Steel structure machining and welding method and device using submerged arc welding machine

Abstract

The invention relates to the technical field of automatic and semi-automatic electric arc welding, in particular to a steel structure machining and welding method and device utilizing a submerged arc welding machine. Acquiring a coupling degradation index at the current moment according to the dispersion degree of the weld penetration sequence at the current moment and the energy distribution uniformity degree of each coupling interference sequence; dividing the coupling degradation indexes at all moments into a plurality of data windows; acquiring an average coupling degradation index at the current moment, and acquiring a dynamic threat level at the current moment by combining the dispersion degree and the change rate of the coupling degradation index in each data window; and then the initial proportionality coefficient in the PID control system is optimized, and the optimized proportionality coefficient is used for adjusting the current of the main welding wire at the next moment. By evaluating the dynamic interference degree at the current moment, the PID control system can make a response more accurately and timely according to the current interference degree.

Description

Technical Field

[0001] This application relates to the technical field of automatic and semi-automatic arc welding, and specifically relates to a steel structure processing and welding method and device using a submerged arc welding machine. Background Art

[0002] Due to the characteristics of the submerged arc welding technology (SAW) where the arc burns under the flux layer, it can effectively isolate air, reduce spatter and defects, making it an ideal choice for thick plate welding. Submerged arc welding can be automatic or semi-automatic, suitable for welding large components, with the characteristics of high current and high efficiency. With the advancement of intelligent manufacturing, the submerged arc welding technology is deeply integrated with industrial robots and digital twin technology, continuously promoting the upgrading of the steel structure industry towards high precision and low energy consumption, and has strategic significance for ensuring the construction of major national projects and enhancing the international competitiveness of the equipment manufacturing industry.

[0003] In the process of multi-wire submerged arc welding (such as double-wire or triple-wire), when multiple welding wires work simultaneously, the electromagnetic fields of the front and rear arcs interfere with each other, resulting in arc drift and increased turbulence in the molten pool, which easily causes defects such as pores and poor fusion. At the same time, the superposition of multi-wire heat input may cause local overheating, resulting in coarse grains or concentrated residual stress in the weld, affecting the mechanical properties of the joint. The existing technology mainly deals with such problems through phase control. For example, an alternating current square wave power supply is used and the current phase difference between the front and rear arcs is set to weaken the magnetic field interference; by adopting the strategy of high current deep penetration for the leading welding wire and low current profiling for the subsequent welding wire, the penetration and formation are balanced. However, the existing automatic submerged arc welding process has insufficient real-time regulation ability for dynamic interference. For example, under high-speed welding or complex groove conditions, the accuracy of the multi-physical field coupling model is limited, resulting in the control system being unable to respond accurately and in a timely manner when instantaneous interference occurs, and it is difficult to accurately and timely eliminate or weaken the instantaneous interference. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a steel structure processing and welding method and device using a submerged arc welding machine, and the specific technical solutions adopted are as follows: In the first aspect, an embodiment of this application provides a steel structure processing and welding method using a submerged arc welding machine, and this method includes the following steps: Obtain the electromagnetic sequence, the current sequences and voltage sequences of two welding wires, the weld penetration sequence during the observation period at each moment during the welding process of the steel structure using the submerged arc welding machine, and the high-temperature ratio sequence composed of the proportion of high-temperature pixel points in the infrared images collected at each moment during the observation period at each moment; Both the electromagnetic sequence, the high-temperature ratio sequence, the current sequences and voltage sequences of two welding wires are called coupling interference sequences; obtain the coupling degradation index at the current moment according to the dispersion degree of the weld penetration sequence at the current moment and the energy distribution uniformity of each coupling interference sequence in the high-frequency band and the low-frequency band; Divide the coupling degradation indices at the current moment and all previous moments into multiple data windows; obtain the average coupling degradation index at the current moment according to the average level after smoothing the coupling degradation indices at the current moment and all previous moments, and combine the degree of dispersion of the coupling degradation indices within each data window and the slope of the fitting line of the coupling degradation indices within each data window to obtain the dynamic threat level at the current moment; then optimize the preset initial proportional coefficient in the PID control system, and use the optimized proportional coefficient to adjust the main wire current at the next moment.

