A pipeline welding construction control method, system, equipment and medium

Abstract

The present application relates to the technical field of pipe welding, and in particular to a pipe welding construction control method, system, equipment and medium. The present application first obtains the material type, dimensional parameters and ambient temperature and humidity data of the pipe to be welded, predicts the optimal temperature curve and welding termination time during the welding process, then collects image information of the welding part through a camera, identifies the weld depth and weld width parameters, and at the same time, collects real-time temperature data through a temperature sensor, combines the predicted optimal temperature curve and the identified weld depth and weld width parameters, and determines whether the welding temperature needs to be adjusted. If adjustment is required, the temperature adjustment value and the control power of the heating device are calculated, the corresponding temperature control instruction is generated, and sent to the heating device for execution, thereby realizing automatic control and quality monitoring of the welding process, reducing dependence on manual experience, and improving welding efficiency and success rate.

Description

Technical Field

[0001] The present application relates to the technical field of pipeline welding, and in particular to a pipeline welding construction control method, system, equipment and medium. Background Art

[0002] At present, a large number of pipes are needed to complete water supply and drainage operations in the water conservancy field. The length of a single pipe is difficult to meet the operation requirements. Usually, several pipes need to be connected to form a water pipeline. When connecting small-diameter PE pipes, a welding machine is often used to heat the pipe interface to complete the welding operation. The traditional welding operation method is to press the two pipe interfaces to be welded on both ends of the welding machine heater at the same time. After heating is completed, the two pipes are quickly squeezed together and maintained for a certain time to complete the pipe welding operation. Manual operation is used for extrusion, which consumes a lot of physical energy for the operators when welding water conservancy pipes.

[0003] Existing pipe welding devices, such as publication number CN204054647U, disclose a socket-type hot-melt pipe welder, which clamps and fixes the pipe by means of a fixed clamp and a sliding clamp. When using this device to weld and fix the pipe, the pipe needs to be inserted into the V-groove first and then clamped and fixed. After the welding is completed, the pipe needs to be erected and the device pulled out from one end of the pipe. However, the welding quality cannot be monitored in real time during operation, and the welding quality and success rate depend on manual experience. This situation needs to be further improved. Summary of the Invention

[0004] In order to solve the problem that existing pipe welding devices cannot monitor welding quality in real time and welding quality and success rate rely on manual experience, this application provides a pipe welding construction control method, system, equipment and medium, which adopts the following technical solutions:

[0005] In a first aspect, the present application provides a pipe welding construction control method, which is applied to a pipe welding device, wherein the pipe welding device includes a temperature sensor and a camera, wherein the temperature sensor is used to monitor the temperature data of the welding part in real time, and the camera is used to capture an image of the welding part. The method includes the following steps:

[0006] Obtaining the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, predicting the optimal temperature curve and predicted welding end time during the welding process, wherein the optimal temperature curve includes the welding start temperature, end temperature, and temperature change rate; obtaining image information of the welding part captured by the camera, and identifying the weld depth parameter and weld width parameter of the welding part based on the image information;

[0007] Acquiring temperature data collected by the temperature sensor, and determining whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the welding depth parameter, and the welding width parameter;

[0008] When the welding temperature needs to be adjusted, the temperature adjustment value is calculated, and the control power of the heating device is calculated according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value;

[0009] A temperature control instruction is generated according to the control power and sent to the heating device, wherein the temperature control instruction includes the control power and the control duration.

[0010] By adopting the above technical solution, in order to solve the problem that existing pipe welding devices cannot monitor welding quality in real time and the welding quality and success rate rely on manual experience, the present application first obtains the material type, dimensional parameters and ambient temperature and humidity data of the pipe to be welded, predicts the optimal temperature curve and welding termination time during the welding process, then uses a camera to capture image information of the welding part and identify the weld depth and weld width parameters. At the same time, real-time temperature data is collected by a temperature sensor. Combined with the predicted optimal temperature curve and the identified weld depth and weld width parameters, it is determined whether the welding temperature needs to be adjusted. If adjustment is required, the temperature adjustment value and the control power of the heating device are calculated, and the corresponding temperature control command is generated and sent to the heating device for execution. First, the introduction of temperature sensors and cameras to monitor the welding process in real time overcomes the limitations of manual monitoring. Second, by predicting the optimal temperature curve and identifying the weld depth and weld width parameters, quantitative evaluation and control of the welding quality are achieved. Third, based on the real-time monitoring data and prediction results, the heating power is dynamically adjusted to ensure optimal control of the welding temperature. The entire control process has a high degree of automation, reduces dependence on manual experience, and improves welding efficiency and success rate.

