods during the production process, that is, the degree of difference between the actual production load and the design load; since hot-dip galvanizing equipment needs to be maintained within a certain temperature range The long-term operation of the detection equipment for the composition of the plating solution, the heat consumption monitoring equipment, etc. will cause unnecessary equipment energy consumption, which is not conducive to energy-saving production. Therefore, in order to find the balance point between the energy consumption of each production equipment and the process control requirements, and reduce the energy consumption and production cost of galvanized steel pipe production, this application obtains load stratification information (stratified according to the load mode and load characteristics, and the stratification standard is to set multiple layers according to the load level and duration) and establishes a compliance model to predict the load change trend of each steel pipe production equipment, so that the model is more in line with the actual production situation and the control accuracy is improved. The fusion process control model can adjust the relevant process parameters according to the actual production needs, and realize the dynamic optimization of the production process, so that the galvanized steel pipe process control system can respond to challenges by adjusting the process parameters in a timely manner even in the face of complex and changing production environments, thereby ensuring production efficiency and product quality. 3. Locating the heat-affected zone based on infrared imaging technology and subdividing the area into several heat-affected analysis units helps to more accurately analyze the impact of heat on galvanized steel pipes during welding, so as to achieve real-time monitoring and precise control of the welding process. By performing real-time comparative analysis of the thickness and distribution changes of the galvanized layer, the flatness of the zinc layer can be quickly and accurately achieved using a non-contact detection method, and problems such as damaged or uneven distribution of the zinc layer can be discovered in a timely manner. By comparing the steel pipe comparison tracking information with the preset change threshold, an intelligent adjustment mechanism is provided to quickly respond to abnormal situations in the welding process, which is helpful in assisting staff to adjust welding parameters or take other treatment measures to eliminate adverse effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a method for controlling a coating process of a galvanized steel pipe according to an embodiment of the present application; Figure 2 This is a flow chart of step S1 in the galvanized steel pipe coating process control method in one embodiment of the present application; Figure 3This is a flow chart of step S2 in the galvanized steel pipe coating process control method in one embodiment of the present application; Figure 4 This is another flow chart of the galvanized steel pipe coating process control method in one embodiment of the present application. DETAILED DESCRIPTION

[0026] The present application is further described in detail below with reference to the accompanying drawings.

[0027] In one embodiment, if Figure 1 As shown, the present application discloses a galvanized steel pipe coating process control method, which specifically includes the following steps: S1: Based on the obtained galvanizing process selection information and the preset galvanizing layer integrity prediction model, obtain galvanizing layer integrity prediction information.

[0028] In this embodiment, the galvanized layer integrity prediction model is used to judge the galvanized layer integrity of the galvanized products currently using the galvanized steel pipe production line equipment under different galvanizing process parameters. It is a mathematical model established based on historical data, material properties, process parameters and other factors, and is used to predict the integrity and quality of the galvanized layer, such as the thickness uniformity and adhesion of the galvanized layer; the operation and management personnel of the galvanized steel pipe process control system can set a control cycle within a predetermined time period by themselves.

[0029] Specifically, the galvanizing process selection information is the galvanizing process type and its related parameters selected according to the specific process standard requirements of the galvanized steel pipe (such as the use environment, corrosion resistance requirements, etc.).

[0030] In this embodiment, the galvanized layer integrity prediction model is obtained according to the following method: 1) Obtain reference data on the characteristics of the galvanized layer, including the thickness of the galvanized layer, the distribution of the zinc layer, environmental conditions, steel pipe material, and galvanizing process parameter data; 2) Model training is performed based on the reference data of galvanized layer characteristics to generate a galvanized layer integrity prediction model.

[0031] Specifically, the galvanized coating characteristic reference data is used to describe the physical and chemical properties of the galvanized coating, including but not limited to the thickness (uniformity) of the galvanized coating, the distribution of the zinc layer on the steel pipe surface, the environmental conditions during the galvanizing process (such as temperature and humidity), the material characteristics of the steel pipe (such as chemical composition and mechanical properties), and the specific parameters of the galvanizing process (such as galvanizing temperature, time, and speed). The galvanized coating integrity prediction model is a mathematical model based on machine learning or statistical methods. It uses the galvanized coating characteristic reference data as input and learns the complex relationships between the data through a training process, thereby predicting the galvanized coating integrity of newly galvanized steel pipes.

