Methods and Systems for Reducing Shielding Gas Consumption and Controlling the Environment in MES Digital Welding

Abstract

This invention discloses a method and system for reducing shielding gas consumption and controlling the environment in digital welding using a MES (Mechanical, Equipment, and Manufacturing) system. It relates to the fields of welding process and environmental control technology, and includes: generating time-series data by collecting current waveforms and shielding gas flow rates during the welding process, and identifying the time boundaries of each welding stage; establishing a mapping relationship between current amplitude and flow rate demand based on data from stable welding stages, and generating graded flow rate commands for different stages; monitoring pipeline pressure during command execution and correcting the flow rate commands accordingly; identifying anomalies and generating identifiers by calculating the deviation between actual flow rate and commanded flow rate and analyzing pressure changes; calculating effective gas savings after removing abnormal data, converting it into carbon emission reductions, and uploading it to the MES platform. This achieves accurate statistics and visual management of energy conservation and emission reduction, effectively reducing production costs and environmental impact.

Description

Technical Field

[0001] This invention relates to the field of welding process and environmental control technology, specifically to a method and system for reducing shielding gas consumption and controlling the environment in MES-based digital welding. Background Technology

[0002] Welding is a widely used joining process in manufacturing, and shielding gas is typically used to prevent oxidation of the weld pool. Traditional welding gas supply methods usually employ constant flow control, maintaining the same gas flow rate throughout the entire welding process. While this method is simple to operate, it suffers from significant gas waste because the actual demand for shielding gas varies at different stages of the welding process.

[0003] Currently, the flow rate of welding shielding gas is usually preset by operators based on experience, lacking precise control that matches the actual operating current. This results in the continued use of high-flow-rate gas during low-current operation and the post-arc blowing phase, leading to significant gas waste.

[0004] Some existing gas flow control technologies have begun to be applied in the welding field, such as PLC-based timing control and simple current detection-based switching control. However, these technologies lack the ability to precisely identify and adapt to the welding process, and cannot dynamically adjust the gas flow according to the actual welding conditions. Existing technologies also lack effective monitoring and handling mechanisms for abnormal gas supply situations, making it difficult to maintain welding quality while conserving gas. Gas usage data during the welding process is often not effectively integrated with the company's production management system, making it difficult for companies to systematically manage gas usage efficiency and carbon emissions. This limits the company's ability to achieve refined management and environmental protection goals.

[0005] To address the aforementioned issues, there is an urgent need for a technical solution that can precisely control the flow rate of shielding gas based on the actual welding conditions, monitor abnormal situations, and integrate with the enterprise's information system, in order to reduce the use of welding shielding gas and control carbon emissions. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for reducing shielding gas consumption and controlling the environment in MES digital welding, aiming to solve at least one of the technical problems existing in the prior art.

[0007] The technical solution of this invention is: a method for reducing shielding gas consumption and controlling the environment in MES digital welding, comprising the following steps: The waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process are collected to generate current-flow time series data. Based on the current-flow time series data, the time series boundaries of the arc initiation stage, the stable welding stage and the arc cessation stage are identified. Based on the time sequence boundary, the current amplitude and corresponding flow rate value of the stable welding stage are extracted from the current-flow time sequence data, a mapping relationship between current amplitude and flow rate requirement is established, and graded flow rate instructions are generated for different stages based on the mapping relationship and time sequence boundary. When executing the graded flow command, the pressure of the gas supply line is monitored, and the graded flow command is corrected according to the fluctuation range of the gas supply line pressure. The corrected graded flow command is then output to the gas supply actuator. The deviation between the actual flow rate in the current-flow time series data and the command flow rate in the corrected graded flow command is calculated. Anomaly patterns are identified by combining the changing trend of the gas supply pipeline pressure, and anomaly indicators are generated. Data corresponding to abnormal periods is removed based on the anomaly identifier. Effective gas reduction is calculated based on the corrected graded flow instructions after removal. Effective gas reduction is converted into carbon emission reduction and uploaded to the MES platform to generate emission reduction statistics.

[0008] The waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process are collected to generate current-flow time series data. Based on the current-flow time series data, the time series boundaries of the arc initiation stage, the stable welding stage, and the arc termination stage are identified, including: The waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process are collected and then aligned with the timestamps to generate current-flow time series data. Scan the operating current waveform in the current-flow time series data, and mark the moment when the operating current waveform starts to rise from zero as the start of the arc initiation stage. Starting from the arc initiation stage, a sliding window is set to scan the working current waveform, and the fluctuation amplitude and average current of the working current waveform within the sliding window are calculated. When the ratio of the fluctuation amplitude of the working current waveform to the average current of the window is lower than the average current of the window, the starting time of the sliding window is marked as the starting point of the stable welding stage. The working current waveform is continuously monitored from the start of the stable welding stage. The moment when the working current waveform begins to decrease from the average current in the window is marked as the start of the arc-stopping stage, and the moment when the working current waveform drops to zero is marked as the end of the arc-stopping stage. The arc initiation stage, stable welding stage, and arc cessation stage are defined based on the start point of the arc initiation stage, the start point of the stable welding stage, the start point of the arc cessation stage, and the end point of the arc cessation stage, thus forming the time sequence boundary.

[0009] Based on the timing boundaries, the current amplitude and corresponding flow rate values ​​for the stable welding stage are extracted from the current-flow timing data. A mapping relationship between current amplitude and flow rate demand is established. Based on the mapping relationship and timing boundaries, graded flow rate instructions are generated for different stages, including: Based on the time sequence boundary, the time period corresponding to the stable welding stage is extracted from the current-flow time sequence data, and the current amplitude and corresponding flow rate value of the stable welding stage are extracted. The current amplitude is divided into multiple current ranges, and the flow center value and flow fluctuation boundary corresponding to each current range are extracted as flow demand to establish a mapping relationship between current amplitude and flow demand. The current change curve of the arc initiation stage is extracted from the current-flow time series data according to the time series boundary. Multiple current change sub-stages are divided according to the curvature change characteristics of the current change curve and the adjustment range of the arc initiation stage is allocated. The graded flow command of the arc initiation stage is generated according to the mapping relationship and the adjustment range of the arc initiation stage. Based on the mapping relationship, the flow center value and flow fluctuation boundary are extracted to construct the flow control interval. Based on the mapping relationship and the flow control interval, the graded flow command for the stable welding stage is generated. The current decay curve of the arc-stopping stage is extracted from the current-flow time series data according to the time series boundary. Multiple current decay sub-stages are divided according to the curvature change characteristics of the current decay curve and the adjustment range of the arc-stopping stage is allocated. The graded flow command of the arc-stopping stage is generated according to the mapping relationship and the adjustment range of the arc-stopping stage.

[0010] The graded flow command is corrected based on the fluctuation range of the gas supply pipeline pressure. The corrected graded flow command is then output to the gas supply actuator, including: Acquire time-series pressure data for the gas supply pipeline; Based on the time sequence boundary, the pressure time sequence data of the gas supply pipeline is divided into pressure data segments for the arc initiation stage, pressure data segments for the stable welding stage, and pressure data segments for the arc cessation stage. The fluctuation amplitude corresponding to each pressure data segment is calculated separately. Using the fluctuation range of the pressure data segment during the stable welding stage as the reference fluctuation range, the arc-starting deviation of the pressure data segment during the arc-starting stage from the reference fluctuation range, and the arc-stopping deviation of the pressure data segment during the arc-stopping stage from the reference fluctuation range are calculated. Time series analysis was performed on the pressure data segments during the arc initiation stage and the pressure data segments during the arc termination stage to extract the trend characteristics of the arc initiation pressure and the arc termination pressure. The arc-starting pressure deviation and the arc-starting pressure change trend characteristics are weighted and fused to generate the arc-starting pressure correction coefficient, and the arc-stopping pressure deviation and the arc-stopping pressure change trend characteristics are weighted and fused to generate the arc-stopping pressure correction coefficient. The graded flow command for the stable welding stage remains unchanged. The graded flow command for the arc initiation stage is modified according to the arc initiation pressure correction coefficient, and the graded flow command for the arc cessation stage is modified according to the arc cessation pressure correction coefficient. The modified graded flow command is then output to the gas supply actuator.

