Welding seam induction heating device of welding machine and control method

Abstract

The invention discloses a welding machine welding seam induction heating device and a control method, and belongs to the technical field of welding seam heating. The method comprises the steps that a weld joint induction heating device is installed in the end face of an upper shear blade, a positioning detector and an infrared thermometer are arranged at the bottom of the weld joint induction heating device, and the positioning detector and the infrared thermometer are connected with a production monitoring system; the problems that in an existing method, a site needs to be additionally added, cost is high, consumed time is long, and manual operation is inconvenient are solved, the weld joint induction heating device is installed in the upper shear blade, the telescopic function is achieved in combination with air cylinder driving, the heating process is remotely controlled based on a production monitoring system, and the extra site and manual operation are not needed; the parameter threshold is set by using historical data, the parameter initial value is predicted by combining the Pearson's correlation coefficient and the neural network model, and the threshold is dynamically adjusted, so that the adaptability and stability of the system are enhanced, the welding efficiency and quality are improved, and the fault rate and cost are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of weld heating device, in particular to a welding machine weld induction heating device and control method. BACKGROUND

[0002] The cold rolling mill group pickling production line, pickling production line, galvanizing production line, continuous annealing production line, etc. are all using laser welder or narrow lap welder to complete the welding of strip head and tail, so that the mill realizes continuous operation. Whether it is a laser welder or a narrow lap welder, in order to improve the stability of the weld quality, the weld induction heating device is increased for annealing treatment of the weld, mainly to eliminate the residual stress in the weld area, refine the weld grain and improve the weld structure, and improve the weld strength. The carbon equivalent of the high-strength steel is greater than or equal to 0.3%, the dual-phase steel, the hot forming steel, the fine blanking steel, or the silicon content of the medium and high grade silicon steel is greater than or equal to 1.8%.

[0003] At present, the cold rolling mill narrow lap welder equipment does not have a weld induction heating device, and a set of weld induction heating device needs to be added at a proper position at the outlet of the welder. After welding is completed, the weld comes to the induction heating device, and manual operation is performed on the weld area for heating annealing treatment. This method needs to increase the site, has high cost, takes a long time, and has the problems of inconvenient manual operation, etc. At present, the cold rolling mill laser welder is equipped with a front heating and rear heating device on the operating side of the C-shaped frame. During the welding process, the weld is heated and treated in front and rear of the C-shaped frame as the C-shaped frame moves. This method has the problems of long C-shaped frame and welder track, large occupied area, inability to set or control the weld heating time, no weld heating temperature curve, and inability to handle the special weld annealing process for high-strength steel, high-carbon steel and high-grade silicon steel with carbon equivalent greater than or equal to 0.6% and silicon content greater than or equal to 2.8%.

[0004] Therefore, the existing needs are not met, and for this purpose, the present application provides a welding machine weld induction heating device and control method. SUMMARY

[0005] The present application aims to provide a welding machine weld induction heating device and control method. The weld induction heating device is installed inside the upper shear blade and realizes the telescopic function by combining with the cylinder drive. The heating process is remotely controlled based on the production monitoring system, without the need for additional site and manual operation. The threshold value is set by using historical data, the initial value of the parameter is predicted by combining the Pearson correlation coefficient and the neural network model, and the threshold value is dynamically adjusted, thereby enhancing the adaptability and stability of the system, improving the welding efficiency and quality, reducing the failure rate and cost, and solving the problems raised in the above background technology.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] The utility model provides a welding machine weld induction heating device, it includes: the double -decked shear box who comprises lower shear blade and upper shear blade, lower shear blade and upper shear blade are installed on the welding machine C -frame, the inside of upper shear blade end surface is equipped with weld induction heating device, the bottom of weld induction heating device is equipped with position detector and infrared thermometer respectively, position detector and infrared thermometer are connected with production monitoring system based on wireless communication technology,

[0008] The production monitoring system comprises a data acquisition unit, a signal processing unit and a human-computer interaction unit.

[0009] The data acquisition unit is configured to acquire the distance between the weld induction heating device and the weld seam and the actual temperature of the weld seam in real time based on the position detector and the infrared thermometer, and to acquire the current, voltage, heating time, holding time, heating power and heating temperature parameters of the weld induction heating device in real time when the weld induction heating device is heating.