[0032] S2: Based on the obtained welding process control parameters and the preset welding quality prediction model, welding quality prediction information is obtained.

[0033] In this embodiment, the welding quality prediction model is used to determine the welding quality of the welded product currently welded using the galvanized steel pipe production line welding equipment under different welding process control parameters. Welding process control parameters are a series of parameters that need to be controlled during the welding process, such as welding current, voltage, welding speed, welding temperature, etc. These parameters directly affect the welding quality. Specifically, the welding quality prediction model is a mathematical model established based on welding process control parameters and other factors in the welding process (such as material properties, environmental conditions, etc.), which is used to predict the quality of the welded joint, such as the strength and air tightness of the weld.

[0034] In one embodiment, the welding quality prediction model is obtained according to the following method: 1) Obtain welding quality reference data, which includes historical welding quality data, welding environment data, welding process parameter data, and welding equipment performance data; 2) Model training is performed based on welding quality reference data to generate a welding quality prediction model.

[0035] Specifically, welding quality reference data records various parameters and results from previous welding processes and is used to evaluate welding quality. This includes historical welding quality data (such as weld strength and airtightness test results), welding environmental conditions (such as temperature, humidity, and wind speed), welding process parameters (such as welding current, voltage, and speed), and welding equipment performance data (such as equipment stability and accuracy).

[0036] S3: Analyze the galvanized layer integrity prediction information and the welding quality prediction information to determine whether the prediction information meets the preset process control indicators. Based on the judgment result, obtain the parameter model input information.

[0037] In this embodiment, the process control indicators are pre-set galvanizing layer integrity indicators and welding quality assessment indicators, such as the galvanizing layer thickness is not less than XX microns, the weld strength is not less than XX MPa, etc.; the parameter model input information is the specific parameter information used to adjust and control the galvanizing welding process and coating process after analysis based on the predicted results of the galvanizing layer integrity and welding quality.

[0038] Specifically, if it is not satisfied, the galvanizing process selection information and welding process control parameters are adjusted according to the prediction results until the process control index requirements are met; the adjusted parameters are used as parameter model input information.

[0039] S4: Inputting the parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information.

[0040] In this embodiment, the coating reference control information includes the recommended coating material type, coating layer thickness and coating speed, etc.; the fusion process control model actually controls the galvanizing welding process and coating process of the galvanized steel pipe based on the parameter control information and coating reference control information output by the fusion process control model. During the galvanizing process, the operation is strictly carried out in accordance with the adjusted galvanizing process parameters; after welding, the appropriate coating material and process parameters are selected for coating according to the coating reference control information to ensure that the overall quality and performance of the galvanized steel pipe meet customer needs.

[0041] Specifically, the fusion process control model is a mathematical model that integrates multiple process control links such as galvanizing, welding and coating. It can comprehensively consider the mutual influence between various process parameters and output the optimal parameter control information and coating reference control information.

[0042] S5: Based on the parameter control information and the coating reference control information, the galvanizing welding process and the coating process of the galvanized steel pipe are controlled.

[0043] Specifically, after welding, appropriate coating materials and process parameters are selected for coating according to the coating reference control information to ensure that the overall quality and performance of the galvanized steel pipe meet the high quality standard requirements. Therefore, the present invention can achieve comprehensive control and optimization of the galvanized steel pipe coating process, improve the integrity of the galvanized layer and the welding quality, reduce the scrap rate and rework rate, and at the same time reduce production costs and improve production efficiency.

[0044] In one embodiment, if Figure 2 As shown, in step S1, based on the obtained galvanizing process selection information and the preset galvanizing layer integrity prediction model, the galvanizing layer integrity prediction information is obtained, specifically including: S11: Obtain galvanizing process selection information of the galvanized steel pipe, the galvanizing process selection information including galvanizing time, galvanizing temperature, galvanizing layer thickness and galvanizing solution composition; galvanized steel pipe associated steel pipe identification.

[0045] In this embodiment, the galvanizing process selection information refers to the process parameters set according to production needs and process requirements during the galvanizing process; the galvanizing time is the time the steel pipe stays in the galvanizing solution, the galvanizing temperature is the temperature of the galvanizing solution, the galvanizing layer thickness refers to the desired galvanizing layer thickness and the galvanizing solution composition is the chemical composition ratio in the galvanizing solution; the steel pipe identification gives each galvanized steel pipe a unique identifier for easy tracking and management.