[0011] The deviation between the actual flow rate in the current-flow time series data and the commanded flow rate in the corrected graded flow command is calculated. Combined with the trend of pressure changes in the gas supply pipeline, abnormal patterns are identified, and abnormality indicators are generated, including: The actual flow rate is extracted from the current-flow time series data, the command flow rate is extracted from the corrected graded flow command, the actual flow rate and the command flow rate are aligned by timestamp, the deviation of the actual flow rate from the command flow rate is calculated, and the deviation time series curve is formed. The pressure of the gas supply pipeline is sampled at the same timestamp, and the rate of pressure change at adjacent sampling times is calculated to form a trend curve. Identify the peak deviation and its corresponding time from the deviation time series curve, identify the extreme value of the pressure change rate and its corresponding time from the change trend curve, calculate the time difference between the peak deviation time and the extreme value of the pressure change rate, mark it as a response hysteresis feature when the time difference is positive and the extreme value of the pressure change rate is negative, and mark it as a gas residue feature when the time difference is negative and the extreme value of the pressure change rate is positive. The deviation fluctuation spectrum and pressure fluctuation spectrum of the stable welding stage are extracted from the deviation time series curve and the trend curve. The ratio of the main frequency of the deviation fluctuation spectrum to the pressure fluctuation spectrum is calculated. When the ratio of the main frequency is less than the preset ratio threshold, it is marked as a damped oscillation characteristic. Based on the response lag characteristics, it is identified as a gas supply response lag abnormal mode; based on the gas residue characteristics, it is identified as a gas residue abnormal mode; based on the damping oscillation characteristics, it is identified as a pipeline damping abnormal mode, and an abnormality identifier corresponding to each abnormal mode is generated.

[0012] Data corresponding to abnormal periods is removed based on the anomaly identifier. The effective gas reduction is calculated based on the corrected graded flow command after the removal, including: Based on the anomaly identifier, locate the start and end times of the abnormal period from the corrected hierarchical traffic command, and extract the traffic gradient before the start time and the traffic gradient after the end time. A flow transition curve is constructed based on the flow gradient to remove the boundary. The data between the start and end times is then interpolated and compensated using the flow transition curve before removal, and the corrected hierarchical flow command after removal is obtained. The initial command flow and duration for each time period are extracted from the graded flow command. The optimized command flow and duration for the corresponding time period are extracted from the corrected graded flow command after removal. The flow difference between the initial command flow and the optimized command flow for each time period within the corresponding duration is calculated. The flow difference is accumulated to obtain the effective gas saving amount.

[0013] The effective gas emission reduction is converted into carbon emission reduction and uploaded to the MES platform to generate emission reduction statistics, including: Extract the welding process parameters corresponding to the corrected graded flow instructions after rejection, and query the baseline carbon emission factor corresponding to the welding process parameters from the carbon emission factor library; Extract the gas supply pressure fluctuation amplitude and current fluctuation amplitude from the corrected graded flow command, dynamically correct the benchmark carbon emission factor to generate a real-time carbon emission factor, and calculate the carbon emission reduction based on the effective gas reduction and the real-time carbon emission factor. Construct emission reduction data records that include carbon emission reduction, welding process parameters, gas supply pressure fluctuation range, and current fluctuation range, and upload them to the MES platform; The MES platform establishes a classification index based on welding process parameters, extracts the gas supply pressure fluctuation range and current fluctuation range from historical emission reduction data records, constructs process stability evaluation indicators, summarizes carbon emission reductions according to the classification index and associates them with process stability evaluation indicators, and generates emission reduction statistics.

[0014] This invention provides a MES digital welding shielding gas reduction and environmental control system, the system comprising: The timing recognition module is used to collect the working current waveform and shielding gas instantaneous flow rate during the welding process, generate current-flow timing data, and identify the timing boundaries of the arc initiation stage, the stable welding stage, and the arc cessation stage based on the current-flow timing data. The instruction generation module is used to extract the current amplitude and corresponding flow rate value of the stable welding stage from the current-flow time series data according to the time series boundary, establish the mapping relationship between the current amplitude and the flow rate requirement, and generate graded flow rate instructions for different stages based on the mapping relationship and the time series boundary. The instruction correction module is used to monitor the gas supply pipeline pressure when executing the graded flow instruction, correct the graded flow instruction according to the fluctuation range of the gas supply pipeline pressure, obtain the corrected graded flow instruction, and output it to the gas supply actuator. The anomaly detection module is used to calculate the deviation between the actual flow rate in the current-flow time series data and the command flow rate in the corrected graded flow command, and to identify the anomaly mode by combining the change trend of the gas supply pipeline pressure and generate anomaly identification. The emission reduction statistics module is used to remove data corresponding to abnormal periods based on the anomaly identifier, calculate the effective gas reduction based on the corrected graded flow instructions after removal, convert the effective gas reduction into carbon emission reduction and upload it to the MES platform to generate emission reduction statistics.

[0015] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0016] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0017] This invention achieves precise, tiered supply of protective gas by real-time acquisition of current-flow data and identification of welding stages, accurately establishing a mapping relationship between current and flow requirements, thus effectively reducing gas waste. Combined with gas supply pipeline pressure monitoring, flow commands can be dynamically adjusted, improving the stability and reliability of gas supply. Anomaly pattern recognition and data rejection mechanisms ensure that the gas reduction process does not affect welding quality, improving process stability. Automatic calculation of gas savings and carbon emission reductions, integrated with the MES platform, enables visualized management of energy-saving and emission-reduction data, providing data support for enterprise carbon asset management. The overall solution precisely controls the use of welding protective gas through digital means, not only reducing production costs and improving resource utilization efficiency but also promoting the green and low-carbon transformation of the manufacturing industry. Attached Figure Description

[0018] Figure 1 A flowchart of the MES digital welding shielding gas reduction and environmental control method provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the structure of the MES digital welding shielding gas reduction and environmental control system according to an embodiment of the present invention. Detailed Implementation

[0019] like Figure 1 As shown, Figure 1 A flowchart of a method for reducing shielding gas consumption and controlling the environment in MES-based digital welding, provided in an embodiment of the present invention, is included in the following steps: Step 101: Collect the working current waveform and shielding gas instantaneous flow rate during the welding process to generate current-flow time series data. Based on the current-flow time series data, identify the time series boundaries of the arc initiation stage, the stable welding stage, and the arc cessation stage.

[0020] In some embodiments of the present invention, step 101 may specifically include the following sub-steps: Sub-step 1011: Collect the working current waveform and shielding gas instantaneous flow rate during the welding process, and generate current-flow time series data after aligning with the timestamps; Sub-step 1012: Scan the working current waveform in the current-flow time series data and mark the moment when the working current waveform starts to rise from zero as the start of the arc initiation stage. Sub-step 1013: Starting from the arc initiation stage, set a sliding window to scan the working current waveform and calculate the fluctuation amplitude and average current of the working current waveform within the sliding window. Sub-step 1014: When the ratio of the fluctuation amplitude of the working current waveform to the average current of the window is lower than the average current of the window, mark the starting time of the sliding window as the starting point of the stable welding stage. Sub-step 1015: Starting from the beginning of the stable welding stage, continuously monitor the working current waveform. The moment when the working current waveform starts to decrease from the average current of the window is marked as the start of the arc-stopping stage, and the moment when the working current waveform drops to zero is marked as the end of the arc-stopping stage. Sub-step 1016: Delineate the arc initiation stage, stable welding stage, and arc cessation stage based on the start point of the arc initiation stage, the start point of the stable welding stage, the start point of the arc cessation stage, and the end point of the arc cessation stage, thus forming the time sequence boundary.

[0021] When acquiring the waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process, a data acquisition device is used to sample the current signal output by the welding power source at a high frequency of 1000Hz to ensure that the rapid changes in the current waveform can be captured. Simultaneously, a flow sensor is installed on the shielding gas supply line to monitor the instantaneous flow rate of the shielding gas in real time. The data acquisition device records timestamps for the acquired working current data and shielding gas flow rate data for subsequent data alignment processing.

[0022] During data alignment, the operating current data and protective gas flow data are precisely aligned according to the timestamp. A linear interpolation method is used to process the data between different sampling points during the alignment process to ensure that the operating current data and protective gas flow data are completely matched in the time dimension, forming a complete current-flow time series dataset.

