Process pipeline welding construction management method and system

Abstract

The invention relates to a process pipeline welding construction management method and system, and the method comprises the steps: calculating the mean value of the iron oxide content of to-be-welded pipeline batches according to the number of pipelines and the iron oxide content of the surface of each to-be-welded pipeline; the protective gas concentration is detected and input into a welding model, and the welding model is constructed based on historical welding data and used for predicting the preset iron oxide content under the given protective gas concentration; based on the relative deviation percentage between the iron oxide content mean value and the preset iron oxide content, the difference value of the to-be-welded pipeline batches is taken; according to the difference value, initial welding energy and initial welding interval time of the welding equipment are set; dynamically adjusting the welding interval time according to the real-time concentration of the protective gas; and according to the adjusted welding interval time, the welding energy corresponding to each welding interval time is determined. Through fusion of a plurality of parameter characteristics, coordination of mutual relations among parameters, intelligent decision making and dynamic control of the welding process, the welding quality is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of industrial welding technology, and in particular to a method and system for construction management of process pipeline welding. Background Technology

[0002] The welding quality of process piping directly affects the safety and reliability of the entire system. Iron oxide on the pipe surface is a key factor influencing weld quality; excessive iron oxide content can lead to defects such as porosity and slag inclusions in the weld. The management of process piping welding construction relies heavily on the experience of the operators. The process typically includes: sampling individual pipes to be welded to test the surface iron oxide content; basic monitoring of the shielding gas concentration during welding; and the welder or process engineer setting the initial welding energy and welding interval based on fixed parameters such as pipe material and wall thickness, combined with their personal experience.

[0003] Current iron oxide content testing methods only involve random sampling of individual pipes, failing to reflect the overall characteristics of the entire pipe batch. Due to differences in storage and transportation environments, the surface conditions of pipes within a batch vary. Welding parameters set based on a single sample cannot be adapted to most pipes within the batch, easily leading to defects such as poor fusion and slag inclusions during welding of some pipes. Shielding gas concentration is used only as an independent monitoring indicator, without correlation analysis with the iron oxide content characteristics of the pipe itself. When the concentration is abnormal, the empirical adjustment measures taken are fixed and fail to consider the specific requirements of the current pipe batch for shielding gas, resulting in a mismatch between the shielding effect and actual working conditions. The setting of initial welding energy and interval time heavily relies on the welder's personal experience, lacking scientific and quantitative decision-making basis. This fixed and empirical parameter setting method is difficult to adapt to the differences in characteristics between different batches of pipes and the dynamic changes in the welding environment, leading to unstable welding quality, low efficiency, and a high risk of rework, increasing costs and project schedule risks.

[0004] In summary, traditional methods lack a quantitative model that can integrate multi-source information (pipeline batch characteristics, ambient gas concentration) and make intelligent decisions. The various links are fragmented and fail to establish their inherent correlation, thus making it impossible to achieve automated, refined, and adaptive adjustment of welding parameters.

[0005] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for construction management of process pipeline welding. By integrating multiple parameter characteristics, coordinating the interrelationships between parameters, and making intelligent decisions and dynamically controlling the welding process, a significant improvement in welding quality is achieved.

[0007] In a first aspect, the present invention provides a method for construction management of process piping welding, comprising the following steps: The iron oxide content on the surface of the pipe to be welded is detected, and the number of pipes in the batch to be welded is calculated. The concentration of the protective gas was detected during the welding of the pipeline; Based on the number of pipes and the iron oxide content on the surface of each pipe to be welded, calculate the average iron oxide content of the batch of pipes to be welded. The shielding gas concentration is input into a pre-built welding model, which is constructed based on historical welding data and is used to predict the preset iron oxide content at a given shielding gas concentration. Obtain the preset iron oxide content, and use the percentage relative deviation between the average iron oxide content and the preset iron oxide content as the difference value of the batch of pipes to be welded; Based on the difference value, the initial welding energy and initial welding interval time of the welding equipment are set; The welding interval time is dynamically adjusted based on the real-time concentration of the protective gas. Based on the adjusted welding interval time, the welding energy corresponding to each welding interval time is determined.