[0046] Specifically, staff set galvanizing process parameters based on production plans and product specifications. For example, for a steel pipe with a diameter of 100 mm and a length of 6 m, the galvanizing time is set to 30 seconds, the galvanizing temperature is set to 450°C, the zinc layer thickness is set to 80 μm, and the galvanizing solution composition is mainly zinc (99%), with appropriate amounts of aluminum and other trace elements. Each galvanized steel pipe is given a unique identification code (e.g., "GZ20240906001") using a barcode or RFID tag. This identification code and the corresponding galvanizing process selection information are recorded in the production management system.

[0047] S12: Based on the galvanizing process selection information and the corresponding steel pipe identification, a preset galvanizing layer integrity prediction model is used to perform analysis to obtain galvanizing layer integrity prediction information.

[0048] In this embodiment, the galvanized layer integrity prediction model is a model obtained by training historical data, and can predict the integrity and uniformity of the galvanized layer based on the input galvanizing process parameters.

[0049] Specifically, the galvanizing process selection information (galvanizing time, galvanizing temperature, galvanizing layer thickness and galvanizing solution composition) obtained in step S11 is input as input parameters into a preset galvanizing layer integrity prediction model.

[0050] Model prediction: Based on the input parameters, the model predicts the integrity and uniformity of the galvanized coating and outputs the galvanized coating integrity prediction information. For example, the prediction results indicate that the galvanized coating of a batch of steel pipes meets the expected integrity and uniformity standards under the set conditions, but there is a certain risk of localized weakness.

[0051] S13: Determine the integrity and uniformity of the galvanized layer of the galvanized steel pipe based on the galvanized layer integrity prediction information.

[0052] In this embodiment, based on the galvanized layer integrity prediction information, technicians can determine whether the galvanized layer of the galvanized steel pipe meets the expected quality standards. If the prediction results indicate that the galvanized layer has defects, it is necessary to adjust the process parameters or take other measures to improve the galvanizing effect.

[0053] In one embodiment, if Figure 3 As shown, in step S2, based on the acquired welding process control parameters and the preset welding quality prediction model, welding quality prediction information is obtained, specifically including: S21: Acquire welding process control parameters, which include welding current, voltage, welding speed, welding wire diameter, and welding trajectory.

[0054] In this embodiment, the welding process control parameters refer to the various parameters that need to be set during the welding process; for example: according to the requirements of the welding process, the welding current is set to 200A, the voltage is set to 25V, the welding speed is set to 30cm / min, the welding wire diameter is set to 1.2mm, and the welding trajectory is set to straight welding. These parameters are input into the control system of the welding equipment to ensure that the welding process is carried out according to the set parameters.

[0055] S22: Based on the welding process control parameters, a preset welding quality prediction model is used for analysis to obtain welding quality prediction information.

[0056] In this embodiment, the welding quality prediction model is trained using historical data. It can predict the quality characteristics of welded joints, such as strength, ductility, and crack resistance, based on input welding process control parameters. The weld quality prediction information is analyzed to evaluate the weld joint's load-bearing capacity (strength), deformation capacity (ductility), and crack propagation resistance (crack resistance) in practical applications.

[0057] Specifically, the welding process control parameters set in step S21 are used as input data into a preset welding quality prediction model. Based on the input parameters, the model predicts the quality characteristics of the weld joint, such as strength, ductility, and crack resistance. For example, the model prediction results show that, based on the set parameters, the weld joint can achieve a tensile strength of 450 MPa and an elongation of 20%, indicating good crack resistance.

[0058] S23: Evaluate the strength, ductility, and crack resistance of the welded joint based on the weld quality prediction information.

[0059] In this embodiment, the performance of the weld joint is evaluated based on the model's prediction results. For example, if the prediction results confirm that the weld joint's strength meets the design requirements, its ductility is good, and its crack resistance meets the standards, the production process can proceed to the next step. If the prediction results are unsatisfactory, the welding process control parameters need to be adjusted and the prediction repeated until the weld joint quality meets the expected standards. The welding quality prediction model can be used to predict welding quality before welding, avoiding welding defects caused by improper parameter settings and improving the quality of the weld joint. At the same time, based on the feedback provided by the welding quality prediction model, the welding process parameters can be continuously optimized to improve welding efficiency and production quality.