[0011] Optionally, the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded are obtained to predict the optimal temperature curve and the welding termination time during the welding process, specifically including the following steps:

[0012] Obtain the material type of the pipe to be welded, and query the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from a preset material database;

[0013] Obtaining the outer diameter, wall thickness, and length parameters of the pipe to be welded, and calculating the surface area and volume of the pipe to be welded;

[0014] Get ambient temperature and relative humidity data;

[0015] The optimal temperature curve and the welding termination time are calculated based on the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0016] By adopting the above technical solution, in order to further improve the accuracy and reliability of pipe welding temperature prediction, this application obtains the material type, dimensional parameters and ambient temperature and humidity data of the pipe to be welded, queries the material property database, calculates the surface area and volume of the pipe, and comprehensively considers environmental factors to predict the optimal temperature curve and welding termination time during the welding process. This can effectively improve the accuracy and reliability of pipe welding temperature prediction and provide a quantitative basis and indicator for subsequent welding temperature control.

[0017] Optionally, obtaining image information of the weld part captured by the camera, and identifying the weld depth parameter and the weld width parameter of the weld part based on the image information specifically includes the following steps:

[0018] Obtaining the front and side images of the welded part captured by the camera, and extracting binary images of the weld depth area and the weld width area according to the grayscale distribution characteristics of the images;

[0019] Based on the binary image of the deep penetration area, the number of pixels in the deep penetration area is determined, and the deep penetration parameter is obtained by conversion according to the preset unit pixel size;

[0020] Based on the binary image of the weld width area, the straight lines at the edge of the weld width are detected, and the distance between the straight lines is calculated to obtain the weld width parameter; the weld depth parameter and the weld width parameter of multiple consecutive frames of images are obtained, and the standard deviation of the weld depth parameter and the weld width parameter is calculated. When the standard deviation is less than the preset threshold, the current weld depth parameter and the weld width parameter are determined to be stable values, and the weld depth parameter and the weld width parameter are obtained.

[0021] By adopting the above-mentioned technical solution, the present application uses a camera to collect front and side images of the welding part, uses the grayscale distribution characteristics of the image to extract the melting depth area and the melting width area, and calculates the melting depth parameters and the melting width parameters through algorithms such as pixel counting and edge detection. Then, through statistical analysis of multiple frames of images, stable and reliable melting depth and width measurement results are obtained, thereby improving the stability and reliability of the measurement results.

[0022] Optionally, obtaining temperature data collected by the temperature sensor and determining whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the penetration depth parameter, and the weld width parameter specifically includes the following steps:

[0023] Calculating a target temperature value at a current moment based on the optimal temperature curve, and comparing the target temperature value with the temperature data to obtain a temperature deviation value;

[0024] Based on the penetration depth parameter and the weld width parameter, determining the temperature adjustment threshold corresponding to the current penetration depth parameter and the weld width parameter by searching a preset penetration depth-temperature mapping table and a preset weld width-temperature mapping table;

[0025] Whether the welding temperature needs to be adjusted is determined according to the temperature deviation value and the temperature adjustment threshold.

[0026] By adopting the above-mentioned technical solution, the present application collects real-time temperature data of the welding part through a temperature sensor, compares it with the predicted optimal temperature curve, and obtains a temperature deviation value; at the same time, the identified welding depth parameters and welding width parameters are used to determine the corresponding temperature adjustment threshold by searching a preset mapping table; finally, the temperature deviation value and the adjustment threshold are comprehensively considered to determine whether the welding temperature needs to be adjusted, thereby realizing dynamic tracking and deviation quantification of the welding temperature, linking the welding quality status with the temperature adjustment, realizing temperature optimization control based on the welding quality, and improving the accuracy and reliability of the temperature adjustment judgment.

[0027] Optionally, when the welding temperature needs to be adjusted, the temperature adjustment value is calculated, and the control power of the heating device is calculated according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value, which specifically includes the following steps:

[0028] Calculate the temperature adjustment value based on the direction and magnitude of the temperature adjustment value, combined with a preset temperature adjustment coefficient and an upper limit of the adjustment; obtain the time difference between the current moment and the predicted welding end moment, and determine the heating power change per unit time based on the time difference and the heating power-time curve corresponding to the material type;

[0029] Calculating the heating power adjustment value required to reach the target temperature within the remaining time according to the temperature adjustment value and the heating power change;

[0030] The current control power of the heating device is added to the heating power adjustment value to obtain a new control power.

[0031] By adopting the above-mentioned technical solution, this application determines the temperature adjustment value based on the temperature deviation, and combines the heating power-time curve corresponding to the remaining welding time and material type to calculate the heating power change per unit time, and then obtains the heating power adjustment value required to reach the target temperature within the remaining time. Finally, by superimposing it with the current control power, a new control power is obtained to realize dynamic adjustment and optimized control of the welding temperature, thereby effectively improving the welding quality and efficiency.