[0008] In some implementations, the welding model is established through the following steps: Obtain shielding gas concentration data at multiple time points during historical welding processes and the average iron oxide content data in the corresponding pipeline batches; The protective gas concentration data and the average iron oxide content data are subjected to noise filtering to obtain a cleaned dataset; The cleaned dataset is divided into multiple continuous intervals according to the protective gas concentration value. For each interval, the mean and variance of the mean iron oxide content are calculated. Based on the mean and variance of each interval, the correspondence between the protective gas concentration and the mean iron oxide content within that interval is determined; The correspondences of all intervals are smoothly connected to form a complete welding model, which is used to predict the preset iron oxide content under a given shielding gas concentration.

[0009] In some embodiments, setting the initial welding energy of the welding equipment based on the difference value includes: Calculate the percentage relative deviation between the average iron oxide content and the preset iron oxide content; Query the preset energy adjustment mapping table, which stores the energy correction coefficients corresponding to different relative deviation percentages; Retrieve the corresponding energy correction factor from the mapping table based on the current relative deviation percentage; The adjusted initial welding energy is obtained by multiplying the default welding energy of the welding equipment by the energy correction coefficient.

[0010] In some implementations, the construction of the energy adjustment map includes the following steps: Collect a large amount of historical welding data, including the percentage deviation of iron oxide content and the corresponding ideal energy adjustment value; The historical data is fitted using an interpolation algorithm to generate a continuous energy correction coefficient curve; The energy correction coefficient curve is discretized into multiple percentage intervals, and an energy correction coefficient value is assigned to each interval; Store each interval and its corresponding energy correction coefficient value to form an energy adjustment mapping table.

[0011] In some embodiments, adjusting the initial welding interval time according to the shielding gas concentration includes: Obtain the total duration of the welding process; Based on the total duration and initial welding energy, calculate the energy distribution rate per unit time; Based on the power characteristics of the welding equipment, determine the basic time unit required for a single welding operation; The welding interval time is calculated based on the total duration, the basic time unit, and the energy distribution rate.

[0012] In some embodiments, dynamically adjusting the welding interval time based on the real-time concentration of the shielding gas includes: Real-time collection of protective gas concentration samples, which are then stored as time-series data; Calculate the difference between the current concentration sample value and the moving average of multiple previous sample values; When the difference exceeds the preset fluctuation threshold, the interval adjustment process is initiated. Calculate the time scaling factor based on the magnitude and direction of the difference, and apply this factor to scale the current welding interval time.

[0013] In some implementations, the computation time scaling factor includes: Determine the absolute value of the concentration difference; The absolute value is compared with multiple preset difference levels, each of which corresponds to a scaling factor range; Select a specific scaling factor value from the predefined scaling factor table based on the difference level to which the value belongs; The scaling factor is adjusted in direction based on the positive or negative direction of the concentration difference.

[0014] In some embodiments, determining the welding energy corresponding to each welding interval time based on the adjusted welding interval time includes: Record the current welding interval time and its historical value for the previous interval time; Calculate the rate of change between the current interval and the historical interval; When the rate of change is within a stable range, the welding energy value corresponding to the historical interval time is directly used. When the rate of change exceeds the stable range, the energy compensation table is consulted based on the magnitude of the rate of change to obtain the energy adjustment amount.

[0015] In some implementations, the construction of the energy compensation table includes: Analyze the relationship between the rate of change of welding intervals and energy demand in historical welding data; The energy compensation values ​​corresponding to different ranges of change rate were determined by regression analysis. The energy compensation value is standardized into a standard lookup table, where the rate of change range corresponds one-to-one with the energy adjustment amount.

[0016] In a second aspect, the present invention provides a process piping welding construction management system, 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 process piping welding construction management method described in any one of the first aspects above.