[0010] The signal processing unit is configured to analyze the acquired parameters, compare the parameters with preset threshold values, and determine whether the operating parameters of the weld induction heating device are abnormal based on the comparison results.

[0011] The human-computer interaction unit is configured to provide an operation interface, and support an operator to manually input adjusted parameter values based on real-time data and abnormal prompts displayed on the monitoring interface.

[0012] Further, the signal processing unit comprises:

[0013] A parameter threshold setting module is configured to obtain historical operating data of the weld induction heating device, pre-process the historical operating data, extract parameter values of the historical operating data in a normal operating state, and set threshold values corresponding to the parameters based on the normal parameter values.

[0014] A parameter comparison module is configured to obtain the parameters acquired in real time by the data acquisition unit, compare the parameters with corresponding threshold values respectively, obtain comparison results of the parameters, and determine whether the parameters of the weld induction heating device need to be adjusted.

[0015] Further, the signal processing unit further comprises:

[0016] An initial parameter generation module is configured to calculate the correlation of the parameters in the historical operating data based on a Pearson correlation coefficient, analyze the correlation of the parameters through a neural network model, and predict initial values of the parameters.

[0017] A parameter dynamic adjustment module is configured to generate adjustment instructions based on the comparison results of the parameters, and adjust the parameters according to the adjustment instructions.

[0018] Further, the signal processing unit further comprises:

[0019] an abnormal frequency analysis module configured to obtain comparison results of each parameter in a period of time, extract parameters in the comparison results that exceed a threshold range, sort the parameters in the threshold range in the period of time, and calculate a frequency of each parameter exceeding the threshold range;

[0020] a threshold dynamic adjustment module configured to dynamically adjust a corresponding parameter threshold according to the frequency of each parameter exceeding the threshold range and in combination with an actual operation state of the weld seam induction heating device.

[0021] Further, the human-computer interaction unit comprises:

[0022] a curve drawing module configured to draw each parameter of the weld seam induction heating device during operation into a curve graph and display the curve graph in a monitoring interface, for analyzing a change trend of each parameter;

[0023] a data storage module configured to receive each parameter collected by the data acquisition unit, store the preprocessed each parameter in a time sequence, for later analysis and model optimization.

[0024] Further, the double-layer shear box is opened by 10-15 cm, wherein the upper shear blade is installed above the inner side of the welder C-shaped frame, the lower shear blade is installed on the end face of the inner side of the welder C-shaped frame, and the side of the lower shear blade is sequentially provided with a rolling wheel and a welding wheel.

[0025] Further, the top of the welder C-shaped frame is provided with a gas cylinder, the gas cylinder is connected with the top end of the weld seam induction heating device through a gas rod, and the gas cylinder is used to push the weld seam induction heating device to make a rotating lifting action in the inside of the upper shear blade end face.

[0026] Further, the weld seam induction heating device is spliced, the length is 1000-2000 mm, and the width is 2-5 mm; according to the actual length of the upper shear blade, the weld seam induction heating device is divided into several parts and is sequentially installed in the inside of the upper shear blade end face.

[0027] A weld seam induction heating device control method, comprising the following steps:

[0028] S1, the strip head and tail are in a welding waiting position, the weld seam induction heating device is in an upper position in the inside of the upper shear blade end face, the upper shear blade is lowered, and the lower shear blade is raised, so that the strip head and tail are sheared;

[0029] S2, after the welding is completed, the welder C-shaped frame returns from the transmission side to the operation side, the inlet clamp and the outlet clamp clamp the strip steel at the same time, and the weld seam is in the center position of the opening of the double-layer shear blade.

[0030] S3, analyze the historical operation data by Pearson correlation coefficient and neural network model, set initial parameters of the induction heating process, drive the weld induction heating device to the lower position through the cylinder, and start heating and annealing treatment on the weld;

[0031] S4, real-time detect and compare whether the initial parameters exceed the corresponding threshold range, judge whether there is an abnormality in the operation of the weld induction heating device, and whether the parameters need to be adjusted;

[0032] S5, display the parameters of the weld induction heating device and abnormal parameter prompts in real time through the man-machine interaction unit;

[0033] S6, after heating, the weld induction heating device automatically rises to the upper position;

[0034] S7, the strip steel starts to release, the weld moves to the edge cutting position, the crescent on both sides is cut, the cupping test machine is used to perform cupping test on the crescent on both sides, and the toughness of the weld joint is verified to be good, and the cupping test is qualified.