[0032] Optionally, the optimal temperature curve and the welding termination time are calculated based on the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0033] Calculate the optimal temperature curve T(t)=(T m -T0)*(1-e -t / τ )+T0;

[0034] Calculate the welding end time t0 = -τ*ln(ΔT / (T m -T0);

[0035] Among them, τ=c*ρ*V / (h*S+λ*S / δ), T m is the melting point temperature, T0 is the ambient temperature, τ is the time constant, ΔT is the difference between the target end temperature and the ambient temperature, c is the specific heat capacity, ρ is the density of the pipe material, V is the volume, h is the convective heat transfer coefficient determined by the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

[0036] By adopting the above-mentioned technical solution, this application obtains the expression of the optimal temperature curve and the termination time of welding through mathematical modeling, comprehensively considering the influence of factors such as the thermal properties of the material, geometric parameters and environmental conditions, and improving the accuracy and applicability of the prediction results; the time constant and convection heat transfer coefficient are introduced to reflect the dynamic characteristics of the heat conduction process and the influence of environmental humidity, making the prediction closer to reality.

[0037] In a second aspect, the present application provides a pipe welding construction control system, which is applied to a pipe welding device. The pipe welding device includes a temperature sensor and a camera. The temperature sensor is used to monitor the temperature data of the welding part in real time, and the camera is used to capture an image of the welding part. The system includes:

[0038] A prediction module is used to obtain the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve and the predicted welding end time during the welding process. The optimal temperature curve includes the welding start temperature, end temperature, and temperature change rate;

[0039] a welding parameter acquisition module, configured to acquire image information of the welding part captured by the camera, and identify the welding depth parameter and the welding width parameter of the welding part based on the image information;

[0040] a judgment module, configured to obtain temperature data collected by the temperature sensor, and judge whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the welding depth parameter, and the welding width parameter;

[0041] A control power calculation module is used to calculate the temperature adjustment value when the welding temperature needs to be adjusted, and calculate the control power of the heating device according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value;

[0042] A temperature control instruction generating module is used to generate a temperature control instruction according to the control power and send it to the heating device, wherein the temperature control instruction includes the control power and the control duration.

[0043] Optionally, the prediction module includes:

[0044] A material parameter acquisition unit is used to obtain the material type of the pipe to be welded and query the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from a preset material database;

[0045] A pipe parameter acquisition unit, configured to acquire the outer diameter, wall thickness, and length parameters of the pipe to be welded, and calculate the surface area and volume of the pipe to be welded;

[0046] Environmental parameter acquisition unit, used to obtain environmental temperature and relative humidity data;

[0047] The calculation unit is used to calculate the optimal temperature curve and the welding termination time according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0048] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned pipeline welding construction control method when executing the computer program.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned pipeline welding construction control method when executed by a processor.

[0050] In summary, this application includes at least one of the following beneficial technical effects:

[0051] 1. This application first obtains the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, predicts the optimal temperature curve and welding termination time during the welding process, then uses a camera to capture image information of the welding site and identify the weld depth and weld width parameters. At the same time, a temperature sensor collects real-time temperature data. Combined with the predicted optimal temperature curve and the identified weld depth and weld width parameters, it is determined whether the welding temperature needs to be adjusted. If adjustment is required, the temperature adjustment value and the control power of the heating device are calculated, and the corresponding temperature control command is generated and sent to the heating device for execution, thereby realizing automated control and quality monitoring of the welding process, reducing dependence on manual experience, and improving welding efficiency and success rate.

[0052] 2. This application obtains the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, queries the material property database, calculates the surface area and volume of the pipe, and comprehensively considers environmental factors to predict the optimal temperature curve and welding termination time during the welding process. This can effectively improve the accuracy and reliability of pipe welding temperature prediction and provide a quantitative basis and indicator for subsequent welding temperature control.

[0053] 3. This application uses a camera to capture front and side images of the weld area, uses the grayscale distribution characteristics of the image to extract the weld depth area and the weld width area, and calculates the weld depth parameters and the weld width parameters through algorithms such as pixel counting and edge detection. Then, through statistical analysis of multiple frames of images, stable and reliable weld depth and width measurement results are obtained, thereby improving the stability and reliability of the measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a pipeline welding construction control method according to an embodiment of the present application;

[0055] Figure 2 This is a flow chart of step S100 in a pipeline welding construction control method according to an embodiment of the present application;

[0056] Figure 3 This is a flow chart of step S200 in a pipeline welding construction control method according to an embodiment of the present application;

[0057] Figure 4 This is a flow chart of step S300 in a pipeline welding construction control method according to an embodiment of the present application;

[0058] Figure 5 This is a flow chart of step S400 in a pipeline welding construction control method according to an embodiment of the present application;

[0059] Figure 6 This is a module diagram of a pipe welding construction control system according to an embodiment of the present application;

[0060] Figure 7 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0062] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0063] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0064] In the first aspect, the present application provides a pipe welding construction control method, which is applied to a pipe welding device, wherein the pipe welding device includes a temperature sensor and a camera, wherein the temperature sensor is used to monitor the temperature data of the welding part in real time, and the camera is used to take an image of the welding part. Figure 1 , the method comprises the following steps:

[0065] S100: Obtain material type, size parameters, and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve and welding termination time during the welding process.