[0017] Beneficial Effects: This solution constructs a welding model as the decision-making center. This model, by learning from historical data, establishes an intrinsic relationship between shielding gas concentration and the ideal iron oxide content in the pipeline. By comparing the real-time measured average batch iron oxide content with the preset value predicted by the model, a quantified difference value is obtained. This difference value serves as a key input, driving the scientific setting and dynamic adjustment of subsequent welding parameters (initial welding energy, welding interval time), ultimately achieving precise and adaptive matching between the welding process and specific working conditions. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the overall principle of the present invention; Figure 2 This is a schematic diagram illustrating the steps involved in establishing the welding model of the present invention. Figure 3 A schematic diagram illustrating the process of setting the initial welding energy of the welding equipment in this invention; Figure 4 This is a schematic diagram illustrating the construction process of the energy adjustment mapping table of the present invention; Figure 5 This is a schematic diagram illustrating the process of adjusting the welding interval time according to the present invention; Figure 6 This is a schematic diagram illustrating the process of dynamically adjusting the welding interval time according to the present invention; Figure 7 This is a schematic diagram illustrating the process of calculating the time scaling factor in this invention; Figure 8 This is a schematic diagram illustrating the process of determining the welding energy corresponding to each welding interval time in this invention. Figure 9 This is a schematic diagram illustrating the construction steps of the energy compensation table of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The welding quality of process piping directly affects the safety and reliability of the entire system. Iron oxide on the pipe surface is a key factor influencing weld quality; excessive iron oxide content can lead to defects such as porosity and slag inclusions in the weld. The management of process piping welding construction relies heavily on the experience of the operators. The process typically includes: sampling individual pipes to be welded to test the surface iron oxide content; basic monitoring of the shielding gas concentration during welding; and the welder or process engineer setting the initial welding energy and welding interval based on fixed parameters such as pipe material and wall thickness, combined with their personal experience.

[0021] Current iron oxide content testing methods only involve random sampling of individual pipes, failing to reflect the overall characteristics of the entire pipe batch. Due to differences in storage and transportation environments, the surface conditions of pipes within a batch vary. Welding parameters set based on a single sample cannot be adapted to most pipes within the batch, easily leading to defects such as poor fusion and slag inclusions during welding of some pipes. Shielding gas concentration is used only as an independent monitoring indicator, without correlation analysis with the iron oxide content characteristics of the pipe itself. When the concentration is abnormal, the empirical adjustment measures taken are fixed, failing to consider the specific requirements of the current pipe batch for shielding gas, resulting in a mismatch between the shielding effect and actual working conditions. The setting of initial welding energy and interval time heavily relies on the welder's personal experience, lacking scientific and quantitative decision-making basis. This fixed and empirical parameter setting method is difficult to adapt to the differences in characteristics between different batches of pipes and the dynamic changes in the welding environment, leading to unstable welding quality, low efficiency, and a high risk of rework, increasing costs and project time risks.

[0022] In summary, traditional methods lack a quantitative model that can integrate multi-source information (pipeline batch characteristics, ambient gas concentration) and make intelligent decisions. The various links are fragmented and fail to establish their inherent correlation, thus making it impossible to achieve automated, refined, and adaptive adjustment of welding parameters.

[0023] Firstly, such as Figure 1 As shown, the present invention provides a method for construction management of process piping welding, comprising the following steps: The iron oxide content on the surface of the pipe to be welded is detected, and the number of pipes in the batch to be welded is calculated. The concentration of the protective gas was detected during the welding of the pipeline; Based on the number of pipes and the iron oxide content on the surface of each pipe to be welded, calculate the average iron oxide content of the batch of pipes to be welded. The shielding gas concentration is input into a pre-built welding model, which is constructed based on historical welding data and is used to predict the preset iron oxide content at a given shielding gas concentration. Obtain the preset iron oxide content, and use the percentage relative deviation between the average iron oxide content and the preset iron oxide content as the difference value of the batch of pipes to be welded; Based on the difference value, the initial welding energy and initial welding interval time of the welding equipment are set; The welding interval time is dynamically adjusted based on the real-time concentration of the protective gas. Based on the adjusted welding interval time, the welding energy corresponding to each welding interval time is determined.