[0035] Further, in S3, the historical operation data is analyzed by Pearson correlation coefficient and neural network model, and the initial parameters of the induction heating process are set, specifically:

[0036] Calculate the average value, maximum value, minimum value and standard deviation of each parameter in the historical operation data, and calculate the correlation between each parameter using Pearson correlation coefficient;

[0037] According to the correlation between each parameter, calculate the correlation coefficient matrix;

[0038] According to the correlation coefficient matrix, analyze the relationship between each parameter;

[0039] According to the correlation analysis result, determine the key parameter that has the greatest influence on the welding quality;

[0040] Establish a neural network model, take the key parameter that has the greatest influence on the welding quality as input data, and predict the relationship between the welding quality and each parameter through the neural network model;

[0041] According to the prediction result of the neural network model, generate the initial value of each parameter.

[0042] Compared with the prior art, the beneficial effects of the present application are:

[0043] 1. In the present application, by installing the weld seam induction heating device inside the upper shear blade and combining with the cylinder drive to make it have telescopic function, it realizes that there is no need for additional space, and there is no need to increase the length of the welding machine C-shaped frame and the welding machine track; through the production monitoring system to remotely control the weld seam heating process, and real-time display of various process parameters and weld seam heating temperature curve, realize that there is no need for manual operation, whether it is a narrow lap welding machine or a laser welding machine, it can meet the requirements of weld seam heating, improve the efficiency of weld seam heating, save time and save space.

[0044] 2. In the present application, by extracting the normal operation parameters in the historical operation data as the threshold of each parameter; combining with the Pearson correlation coefficient and the neural network model to predict the initial value of the parameter, and real-time analysis of the parameters exceeding the threshold range, to ensure that the weld seam induction heating device is always in the best operating state; detect the frequency of parameters exceeding the threshold range for a long time, and dynamically adjust the corresponding parameter threshold according to the actual operating state of the weld seam induction heating device, further enhance the adaptability and stability of the production monitoring system, significantly improve the welding efficiency and quality, reduce the equipment failure rate, and reduce the production cost. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The welding machine weld seam induction heating method flowchart of the present application;

[0046] Figure 2 The welding machine weld seam induction heating device schematic diagram of the present application;

[0047] Figure 3 Another state schematic diagram of the welding machine weld seam induction heating device of the present application;

[0048] Figure 4 The welding machine weld seam induction heating device control method flowchart of the present application.

[0049] In the figure: 1, rolling wheel; 2, welding wheel; 3, lower shear blade; 4, welding machine C-shaped frame; 5, weld seam induction heating device; 5a, lower position; 5b, upper position; 6, cylinder; 7, upper shear blade; 8, positioning detector; 9, infrared thermometer. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] In order to solve the technical problems of high cost, long time and inconvenient manual operation in the prior art, please refer to Figure 1 Figure 4 The embodiment provides the following technical solutions:

[0052] A welding machine weld seam induction heating device, comprising: a double-layer shearing box composed of a lower shear blade 3 and an upper shear blade 7, the double-layer shearing box is opened by 10-15 cm, wherein the upper shear blade 7 is installed above the inner side of the welding machine C-shaped frame 4, the lower shear blade 3 is installed on the end face of the inner side of the welding machine C-shaped frame 4, and the side of the lower shear blade 3 is further provided with a rolling wheel 1 and a welding wheel 2 in sequence, the inner side of the end face of the upper shear blade 7 is provided with a weld seam induction heating device 5, the weld seam induction heating device 5 is spliced, the length is 1000-2000 mm, and the width is 2-5 mm; according to the actual length of the upper shear blade 7, the weld seam induction heating device 5 is divided into several parts and is installed in the inner side of the end face of the upper shear blade 7 in sequence to meet the requirement that the whole weld seam is heated; the top of the welding machine C-shaped frame 4 is provided with a pneumatic cylinder 6, the pneumatic cylinder 6 is connected with the top end of the weld seam induction heating device 5 through a pneumatic rod, and the pneumatic cylinder 6 is used to push the weld seam induction heating device 5 to make rotary lifting action in the inner side of the end face of the upper shear blade 7, so that the distance between the weld seam induction heating device 5 and the strip steel weld seam is 5-10 mm; the bottom of the weld seam induction heating device 5 is respectively provided with a positioning detector 8 and an infrared temperature detector 9, and the positioning detector 8 and the infrared temperature detector 9 are connected with a production monitoring system based on wireless communication technology. The strip steel base plate steel grade includes but is not limited to: CQ-HSS, IF-HSS, P-HSS, dual-phase steel DP600, DP800, DP1180, medium and high grade silicon steel, hot forming steel, fine punching steel 45Mn, 65Mn and the like.