[0060] In one embodiment, if Figure 4 As shown, the galvanized steel pipe coating process control method also includes: The fusion process control model is obtained by: S10: Acquire physical process data and steel pipe production equipment data, and construct a process physical equipment model based on the physical process data and steel pipe production equipment data.

[0061] In this embodiment, the physical process data refers to various parameter data related to the galvanized steel pipe coating process during the actual production process, such as temperature, pressure, speed, coating thickness, etc.; the steel pipe production equipment data refers to data related to the production equipment, including equipment model, production capacity, maintenance records, fault history and other information.

[0062] Specifically, sensors installed on the production line collect real-time process data, such as galvanizing tank temperature, galvanizing solution concentration, and line speed, to obtain physical process data. Basic information about production equipment, such as model, production date, and maintenance records, is obtained from the equipment management system to capture steel pipe production equipment data. Combining physics and engineering knowledge, a process physics equipment model is built to simulate the production line's operating process. For example, process parameters can be input into simulation software to simulate the coating formation process on galvanized steel pipes at different temperatures.

[0063] S20: Obtain historical process load data of steel pipe production equipment at different production stages, determine the process load difference based on the historical process load data, and obtain load stratification information.

[0064] In this embodiment, the process load difference refers to the difference between the actual load of the production equipment and its design load or target load at different production stages, reflecting the fluctuations in the production process; the load stratification information is based on historical process load data. By analyzing the load characteristics of different production stages, the load is divided into several levels or categories to facilitate more detailed management of the production process.

[0065] Specifically, the production line's operating records over the past period of time (e.g., the past month) are extracted from the production database, including daily production volume and equipment working hours. These data are processed using statistical analysis tools to calculate the load differences in different production stages. Based on the size of the load differences, the production stages are divided into several different load levels, such as low load, medium load, and high load.

[0066] S30: Establish a load model based on the load stratification information.

[0067] In this embodiment, the load model is a mathematical model established based on load stratification information, and is used to describe the working status and efficiency of equipment at different load levels.

[0068] S40: Obtain adjustable process parameter information of each steel pipe production equipment, integrate the process physical equipment model and the load model, use the adjustable process parameter information as a model input, and generate a fusion process control model.

[0069] Specifically, all adjustable process parameters on the production line are identified, such as galvanizing tank temperature, production line speed, etc.; the above models are integrated together to form a comprehensive control model, which can dynamically adjust process parameters based on real-time data to achieve the best production results.

[0070] In one embodiment, in step S40, the adjustable process parameter information of each steel pipe production equipment is obtained, the process physical equipment model and the load model are integrated, and the adjustable process parameter information is used as a model input to generate a fusion process control model, which specifically includes: S401: Obtain adjustable process parameter information and historical process parameter adjustment information of each steel pipe production equipment, and use the historical process parameter adjustment information and historical process load data as a training data set.

[0071] In this embodiment, adjustable process parameter information refers to process parameters that can be adjusted according to actual conditions during the galvanized steel pipe coating process, such as temperature, pressure, speed, coating thickness, etc., which can be automatically adjusted through the galvanized steel pipe process control system; historical process parameter adjustment information refers to the adjustment records made for various process parameters in the past production process, including the time point of adjustment, the adjusted parameters and the information before and after the adjustment; historical process load data refers to the actual workload data of production equipment at different production stages within a certain period of time, including the working time of the equipment, capacity utilization rate, etc. The process physical equipment model is a mathematical model based on physical principles and equipment characteristics, which is used to describe the working status and performance of production equipment under specific process conditions. The load model is a mathematical model established based on historical process load data and the load characteristics of different production stages to predict the performance of equipment under different load conditions.

[0072] Specifically, various adjustable process parameter information on the production line, such as temperature setting value, spraying speed, coating thickness, etc., are collected, and past process parameter adjustment records are extracted from the production database, including the time of each adjustment, the adjusted parameters and the reasons for the adjustment.

[0073] S402: Based on a preset model fusion algorithm, the process physical equipment model and the load model are integrated to generate a fusion model. The model fusion algorithm includes a linear fitting algorithm, a process control fitting algorithm, and a cross fusion algorithm.