[0023] When scanning the operating current waveform in the current-flow time series data, an initial detection threshold of 5A is set. When the operating current value is detected to rise from zero and exceed this threshold, that moment is marked as the start of the arc ignition stage. Accurate identification of the start of the arc ignition stage is crucial for the division of subsequent welding stages. Therefore, a continuous judgment mechanism with multiple sampling points is adopted to avoid misjudgments caused by instantaneous interference.

[0024] Starting from the marked arc initiation stage, a sliding window is set to scan the operating current waveform. The sliding window size is set to 100 sampling points, corresponding to an actual time of 0.1s. At each sliding window position, the fluctuation amplitude of the operating current waveform within the window and the average current within the window are calculated. The fluctuation amplitude is obtained by calculating the difference between the maximum and minimum current values ​​within the window, and the average current within the window is obtained by calculating the arithmetic mean of all current sampling points within the window.

[0025] The sliding window moves forward 10 sampling points each time, recalculating the fluctuation amplitude and the average current within the window, and calculating the ratio of the fluctuation amplitude to the average current. When this ratio is lower than a preset threshold of 0.1, the operating current is determined to have entered a stable state, and the start time of the current sliding window is marked as the start of the stable welding stage. The marking of the start of the stable welding stage is based on a significant improvement in the stability of the operating current waveform and a reduction in the ratio of fluctuation amplitude to the average current value to an acceptable range.

[0026] Starting from the marked stable welding stage, the working current waveform is continuously monitored, and the sliding window technique is used to calculate the average current value of the window in real time. When the working current is detected to be continuously decreasing from the average current value of the window, and the decrease exceeds 20% of the average current value of the window, this moment is marked as the start of the arc-stopping stage. The characteristic of this stage is that the working current begins to decrease significantly, indicating that the welding operation is about to end.

[0027] Continue monitoring the operating current waveform. When the operating current value drops to near zero (below 5A) and remains stable, mark this moment as the end of the arc-stopping phase. The determination of the end of the arc-stopping phase uses a judgment logic based on multiple consecutive sampling points to ensure that the operating current has indeed dropped to near zero and remained stable, avoiding misjudgments caused by instantaneous fluctuations.

[0028] Based on the identified start points of the arc initiation phase, the stable welding phase, the arc cessation phase, and the arc cessation phase, the arc initiation phase, stable welding phase, and arc cessation phase of the entire welding process are delineated, forming complete temporal boundaries. These temporal boundaries serve as the foundational data for welding process analysis and are used for subsequent shielding gas consumption analysis and control strategy formulation.

[0029] After the time-series boundary was defined, statistical analysis was performed on the current-flow time-series data for each stage, calculating statistical characteristics such as the average current value, average flow rate value, current standard deviation, and flow rate standard deviation for each stage. These statistical characteristics reflect the characteristics and stability of the welding process at each stage, providing data support for subsequent shielding gas reduction control.

[0030] This invention, through the aforementioned current-flow time-series data analysis method, achieves precise identification of the arc initiation, stable welding, and arc termination stages during welding, laying the foundation for precise control of shielding gas. By accurately identifying different stages in the welding process, refined control of the shielding gas supply is achieved, reducing shielding gas consumption, minimizing resource waste, and improving the environmental friendliness of the welding process. Accurate time-series boundary identification provides an important basis for welding quality assessment, helping to improve the stability and consistency of welding quality, increase production efficiency, and reduce production costs.

[0031] Step 102: Based on the timing boundary, extract the current amplitude and corresponding flow rate value of the stable welding stage from the current-flow timing data, establish the mapping relationship between current amplitude and flow rate requirement, and generate graded flow rate instructions for different stages based on the mapping relationship and timing boundary.

[0032] In some embodiments of the present invention, step 102 may specifically include the following sub-steps: Sub-step 1021: Based on the time sequence boundary, extract the time period corresponding to the stable welding stage from the current-flow time sequence data, and extract the current amplitude and corresponding flow rate value of the stable welding stage. Sub-step 1022: Divide the current amplitude into multiple current intervals, extract the flow center value and flow fluctuation boundary corresponding to each current interval as the flow demand, and establish the mapping relationship between current amplitude and flow demand. Sub-step 1023: Extract the current change curve of the arc initiation stage from the current-flow time series data according to the time series boundary, divide the current change into multiple current change sub-stages according to the curvature change characteristics of the current change curve and allocate the adjustment range of the arc initiation stage, and generate the graded flow command of the arc initiation stage according to the mapping relationship and the adjustment range of the arc initiation stage. Sub-step 1024: Extract the flow center value and flow fluctuation boundary according to the mapping relationship to construct the flow control interval, and generate the graded flow command for the stable welding stage according to the mapping relationship and the flow control interval; Sub-step 1025: Extract the current decay curve of the arc-stopping stage from the current-flow time series data according to the time series boundary, divide the current decay curve into multiple current decay sub-stages according to the curvature change characteristics of the current decay curve and allocate the arc-stopping stage adjustment range, and generate the graded flow command of the arc-stopping stage according to the mapping relationship and the arc-stopping stage adjustment range.

[0033] Based on the identified time-series boundaries, time periods corresponding to the stable welding stage are extracted from the current-flow-rate time-series data, and the current amplitude and corresponding flow rate values ​​within this stage are extracted. During the extraction process, to ensure data quality, bidirectional filtering is used to remove transient noise interference and retain the true correspondence between current amplitude and flow rate values. The extracted current amplitude is calculated using the mean method, taking the arithmetic mean of the current values ​​within the stable welding stage as the representative value of the current amplitude. Simultaneously, the flow rate values ​​at the corresponding times are recorded, forming a set of current-flow-rate data pairs.

[0034] The extracted current amplitude is categorized into multiple current ranges based on welding process characteristics. A specific categorization method uses equal-width intervals, with each interval width set to 30A. Taking a welding current range of 80A to 350A as an example, it can be divided into nine current ranges: 80A to 110A, 110A to 140A, 140A to 170A, etc. For each current range, all corresponding flow rate values ​​within that range are statistically analyzed, and the flow rate center value and flow rate fluctuation boundary are calculated. The flow rate center value is calculated using the median method, taking the median of all flow rate values ​​within the current range; the flow rate fluctuation boundary is determined using the percentile method, taking the 10% to 90% percentile range of the flow rate value distribution to form the flow rate demand characteristics. A mapping table between current amplitude and flow rate demand is established using the current ranges and their corresponding flow rate demand characteristics.

[0035] The current variation curve for the arc initiation phase is extracted from the current-flow time series data based on the time series boundaries, and the curvature value of the current variation curve at each time point is calculated. The curvature calculation uses a three-point method: for time point t, a quadratic curve is fitted using three points t-Δt, t, and t+Δt, and the second derivative of this curve at point t is calculated as an approximate curvature value. Δt is set to 5 ms. Based on the curvature variation characteristics, the arc initiation phase is divided into multiple current variation sub-phases. When the curvature value changes significantly (the change exceeds 50%), it is marked as the sub-phase boundary point. Typically, the arc initiation phase can be divided into three sub-phases: initial triggering, rapid rise, and transition stabilization.

[0036] For each sub-stage of current change, different adjustment ranges are assigned to the arc-starting stage. The allocation of adjustment ranges is based on the current change characteristics and stability requirements of each sub-stage: 120% adjustment range for the initial triggering sub-stage, 150% for the rapid rise sub-stage, and 110% for the transitional stabilization sub-stage. Based on the established mapping relationship between current amplitude and flow demand, and combined with the adjustment ranges for each sub-stage, a graded flow command for the arc-starting stage is generated. The calculation method is: Sub-stage flow command = Flow center value corresponding to the average current of the sub-stage × Sub-stage adjustment range.

[0037] For the stable welding stage, the flow center value and flow fluctuation boundary of the corresponding current amplitude are extracted according to the mapping relationship to construct a flow control interval. The lower limit of the flow control interval is taken as the lower limit of the flow fluctuation boundary, and the upper limit is taken as the weighted average of the flow center value and the upper limit of the flow fluctuation boundary, with weights of 0.7 and 0.3, respectively. Based on the constructed flow control interval, a graded flow command for the stable welding stage is generated. When the actual flow is lower than the lower limit of the control interval, the flow command is increased; when the actual flow is higher than the upper limit of the control interval, the flow command is decreased; when the actual flow is within the control interval, the current flow command remains unchanged. The increase or decrease increment is set to 5% of the current flow to ensure smooth flow adjustment.