[0053] The production monitoring system comprises a data acquisition unit, a signal processing unit and a man-machine interaction unit.

[0054] The data acquisition unit is configured to acquire the distance between the weld seam induction heating device 5 and the weld seam and the actual temperature of the weld seam in real time based on the positioning detector 8 and the infrared temperature detector 9, and acquire the current, voltage, heating time, holding time, heating power and heating temperature parameters of the weld seam induction heating device 5 when heating in real time.

[0055] The signal processing unit is configured to analyze the collected parameters, compare the parameters with preset threshold values, and judge whether the operation parameters of the weld seam induction heating device 5 are abnormal according to the comparison result; the signal processing unit comprises:

[0056] ​A parameter threshold setting module is configured to obtain historical operation data of the weld induction heating device 5, and pre-process the historical operation data, including cleaning, noise reduction and normalization processing; extract parameter values in the normal operation state from the historical operation data, and set threshold values corresponding to each parameter according to each normal parameter value; for example, the following threshold values are set:

[0057] Parameter Lower threshold value Upper threshold value Unit Current I 10 30 A Voltage V 200 240 V Heating time 30 60 s Soak time 10 30 s Heating power 2000 6000 W Heating temperature 200 300 ℃

[0058] Table 1, threshold table of each operation parameter

[0059] A parameter comparison module is configured to obtain each parameter collected in real time by the data acquisition unit, and compare each parameter with the corresponding threshold value to obtain the comparison result of each parameter, and determine whether each parameter of the weld induction heating device 5 needs to be adjusted; for example, when heating, the current parameter is 40A, which has exceeded the upper threshold value, so the current parameter of the weld induction heating device 5 is adjusted in time.

[0060] An initial parameter generation module is configured to calculate the correlation of each parameter in the historical operation data based on the Pearson correlation coefficient, analyze the correlation of each parameter through a neural network model, and predict the initial value of each parameter; specifically:

[0061] Calculate the average value, maximum value, minimum value and standard deviation of each parameter in the historical operation data, calculate the correlation between each parameter using the Pearson correlation coefficient; calculate the correlation coefficient matrix according to the correlation between each parameter; analyze the relationship between each parameter according to the correlation coefficient matrix; for example, if the correlation coefficient of current and heating power is close to 1, it means that the current has a strong positive correlation with the heating power; if the correlation coefficient of heating time and heating temperature is low, it means that the heating time has little effect on the heating temperature; according to the correlation analysis result, determine the key parameter that has the greatest impact on the welding quality; for example, if the heating temperature and the holding time have a great impact on the welding quality, then these two parameters are the focus of optimization; establish a neural network model, take the key parameter that has the greatest impact on the welding quality as the input data, and predict the relationship between the welding quality and each parameter through the neural network model; according to the prediction result of the neural network model, generate the initial value of each parameter to improve the welding quality; for example, if the neural network model predicts that the heating temperature has the greatest impact on the welding quality, adjust the heating temperature value and improve the threshold range, so as to optimize the welding quality.

[0062] A parameter dynamic adjustment module is configured to generate an adjustment instruction based on the comparison result of each parameter, and adjust each parameter according to the adjustment instruction.

[0063] an abnormal frequency analysis module configured to obtain comparison results of various parameters in a period of time, extract parameters in the comparison results that exceed a threshold range, sort the parameters in the threshold range in the period of time, and calculate a frequency of each parameter exceeding the threshold range;

[0064] a threshold dynamic adjustment module configured to dynamically adjust a corresponding parameter threshold according to the frequency of each parameter exceeding the threshold range and in combination with an actual running state of the weld seam induction heating device 5; for example, if it is found in actual running that the heating temperature is often close to the upper limit threshold 300℃, but the welding quality is still good, the upper limit threshold can be increased to 320℃; if the current is often lower than the lower limit threshold 10A, but the device runs normally, the lower limit threshold can be considered to be reduced to 8A.