[0005] Preferably, the proportion of high-temperature pixel points in the infrared image collected at each moment is the ratio of the number of pixel points in the clustering cluster with the highest average pixel value to the total number of pixel points in the infrared image after clustering all pixel points in the infrared image.

[0006] Preferably, the calculation formula for the coupling degradation index at the current moment is: ; where is the coupling degradation index at the current moment, is the standard deviation of the weld penetration depth sequence at the current moment, is the energy entropy value of the i-th coupling interference sequence at the current moment in the high-frequency band, is the energy entropy value of the i-th coupling interference sequence at the current moment in the low-frequency band, is the total number of coupling interference sequences at the current moment.

[0007] Preferably, the energy entropy values of the low-frequency band and the high-frequency band of the coupling interference sequence are obtained through the wavelet energy entropy algorithm.

[0008] Preferably, the specific process of dividing the coupling degradation indices of all moments into multiple data windows is: taking every r data of the coupling degradation indices of all moments in chronological order from front to back as a data window, where r is a preset positive integer.

[0009] Preferably, the process of obtaining the average coupling degradation index at the current moment is: using the coupling degradation indices at the current moment and all previous moments as the input of the moving average algorithm, outputting the smoothed coupling degradation indices and taking the mean value, and taking the mean value as the average coupling degradation index at the current moment.

[0010] Preferably, the calculation formula for the dynamic threat level at the current moment is: ; where is the dynamic threat level at the current moment, is the smoothed coupling degradation index at the current moment, is the coefficient of variation within the j-th data window among all data windows before the current moment, is the slope of the fitting line of the j-th data window among all data windows before the current moment, and N is the total number of all data windows before the current moment.

[0011] Preferably, the preset initial proportional coefficient in the PID control system is optimized, and the calculation formula of the optimized proportional coefficient is: ; in the formula, is the optimized proportional coefficient at the current moment, is the preset initial proportional coefficient, is the dynamic threat level at the current moment, is the hyperbolic tangent function.

[0012] Preferably, the specific process of adjusting the main wire welding current at the next moment by using the optimized proportional coefficient is: using the optimized proportional coefficient at the current moment as the proportional coefficient of the PID control system at the next moment to dynamically adjust the main wire welding current compensation amount.

[0013] In a second aspect, an embodiment of the present application further provides a steel structure processing and welding device using a submerged arc welding machine, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method for processing and welding steel structures using a submerged arc welding machine as described in any one of the above.

[0014] The present application has at least the following beneficial effects: Aiming at the problem that the PID control system has a response lag during the multi-wire automatic submerged arc welding process when there is an instantaneous interference and cannot accurately and timely respond according to the current interference degree, the present application first quantifies the synergistic effect of the transient interference intensity and the steady-state interference level through wavelet energy entropy, and combines the discreteness of the weld penetration sequence to construct a coupling degradation index, which can reflect the comprehensive degradation risk of multi-dimensional interference on the weld penetration fluctuation, so that the subsequent control system can accurately make adjustments according to the current interference; then, through the mean value, volatility and degradation trend slope of the smoothed coupling degradation index, a dynamic threat level is constructed to accurately evaluate the dynamic threat degree at the current moment during the welding process, and further the proportional parameters of the PID control system can be optimized more accurately and timely, and the distribution of the wire welding current can be optimized in real time, so that the PID control system can respond more accurately and timely according to the current interference degree. Description of the Drawings

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of the steps of a steel structure processing and welding method using a submerged arc welding machine provided by an embodiment of the present application; Figure 2 It is a flowchart for obtaining the dynamic threat level at the current moment provided by an embodiment of the present application. Detailed implementation manners

[0017] To further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a steel structure processing and welding method and device using a submerged arc welding machine proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0019] The following specifically describes the specific solutions of a steel structure processing and welding method and device using a submerged arc welding machine provided by the present application with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a flowchart of the steps of a steel structure processing and welding method using a submerged arc welding machine provided by an embodiment of the present application. The method includes the following steps: Step 1: Obtain the electromagnetic sequence, the current sequences and voltage sequences of two welding wires, the weld penetration sequence during the welding process of the steel structure using a submerged arc welding machine at each moment within the observation period, and the high-temperature ratio sequence composed of the ratio of high-temperature pixel points in the infrared images collected at each moment within the observation period of each moment.