[0066] The optimal temperature curve includes the welding starting temperature, ending temperature and temperature change rate.

[0067] In this embodiment, the material type of the pipe to be welded, such as stainless steel, carbon steel, etc., as well as the pipe's outer diameter, wall thickness, and other dimensional parameters are obtained through manual input or automatic recognition. At the same time, a temperature and humidity sensor is used to measure the temperature and relative humidity of the welding environment.

[0068] Specifically, based on the material type, the corresponding thermophysical parameters, such as melting point, thermal conductivity, and specific heat capacity, are retrieved from a pre-set material property database. Then, based on the pipe dimensions and ambient temperature and humidity data, a pre-set formula is used to derive the optimal temperature curve for the welding process. The starting and ending temperatures, as well as the temperature change rate, are fitted. Furthermore, a pre-set formula is used to calculate the predicted end time of the welding process.

[0069] S200 , obtaining image information of the weld part captured by a camera, and identifying weld depth parameters and weld width parameters of the weld part based on the image information.

[0070] In this embodiment, a camera is used to capture real-time images of the welded area, and the image data is transmitted to a computer for processing via an image acquisition card. The camera's shooting angle and position can be adjusted according to actual needs to obtain a clear and comprehensive image of the welded area.

[0071] Specifically, the captured images undergo preprocessing, such as grayscaling, filtering, and enhancement, to improve image quality. Next, image processing algorithms, such as edge detection and threshold segmentation, are used to extract characteristic information about the depth and width of the weld. For the depth of the weld, pixel statistics and scale conversion are used to determine the depth of the weld. For the width of the weld, a straight line is fitted to the weld edge and the distance is calculated to determine the weld width.

[0072] S300 , obtaining temperature data collected by a temperature sensor, and determining whether the welding temperature needs to be adjusted based on the temperature data, an optimal temperature curve, a welding depth parameter, and a welding width parameter.

[0073] In this embodiment, temperature sensors such as thermocouples and thermal resistors are used and fixed near the weld site via sensor mounting brackets to collect real-time temperature data during the welding process. The placement and number of sensors can be optimized based on the pipe size and temperature measurement requirements.

[0074] Specifically, the real-time temperature data collected by the temperature sensor is compared with the predicted optimal temperature curve to calculate the temperature deviation. Simultaneously, based on the identified weld depth and weld width parameters, the temperature adjustment threshold required for the current weld quality is determined using preset weld depth-temperature and weld width-temperature mapping tables. Taking both the temperature deviation and the adjustment threshold into account, if the deviation exceeds the threshold range, the weld temperature is determined to need adjustment; otherwise, the current temperature is maintained and welding continues.

[0075] S400: When the welding temperature needs to be adjusted, the temperature adjustment value is calculated, and the control power of the heating device is calculated according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value.

[0076] In this embodiment, the temperature adjustment value is calculated based on the size and direction of the temperature deviation, combined with the preset adjustment coefficient and adjustment upper limit. At the same time, the remaining time between the current time and the predicted welding end time is obtained as the temperature adjustment time window.

[0077] Specifically, based on the heating power-time curve corresponding to the material type, the change in heating power per unit time is determined through interpolation or fitting. The temperature adjustment value is then divided by the power change per unit time to obtain the required heating power adjustment value. Finally, the current heating device control power is added to the power adjustment value to obtain the new control power to ensure that the target temperature is reached within the remaining time.

[0078] S500: Generate a temperature control instruction according to the control power and send it to the heating device. The temperature control instruction includes the control power and the control duration.

[0079] In one embodiment, referring to Figure 2 In step S100, the material type, size parameters and ambient temperature and humidity data of the pipe to be welded are obtained, and the optimal temperature curve and the predicted welding end time during the welding process are predicted, which specifically includes the following steps:

[0080] S110: Obtain the material type of the pipe to be welded, and query the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from a preset material database.

[0081] In this embodiment, the material grade of the pipeline is obtained by manual input or barcode scanning, such as 304 stainless steel, Q235 carbon steel, etc. Then, a pre-established material attribute database is accessed and the corresponding thermophysical property parameters are searched according to the index of the material grade.

[0082] S120: Obtain outer diameter, wall thickness, and length parameters of the pipe to be welded, and calculate the surface area and volume of the pipe to be welded.