[0038] The current decay curve for the arc-ending phase is extracted from the current-flow time-series data based on the time-series boundaries. Using the same curvature calculation method as for the arc-starting phase, the curvature value of the current decay curve at each time point is calculated. Based on the curvature change characteristics, the arc-ending phase is divided into multiple current decay sub-phases. A significant change in curvature value is marked as a sub-phase boundary. Typically, the arc-ending phase can be divided into three sub-phases: initial descent, rapid decay, and residual current elimination.

[0039] For each of the defined current attenuation sub-stages, different adjustment ranges are assigned to the arc-stopping stage: 90% adjustment range for the initial descent sub-stage, 60% for the rapid attenuation sub-stage, and 30% for the residual elimination sub-stage. Based on the established mapping relationship between current amplitude and flow demand, and combined with the adjustment ranges for each sub-stage, a tiered flow command for the arc-stopping stage is generated. The calculation method is: Sub-stage flow command = Flow center value corresponding to the average current of the sub-stage × Sub-stage adjustment range.

[0040] This invention establishes a mapping relationship between current amplitude and flow rate demand, achieving precise control of shielding gas flow rate during welding and significantly reducing shielding gas consumption. Based on the characteristics of each stage of the welding process, graded flow commands are generated, ensuring welding quality while minimizing resource waste. The adaptive flow control strategy automatically adjusts the gas flow rate according to current changes, reducing the workload of operators and improving the automation level and production efficiency of the welding process.

[0041] Step 103: When executing the graded flow command, monitor the pressure of the gas supply pipeline, correct the graded flow command according to the fluctuation range of the gas supply pipeline pressure, obtain the corrected graded flow command, and output it to the gas supply actuator.

[0042] In some embodiments of the present invention, step 103, which involves correcting the graded flow command based on the fluctuation range of the gas supply pipeline pressure to obtain the corrected graded flow command and outputting it to the gas supply actuator, may specifically include the following sub-steps: Sub-step 1031: Obtain the timing data of the gas supply pipeline pressure; Sub-step 1032: Divide the gas supply pipeline pressure time series data into the arc-starting stage pressure data segment, the stable welding stage pressure data segment, and the arc-stopping stage pressure data segment according to the time series boundary, and calculate the fluctuation amplitude corresponding to each pressure data segment respectively. Sub-step 1033: Using the fluctuation amplitude of the pressure data segment in the stable welding stage as the reference fluctuation amplitude, calculate the arc-starting deviation between the fluctuation amplitude of the pressure data segment in the arc-starting stage and the reference fluctuation amplitude, and the arc-stopping deviation between the fluctuation amplitude of the pressure data segment in the arc-stopping stage and the reference fluctuation amplitude. Sub-step 1034: Perform time series analysis on the pressure data segments during the arc initiation stage and the pressure data segments during the arc termination stage, and extract the trend characteristics of the arc initiation pressure and the trend characteristics of the arc termination pressure. Sub-step 1035: The arc-starting pressure deviation and the arc-starting pressure change trend characteristics are weighted and fused to generate the arc-starting pressure correction coefficient; the arc-stopping pressure deviation and the arc-stopping pressure change trend characteristics are weighted and fused to generate the arc-stopping pressure correction coefficient. Sub-step 1036: The graded flow command for the stable welding stage remains unchanged. The graded flow command for the arc ignition stage is modified according to the arc ignition pressure correction coefficient. The graded flow command for the arc cessation stage is modified according to the arc cessation pressure correction coefficient. The modified graded flow command is obtained and output to the gas supply actuator.

[0043] When executing graded flow commands, pressure time-series data from the gas supply pipeline is acquired via a pressure sensor. The pressure sensor is installed on the pipeline between the gas supply actuator and the welding torch, with a sampling frequency set to 100Hz to ensure the capture of subtle pressure fluctuations. The acquired pressure data is pre-processed and filtered before being stored as pressure time-series data, with each data point containing a timestamp and the corresponding pressure value. The pre-processing filtering employs a 20-point moving average method to effectively reduce signal noise while preserving pressure change trend information.

[0044] Based on the time-series boundaries obtained in the preceding steps, the gas supply pipeline pressure time-series data is divided into three segments: the arc-starting stage pressure data segment, the stable welding stage pressure data segment, and the arc-stopping stage pressure data segment. A precise timestamp comparison method is used during this division to ensure the accuracy of the data for each stage. The fluctuation amplitude is calculated for each pressure data segment. The fluctuation amplitude is defined as the ratio of the difference between the maximum and minimum pressure values ​​within the data segment to the average pressure value. The calculation formula is: Fluctuation Amplitude = (Maximum Pressure Value - Minimum Pressure Value) / Average Pressure Value. For the arc-starting stage pressure data segment, the entire data segment is used to calculate the fluctuation amplitude; for the stable welding stage pressure data segment, the fluctuation amplitude is calculated in multiple windows and the average value is taken; for the arc-stopping stage pressure data segment, the entire data segment is used to calculate the fluctuation amplitude in the same way.

[0045] Using the fluctuation amplitude of the pressure data segment during the stable welding stage as the benchmark fluctuation amplitude, the arc-starting deviation of the pressure data segment during the arc-starting stage is calculated as follows: Arc-starting deviation = (Arc-starting stage fluctuation amplitude - Benchmark fluctuation amplitude) / Benchmark fluctuation amplitude. Similarly, arc-stopping deviation = (Arc-stopping stage fluctuation amplitude - Benchmark fluctuation amplitude) / Benchmark fluctuation amplitude. A positive deviation indicates that the fluctuation amplitude of the corresponding stage is greater than the benchmark fluctuation amplitude, while a negative value indicates that it is less than the benchmark fluctuation amplitude. The larger the absolute value, the greater the degree of deviation.

[0046] Time-series analysis was performed on the pressure data segments during the arc initiation and arc termination phases to extract the pressure change trend characteristics during arc initiation and arc termination. The time-series analysis employed a piecewise linear fitting method, dividing both the arc initiation and arc termination phases into three sub-segments. Linear fitting was performed on each sub-segment to obtain the slope value as the pressure change rate. For the arc initiation phase, the average slope during the pressure rise, the slope before the peak, and the slope after the peak were calculated. For the arc termination phase, the average slope during the initial descent, the average slope during the rapid descent, and the average slope during the stable phase were calculated. These slope values ​​constitute the pressure change trend characteristics. The slope values ​​were normalized to fall within the range of 0 to 1, facilitating subsequent weighted fusion.

[0047] The arc-starting pressure deviation and the arc-starting pressure change trend characteristics are weighted and fused to generate the arc-starting pressure correction coefficient Cs. The calculation formula is: Cs = 0.6Ds + 0.4(0.5k1 + 0.3k2 + 0.2k3), where Ds represents the normalized arc-starting deviation, k1 represents the normalized rising slope, k2 represents the normalized pre-peak slope, and k3 represents the normalized post-peak slope. Similarly, the arc-stopping pressure correction coefficient Ce is calculated as: Ce = 0.7De + 0.3(0.5k4 + 0.4k5 + 0.1k6), where De represents the normalized arc-stopping deviation, k4 represents the normalized initial falling slope, k5 represents the normalized rapid falling slope, and k6 represents the normalized stable slope. The weighting coefficients are set based on the degree of influence of each parameter on gas flow control; a higher deviation weight indicates that the fluctuation amplitude is the main consideration.

[0048] The graded flow command for the stable welding stage remains unchanged to ensure the protective effect during this stage. The graded flow command for the arc ignition stage is corrected according to the arc ignition pressure correction coefficient, using the formula: Qs′=Qs(1+0.2Cs), where the original arc ignition stage flow command is Qs, and the corrected arc ignition stage flow command is Qs′. A positive correction coefficient increases the flow command, while a negative correction coefficient decreases the flow command, with a maximum correction of 20% of the original command. The graded flow command for the arc cessation stage is corrected according to the arc cessation pressure correction coefficient, using the formula: Qe′=Qe(1−0.3Ce), where the original arc cessation stage flow command is Qe, and the corrected arc cessation stage flow command is Qe′. A positive correction coefficient decreases the flow command, while a negative correction coefficient increases the flow command, with a maximum correction of 30% of the original command.