[0074] Specifically, historical process load data, including equipment operating time, production batches, output and other information, is exported from the production management system, and the collected historical process load data is cleaned and organized to ensure the accuracy and completeness of the data.

[0075] S403: Using the adjustable process parameter information as model input, training the fusion model based on the training data set, and generating a fusion process control model.

[0076] In this embodiment, the adjustable process parameter information is used as input, and the fusion model is trained in combination with historical process parameter adjustment information and historical process load data. After model training, a fusion process control model is generated. The model can predict the production effect based on the current process parameters and recommend the optimal process parameter combination. Through the fusion model, the process parameters in the production process can be controlled more accurately, unnecessary debugging time can be reduced, and production efficiency can be improved.

[0077] In one embodiment, after step S5, the galvanized steel pipe coating process control method further includes: S51: Collect temperature data of the heat-affected zone during welding.

[0078] In this embodiment, the heat-affected zone (HAZ) temperature data refers to the temperature changes in the weld area and surrounding materials during the welding process due to high temperatures. This area is called the HAZ. The HAZ temperature data refers to the temperature changes in this area during the welding process.

[0079] Specifically, during the welding process, a temperature sensor or infrared thermal imager is used to monitor the temperature changes in the heat-affected zone in real time and record the data; for example, when welding galvanized steel pipes, temperature data is collected every few seconds and saved.

[0080] S52: Acquire an infrared image of the galvanized steel pipe during the welding process in combination with the temperature data of the heat-affected zone, track the heat conduction path of the galvanized steel pipe during welding based on the infrared image, locate the heat-affected zone, and obtain a number of heat-affected analysis units.

[0081] In this embodiment, the infrared image is a thermal image obtained using infrared imaging technology that can reflect the temperature distribution on the surface of an object. During the welding process, the infrared image can help identify the location and range of the heat-affected zone. The heat-affected zone determined by the infrared image is divided into multiple analysis units, each representing an area with similar temperature characteristics. Specifically, the collected temperature data is converted into a visual infrared image, and the temperature is represented by the depth of color.

[0082] S53: Integrated element spectrum analyzer, compares the zinc layer thickness and distribution changes of the galvanized steel pipe in the corresponding area of ​​the heat impact analysis unit, and obtains steel pipe comparison and tracking information.

[0083] In this embodiment, the steel pipe comparison and tracking information is the data information of the thickness and distribution changes of the zinc layer of the galvanized steel pipe obtained by the elemental spectrometer, which is used to compare the changes of the galvanized layer before and after welding; by analyzing the infrared imaging image, the direction and path of heat conduction can be tracked, thereby locating the heat-affected zone; and the heat-affected zone is divided into several analysis units, each unit representing a region with similar temperature characteristics, which is convenient for subsequent detailed analysis.

[0084] Specifically, within each heat impact analysis unit, an elemental spectrometer is used to measure the zinc coating thickness and distribution changes of the galvanized steel pipe. For example, a galvanized steel pipe can be measured once before welding and again after welding. The difference between the two measurements is compared, and the zinc coating thickness and distribution change data for each unit is recorded to form steel pipe comparison and tracking information.

[0085] S54: performing a range comparison on the steel pipe comparison and tracking information and a preset change threshold value. If the steel pipe comparison and tracking information is outside the preset change threshold range, a parameter adjustment instruction is triggered.

[0086] In this embodiment, the preset change threshold is a pre-set standard value used to determine whether the change in the thickness and distribution of the galvanized layer during welding exceeds a normal range.

[0087] Specifically, based on process requirements and empirical data, a change threshold is pre-set to determine whether the zinc layer thickness and distribution changes are within the allowable range; the steel pipe comparison tracking information is compared with the preset change threshold. If it is found that the zinc layer thickness or distribution changes exceed the preset threshold, it is considered that the galvanized layer may be damaged or uneven; once it is found that the zinc layer thickness or distribution changes exceed the preset threshold, the parameter adjustment instruction is immediately triggered to adjust the welding parameters (such as current, voltage, speed, etc.) to ensure the integrity of the galvanized layer.

[0088] It should be understood that the serial numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0089] In one embodiment, a galvanized steel pipe coating process control system is provided, which corresponds to the galvanized steel pipe coating process control method in the above embodiment.