[0065] a man-machine interaction unit configured to provide an operation interface and support an operator to manually input an adjusted parameter value according to real-time data and abnormal prompts displayed on the monitoring interface; the man-machine interaction unit comprises:

[0066] a curve drawing module configured to draw various parameters during running of the weld seam induction heating device 5 into a curve graph and display the curve graph in the monitoring interface, for analyzing a change trend of the various parameters.

[0067] a data storage module configured to receive various parameters collected by the data collection unit, store the various parameters in a time sequence after preprocessing, and use the various parameters for later analysis and model optimization.

[0068] Embodiment one: it is assumed that a narrow lap welding machine is used for welding, the opening width of the double-layer shear box is 10 cm, the weld seam induction heating device 5 is installed inside the end face of the upper shear blade 7, the welding steel type is dual-phase steel DP800, and the thickness is 2.0 mm.

[0069] A welding machine weld seam induction heating device control method comprises the following steps:

[0070] S1, the strip head and tail are in a welding waiting position, the weld seam induction heating device 5 is in an upper position 5b inside the end face of the upper shear blade 7, that is, the weld seam induction heating device 5 does not exceed the end of the upper shear blade 7, the upper shear blade 7 is lowered, the lower shear blade 3 is raised, and the strip head and tail are sheared;

[0071] S2, after welding is completed, the welding machine C-shaped frame 4 returns from the transmission side to the operation side, the inlet clamp and the outlet clamp clamp the strip steel at the same time, and the weld seam is in the center position of the opening of the double-layer shear blades;

[0072] S3, analyze the historical operation data by Pearson correlation coefficient and neural network model, set the initial parameters of the induction heating process, for example: heating power is 60%, heating time is 40s, holding time is 10s, the weld induction heating device 5 is driven by the air cylinder 6 to descend to the lower position 5a, the distance from the weld to the strip steel is 5mm, and the heating annealing treatment of the weld is started;

[0073] S4, real-time detection and comparison of whether the initial parameters exceed the corresponding threshold range, judgment of whether there is an abnormality in the operation of the weld induction heating device 5, whether the parameters need to be adjusted;

[0074] S5, real-time display of the parameters of the weld induction heating device 5 and abnormal parameter prompt through the man-machine interaction unit;

[0075] S6, after the heating is completed, the weld induction heating device 5 automatically rises to the upper position 5b, i.e. the weld induction heating device 5 does not exceed the end of the upper shear blade 7;

[0076] S7, the strip steel starts to release, the weld moves to the edge cutting position, the crescent on both sides is cut, the cupping test machine is used to perform the cupping test on the crescent on both sides, and the weld joint toughness is verified to be good, and the cupping test is qualified.

[0077] The beneficial effects achieved by the above content are: the length of the welding machine C-shaped frame 4 and the track is reduced by using the weld induction heating device 5, the site and the production and installation cost are saved; and the weld induction heating device 5 has the telescopic function through cooperation with the air cylinder 6, which does not affect the cutting of the strip head and tail, protects the weld induction heating device 5 from being scratched and impacted by the strip steel, realizes remote heating of the weld, does not need manual operation, can set different heating process parameters according to different steel specifications, and meets the requirements of weld heating annealing whether it is a narrow lap welding machine or a laser welding machine.

[0078] Example two: assuming that a laser welding machine is used for welding, there is no front induction heating and rear induction heating device on the welding machine C-shaped frame 4, the opening width of the double-layer shear box is 10cm, the weld induction heating device 5 is installed inside the end face of the upper shear blade 7, and the welding steel is high-grade silicon steel MGW310 with a thickness of 2.3mm.

[0079] A welding machine weld induction heating device control method, comprising the following steps:

[0080] S1, the strip head and tail are in the welding waiting position, the weld induction heating device 5 is in the upper position 5b inside the end face of the upper shear blade 7, i.e. the weld induction heating device 5 does not exceed the end of the upper shear blade 7; the lower shear blade 3 rises and the upper shear blade 7 descends, and the cutting of the strip head and tail is completed;

[0081] S2, after welding, the welding machine C frame 4 returns from the transmission side to the operation side, the entrance clamp, the exit clamp clamps the strip steel at the same time, and the welding seam is in the center position of the double-layer shear box opening;

[0082] S3, set the appropriate induction heating process parameters, for example: heating power is 40%, heating time is 30s, holding time is 5s, the welding seam induction heating device 5 starts to be put into use, the cylinder 6 drives the welding seam induction heating device 5 to descend to the lower position 5a, the distance from the strip steel welding seam is 8mm;

[0083] S4, start heating annealing treatment for the welding seam, the man-machine interaction unit displays the actual power, heating current, voltage, heating time and actual heating temperature.