[0049] The revised graded flow command is output to the gas supply actuator via a digital communication interface. The gas supply actuator includes an electronic flow control valve and an actuator controller. Upon receiving the revised graded flow command, the actuator controller converts the command into a control signal, driving the electronic flow control valve to adjust its opening, thereby achieving precise control of the protective gas flow. The control signal update frequency is set to 20Hz to ensure the timeliness and stability of the flow adjustment.

[0050] This invention achieves closed-loop precise control of protective gas flow by dynamically correcting graded flow commands through real-time monitoring of pressure fluctuations in the gas supply pipeline. By considering both the amplitude and trend of pressure fluctuations, the stability and adaptability of flow control are significantly improved. This method further optimizes the efficiency of protective gas usage, reduces gas waste, and lowers production costs and environmental impact while ensuring welding quality.

[0051] Step 104: Calculate the deviation between the actual flow rate in the current-flow time series data and the commanded flow rate in the corrected graded flow command, and determine the abnormal mode by combining the change trend of the gas supply pipeline pressure, and generate an abnormality label.

[0052] In some embodiments of the present invention, step 104 may specifically include the following sub-steps: Sub-step 1041: Extract the actual flow rate from the current-flow time series data, extract the command flow rate from the corrected graded flow command, align the actual flow rate and command flow rate according to the timestamp, calculate the deviation of the actual flow rate from the command flow rate, and form a deviation time series curve. Sub-step 1042: Sample the pressure of the gas supply pipeline at the same timestamp, calculate the pressure change rate at adjacent sampling times, and form a trend curve. Sub-step 1043: Identify the peak value of deviation and its corresponding time from the deviation time series curve, identify the extreme value of pressure change rate and its corresponding time from the change trend curve, calculate the time difference between the peak value of deviation and the extreme value of pressure change rate, mark it as a response hysteresis feature when the time difference is positive and the extreme value of pressure change rate is negative, and mark it as a gas residue feature when the time difference is negative and the extreme value of pressure change rate is positive. Sub-step 1044: Extract the deviation fluctuation spectrum and pressure fluctuation spectrum of the stable welding stage from the deviation time series curve and the trend curve, calculate the ratio of the main frequency of the deviation fluctuation spectrum to the pressure fluctuation spectrum, and mark it as a damped oscillation feature when the ratio of the main frequency is less than the preset ratio threshold. Sub-step 1045: Based on the response lag characteristics, identify the abnormal mode as gas supply response lag; based on the gas residue characteristics, identify the abnormal mode as gas residue; based on the damping oscillation characteristics, identify the abnormal mode as pipeline damping; and generate the abnormal identifiers corresponding to each abnormal mode.

[0053] The actual flow rate is extracted from the current-flow time series data, and the commanded flow rate is extracted from the corrected graded flow command. The two sets of data are aligned using timestamps. The actual flow rate comes from the value collected by the flow sensor and processed by signal processing, while the commanded flow rate comes from the value in the corrected graded flow command output to the gas supply actuator in the previous steps. Nearest neighbor interpolation is used for timestamp alignment to ensure a one-to-one correspondence between the two sets of data in the time dimension. The deviation between the actual flow rate and the commanded flow rate is calculated as: Deviation = (Actual Flow Rate - Commanded Flow Rate) / Commanded Flow Rate × 100%. A positive deviation indicates that the actual flow rate is greater than the commanded flow rate, and a negative deviation indicates that the actual flow rate is less than the commanded flow rate. The larger the absolute value of the deviation, the greater the degree of deviation. Based on the calculated deviation values ​​and corresponding timestamps, a deviation time series curve is generated.

[0054] The gas supply pipeline pressure is sampled at the same timestamps as the deviation time-series curve to obtain the pressure values ​​at each sampling moment. The pressure change rate between adjacent sampling moments is calculated as: Pressure change rate = (Current pressure - Previous pressure) / Time interval. A positive pressure change rate indicates a pressure increase, while a negative rate indicates a pressure decrease. The larger the absolute value of the rate of change, the faster the pressure change. Based on the calculated pressure change rate values ​​and corresponding timestamps, a trend curve is generated. To reduce noise, a 5-point median filter is applied to the trend curve to improve its smoothness and analyzability.

[0055] To identify deviation peaks and their corresponding moments from the deviation time-series curves, a local extremum detection algorithm is used. The detection window width is set to 100ms. When the deviation value at a certain moment is greater than the deviation values ​​at other moments within the window and exceeds the average deviation value by 30%, it is marked as a deviation peak. The value of each deviation peak and its corresponding timestamp are recorded. Similarly, to identify pressure change rate extrema and their corresponding moments from the trend curves, the detection method is similar to that for deviation peak detection. The detection window width is set to 80ms. When the absolute value of the pressure change rate at a certain moment is greater than the absolute value of the pressure change rate at other moments within the window and exceeds the average absolute value of the pressure change rate by 25%, it is marked as a pressure change rate extrema. The value of each pressure change rate extrema and its corresponding timestamp are recorded.

[0056] Calculate the time difference between the peak deviation and the extreme value of the pressure change rate. When the time difference is positive and greater than a preset time threshold of 20ms, and the extreme value of the pressure change rate is negative, it is marked as a response lag characteristic. The response lag characteristic indicates that after the pressure drops, the actual flow rate fails to follow the commanded flow rate change in a timely manner, exhibiting a response lag phenomenon. When the time difference is negative and its absolute value is greater than a preset time threshold of 15ms, and the extreme value of the pressure change rate is positive, it is marked as a gas residue characteristic. The gas residue characteristic indicates that the actual flow rate has changed before the pressure rises, indicating the presence of gas residue in the pipeline.

[0057] The deviation data segment of the stable welding stage was extracted from the deviation time series curve, and the deviation fluctuation spectrum was calculated using Fast Fourier Transform (FFT). The FFT parameters were set as follows: sampling window length of 2048 points, Hanning window function, and 75% overlap. The frequency component with the largest amplitude was identified from the spectrum and recorded as the dominant frequency of the deviation fluctuation. The pressure change rate data segment of the stable welding stage was extracted from the trend curve, and the pressure fluctuation spectrum was calculated using the same FFT parameters as the deviation fluctuation spectrum calculation. The frequency component with the largest amplitude was identified from the spectrum and recorded as the dominant frequency of the pressure fluctuation. The ratio of the dominant frequency of the deviation fluctuation to the dominant frequency of the pressure fluctuation was calculated. When the ratio was less than a preset threshold of 0.8, it was marked as a damped oscillation characteristic. The damped oscillation characteristic indicates an abnormal damping characteristic in the pipeline system, which may lead to unstable flow control.

[0058] Based on the identified features, abnormal patterns are determined. When a lag characteristic is detected, it is identified as a gas supply response lag abnormal pattern, and a gas supply response lag abnormality identifier is generated. The abnormality identifier includes information such as abnormality type, occurrence time, duration, and severity. The severity is calculated based on the time difference and frequency of occurrence, using the formula: Severity = Absolute value of time difference × Frequency of occurrence / 10.

[0059] When residual gas characteristics are detected, it is identified as an abnormal residual gas pattern, and an abnormal residual gas identifier is generated. The identifier also includes information such as the abnormality type, occurrence time, duration, and severity. The severity is calculated based on the absolute value of the time difference and the peak value of the deviation, using the following formula: Severity = Absolute value of time difference × Absolute value of peak deviation / 5.

[0060] When damped oscillation characteristics are detected, it is identified as an abnormal pipeline damping mode, and a pipeline damping abnormality identifier is generated. The abnormality identifier includes information such as abnormality type, occurrence time, duration, and severity. The severity is calculated based on the difference between the dominant frequency ratio and a preset ratio threshold, using the formula: Severity = (Preset ratio threshold - Dominant frequency ratio) × 100.

[0061] The generated anomaly identifier is in JSON format, containing four main fields: anomaly type, timestamp, duration, and severity. The anomaly type is an enumeration value, including three types: "Gas Supply Response Delay," "Gas Residue," and "Pipeline Damping." The timestamp records the anomaly start time with millisecond precision, the duration records the number of milliseconds the anomaly lasted, and the severity is an integer value from 0 to 100, with higher values ​​indicating more severe anomalies. The anomaly identifier is transmitted to the MES system in real time as a basis for welding quality assessment and equipment maintenance decisions.