[0083] In this embodiment, measuring tools such as a vernier caliper or a laser rangefinder are used to measure geometrical dimension parameters such as the outer diameter, wall thickness and length of the pipe, which are then imported into a computer for processing via a digital interface or manual entry.

[0084] Specifically, the outer surface area of the pipe is calculated based on its outer diameter and length using the cylindrical surface area formula. Simultaneously, the solid volume of the pipe is calculated based on its outer diameter, wall thickness, and length using the cylindrical volume formula. Finally, these surface area and volume parameters are passed to the heat conduction calculation module to derive the optimal temperature curve.

[0085] S130: Acquire ambient temperature and relative humidity data.

[0086] S140. Calculate the optimal temperature curve and welding termination time based on the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0087] Specifically, calculate the optimal temperature curve T(t)=(T m -T0)*(1-e -t / τ )+T0;

[0088] Calculate the welding end time t0 = -τ*ln(ΔT / (T m -T0);

[0089] Among them, τ=c*ρ*V / (h*S+λ*S / δ), T m is the melting point temperature, T0 is the ambient temperature, τ is the time constant, ΔT is the difference between the target end temperature and the ambient temperature, c is the specific heat capacity, ρ is the density of the pipe material, V is the volume, h is the convective heat transfer coefficient determined by the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

[0090] It should be noted that in actual applications, due to differences in pipe shape, material, environmental conditions, and other factors, the calculated results of the optimal temperature curve and termination time may deviate to a certain extent. To improve prediction accuracy, empirical parameters in the calculation model can be modified through experimental testing and data fitting, such as the relationship between the convective heat transfer coefficient h and humidity, and the range of values for the time constant τ.

[0091] In one embodiment, referring to Figure 3 In step S200, image information of the weld part captured by the camera is obtained, and the weld depth parameter and the weld width parameter of the weld part are identified based on the image information, which specifically includes the following steps:

[0092] S210 , obtaining the front and side images of the welded part captured by the camera, and extracting binary images of the weld depth area and the weld width area according to the grayscale distribution characteristics of the images.

[0093] In this embodiment, an industrial camera is used to photograph the welding position from the front and side, respectively, to obtain a digital image of the molten pool morphology.

[0094] Specifically, the grayscale histogram of the image is used to analyze the grayscale difference between the melt pool and the base material, and an appropriate threshold is selected for binarization. For the frontal image, the melt pool region exhibits a higher grayscale value, corresponding to the melt width parameter; for the side image, the melt pool region exhibits a gradual grayscale distribution along the depth direction, corresponding to the melt depth parameter.

[0095] S220 , based on the binary image of the penetration area, determine the number of pixels in the penetration area, and obtain a penetration parameter by converting it according to a preset unit pixel size.

[0096] In this embodiment, pixel counts are performed on the extracted binary image of the deep penetration area to obtain the number of white pixels. Since the image resolution and size are known, a calibration relationship between the number of pixels and the actual length can be established.

[0097] Specifically, the actual penetration parameter is calculated by multiplying the number of pixels in the penetration area by the unit pixel size. This represents the maximum depth dimension of the melt pool. In practical applications, geometric corrections must be performed on the measurement results to improve the accuracy of the penetration parameter calculation, taking into account factors such as the camera's installation angle and distortion.

[0098] S230 , based on the binary image of the weld width area, detecting straight lines at the weld width edge, and calculating the distance between the straight lines to obtain weld width parameters.

[0099] Specifically, two lines perpendicular to the weld direction are selected from the extracted edge lines, corresponding to the left and right boundaries of the melt pool. The horizontal distance between these two lines is then calculated, representing the melt width parameter, which represents the maximum width of the melt pool. It should be noted that the melt pool boundary may be irregular or discontinuous, requiring curve fitting and endpoint connection.

[0100] S240, obtaining the penetration depth parameter and the weld width parameter of the continuous multiple-frame image, calculating the standard deviation of the penetration depth parameter and the weld width parameter, and when the standard deviation is less than a preset threshold, determining that the current penetration depth parameter and the weld width parameter are stable values, and obtaining the penetration depth parameter and the weld width parameter.

[0101] Because the melt pool morphology changes with fluctuations in welding conditions, the measurement results of a single frame image may contain occasional errors. The arithmetic mean and standard deviation of the melt depth and width parameters for multiple consecutive frames, such as 20 frames, are calculated. When the standard deviation is less than a preset threshold, the melt pool morphology is considered to have reached a steady state, and the melt depth and width parameters at this time are the final melt depth and width parameters.

[0102] In one embodiment, referring to Figure 4 In step S300, the temperature data collected by the temperature sensor is obtained, and based on the temperature data, the optimal temperature curve, the penetration depth parameter and the weld width parameter, it is determined whether the welding temperature needs to be adjusted. Specifically, the following steps are included:

[0103] S310 . Calculate the target temperature value at the current moment based on the optimal temperature curve, compare the target temperature value with the temperature data, and obtain a temperature deviation value.