[0024] In this scheme, firstly, the initial state of the pipe surface (iron oxide content) and the welding environment state (shielding gas concentration) are obtained through detection (e.g., using spectral analysis or chemical titration). Then, a pre-trained welding model (essentially an input-output mapping based on historical data) is used to map the current gas concentration to an ideal preset iron oxide content. This preset value represents the level of iron oxide content that the pipe surface should achieve for good welding under the current gas environment. Next, the difference between the average iron oxide content in the actual measured batches and this preset value is calculated; this difference quantifies the gap between the current operating conditions and the ideal operating conditions. Based on this difference, the system inversely derives the initial welding energy that needs compensation (higher energy to remove more oxide layer, lower energy to prevent overheating) and a reasonable welding interval time (providing a time window for energy accumulation and heat diffusion). Finally, the system is not static but continuously monitors the shielding gas concentration, as even small fluctuations in the gas concentration can affect the shielding effect. Therefore, the welding interval time needs to be fine-tuned in real time, and the energy of each weld point is dynamically allocated according to the adjusted interval time, forming a real-time, dynamic, and adaptive feedback control loop.

[0025] This solution represents a qualitative shift in welding processes, moving from static, experience-based settings to dynamic, data-driven approaches. Firstly, it improves the consistency of welding quality by averaging the iron oxide content across the entire batch and uniformly adjusting parameters, effectively reducing fluctuations in welding quality between different pipe sections within the same batch. Secondly, it enhances the adaptability of the process, enabling dynamic adjustments based on real-time changes in shielding gas concentration, offsetting environmental interference and ensuring stable welding results even under non-ideal gas conditions. Thirdly, it achieves precision and automation in the welding process, reducing reliance on human experience, lowering the operational threshold and the risk of human error, thereby significantly improving production efficiency and product qualification rate.

[0026] like Figure 2 As shown, the welding model is established through the following steps: Obtain shielding gas concentration data at multiple time points during historical welding processes and the average iron oxide content data in the corresponding pipeline batches; The protective gas concentration data and the average iron oxide content data are subjected to noise filtering to obtain a cleaned dataset; The cleaned dataset is divided into multiple continuous intervals according to the protective gas concentration value. For each interval, the mean and variance of the mean iron oxide content are calculated. Based on the mean and variance of each interval, the correspondence between the protective gas concentration and the mean iron oxide content within that interval is determined; The correspondences of all intervals are smoothly connected to form a complete welding model, which is used to predict the preset iron oxide content under a given shielding gas concentration.

[0027] The construction of the welding model is essentially a data-driven empirical model building method. First, historical data is collected to establish a data knowledge base. Noise reduction is achieved using methods such as moving average filtering or median filtering to eliminate abnormal data such as sensor misreadings and operational errors, improving data quality and ensuring model robustness. Then, continuous shielding gas concentration data is divided into multiple intervals, approximating complex nonlinear relationships as linear or stationary relationships within local intervals. The average and variance of iron oxide content within each interval are calculated. The average represents the "typical" iron oxide level for that gas concentration interval, while the variance measures the dispersion of the data, providing a confidence reference for model prediction. Finally, the correspondences of all intervals are smoothly connected (e.g., using spline interpolation or linear connections), forming a continuous prediction model covering the entire gas concentration range. This ensures that for any given gas concentration, a smoothly transitioning preset iron oxide content value is output. The construction of this welding model guarantees the accuracy and stability of the prediction results.

[0028] like Figure 3 As shown, based on the difference value, the initial welding energy of the welding equipment is set, including: Calculate the percentage relative deviation between the average iron oxide content and the preset iron oxide content; Query the preset energy adjustment mapping table, which stores the energy correction coefficients corresponding to different relative deviation percentages; Retrieve the corresponding energy correction factor from the mapping table based on the current relative deviation percentage; The adjusted initial welding energy is obtained by multiplying the default welding energy of the welding equipment by the energy correction coefficient.