[0021] This application is directed to the process of a multi - power series double - wire submerged arc welding machine in steel structure processing. In order to optimize the automatic welding process of the submerged arc welding machine, high - precision Hall sensors are symmetrically integrated on both sides of the welding torch's conductive nozzle to ensure its stability in a complex electromagnetic environment. The vector sum of the three - axis magnetic field components (Bx, By, Bz) collected by the Hall sensor at each moment is denoted as the electromagnetic data at each moment, which is used to characterize the total magnetic field intensity at each moment. An infrared thermal imager is used to obtain the infrared images at each moment during the steel structure processing and welding. Taking all the pixel values in each infrared image as inputs respectively, the K - means clustering algorithm with K = 2 is used to cluster all the pixel points in each infrared image. The ratio of the number of pixel points in the clustering cluster with the highest pixel value mean to the total number of pixel points in the corresponding infrared image is denoted as the high - temperature proportion data of each infrared image. Each welding wire in the multi - power series double - wire submerged arc welding machine is independently powered by a power source. Current transformers and voltage probes are installed at the two power sources respectively, and the current and voltage of the main welding wire and the slave welding wire can be collected respectively. A laser displacement sensor is installed on the moving platform of the welding trolley during steel structure welding, aiming at the weld seam 10 (x = 10 in this embodiment) centimeters behind the welding torch to measure the weld penetration data in real - time. Among them, the vector sum and the K - means clustering algorithm are both well - known technologies in the art, and the specific process will not be elaborated here.

[0022] Each sensor is synchronized through a high - precision timestamp and data is collected once per second to ensure the synchronization of the acquisition of electromagnetic data, high - temperature proportion data, welding wire current, welding wire voltage, and weld penetration data. The collected data of each type is subjected to real - time overall normalization processing, and the previous t seconds of the current moment are used as the observation period of the current moment. It should be noted that if the data acquisition time before the current moment is less than t seconds, the actual existing time period is used as the observation period of the current moment. In this embodiment, t is taken as 60. According to the normalized data within the observation period of each moment, electromagnetic sequences, high - temperature proportion sequences, current sequences and voltage sequences of the main welding wire, current sequences and voltage sequences of the slave welding wire, and weld penetration sequences are constructed at each moment, providing accurate inputs for subsequent coupled interference analysis. Among them, the normalization method can be the maximum - minimum normalization method, the Z - score normalization method, or the maximum value normalization method, which is not limited in this embodiment.

[0023] Step 2: The electromagnetic sequence, high - temperature proportion sequence, current sequences and voltage sequences of the two welding wires are all called coupled interference sequences; the coupled degradation index at the current moment is obtained according to the dispersion degree of the weld penetration sequence at the current moment and the energy distribution uniformity of each coupled interference sequence in the high - frequency band and the low - frequency band.

[0024] Since multiple welding wires work simultaneously in the multi-wire submerged arc welding process, the electromagnetic fields of the front and rear arcs interfere with each other, causing arc drift and increased turbulence in the molten pool, which in turn causes defects such as porosity and poor fusion. At the same time, the superposition of multi-wire heat input can easily cause local overheating, affecting the mechanical properties of the weld. Among the collected data, the collected data can be divided into two categories from the perspective of cause and effect. Specifically, the electromagnetic sequence, high temperature proportion sequence, and the current sequence and voltage sequence of the two welding wires are divided into one type of data, which characterizes the input state of the dynamic interference source in the welding process, and these sequences are called coupled interference sequences; the weld penetration sequence is divided into another category separately, which characterizes the quality response of the final output of the welding process.

[0025] In the manufacturing process of welding equipment, the wavelet energy entropy algorithm is integrated into the equipment control unit. Each data sequence in the coupled interference sequence is taken as input, and wavelet energy entropy analysis is used. The Daubechies 4 wavelet basis is set for 5-layer decomposition to extract the energy entropy value of each data sequence in the high frequency band and the low frequency band. In this embodiment, the frequency range of the high frequency band is 150-500Hz, and the frequency range of the low frequency band is 0-50Hz.