[0104] This embodiment calculates the current target temperature value in real time based on a pre-established optimal temperature curve model. This target value is then subtracted from the actual temperature data collected by the temperature sensor to produce a temperature deviation. A positive value indicates that the actual temperature is higher than the target temperature, while a negative value indicates that the actual temperature is lower than the target temperature. The magnitude of the temperature deviation reflects the quality of temperature control; smaller deviations indicate that the temperature is closer to the optimal state.

[0105] S320 : Based on the penetration depth parameter and the weld width parameter, determine the temperature adjustment thresholds corresponding to the current penetration depth parameter and the weld width parameter by searching a preset penetration depth-temperature mapping table and a preset weld width-temperature mapping table.

[0106] This application pre-constructs a mapping table between penetration depth, weld width, and temperature, revealing the sensitivity of the melt pool geometry to temperature changes. The temperature adjustment threshold reflects the degree to which the temperature can deviate from the optimal value under the current penetration depth / weld width conditions. A smaller threshold indicates a lower tolerance for temperature fluctuations.

[0107] S330: Determine whether the welding temperature needs to be adjusted based on the temperature deviation value and the temperature adjustment threshold.

[0108] This embodiment determines the necessity of temperature control by comparing the temperature deviation value with the adjustment threshold. If the absolute value of the temperature deviation value is less than the threshold, the current temperature state is acceptable and no adjustment is required. Conversely, if the deviation value exceeds the threshold, timely measures must be taken to bring the temperature back to a reasonable range.

[0109] It is worth noting that the temperature adjustment amplitude of the present application is proportional to the size of the deviation value, thereby avoiding overshoot or oscillation.

[0110] In one embodiment, referring to Figure 5 In step S400, when the welding temperature needs to be adjusted, the temperature adjustment value is calculated, and the control power of the heating device is calculated according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value. Specifically, the steps include:

[0111] S410 , calculating the temperature adjustment value according to the direction and magnitude of the temperature adjustment value in combination with a preset temperature adjustment coefficient and an adjustment upper limit.

[0112] In this embodiment, based on the known direction and magnitude of the temperature deviation, a temperature adjustment coefficient and an upper limit parameter are introduced to correct the original temperature adjustment value to avoid excessive temperature fluctuation or exceeding a safe range.

[0113] S420: Obtain the time difference between the current moment and the predicted welding termination moment, and determine the heating power change per unit time based on the time difference and the heating power-time curve corresponding to the material type.

[0114] In this embodiment, the rate of change of power per unit time during the temperature adjustment process is determined based on the remaining time from the current moment to the end of welding and the dynamic relationship between material properties and power response. A longer remaining time allows for a gentler power adjustment strategy; a shorter remaining time requires a more aggressive power adjustment strategy.

[0115] S430: Calculate the heating power adjustment value required to reach the target temperature within the remaining time according to the temperature adjustment value and the heating power change.

[0116] Specifically, the corrected temperature adjustment value is divided by the heating power change per unit time, and then multiplied by the remaining time to obtain the heating power adjustment value.

[0117] S440: Add the current control power of the heating device to the heating power adjustment value to obtain a new control power.

[0118] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0119] In the second aspect, the present application provides a pipeline welding construction control system. The pipeline welding construction control system of the present application is described below in combination with the above-mentioned pipeline welding construction control method.

[0120] Reference Figure 6A pipe welding construction control system is applied to a pipe welding device. The pipe welding device includes a temperature sensor and a camera. The temperature sensor is used to monitor the temperature data of the welding part in real time, and the camera is used to capture the image of the welding part. The system includes:

[0121] The prediction module is used to obtain the material type, dimensional parameters and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve and the predicted welding end time during the welding process. The optimal temperature curve includes the welding start temperature, end temperature and temperature change rate;

[0122] A welding parameter acquisition module is used to obtain image information of the welding part collected by the camera, and identify the welding depth parameter and the welding width parameter of the welding part based on the image information;

[0123] A judgment module is used to obtain temperature data collected by the temperature sensor and determine whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the welding depth parameter and the welding width parameter;

[0124] The control power calculation module is used to calculate the temperature adjustment value when the welding temperature needs to be adjusted. The control power of the heating device is calculated based on the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value;

[0125] The temperature control instruction generation module is used to generate a temperature control instruction according to the control power and send it to the heating device. The temperature control instruction includes the control power and the control duration.