[0029] Calculating the percentage of relative deviation standardizes the difference, facilitating comparison and application across different levels of iron oxide content. The pre-defined "energy adjustment mapping table" is essentially a discretized representation of a lookup dictionary or empirical formula, storing "deviation-compensation" relationships validated through extensive practice. For example, when the iron oxide content is high (positive deviation), the mapping table provides an energy correction coefficient greater than 1, proportionally increasing the welding energy to fully dissolve the oxide layer; conversely, it decreases the energy. Finally, a multiplier combines the correction coefficient with the equipment's base parameters (preset welding energy) to achieve precise control of the equipment output. This process avoids the hassle of manual calculations based on complex formulas for engineers. Through simple table lookups and multiplication, initial parameters can be set quickly and accurately, significantly improving response speed and ease of operation, and ensuring the consistency and repeatability of energy adjustments.

[0030] like Figure 4As shown, the construction of the energy adjustment mapping table includes the following steps: Collect a large amount of historical welding data, including the percentage deviation of iron oxide content and the corresponding ideal energy adjustment value; The historical data is fitted using an interpolation algorithm to generate a continuous energy correction coefficient curve; The energy correction coefficient curve is discretized into multiple percentage intervals, and an energy correction coefficient value is assigned to each interval; Store each interval and its corresponding energy correction coefficient value to form an energy adjustment mapping table.

[0031] Based on data fitting and discretization, historical welding data is collected to obtain actual welding data. Interpolation algorithms such as linear interpolation and polynomial interpolation are used to construct a continuous "energy correction coefficient curve" from discrete data points. This curve completely describes the energy adjustment strategy required across the entire range from negative to positive deviation. Discretizing the continuous curve into multiple percentage intervals and assigning correction coefficient values ​​facilitates storage and rapid retrieval in digital systems (such as PLCs and microcontrollers), representing an engineering optimization while ensuring accuracy. Discretizing into multiple percentage regions also provides greater tolerance and facilitates faster processing. This method ensures the scientific validity and practicality of the energy adjustment mapping table. Through data fitting, the logic behind the mapping table is derived from practice rather than subjective assumptions, improving the scientific nature of decision-making. Discretization allows this advanced algorithm to be easily embedded into controllers in industrial settings, achieving an effective combination of advanced algorithms and industrial hardware.

[0032] like Figure 5 As shown, adjusting the initial welding interval time according to the shielding gas concentration includes: Obtain the total duration of the welding process; Based on the total duration and initial welding energy, calculate the energy distribution rate per unit time; Based on the power characteristics of the welding equipment, determine the basic time unit required for a single welding operation; The initial welding interval time is calculated based on the total duration, the basic time unit, and the energy distribution rate.

[0033] Under the constraints of total welding energy and total time, the energy distribution rate per unit time is calculated. Combined with the basic time required for a single welding operation, a theoretically reasonable interval time is derived to ensure the uniform distribution and accumulation of heat. This welding interval time is the default initial welding time, which is universal and can guarantee the overall efficiency and thermal balance of the welding process, avoiding local overheating or incomplete penetration.

[0034] like Figure 6As shown, the welding interval time is dynamically adjusted according to the real-time concentration of the shielding gas, including: Real-time collection of protective gas concentration samples, which are then stored as time-series data; Calculate the difference between the current concentration sample value and the moving average of multiple previous sample values; When the difference exceeds the preset fluctuation threshold, the interval adjustment process is initiated. Calculate the time scaling factor based on the magnitude and direction of the difference, and apply this factor to scale the current welding interval time.

[0035] From a real-time feedback perspective, the stability of the protective gas concentration is monitored. A moving average is calculated to eliminate instantaneous fluctuations and obtain a stable concentration trend. When the difference between the real-time concentration and the trend value exceeds a fluctuation threshold (set according to different needs, such as 0.3%, 0.5%, 1%, etc.), it indicates a significant change in the protective environment (e.g., insufficient cylinder pressure, pipeline leakage). At this point, the system adjusts the interval time using a time scaling factor: if the gas concentration decreases, the interval time is extended to allow more time for heat dissipation from the previous weld joint and to allow the gas to recover, preventing overheating and oxidation; if the gas concentration increases, the interval time can be appropriately shortened. Dynamic adjustments to address changes in the gas environment effectively suppress quality risks caused by protective gas fluctuations and greatly enhance the robustness of the welding process against interference.