[0026] As a preferred implementation, the coupling degradation index at the current moment is obtained according to the discrete degree of the weld penetration sequence at the current moment and the uniformity of the energy distribution of each coupling interference sequence in the high frequency band and the low frequency band, which is used to characterize the significance of the synergistic deterioration effect of transient arc disturbance, steady-state interference level and steady-state thermal imbalance in the observation period at the current moment.

[0027] In this embodiment, the coupling degradation index at the current moment is recorded as A, and the specific calculation relationship is: ; In the formula, is the coupling degradation index at the current moment, is the standard deviation of the weld penetration sequence at the current moment, is the energy entropy value of the i-th coupling interference sequence in the high frequency band at the current moment, is the energy entropy value of the i-th coupling interference sequence in the low frequency band at the current moment, is the total number of coupled interference sequences at the current moment, and in this embodiment, m=6.

[0028] Directly reflects the stability of the penetration data during welding. The higher the value, the more dramatic the dynamic changes of the molten pool, resulting in more uneven melting depth; Characterizes the transient interference intensity of the i-th coupled interference sequence at the current moment, Characterize the steady-state interference level of the i-th coupled interference sequence at the current moment. High-frequency interference stems from short-time rapid dynamic events (such as arc mutations, electromagnetic pulses), while low-frequency interference is dominated by long-period slow-varying processes (such as heat conduction, parameter gradual changes). The product of the two characterizes the synergistic amplification effect of transient and steady-state interference; A reflects the significant degree of the synergistic deterioration effect of transient arc disturbance, steady-state interference level, and steady-state thermal imbalance during the observation period at the current moment. When the value of A is larger, it indicates that the synergistic deterioration effect of transient arc disturbance, steady-state interference level, and steady-state thermal imbalance is more significant, the penetration data is more unstable, and the risks of fusion defects and mechanical property deterioration are higher.

[0029] Step 3: Divide the coupled deterioration indices at the current moment and all previous moments into multiple data windows; obtain the average coupled deterioration index at the current moment according to the average level after smoothing the coupled deterioration indices at the current moment and all previous moments, and combine the dispersion degree of the coupled deterioration indices within each data window and the slope of the fitting line of the coupled deterioration indices within each data window to obtain the dynamic threat level at the current moment; furthermore, optimize the preset initial proportional coefficient in the PID control system, and use the optimized proportional coefficient to adjust the main wire current at the next moment.

[0030] Furthermore, in the multi-wire submerged arc welding process, the simultaneous operation of multiple wires leads to mutual interference of electromagnetic fields and superposition of heat inputs, which causes arc drift and intensified molten pool turbulence, and further generates defects such as pores and poor fusion. However, the existing technology has insufficient real-time regulation ability for dynamic interference and is difficult to effectively suppress the synergistic deterioration effect of instantaneous electromagnetic disturbance and steady-state thermal imbalance.

[0031] Therefore, calculate the coupled deterioration indices at the current moment and all previous moments according to the same steps as above, and use the moving average algorithm to calculate the mean value of all coupled deterioration indices. The moving average algorithm can be the simple moving average method, weighted moving average method, and exponential moving average method. In this embodiment, the weighted moving average method is taken as an example for implementation. Specifically, use the coupled deterioration indices at the current moment and all previous moments as the input of the weighted moving average method, set the length of the sliding window as u (u is taken as 7 in this embodiment), the window sliding step size is the same as the window length, that is, each time it slides u data, the weights within the window are distributed according to the Gaussian distribution, output the coupled deterioration indices of each sliding window after smoothing, and take the mean value of the coupled deterioration indices of all sliding windows after smoothing as the average coupled deterioration index at the current moment. Its value represents the average intensity of interference energy within the local time window, reflects the steady-state deterioration trend of the welding process by suppressing instantaneous noise, and optimizes the steady-state evaluation of dynamic interference in the manufacture of welding equipment.

[0032] The coupling degradation index of the current moment and all previous moments is taken as a data window every r (10 in this embodiment) data from the beginning to the end in chronological order, and the coefficient of variation of all data in each data window is calculated. All coupling degradation indexes in each data window are used as input, and the least squares method is used to obtain the fitting straight line of each data window and the slope of each fitting straight line, which are used to predict the accelerated risk of degradation trend in welding equipment manufacturing and provide decision-making basis for equipment parameter optimization.