[0090] The galvanized steel pipe coating process control system includes a galvanized layer integrity prediction module, a welding quality prediction module, a joint analysis module, a model fusion analysis module, and a control module. Detailed descriptions of each functional module are as follows: A galvanizing layer integrity prediction module is used to obtain galvanizing layer integrity prediction information based on the obtained galvanizing process selection information and a preset galvanizing layer integrity prediction model; A welding quality prediction module is used to obtain welding quality prediction information based on the acquired welding process control parameters and a preset welding quality prediction model; The joint analysis module is used to analyze the galvanized layer integrity prediction information and the welding quality prediction information to determine whether the prediction information meets the preset process control indicators. Based on the judgment results, the parameter model input information is obtained; A model fusion analysis module is used to input parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information; The control module is used to control the galvanizing welding process and coating process of the galvanized steel pipe based on parameter control information and coating reference control information.

[0091] Optionally, the galvanized steel pipe coating process control system also includes: Thermal data acquisition module, used to collect temperature data of the heat-affected zone during welding; The heat impact analysis module is used to obtain infrared images of the galvanized steel pipe during the welding process based on the heat-affected zone temperature data. Based on the infrared images, the heat conduction path of the galvanized steel pipe during welding is tracked, the heat-affected zone is located, and several heat impact analysis units are obtained. The spectrum analysis and change tracking module is used to integrate an element spectrum analyzer to compare the zinc layer thickness and distribution changes of the galvanized steel pipe in the corresponding area of ​​the heat impact analysis unit to obtain steel pipe comparison and tracking information; The parameter adjustment control module is used to compare the steel pipe comparison and tracking information with the preset change threshold. If the steel pipe comparison and tracking information is outside the preset change threshold range, the parameter adjustment instruction is triggered.

[0092] For the specific limitations of the galvanized steel pipe coating process control system, please refer to the limitations of the galvanized steel pipe coating process control method above, which will not be repeated here; the various modules in the above-mentioned galvanized steel pipe coating process control system can be implemented in whole or in part through software, hardware and their combination; the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0093] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0094] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A galvanized steel pipe coating process control method, characterized in that: include: Based on the obtained galvanizing process selection information and the preset galvanizing layer integrity prediction model, the galvanizing layer integrity prediction information is obtained; Based on the acquired welding process control parameters and the preset welding quality prediction model, welding quality prediction information is obtained; Analyze the galvanized layer integrity prediction information and the welding quality prediction information to determine whether the prediction information meets the preset process control index, and obtain parameter model input information according to the determination result; Inputting the parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information; Based on the parameter control information and the coating reference control information, the galvanizing welding process and the coating process of the galvanized steel pipe are controlled.

2. The galvanized steel pipe coating process control method according to claim 1, characterized in that: The method of obtaining the galvanizing layer integrity prediction information based on the obtained galvanizing process selection information and the preset galvanizing layer integrity prediction model specifically includes: Obtaining galvanizing process selection information of the galvanized steel pipe, wherein the galvanizing process selection information includes galvanizing time, galvanizing temperature, galvanizing layer thickness and galvanizing solution composition; the galvanized steel pipe is associated with a steel pipe identification; Based on the galvanizing process selection information and the corresponding steel pipe identification, a preset galvanizing layer integrity prediction model is used to perform analysis to obtain galvanizing layer integrity prediction information; The integrity and uniformity of the galvanized layer of the galvanized steel pipe are determined based on the galvanized layer integrity prediction information.

3. The galvanized steel pipe coating process control method according to claim 1, characterized in that: The step of obtaining welding quality prediction information based on the obtained welding process control parameters and the preset welding quality prediction model specifically includes: Acquiring welding process control parameters, wherein the welding process control parameters include welding current, voltage, welding speed, welding wire diameter and welding trajectory; Based on the welding process control parameters, a preset welding quality prediction model is used for analysis to obtain welding quality prediction information; The strength, ductility and crack resistance of the welded joint are evaluated based on the welding quality prediction information.

4. The galvanized steel pipe coating process control method according to claim 1, characterized in that: The galvanized layer integrity prediction model is obtained according to the following method: Acquire galvanized layer characteristic reference data, wherein the galvanized layer characteristic reference data includes galvanized layer thickness, zinc layer distribution data, environmental condition data, steel pipe material data and galvanizing process parameter data; Performing model training based on the galvanized layer characteristic reference data to generate a galvanized layer integrity prediction model; The welding quality prediction model is obtained according to the following method: Acquiring welding quality reference data, wherein the welding quality reference data includes historical welding quality data, welding environment data, welding process parameter data, and welding equipment performance data; Model training is performed based on the welding quality reference data to generate a welding quality prediction model.