[0036] like Figure 7 As shown, the computation time scaling factor includes: Determine the absolute value of the concentration difference; The absolute value is compared with multiple preset difference levels, each of which corresponds to a scaling factor range; Select a specific scaling factor value from the predefined scaling factor table based on the difference level to which the value belongs; The scaling factor is adjusted in direction based on the positive or negative direction of the concentration difference.

[0037] The absolute magnitude of concentration differences is divided into multiple levels, such as "slight," "moderate," and "severe," with each level corresponding to a scaling factor range. This hierarchical approach avoids the system from becoming overly sensitive to small fluctuations while still allowing for sufficient adjustments when larger deviations occur. Combining the positive or negative direction of the difference (concentration increase or decrease), the system ultimately determines whether to shorten or lengthen the interval, giving the control action directionality. This hierarchical control strategy makes the system response smoother and more reasonable. It avoids welding process jitter caused by sudden parameter changes, ensuring stable production. Simultaneously, it simplifies complex continuous calculations into simple level judgment and table lookup, reducing the controller's computational burden and improving the system's real-time performance.

[0038] like Figure 8 As shown, determining the welding energy corresponding to each welding interval time based on the adjusted welding interval time includes: Record the current welding interval time and its historical value for the previous interval time; Calculate the rate of change between the current interval and the historical interval; When the rate of change is within a stable range, the welding energy value corresponding to the historical interval time is directly used. When the rate of change exceeds the stable range, the energy compensation table is consulted based on the magnitude of the rate of change to obtain the energy adjustment amount.

[0039] A feedback compensation mechanism based on interval time variations was established. Recording historical interval times and calculating the rate of change helps to detect trends in welding cycle time. When the rate of change is within a stable range, the system is operating smoothly, requiring no energy adjustment and continuing with the previous energy value. When the rate of change exceeds the stable range, it indicates a change in heat input conditions, necessitating consulting the energy compensation table for energy compensation. For example, if the interval time is significantly extended, reducing heat input per unit time, the welding energy needs to be appropriately increased to compensate. This achieves coordinated energy-time interaction, ensuring the entire welding heat input system maintains dynamic balance even when the interval time is dynamically adjusted due to environmental factors. This prevents problems such as incomplete penetration or burn-through caused by cycle time variations, ultimately achieving high stability in welding quality under dynamically changing conditions.

[0040] like Figure 9 As shown, the construction of the energy compensation table includes: Analyze the relationship between the rate of change of welding intervals and energy demand in historical welding data; The energy compensation values ​​corresponding to different ranges of change rate were determined by regression analysis. The energy compensation value is standardized into a standard lookup table, where the rate of change range corresponds one-to-one with the energy adjustment amount.

[0041] An energy compensation table establishes a quantitative relationship between the "rate of change over time" and the "required energy compensation amount" by using regression analysis of historical data.

[0042] like Figures 1 to 9 As shown, a method for construction management of process piping welding includes, more specifically: The iron oxide content on the surface of the pipe to be welded is detected, and the number of pipes in the batch to be welded is calculated; the concentration of the shielding gas during the welding of the pipe is also detected. Based on the number of pipes and the iron oxide content on the surface of each pipe to be welded, calculate the average iron oxide content of the batch of pipes to be welded. The shielding gas concentration is input into a pre-built welding model, which is obtained based on historical welding data through noise filtering and interval division, and is used to predict the preset iron oxide content under a given shielding gas concentration. The percentage relative deviation between the average iron oxide content and the preset iron oxide content is calculated as the difference value. Based on the difference value, a preset energy adjustment mapping table is queried to obtain the energy correction coefficient, and the initial welding energy of the welding equipment is set to the product of the default welding energy and the energy correction coefficient; Based on the difference value, the initial welding interval time of the welding equipment is set; Based on the detected real-time shielding gas concentration, the difference between the moving average of the shielding gas concentration sampling value and the current value is calculated, and when the difference exceeds a preset fluctuation threshold, the welding interval time is adjusted by applying a time scaling factor. Based on the adjusted welding interval time, the welding energy corresponding to each welding interval time is determined by consulting the energy compensation table.