[0033] As a preferred implementation, the dynamic threat level at the current moment is obtained based on the average coupling degradation index at the current moment, combined with the discrete degree of the coupling degradation index in each data window, and the slope of the fitting line of the coupling degradation index in each data window, to characterize the dynamic threat level of multi-factor coupling interference to welding quality. The acquisition process of the dynamic threat level at the current moment is as follows: Figure 2 shown.

[0034] In this embodiment, the dynamic threat level at the current moment is recorded as B, and the specific calculation relationship is: ; In the formula, is the dynamic threat level at the current moment, is the average coupling degradation index at the current moment, is the coefficient of variation in the jth data window among all data windows before the current moment, is the slope of the fitting line of the jth data window among all data windows before the current moment, and N is the total number of all data windows before the current moment.

[0035] The original coupling degradation index is smoothed by weighted moving average method to suppress instantaneous noise and highlight the steady-state degradation trend. The larger the value, the greater the average influence of various influencing factors on welding. It is used to measure the relative volatility of all coupling degradation indexes in the jth window among all data windows before the current moment. The larger the value, the more drastic the fluctuation in the jth window, and the more unstable the welding process is. It represents the changing trend of the coupling degradation index in the jth window among all the data windows before the current moment. The larger the absolute value is, the faster the changing rate of the coupling degradation index in the time period corresponding to the window is, and the more urgent the threat to the stability of the melting depth is.

[0036] B quantifies the dynamic threat level of multi-factor coupled interference to welding quality. The larger the value, the more dangerous the current welding process is in a complex state of high interference intensity, high volatility, and accelerated degradation, and the risk of collapse of penetration stability increases sharply.

[0037] Regarding the problem of unstable penetration caused by the superposition of electromagnetic interference and heat input in multi-wire submerged arc welding, a PID control system is used to act on the current feedback loop of the welding power supply to ensure the rapid response of the double-wire current distribution during the manufacture of welding equipment. In this embodiment, the preset initial proportional coefficient Kp is set to 0.8 to quickly respond to the fluctuation of the B value; the preset initial integral coefficient Ki is set to 0.2 to eliminate the steady-state cumulative error; the initial differential coefficient Kd is set to 0.4 to suppress overshoot.

[0038] In multi-wire submerged arc welding, the fluctuation of the dynamic threat level B is directly related to the penetration stability. The original fixed proportional coefficient has insufficient response during severe interference, resulting in a lag in penetration adjustment. To improve the sensitivity of the system to the fluctuation of the B value, in this embodiment, the initial proportional parameter in the PID control system is optimized based on the dynamic threat level at the current moment, and its specific adjustment relationship formula is: ; where is the optimized proportional coefficient at the current moment, is the preset initial proportional coefficient, is the dynamic threat level at the current moment, is the hyperbolic tangent function used to normalize the dynamic threat level.

[0039] By adjusting the proportional coefficient in this way, the proportional coefficient can be adaptively enhanced when the B value is larger to quickly suppress interference; when the B value is smaller, the proportional coefficient is closer to the initial proportional coefficient to avoid overshoot.

[0040] The PID control system uses the optimized proportional coefficient at the current moment as the proportional coefficient for the next moment to dynamically adjust the main wire current compensation amount, and supplements the pre-adjustment of the slave wire current with the differential term to achieve transient interference suppression and steady-state heat input rebalancing of the arc-molten pool system, thereby reducing the fluctuation of penetration data, reducing the porosity, and improving the stability of the dynamic balance of the molten pool during the manufacture of welding equipment. The improved submerged arc welding machine equipment realizes adaptive optimization under multi-interference coupling through the real-time calculation and PID adjustment of the B value.

[0041] So far, a steel structure processing and welding method using a submerged arc welding machine is completed.

[0042] Based on the same inventive concept as the above method, the embodiment of the present application also provides a steel structure processing and welding device using a submerged arc welding machine, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it realizes the steps of any one of the above methods of the steel structure processing and welding method using a submerged arc welding machine.

[0043] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described. Moreover, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0044] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and each embodiment focuses on the differences from other embodiments.

[0045] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included within the protection scope of the present application.