[0084] S5, after heating, the welding seam induction heating device 5 automatically rises to the upper position 5b, that is, the welding seam induction heating device 5 does not exceed the end of the upper shear blade 7;

[0085] S6, the strip steel starts to release, the welding seam comes to the edge cutting position, the two sides of the crescent are cut, the cupping tester is used to carry out the cupping test on the two sides of the crescent, the welding seam joint has good toughness, and the cupping test is qualified.

[0086] Working principle: by installing the welding seam induction heating device 5 in the upper shear blade 7 and combining the cylinder 6 to realize the telescopic function, the heating process is remotely controlled based on the production monitoring system, without additional space and manual operation; the threshold value of the parameter is set by using the historical data, the initial value of the parameter is predicted by combining the Pearson correlation coefficient and the neural network model, and the threshold value is dynamically adjusted, the adaptability and stability of the system are enhanced, the welding efficiency and quality are improved, and the failure rate and cost are reduced.

[0087] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0088] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A weld machine weld seam induction heating apparatus comprising: The double-layer shearing box composed of the lower shearing blade (3) and the upper shearing blade (7) is characterized in that: the lower shearing blade (3) and the upper shearing blade (7) are installed on the welding machine C-shaped frame (4), the inner part of the end face of the upper shearing blade (7) is provided with a weld seam induction heating device (5), the bottom of the weld seam induction heating device (5) is respectively provided with a positioning detector (8) and an infrared temperature detector (9), and the positioning detector (8) and the infrared temperature detector (9) are connected with a production monitoring system based on wireless communication technology; The production monitoring system comprises a data acquisition unit, a signal processing unit and a man-machine interaction unit. The data acquisition unit is configured to acquire the distance between the weld seam induction heating device (5) and the weld seam and the actual temperature of the weld seam in real time based on the positioning detector (8) and the infrared temperature detector (9), and acquire the current, voltage, heating time, holding time, heating power and heating temperature parameters of the weld seam induction heating device (5) when heating in real time. The signal processing unit is configured to analyze the collected parameters, compare the parameters with preset threshold values, and judge whether the operation parameters of the weld seam induction heating device (5) are abnormal according to the comparison result. The man-machine interaction unit is configured to provide an operation interface, and support an operator to manually input adjusted parameter values according to real-time data and abnormal prompts displayed on the monitoring interface.

2. A weld seam induction heating device for a welding machine as claimed in claim 1, characterized in that The signal processing unit comprises: A parameter threshold setting module is configured to acquire historical operation data of the weld seam induction heating device (5), pre-process the historical operation data, extract parameter values in a normal operation state from the historical operation data, and set threshold values corresponding to the parameters according to the normal parameter values. A parameter comparison module is configured to acquire the parameters collected by the data acquisition unit in real time, compare each parameter with the corresponding threshold value, obtain the comparison result of each parameter, and judge whether the parameters of the weld seam induction heating device (5) need to be adjusted.

3. A weld seam induction heating device for a welding machine as claimed in claim 2, characterized in that The signal processing unit further comprises: An initial parameter generation module is configured to calculate the correlation of the parameters in the historical operation data based on the Pearson correlation coefficient, analyze the correlation of the parameters through a neural network model, and predict the initial values of the parameters. A parameter dynamic adjustment module is configured to generate an adjustment instruction based on the comparison result of each parameter, and adjust each parameter according to the adjustment instruction.

4. A weld seam induction heating device for a welding machine as claimed in claim 3, characterized in that The signal processing unit further comprises: An abnormal frequency analysis module is configured to acquire the comparison results of the parameters in a period of time, extract parameters exceeding the threshold range from the comparison results, sort the parameters exceeding the threshold range in the period of time, and calculate the frequency of each parameter exceeding the threshold range. A threshold dynamic adjustment module is configured to dynamically adjust the corresponding parameter threshold value according to the frequency of each parameter exceeding the threshold range and in combination with the actual operation state of the weld seam induction heating device (5).