[0043] This embodiment refines the implementation details of each step, overcoming the over-reliance on manual experience and the isolation and rigidity of parameter settings in existing process pipeline welding management. This includes upgrading the random sampling inspection of individual pipelines to calculating the statistical average of iron oxide content for the entire pipeline batch, obtaining data representative of the overall batch characteristics, and avoiding welding parameter mismatches caused by individual differences. A welding model is constructed to correlate the two previously independent key parameters—pipeline iron oxide content and shielding gas concentration—changing their independent monitoring and isolated adjustment to achieve collaborative decision-making between parameters. Mapping tables based on historical data (such as energy adjustment mapping tables and energy compensation tables) and quantitative algorithms (such as calculating time scaling factors) are established to transform qualitative decisions relying on individual welder experience into reproducible quantitative decisions based on models and data, thereby reducing quality fluctuations and improving the stability and reliability of welding quality.

[0044] In a second aspect, the present invention provides a process piping welding construction management system, 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 process piping welding construction management method described in any one of the first aspects above.

[0045] The above solution is illustrated below with a specific embodiment, which is as follows: Initialization and Inspection: For a batch of 10 process pipes to be welded, the iron oxide content of specific welding areas on the outer surface of each pipe was detected using a spectrometer and recorded. Simultaneously, a gas sensor was installed near the welding torch to monitor the concentration of argon shielding gas in real time.

[0046] Model Application and Difference Calculation: The industrial control computer calls a welding model pre-built based on historical data. The currently monitored average shielding gas concentration (99.2%) is input into the model, resulting in a preset iron oxide content of 0.15%. The calculated average measured iron oxide content of this batch of pipelines is 0.18%, therefore the relative deviation percentage is... .

[0047] Setting initial parameters: Based on a +20% deviation, the energy adjustment mapping table is consulted to obtain an energy correction factor of 1.15. The default welding energy is 100J, therefore the initial welding energy is set as follows: At the same time, based on this difference value, the system sets a basic initial welding interval time of 2 seconds.

[0048] Real-time adjustment: After welding begins, the gas concentration sensor continuously samples. At a certain moment, it detects that the concentration drops from 99.2% to 98.5% within a short period. The system calculates that the difference between this and the recent moving average exceeds a preset threshold (0.3%), and based on the magnitude and direction of the drop, determines a time scaling factor of 1.2 (i.e., an extension of 20%) from the scaling factor table. Therefore, the current welding interval is adjusted from 2 seconds to... Second.

[0049] The final energy setting was determined as follows: When the interval changed from 2 seconds to 2.4 seconds, the system calculated a change rate of +20%, exceeding the stable range (±5%). Consulting the energy compensation table revealed that for a +20% change rate, the welding energy needed to be increased by 5% to compensate for the heat input. Therefore, the final welding energy set for this weld point was... Welding is performed according to the welding energy and welding time interval determined above.

[0050] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A method for construction management of process piping welding, characterized in that, Includes the following steps: The iron oxide content on the surface of the pipe to be welded is detected, and the number of pipes in the batch to be welded is calculated. The concentration of the protective gas was detected during the welding of the pipeline; Based on the number of pipes and the iron oxide content on the surface of each pipe to be welded, calculate the average iron oxide content of the batch of pipes to be welded. The shielding gas concentration is input into a pre-built welding model, which is constructed based on historical welding data and is used to predict the preset iron oxide content at a given shielding gas concentration. Obtain the preset iron oxide content, and use the percentage relative deviation between the average iron oxide content and the preset iron oxide content as the difference value of the batch of pipes to be welded; Based on the difference value, the initial welding energy and initial welding interval time of the welding equipment are set; The welding interval time is dynamically adjusted based on the real-time concentration of the protective gas. Based on the adjusted welding interval time, the welding energy corresponding to each welding interval time is determined.