Claims

1. A steel structure processing and welding method using a submerged arc welding machine, characterized in that, The method comprises the following steps: Obtain an electromagnetic sequence, current sequences and voltage sequences of two welding wires, a weld penetration depth sequence, and a high-temperature proportion sequence composed of the proportion of high-temperature pixel points in the infrared images collected at each moment within the observation period during the welding process of the steel structure by using a submerged arc welding machine; Both the electromagnetic sequence, the high-temperature proportion sequence, the current sequences and voltage sequences of the two welding wires are referred to as coupling interference sequences; obtain the coupling degradation index at the current moment according to the dispersion degree of the weld penetration depth sequence at the current moment and the energy distribution uniformity of each coupling interference sequence in the high-frequency band and the low-frequency band; Divide the coupling degradation indices at the current moment and all previous moments into multiple data windows; obtain the average coupling degradation index at the current moment according to the average level after smoothing the coupling degradation indices at the current moment and all previous moments, and combine the dispersion degree of the coupling degradation indices within each data window and the slope of the fitting straight line of the coupling degradation indices within each data window to obtain the dynamic threat level at the current moment; furthermore, optimize the preset initial proportional coefficient in the PID control system, and use the optimized proportional coefficient to adjust the main welding wire current at the next moment.

2. The steel structure processing and welding method using a submerged arc welding machine according to claim 1, characterized in that, The proportion of high-temperature pixel points in the infrared image collected at each moment is the ratio of the number of pixel points in the clustering cluster with the highest average pixel value to the total number of pixel points in the infrared image after clustering all pixel points in the infrared image.

3. A steel structure processing and welding method using a submerged arc welding machine as described in claim 1, characterized in that, The calculation formula for the coupling degradation index at the current moment is as follows: ; In the formula, is the coupling degradation index at the current moment, is the standard deviation of the weld penetration depth sequence at the current moment, is the energy entropy value of the i-th coupling interference sequence in the high-frequency band at the current moment, is the energy entropy value of the i-th coupling interference sequence in the low-frequency band at the current moment, is the total number of coupling interference sequences at the current moment.

4. A steel structure processing and welding method using a submerged arc welding machine as described in claim 3, characterized in that, The energy entropy values of the low-frequency band and the high-frequency band of the coupling interference sequence are obtained through the wavelet energy entropy algorithm.

5. A steel structure processing and welding method using a submerged arc welding machine according to claim 1, characterized in that, The specific process of dividing the coupling degradation indices at all moments into multiple data windows is as follows: take every r data of the coupling degradation indices at all moments in chronological order from front to back as a data window, where r is a preset positive integer.

6. A steel structure processing and welding method using a submerged arc welding machine according to claim 1, characterized in that, The process of obtaining the average coupling degradation index at the current moment is as follows: use the coupling degradation indices at the current moment and all previous moments as the input of the moving average algorithm, output the smoothed coupling degradation indices and calculate the mean value, and take the mean value as the average coupling degradation index at the current moment.

7. A steel structure processing and welding method using a submerged arc welding machine as described in claim 1, characterized in that, The calculation formula for the dynamic threat level at the current moment is as follows: ; In the formula, is the dynamic threat level at the current moment, is the smooth coupling degradation index at the current moment, is the coefficient of variation within the j-th data window among all data windows before the current moment, is the slope of the fitting straight line of the j-th data window among all data windows before the current moment, and N is the total number of all data windows before the current moment.

8. A steel structure processing and welding method using a submerged arc welding machine according to claim 1, characterized in that, The optimization of the preset initial proportional coefficient in the PID control system is carried out, and the calculation formula of the optimized proportional coefficient is as follows: ; In the formula, is the optimized proportional coefficient at the current moment, is the preset initial proportional coefficient, is the dynamic threat level at the current moment, is the hyperbolic tangent function.

9. A steel structure processing and welding method using a submerged arc welding machine according to claim 1, characterized in that, The specific process of using the optimized proportional coefficient to adjust the main welding wire current at the next moment is as follows: use the optimized proportional coefficient at the current moment as the proportional coefficient of the PID control system at the next moment to dynamically adjust the main welding wire current compensation amount.

10. A steel structure processing and welding device using a submerged arc welding machine, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for processing and welding a steel structure using a submerged arc welding machine as described in any one of claims 1-9.

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