[0062] This invention establishes an anomaly detection mechanism for welding shielding gas supply systems by comparing the deviation between actual and commanded flow rates in real time, combined with the pressure change trend of the gas supply pipeline. It can accurately identify three typical anomaly modes: delayed gas supply response, gas residue, and pipeline damping, and promptly generate standardized anomaly identifiers. This enables early warning of gas supply anomalies, avoiding adverse effects on welding quality. Through dual frequency and time domain analysis, the accuracy and reliability of anomaly detection are improved, providing data support for preventative maintenance of equipment in the MES system, extending equipment lifespan, and ensuring the continuity and stability of welding production.

[0063] Step 105: Remove the data corresponding to the abnormal period according to the abnormal identifier, calculate the effective gas reduction based on the corrected graded flow instructions after removal, convert the effective gas reduction into carbon emission reduction and upload it to the MES platform to generate emission reduction statistics.

[0064] In some embodiments of the present invention, step 105, which involves removing data corresponding to abnormal time periods based on anomaly identifiers and calculating the effective gas reduction based on the corrected graded flow command after removal, may specifically include the following sub-steps: Sub-step 10511: Locate the start and end times of the abnormal period from the corrected hierarchical traffic instructions based on the abnormality identifier, and extract the traffic gradient before the start time and the traffic gradient after the end time. Sub-step 10512: Construct a flow transition curve for the removal boundary based on the flow gradient. After interpolating and compensating the data between the start and end times using the flow transition curve, remove the data and obtain the corrected graded flow instructions after removal. Sub-step 10513: Extract the initial command flow and duration of each time period from the graded flow command; extract the optimized command flow and duration of the corresponding time period from the corrected graded flow command after removal; calculate the flow difference between the initial command flow and the optimized command flow in each time period within the corresponding duration; and accumulate the flow difference to obtain the effective gas saving amount.

[0065] Based on the anomaly identifier, the start and end times of the abnormal period are located from the revised tiered traffic command. The anomaly identifier contains information such as the anomaly type, occurrence time, and duration. By parsing the occurrence time and duration in the anomaly identifier, the start and end times of the abnormal period are calculated. Specifically, the occurrence time in the anomaly identifier is used as the start time, and the duration is added to the occurrence time to obtain the end time. Traffic data before the start time is extracted from the revised tiered traffic command, and its traffic gradient is calculated. The traffic gradient is calculated by dividing the traffic difference between adjacent time points by the time interval, and the average traffic gradient of the 10 time points before the start time is taken as the starting traffic gradient. Similarly, traffic data after the end time is extracted, its traffic gradient is calculated, and the average traffic gradient of the 10 time points after the end time is taken as the ending traffic gradient.

[0066] A flow transition curve is constructed based on the flow gradient to remove boundary conditions and achieve a smooth transition of data during abnormal periods. The flow transition curve is constructed using cubic spline interpolation to ensure the continuity of the curve's values ​​and first derivative at connection points. Four control points are set during the construction process: the last valid data point before the start time, the start time point, the end time point, and the first valid data point after the end time point. At the start time point, the tangent direction is set to be consistent with the initial flow gradient, and at the end time point, the tangent direction is set to be consistent with the final flow gradient to ensure the continuity and smoothness of flow changes. Interpolated compensation data between the start and end times is generated using the flow transition curve to replace the original abnormal period data. Specifically, the cubic spline interpolation curve is discretized into sampling intervals identical to the original data to generate replacement data points. After replacement, the corrected graded flow command after removing abnormal periods is obtained.

[0067] The initial command flow rate and duration for each time period are extracted from the graded flow command. The graded flow command includes three main time periods: arc initiation, welding stabilization, and arc cessation, each of which may contain multiple sub-time periods. The initial command flow rate and duration for each sub-time period are extracted. The initial command flow rate is the set flow rate value for that sub-time period, and the duration is the sub-time period's end time minus its start time. The optimized command flow rate and duration for the corresponding time period are extracted from the corrected graded flow command after removing abnormal time periods. The optimized command flow rate is the flow rate value after the aforementioned steps to correct and remove abnormal time periods, and its duration is consistent with the duration of the corresponding time period of the initial command. For certain special cases, such as time period changes due to anomalies, the duration needs to be adjusted to maintain the correspondence between the two sets of data.

[0068] Calculate the difference between the initial commanded flow rate and the optimized commanded flow rate for each time period within the corresponding duration to obtain the gas reduction for that time period. Let the initial commanded flow rate for the i-th time period be Q. i0 Optimize instruction flow to Q i1 The duration is Ti The amount reduced during that period is S. i The calculation formula is: S i =(Q i0 -Q i1 )T i , where Q i1 i0 At that time, S i A positive value for Q indicates that gas was saved during that period; when Q... i1 Q i0 At that time, S i A negative value indicates that gas consumption increased during that period.

[0069] The total effective gas reduction is obtained by summing the reduction amounts over all time periods. Let the weighting factor be w. i The formula for calculating the effective gas reduction S is: The total effective gas reduction is obtained by summing the reductions over all time periods. Let the weighting factor be w. i The formula for calculating the effective gas reduction S is: Where n represents the number of time periods in the welding process; when the i-th time period belongs to the arc-starting stage or the arc-stopping stage, the weighting factor w i =1.2; when the i-th time period belongs to the stable welding stage, the weighting factor w i =1.0.

[0070] Effective gas reductions are converted into carbon emission reductions. The conversion uses standard carbon emission calculation methods, considering carbon emissions from gas types, production processes, and transportation. The formula for calculating the carbon emission reduction C is: C = Sf g +Sf p +Sf t , where f g f represents the gaseous carbon emission factor. p f represents the carbon emission factor from gas production. t This indicates the carbon emission factor for gas transportation.

[0071] The carbon emission factor for gases is determined based on the type of protective gas. The carbon emission factor for argon is 0.295, and for carbon dioxide it is 1.000. For mixed gases, the emission factors are calculated proportionally. The carbon emission factors for gas production and gas transportation are 0.085 and 0.022, respectively. These parameters can be adjusted according to actual conditions.

[0072] ​The calculated carbon emission reduction is uploaded to the MES platform. Uploaded data includes the production batch number, welding station number, timestamp, effective gas saving amount, and carbon emission reduction amount. The data is in JSON format and transmitted via a standardized API interface. The upload frequency is set to once after each welded workpiece is completed; for long-term continuous welding tasks, intermediate data is uploaded every 30 minutes. The MES platform receives the data, stores and processes it, and generates emission reduction statistics. The emission reduction statistics include four levels: hourly, shift, daily, and monthly. Each level contains four core indicators: cumulative gas saving, average gas saving rate, cumulative carbon reduction, and economic benefits. A sliding window method is used to ensure the real-time nature and continuity of the data.

[0073] This invention ensures the accuracy and reliability of gas reduction calculations by precisely identifying and eliminating data from abnormal periods. A smooth transition curve is constructed using flow gradient and cubic spline interpolation to avoid calculation errors caused by data breaks. A quantitative relationship between welding shielding gas usage and carbon emissions is established, achieving a precise conversion from gas reduction to carbon emission reduction. Data integration and statistical analysis through a MES platform provide a multi-dimensional assessment of emission reduction effects, offering data support for enterprises' green production decisions. This invention enables refined management of shielding gas usage during welding production, minimizing resource waste while ensuring welding quality, reducing production costs, and improving environmental benefits, providing an effective pathway for manufacturing enterprises to achieve green and low-carbon transformation.

[0074] Step 105, which converts effective gas emission reductions into carbon emission reductions and uploads them to the MES platform, also includes generating emission reduction statistics: Sub-step 10521: Extract the welding process parameters corresponding to the corrected graded flow instructions after rejection, and query the benchmark carbon emission factor corresponding to the welding process parameters from the carbon emission factor library. Sub-step 10522: Extract the gas supply pressure fluctuation amplitude and current fluctuation amplitude from the corrected graded flow command, dynamically correct the benchmark carbon emission factor to generate a real-time carbon emission factor, and calculate the carbon emission reduction based on the effective gas reduction and the real-time carbon emission factor. Sub-step 10523: Construct emission reduction data records including carbon emission reduction, welding process parameters, gas supply pressure fluctuation range and current fluctuation range, and upload them to the MES platform; In sub-step 10524, the MES platform establishes a classification index based on welding process parameters, extracts the gas supply pressure fluctuation range and current fluctuation range from historical emission reduction data records, constructs process stability evaluation indicators, summarizes the carbon emission reduction amount according to the classification index and associates it with the process stability evaluation indicators, and generates emission reduction statistics.