2. The process piping welding construction management method as described in claim 1, characterized in that, The welding model is established through the following steps: Obtain shielding gas concentration data at multiple time points during historical welding processes and the average iron oxide content data in the corresponding pipeline batches; The protective gas concentration data and the average iron oxide content data are subjected to noise filtering to obtain a cleaned dataset; The cleaned dataset is divided into multiple continuous intervals according to the protective gas concentration value. For each interval, the mean and variance of the mean iron oxide content are calculated. Based on the mean and variance of each interval, the correspondence between the protective gas concentration and the mean iron oxide content within that interval is determined; The correspondences of all intervals are smoothly connected to form a complete welding model, which is used to predict the preset iron oxide content under a given shielding gas concentration.

3. The process piping welding construction management method as described in claim 1 or 2, characterized in that, Based on the difference value, the initial welding energy of the welding equipment is set, including: Calculate the percentage relative deviation between the average iron oxide content and the preset iron oxide content; Query the preset energy adjustment mapping table, which stores the energy correction coefficients corresponding to different relative deviation percentages; Retrieve the corresponding energy correction factor from the mapping table based on the current relative deviation percentage; The adjusted initial welding energy is obtained by multiplying the default welding energy of the welding equipment by the energy correction coefficient.

4. The process piping welding construction management method according to any one of claims 1 to 3, characterized in that, The construction of the energy adjustment mapping table includes the following steps: Collect a large amount of historical welding data, including the percentage deviation of iron oxide content and the corresponding ideal energy adjustment value; The historical data is fitted using an interpolation algorithm to generate a continuous energy correction coefficient curve; The energy correction coefficient curve is discretized into multiple percentage intervals, and an energy correction coefficient value is assigned to each interval; Store each interval and its corresponding energy correction coefficient value to form an energy adjustment mapping table.

5. The process piping welding construction management method according to any one of claims 1 to 4, characterized in that, Adjusting the initial welding interval time based on the shielding gas concentration includes: Obtain the total duration of the welding process; Based on the total duration and initial welding energy, calculate the energy distribution rate per unit time; Based on the power characteristics of the welding equipment, determine the basic time unit required for a single welding operation; The initial welding interval time is calculated based on the total duration, the basic time unit, and the energy distribution rate.

6. The process piping welding construction management method according to any one of claims 1 to 5, characterized in that, The welding interval time is dynamically adjusted based on the real-time concentration of the shielding gas, including: Real-time collection of protective gas concentration samples, which are then stored as time-series data; Calculate the difference between the current concentration sample value and the moving average of multiple previous sample values; When the difference exceeds the preset fluctuation threshold, the interval adjustment process is initiated. Calculate the time scaling factor based on the magnitude and direction of the difference, and apply this factor to scale the current welding interval time.

7. The process piping welding construction management method according to any one of claims 1 to 6, characterized in that, The computation time scaling factor includes: Determine the absolute value of the concentration difference; The absolute value is compared with multiple preset difference levels, each of which corresponds to a scaling factor range; Select a specific scaling factor value from the predefined scaling factor table based on the difference level to which the value belongs; The scaling factor is adjusted in direction based on the positive or negative direction of the concentration difference.

8. The process piping welding construction management method according to any one of claims 1 to 7, characterized in that, The step of determining the welding energy corresponding to each welding interval time based on the adjusted welding interval time includes: Record the current welding interval time and its historical value for the previous interval time; Calculate the rate of change between the current interval and the historical interval; When the rate of change is within a stable range, the welding energy value corresponding to the historical interval time is directly used. When the rate of change exceeds the stable range, the energy compensation table is consulted based on the magnitude of the rate of change to obtain the energy adjustment amount.

9. The process piping welding construction management method according to any one of claims 1 to 8, characterized in that, The construction of the energy compensation table includes: Analyze the relationship between the rate of change of welding intervals and energy demand in historical welding data; The energy compensation values ​​corresponding to different ranges of change rate were determined by regression analysis. The energy compensation value is standardized into a standard lookup table, where the rate of change range corresponds one-to-one with the energy adjustment amount.

10. A process piping welding construction management system, 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 the process pipeline welding construction management method according to any one of claims 1 to 9.

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