5. The galvanized steel pipe coating process control method according to claim 1, characterized in that: The fusion process control model is obtained by: Acquire physical process data and steel pipe production equipment data, and construct a process physical equipment model based on the physical process data and steel pipe production equipment data; Obtain historical process load data of steel pipe production equipment at different production stages, determine the process load drop based on the historical process load data, and obtain load stratification information; Based on the load stratification information, establishing a load model; The adjustable process parameter information of each steel pipe production equipment is obtained, the process physical equipment model and the load model are integrated, the adjustable process parameter information is used as a model input, and the integrated process control model is generated.

6. The galvanized steel pipe coating process control method according to claim 5, characterized in that: The step of obtaining the adjustable process parameter information of each steel pipe production equipment, fusing the process physical equipment model and the load model, using the adjustable process parameter information as a model input, and generating the fusion process control model includes: Obtaining adjustable process parameter information and historical process parameter adjustment information of each steel pipe production equipment, and using the historical process parameter adjustment information and the historical process load data as a training data set; Based on a preset model fusion algorithm, the process physical equipment model and the load model are fused to generate a fusion model, wherein the model fusion algorithm includes a linear fitting algorithm, a process control fitting algorithm and a cross fusion algorithm; The adjustable process parameter information is used as a model input, and the fusion model is trained based on the training data set to generate a fusion process control model.

7. The galvanized steel pipe coating process control method according to claim 6, characterized in that: After the galvanizing welding process and coating process of the galvanized steel pipe are controlled based on the parameter control information and the coating reference control information, the method further includes: Collect temperature data of heat-affected zone during welding; Acquire an infrared image of the galvanized steel pipe during welding in combination with the heat-affected zone temperature data, track the heat conduction path of the galvanized steel pipe during welding based on the infrared image, locate the heat-affected zone, and obtain a plurality of heat-affected analysis units; An integrated element spectrum analyzer is used to compare the zinc layer thickness and distribution changes of the galvanized steel pipe in the corresponding area of ​​the heat impact analysis unit to obtain steel pipe comparison and tracking information; The steel pipe comparison and tracking information is compared with a preset change threshold value. If the steel pipe comparison and tracking information is outside the preset change threshold range, a parameter adjustment instruction is triggered.

8. Galvanized steel pipe coating process control system, characterized in that: Applied to the galvanized steel pipe coating process control method according to any one of claims 1 to 7, the system comprises: A galvanizing layer integrity prediction module is used to obtain galvanizing layer integrity prediction information based on the obtained galvanizing process selection information and a preset galvanizing layer integrity prediction model; A welding quality prediction module, used to obtain welding quality prediction information based on the acquired welding process control parameters and a preset welding quality prediction model; A joint analysis module, used to jointly analyze the galvanized layer integrity prediction information and the welding quality prediction information, determine whether the prediction information meets the preset process control index, and obtain parameter model input information according to the determination result; A model fusion analysis module, used for inputting the parameter model input information into a preset fusion process control model to obtain parameter control information and coating reference control information; The control module is used to control the galvanizing welding process and coating process of the galvanized steel pipe based on the parameter control information and the coating reference control information.

9. The galvanized steel pipe coating process control system according to claim 8, characterized in that: The system also includes: Thermal data acquisition module, used to collect temperature data of heat-affected zone during welding; A heat impact analysis module, which is used to obtain an infrared image of the galvanized steel pipe during the welding process in combination with the heat affected zone temperature data, track the heat conduction path of the galvanized steel pipe during welding based on the infrared image, locate the heat affected zone, and obtain a plurality of heat impact analysis units; A spectrum analysis and change tracking module is used to integrate an element spectrum analyzer to compare the zinc layer thickness and distribution changes of the galvanized steel pipe in the corresponding area of ​​the heat impact analysis unit to obtain steel pipe comparison and tracking information; The parameter adjustment control module is used to compare the steel pipe comparison and tracking information with a preset change threshold value, and if the steel pipe comparison and tracking information is outside the preset change threshold range, a parameter adjustment instruction is triggered.

Images