[0075] After calculating the effective gas emission reduction, the effective gas emission reduction is converted into the corresponding carbon emission reduction, and the relevant data is uploaded to the MES platform to generate emission reduction statistics. During implementation, the corrected graded flow commands, after removing abnormal fluctuations, are first parsed to extract the corresponding welding process parameters. These welding process parameters include welding method, shielding gas type, shielding gas ratio, welding current setting, welding voltage setting, and welding speed. Based on the extracted welding process parameters, a matching query is performed in a pre-established carbon emission factor database. This database establishes a correspondence table according to the welding process parameter categories, with each set of welding process parameters corresponding to a set of benchmark carbon emission factors. By matching and retrieving the welding process parameters, the benchmark carbon emission factor corresponding to the current welding process parameters is obtained, and this benchmark carbon emission factor is used as the basis for subsequent carbon emission calculations.

[0076] After obtaining the baseline carbon emission factor, the process fluctuation status during the execution of the corrected graded flow command is extracted and analyzed. Specifically, real-time monitoring data of gas supply pressure and welding current are obtained from the welding equipment acquisition system, and the fluctuation amplitudes of gas supply pressure and current are calculated within the corresponding welding periods. The gas supply pressure fluctuation amplitude is obtained by calculating the difference between the maximum and minimum gas supply pressure values ​​within that period; the current fluctuation amplitude is obtained by calculating the difference between the maximum and minimum welding current values. To reflect the impact of actual process stability on carbon emissions, a dynamic correction mechanism is introduced to correct the baseline carbon emission factor. Specifically, a correction function is constructed based on the fluctuation amplitudes of gas supply pressure and current to dynamically adjust the baseline carbon emission factor, obtaining the real-time carbon emission factor. The real-time carbon emission factor reflects the changes in energy consumption and gas utilization efficiency caused by process fluctuations during the current welding process, thus making the carbon emission calculation results more consistent with actual production conditions. After obtaining the real-time carbon emission factor, it is calculated with the effective gas reduction amount to obtain the corresponding carbon emission reduction, expressed in the form of carbon dioxide equivalent.

[0077] After obtaining the carbon emission reduction amount, emission reduction data records are constructed. These records include information such as the carbon emission reduction amount, welding process parameters, gas supply pressure fluctuation range, and current fluctuation range. Specifically, the carbon emission reduction amount represents the emission reduction effect achieved in this welding task; the welding process parameters identify the specific welding process type; and the gas supply pressure and current fluctuation ranges reflect the process stability of the welding process. To facilitate subsequent data management and statistical analysis, corresponding data identification information, such as timestamps, equipment numbers, or welding task numbers, can be generated for each emission reduction data record. Subsequently, the constructed emission reduction data records are uploaded to the MES platform for centralized management via an industrial communication interface. Upon receiving the emission reduction data records, the MES platform writes them into the emission reduction data database and stores them according to a preset data structure for subsequent querying and statistical analysis.

[0078] After data storage is completed, the MES platform establishes a classification index structure based on welding process parameters. Specifically, parameters such as welding method, shielding gas type, and welding current range are used as classification dimensions, and corresponding classification index tables are established in the database to achieve classified management of emission reduction data under different welding process conditions. After establishing the classification index, the MES platform extracts gas supply pressure fluctuation amplitude and current fluctuation amplitude data from historical emission reduction data records to comprehensively evaluate the process stability of the welding process. In practice, a process stability evaluation index can be constructed based on the gas supply pressure fluctuation amplitude and current fluctuation amplitude. For example, a stability evaluation value can be obtained by weighting the two to reflect the comprehensive situation of gas supply stability and arc stability during the welding process. This process stability evaluation index is correlated with the corresponding welding process parameters to characterize the stability level under different welding process conditions.

[0079] After calculating the process stability evaluation indicators, the MES platform statistically summarizes the carbon emission reductions based on the aforementioned classification index. Under the same welding process parameter category, the corresponding carbon emission reductions are accumulated or statistically analyzed, and the statistical results are correlated with the corresponding process stability evaluation indicators to form statistical results containing both emission reduction and process stability information. The final emission reduction statistics reflect both the carbon emission reductions achieved under different welding process conditions and the stability level of the corresponding process conditions. This approach not only enables a quantitative assessment of the emission reduction effect of the welding production process but also provides data support for subsequent welding process optimization, allowing the production management system to further improve gas utilization efficiency and reduce carbon emission levels while ensuring welding quality and process stability.

[0080] like Figure 2 As shown, Figure 2This is a schematic diagram of the structure of the MES digital welding shielding gas reduction and environmental control system provided in an embodiment of the present invention. The system includes: The timing recognition module 201 is used to collect the working current waveform and the instantaneous flow rate of the shielding gas during the welding process, generate current-flow timing data, and identify the timing boundaries of the arc initiation stage, the stable welding stage and the arc cessation stage based on the current-flow timing data. The instruction generation module 202 is used to extract the current amplitude and corresponding flow rate value of the stable welding stage from the current-flow time series data according to the time series boundary, establish the mapping relationship between the current amplitude and the flow rate requirement, and generate graded flow rate instructions for different stages based on the mapping relationship and the time series boundary. The instruction correction module 203 is used to monitor the gas supply pipeline pressure when executing the graded flow instruction, correct the graded flow instruction according to the fluctuation range of the gas supply pipeline pressure, obtain the corrected graded flow instruction, and output it to the gas supply actuator. The anomaly detection module 204 is used to calculate the deviation between the actual flow rate in the current-flow time series data and the command flow rate in the corrected graded flow command, and to detect the anomaly mode by combining the change trend of the gas supply pipeline pressure and generate an anomaly identifier. The emission reduction statistics module 205 is used to remove data corresponding to abnormal periods based on the abnormal identifier, calculate the effective gas reduction based on the corrected graded flow instructions after removal, convert the effective gas reduction into carbon emission reduction and upload it to the MES platform to generate emission reduction statistics.

[0081] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0082] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0083] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for reducing shielding gas consumption and controlling the environment in MES-based digital welding, characterized in that, Includes the following steps: The waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process are collected to generate current-flow time series data. Based on the current-flow time series data, the time series boundaries of the arc initiation stage, the stable welding stage and the arc cessation stage are identified. Based on the time sequence boundary, the current amplitude and corresponding flow rate value of the stable welding stage are extracted from the current-flow time sequence data, a mapping relationship between current amplitude and flow rate requirement is established, and graded flow rate instructions are generated for different stages based on the mapping relationship and time sequence boundary. When executing the graded flow command, the pressure of the gas supply line is monitored, and the graded flow command is corrected according to the fluctuation range of the gas supply line pressure. The corrected graded flow command is then output to the gas supply actuator. The deviation between the actual flow rate in the current-flow time series data and the command flow rate in the corrected graded flow command is calculated. Anomaly patterns are identified by combining the changing trend of the gas supply pipeline pressure, and anomaly indicators are generated. Data corresponding to abnormal periods is removed based on the anomaly identifier. Effective gas reduction is calculated based on the corrected graded flow instructions after removal. Effective gas reduction is converted into carbon emission reduction and uploaded to the MES platform to generate emission reduction statistics.

2. The method according to claim 1, characterized in that, The waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process are collected to generate current-flow time series data. Based on the current-flow time series data, the time series boundaries of the arc initiation stage, the stable welding stage, and the arc termination stage are identified, including: The waveform of the working current and the instantaneous flow rate of the shielding gas during the welding process are collected and then aligned with the timestamps to generate current-flow time series data. Scan the operating current waveform in the current-flow time series data, and mark the moment when the operating current waveform starts to rise from zero as the start of the arc initiation stage. Starting from the arc initiation stage, a sliding window is set to scan the working current waveform, and the fluctuation amplitude and average current of the working current waveform within the sliding window are calculated. When the ratio of the fluctuation amplitude of the working current waveform to the average current of the window is lower than the average current of the window, the starting time of the sliding window is marked as the starting point of the stable welding stage. The working current waveform is continuously monitored from the start of the stable welding stage. The moment when the working current waveform begins to decrease from the average current in the window is marked as the start of the arc-stopping stage, and the moment when the working current waveform drops to zero is marked as the end of the arc-stopping stage. The arc initiation stage, stable welding stage, and arc cessation stage are defined based on the start point of the arc initiation stage, the start point of the stable welding stage, the start point of the arc cessation stage, and the end point of the arc cessation stage, thus forming the time sequence boundary.