[0126] In one embodiment, the prediction module includes:

[0127] The material parameter acquisition unit is used to obtain the material type of the pipe to be welded and query the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from the preset material database;

[0128] A pipe parameter acquisition unit is used to obtain the outer diameter, wall thickness and length parameters of the pipe to be welded, and calculate the surface area and volume of the pipe to be welded;

[0129] Environmental parameter acquisition unit, used to obtain environmental temperature and relative humidity data;

[0130] The calculation unit is used to calculate the optimal temperature curve and the welding termination time according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0131] In one embodiment, the fusion parameter acquisition module includes:

[0132] An image acquisition unit is used to acquire the front and side images of the weld part captured by the camera, and extract binary images of the weld depth area and the weld width area according to the grayscale distribution characteristics of the image;

[0133] A penetration parameter calculation unit is used to determine the number of pixels in the penetration area based on the binary image of the penetration area, and obtain the penetration parameter according to a preset unit pixel size;

[0134] A weld width parameter calculation unit is used to detect straight lines at the weld width edge based on a binary image of the weld width area, and calculate the distance between the straight lines to obtain the weld width parameter;

[0135] The parameter determination unit is used to obtain the melt depth parameters and melt width parameters of multiple consecutive frames of images, calculate the standard deviation of the melt depth parameters and melt width parameters, and when the standard deviation is less than a preset threshold, determine that the current melt depth parameters and melt width parameters are stable values, and obtain the melt depth parameters and melt width parameters.

[0136] In one embodiment, the judgment module includes:

[0137] A comparison unit is used to calculate the target temperature value at the current moment based on the optimal temperature curve, and compare the target temperature value with the temperature data to obtain a temperature deviation value;

[0138] An adjustment threshold determination unit is used to determine the temperature adjustment threshold corresponding to the current penetration depth parameter and the weld width parameter by searching a preset penetration depth-temperature mapping table and a preset weld width-temperature mapping table based on the penetration depth parameter and the weld width parameter;

[0139] The judgment unit is used to judge whether the welding temperature needs to be adjusted according to the temperature deviation value and the temperature adjustment threshold.

[0140] In one embodiment, the control power calculation module includes:

[0141] a temperature adjustment value calculation unit, configured to calculate the temperature adjustment value according to the direction and magnitude of the temperature adjustment value, in combination with a preset temperature adjustment coefficient and an adjustment upper limit;

[0142] A power variation determination unit is configured to obtain a time difference between the current moment and the predicted welding termination moment, and determine a heating power variation per unit time based on the time difference and a heating power-time curve corresponding to the material type;

[0143] a power adjustment value calculation unit, configured to calculate a heating power adjustment value required to reach a target temperature within a remaining time according to the temperature adjustment value and the heating power variation;

[0144] The control power calculation unit is used to add the current control power of the heating device to the heating power adjustment value to obtain a new control power.

[0145] In one embodiment, the optimal temperature curve and the welding termination time are calculated based on the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0146] Calculate the optimal temperature curve T(t)=(T m -T0)*(1-e -t / τ )+T0;

[0147] Calculate the welding end time t0 = -τ*ln(ΔT / (T m -T0);

[0148] Among them, τ=c*ρ*V / (h*S+λ*S / δ), T m is the melting point temperature, T0 is the ambient temperature, τ is the time constant, ΔT is the difference between the target end temperature and the ambient temperature, c is the specific heat capacity, ρ is the density of the pipe material, V is the volume, h is the convective heat transfer coefficient determined by the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

[0149] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The electronic device includes a processor, a memory, and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a pipeline welding construction control method is implemented.

[0150] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0151] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0152] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0153] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A pipeline welding construction control method, characterized in that: The method is applied to a pipe welding device, the pipe welding device including a temperature sensor and a camera, the temperature sensor is used to monitor the temperature data of the welding part in real time, and the camera is used to capture an image of the welding part. The method includes the following steps: Obtain the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve and the predicted welding end time during the welding process. The optimal temperature curve includes the welding start temperature, end temperature, and temperature change rate; Acquiring image information of the weld part captured by the camera, and identifying a weld depth parameter and a weld width parameter of the weld part based on the image information; Acquiring temperature data collected by the temperature sensor, and determining whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the welding depth parameter, and the welding width parameter; When the welding temperature needs to be adjusted, the temperature adjustment value is calculated, and the control power of the heating device is calculated according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value; Generate a temperature control instruction according to the control power and send it to the heating device, the temperature control instruction including the control power and the control duration; The following steps are involved in obtaining the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, and predicting the optimal temperature curve and the welding termination time during the welding process: Obtain the material type of the pipe to be welded, and query the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from a preset material database; Obtaining the outer diameter, wall thickness, and length parameters of the pipe to be welded, and calculating the surface area and volume of the pipe to be welded; Get ambient temperature and relative humidity data; Calculate the optimal temperature curve T(t)=(T m -T0)*(1-e -t / τ )+T0; Calculate the welding end time t0 = -τ*ln(ΔT / (T m -T0); Among them, τ=c*ρ*V / (h*S+λ*S / δ), T m is the melting point temperature, T0 is the ambient temperature, τ is the time constant, ΔT is the difference between the target end temperature and the ambient temperature, c is the specific heat capacity, ρ is the density of the pipe material, V is the volume, h is the convective heat transfer coefficient determined by the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