5. A weld seam induction heating device for a welding machine as defined in claim 4, characterized in that The man-machine interaction unit comprises: A curve drawing module is configured to draw the parameters of the weld seam induction heating device (5) during operation into a curve graph and display the curve graph in the monitoring interface, so as to analyze the change trend of the parameters. The data storage module is configured to receive various parameters collected by the data collection unit, pre-process the various parameters, and store the various parameters in time sequence for later analysis and model optimization.

6. A weld seam induction heating device for a welding machine as defined in claim 1, characterized in that The double-layer shear box is opened by 10-15 cm, wherein the upper shear blade (7) is installed above the inner side of the welding machine C-shaped frame (4), and the lower shear blade (3) is installed on the end face of the inner side of the welding machine C-shaped frame (4), and one side of the lower shear blade (3) is further sequentially provided with a rolling wheel (1) and a welding wheel (2).

7. A weld seam induction heating device for a welding machine as defined in claim 6, characterized in that The top of the welding machine C-shaped frame (4) is provided with a gas cylinder (6), the gas cylinder (6) is connected with the top end of the welding seam induction heating device (5) through a gas rod, and the gas cylinder (6) is used to push the welding seam induction heating device (5) to make a rotating lifting action in the inside of the end face of the upper shear blade (7).

8. A weld seam induction heating device for a welding machine as defined in claim 7, characterized in that The welding seam induction heating device (5) is spliced, the length is 1000mm-2000mm, and the width is 2-5mm; according to the actual length of the upper shear blade (7), the welding seam induction heating device (5) is divided into several parts and is sequentially installed in the inside of the end face of the upper shear blade (7).

9. A method for controlling a welding seam induction heating device of a welding machine, implemented by the welding seam induction heating device of any one of claims 1-8, characterized in that, The method comprises the following steps: S1, the strip head and tail are in the welding waiting position, the welding seam induction heating device (5) is in the upper position (5b) in the inside of the end face of the upper shear blade (7), the upper shear blade (7) is lowered, the lower shear blade (3) is raised, and the cutting of the strip head and tail is completed; S2, after the welding is completed, the welding machine C-shaped frame (4) returns from the transmission side to the operation side, the inlet clamp and the outlet clamp clamp the strip steel at the same time, and the welding seam is at the center position of the double-layer shear blade opening; S3, the historical operation data is analyzed through the Pearson correlation coefficient and the neural network model, the initial parameters of the induction heating process are set, the welding seam induction heating device (5) is driven to descend to the lower position (5a) through the gas cylinder (6), and the heating annealing treatment of the welding seam is started; S4, whether the initial parameters exceed the corresponding threshold range is detected and compared in real time, whether there is an abnormality in the operation process of the welding seam induction heating device (5) is judged, and whether the parameters need to be adjusted is judged; S5, the parameters of the welding seam induction heating device (5) and the abnormal parameter prompt are displayed in real time through the man-machine interaction unit; S6, after the heating is completed, the welding seam induction heating device (5) automatically rises to the upper position (5b); S7, the strip steel starts to be released, the welding seam moves to the edge cutting position, the two side crescent moons are cut, the cupping test machine is used to carry out the cupping test on the two side crescent moons, the toughness of the welding seam joint is verified to be good, and the cupping test is qualified.

10. A method of controlling a weld seam induction heating device of a welding machine as defined in claim 9, characterized in that In S3, the historical operation data is analyzed through the Pearson correlation coefficient and the neural network model, the initial parameters of the induction heating process are set, and the specific steps are as follows: The average value, the maximum value, the minimum value and the standard deviation of each parameter in the historical operation data are calculated, and the correlation between the parameters is calculated using the Pearson correlation coefficient; According to the correlation between the parameters, the correlation coefficient matrix is calculated; According to the correlation coefficient matrix, the relationship between the parameters is analyzed; According to the correlation analysis result, the key parameter which has the greatest influence on the welding quality is determined; The neural network model is established, the key parameter which has the greatest influence on the welding quality is taken as input data, and the relationship between the welding quality and the parameters is predicted through the neural network model. According to the prediction result of the neural network model, initial values of various parameters are generated.

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