3. The method according to claim 1, characterized in that, Based on the timing boundaries, the current amplitude and corresponding flow rate values ​​for the stable welding stage are extracted from the current-flow timing data. A mapping relationship between current amplitude and flow rate demand is established. Based on the mapping relationship and timing boundaries, graded flow rate instructions are generated for different stages, including: Based on the time sequence boundary, the time period corresponding to the stable welding stage is extracted from the current-flow time sequence data, and the current amplitude and corresponding flow rate value of the stable welding stage are extracted. The current amplitude is divided into multiple current ranges, and the flow center value and flow fluctuation boundary corresponding to each current range are extracted as flow demand to establish a mapping relationship between current amplitude and flow demand. The current change curve of the arc initiation stage is extracted from the current-flow time series data according to the time series boundary. Multiple current change sub-stages are divided according to the curvature change characteristics of the current change curve and the adjustment range of the arc initiation stage is allocated. The graded flow command of the arc initiation stage is generated according to the mapping relationship and the adjustment range of the arc initiation stage. Based on the mapping relationship, the flow center value and flow fluctuation boundary are extracted to construct the flow control interval. Based on the mapping relationship and the flow control interval, the graded flow command for the stable welding stage is generated. The current decay curve of the arc-stopping stage is extracted from the current-flow time series data according to the time series boundary. Multiple current decay sub-stages are divided according to the curvature change characteristics of the current decay curve and the adjustment range of the arc-stopping stage is allocated. The graded flow command of the arc-stopping stage is generated according to the mapping relationship and the adjustment range of the arc-stopping stage.

4. The method according to claim 1, characterized in that, The graded flow command is corrected based on the fluctuation range of the gas supply pipeline pressure. The corrected graded flow command is then output to the gas supply actuator, including: Acquire time-series pressure data for the gas supply pipeline; Based on the time sequence boundary, the pressure time sequence data of the gas supply pipeline is divided into pressure data segments for the arc initiation stage, pressure data segments for the stable welding stage, and pressure data segments for the arc cessation stage. The fluctuation amplitude corresponding to each pressure data segment is calculated separately. Using the fluctuation range of the pressure data segment during the stable welding stage as the reference fluctuation range, the arc-starting deviation of the pressure data segment during the arc-starting stage from the reference fluctuation range, and the arc-stopping deviation of the pressure data segment during the arc-stopping stage from the reference fluctuation range are calculated. Time series analysis was performed on the pressure data segments during the arc initiation stage and the pressure data segments during the arc termination stage to extract the trend characteristics of the arc initiation pressure and the arc termination pressure. The arc-starting pressure deviation and the arc-starting pressure change trend characteristics are weighted and fused to generate the arc-starting pressure correction coefficient, and the arc-stopping pressure deviation and the arc-stopping pressure change trend characteristics are weighted and fused to generate the arc-stopping pressure correction coefficient. The graded flow command for the stable welding stage remains unchanged. The graded flow command for the arc initiation stage is modified according to the arc initiation pressure correction coefficient, and the graded flow command for the arc cessation stage is modified according to the arc cessation pressure correction coefficient. The modified graded flow command is then output to the gas supply actuator.

5. The method according to claim 1, characterized in that, The deviation between the actual flow rate in the current-flow time series data and the commanded flow rate in the corrected graded flow command is calculated. Combined with the trend of pressure changes in the gas supply pipeline, abnormal patterns are identified, and abnormality indicators are generated, including: The actual flow rate is extracted from the current-flow time series data, the command flow rate is extracted from the corrected graded flow command, the actual flow rate and the command flow rate are aligned by timestamp, the deviation of the actual flow rate from the command flow rate is calculated, and the deviation time series curve is formed. The pressure of the gas supply pipeline is sampled at the same timestamp, and the rate of pressure change at adjacent sampling times is calculated to form a trend curve. Identify the peak deviation and its corresponding time from the deviation time series curve, identify the extreme value of the pressure change rate and its corresponding time from the change trend curve, calculate the time difference between the peak deviation time and the extreme value of the pressure change rate, mark it as a response hysteresis feature when the time difference is positive and the extreme value of the pressure change rate is negative, and mark it as a gas residue feature when the time difference is negative and the extreme value of the pressure change rate is positive. The deviation fluctuation spectrum and pressure fluctuation spectrum of the stable welding stage are extracted from the deviation time series curve and the trend curve. The ratio of the main frequency of the deviation fluctuation spectrum to the pressure fluctuation spectrum is calculated. When the ratio of the main frequency is less than the preset ratio threshold, it is marked as a damped oscillation characteristic. Based on the response lag characteristics, it is identified as a gas supply response lag abnormal mode; based on the gas residue characteristics, it is identified as a gas residue abnormal mode; based on the damping oscillation characteristics, it is identified as a pipeline damping abnormal mode, and an abnormality identifier corresponding to each abnormal mode is generated.

6. The method according to claim 1, characterized in that, Data corresponding to abnormal periods is removed based on the anomaly identifier. The effective gas reduction is calculated based on the corrected graded flow command after the removal, including: Based on the anomaly identifier, locate the start and end times of the abnormal period from the corrected hierarchical traffic command, and extract the traffic gradient before the start time and the traffic gradient after the end time. A flow transition curve is constructed based on the flow gradient to remove the boundary. The data between the start and end times is then interpolated and compensated using the flow transition curve before removal, and the corrected hierarchical flow command after removal is obtained. The initial command flow and duration for each time period are extracted from the graded flow command. The optimized command flow and duration for the corresponding time period are extracted from the corrected graded flow command after removal. The flow difference between the initial command flow and the optimized command flow for each time period within the corresponding duration is calculated. The flow difference is accumulated to obtain the effective gas saving amount.

7. The method according to claim 1, characterized in that, The effective gas emission reduction is converted into carbon emission reduction and uploaded to the MES platform to generate emission reduction statistics, including: Extract the welding process parameters corresponding to the corrected graded flow instructions after rejection, and query the baseline carbon emission factor corresponding to the welding process parameters from the carbon emission factor library; Extract the gas supply pressure fluctuation amplitude and current fluctuation amplitude from the corrected graded flow command, dynamically correct the benchmark carbon emission factor to generate a real-time carbon emission factor, and calculate the carbon emission reduction based on the effective gas reduction and the real-time carbon emission factor. Construct emission reduction data records that include carbon emission reduction, welding process parameters, gas supply pressure fluctuation range, and current fluctuation range, and upload them to the MES platform; The MES platform establishes a classification index based on welding process parameters, extracts the gas supply pressure fluctuation range and current fluctuation range from historical emission reduction data records, constructs process stability evaluation indicators, summarizes carbon emission reductions according to the classification index and associates them with process stability evaluation indicators, and generates emission reduction statistics.

8. A MES digital welding shielding gas reduction and environmental control system, used to implement the method described in any one of claims 1-7, characterized in that, The system includes: The timing recognition module is used to collect the working current waveform and shielding gas instantaneous flow rate during the welding process, generate current-flow timing data, and identify the timing boundaries of the arc initiation stage, the stable welding stage, and the arc cessation stage based on the current-flow timing data. The instruction generation module is used to extract the current amplitude and corresponding flow rate value of the stable welding stage from the current-flow time series data according to the time series boundary, establish the mapping relationship between the current amplitude and the flow rate requirement, and generate graded flow rate instructions for different stages based on the mapping relationship and the time series boundary. The instruction correction module is used to monitor the gas supply pipeline pressure when executing the graded flow instruction, correct the graded flow instruction according to the fluctuation range of the gas supply pipeline pressure, obtain the corrected graded flow instruction, and output it to the gas supply actuator. The anomaly detection module is used to calculate the deviation between the actual flow rate in the current-flow time series data and the command flow rate in the corrected graded flow command, and to identify the anomaly mode by combining the change trend of the gas supply pipeline pressure and generate anomaly identification. The emission reduction statistics module is used to remove data corresponding to abnormal periods based on the anomaly identifier, calculate the effective gas reduction based on the corrected graded flow instructions after removal, convert the effective gas reduction into carbon emission reduction and upload it to the MES platform to generate emission reduction statistics.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 7.

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