2. The pipeline welding construction control method according to claim 1, characterized in that: Obtaining image information of the weld part captured by the camera, and identifying the weld depth parameter and the weld width parameter of the weld part based on the image information, specifically includes the following steps: Obtaining the front and side images of the welded part captured by the camera, and extracting binary images of the weld depth area and the weld width area according to the grayscale distribution characteristics of the images; Based on the binary image of the deep penetration area, the number of pixels in the deep penetration area is determined, and the deep penetration parameter is obtained by conversion according to the preset unit pixel size; Based on the binary image of the weld width area, the straight lines at the weld width edge are detected, and the distance between the straight lines is calculated to obtain the weld width parameters; The depth and width parameters of the continuous multi-frame images are obtained, and the standard deviation of the depth and width parameters is calculated. When the standard deviation is less than a preset threshold, the current depth and width parameters are determined to be stable values, and the depth and width parameters are obtained.

3. The pipeline welding construction control method according to claim 1, characterized in that: Acquiring temperature data collected by the temperature sensor, and judging whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the penetration depth parameter, and the weld width parameter, specifically includes the following steps: Calculating a target temperature value at a current moment based on the optimal temperature curve, and comparing the target temperature value with the temperature data to obtain a temperature deviation value; Based on the penetration depth parameter and the weld width parameter, determining the temperature adjustment threshold corresponding to the current penetration depth parameter and the weld width parameter by searching a preset penetration depth-temperature mapping table and a preset weld width-temperature mapping table; Whether the welding temperature needs to be adjusted is determined according to the temperature deviation value and the temperature adjustment threshold.

4. The pipeline welding construction control method according to claim 1, characterized in that: When the welding temperature needs to be adjusted, the temperature adjustment value is calculated, and the control power of the heating device is calculated according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value, which specifically includes the following steps: Calculate the temperature adjustment value based on the direction and magnitude of the temperature adjustment value, combined with a preset temperature adjustment coefficient and an upper limit of the adjustment; obtain the time difference between the current moment and the predicted welding end moment, and determine the heating power change per unit time based on the time difference and the heating power-time curve corresponding to the material type; Calculating the heating power adjustment value required to reach the target temperature within the remaining time according to the temperature adjustment value and the heating power change; The current control power of the heating device is added to the heating power adjustment value to obtain a new control power.

5. A pipe welding construction control system, characterized in that: Applied to a pipe welding device, the pipe welding device includes a temperature sensor and a camera. The temperature sensor is used to monitor the temperature data of the welding part in real time, and the camera is used to capture the image of the welding part. The system includes: A prediction module is used to obtain the material type, dimensional parameters, and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve and the predicted welding end time during the welding process. The optimal temperature curve includes the welding start temperature, end temperature, and temperature change rate; a welding parameter acquisition module, configured to acquire image information of the welding part captured by the camera, and identify the welding depth parameter and the welding width parameter of the welding part based on the image information; a judgment module, configured to obtain temperature data collected by the temperature sensor, and judge whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the welding depth parameter, and the welding width parameter; A control power calculation module is used to calculate the temperature adjustment value when the welding temperature needs to be adjusted, and calculate the control power of the heating device according to the time difference between the current moment and the predicted welding end moment, the material type and the temperature adjustment value; a temperature control instruction generating module, configured to generate a temperature control instruction according to the control power and send the instruction to the heating device, wherein the temperature control instruction includes the control power and the control duration; Wherein, the prediction module includes: A material parameter acquisition unit is used to obtain the material type of the pipe to be welded and query the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from a preset material database; A pipe parameter acquisition unit, configured to acquire the outer diameter, wall thickness, and length parameters of the pipe to be welded, and calculate the surface area and volume of the pipe to be welded; Environmental parameter acquisition unit, used to obtain environmental temperature and relative humidity data; a calculation unit, for calculating an optimal temperature curve and a welding termination time according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data; The calculation unit specifically performs the following steps: Calculate the optimal temperature curve T(t)=(T m -T0)*(1-e -t / τ )+T0; Calculate the welding end time t0 = -τ*ln(ΔT / (T m -T0); Among them, τ=c*ρ*V / (h*S+λ-S / δ), T m is the melting point temperature, T0 is the ambient temperature, τ is the time constant, ΔT is the difference between the target end temperature and the ambient temperature, c is the specific heat capacity, ρ is the density of the pipe material, V is the volume, h is the convective heat transfer coefficient determined by the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

6. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the pipeline welding construction control method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the pipeline welding construction control method according to any one of claims 1 to 4 are implemented.

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