Pipeline welding construction control method, system and equipment and medium

Abstract

The invention relates to the technical field of pipeline welding, in particular to a pipeline welding construction control method, system and equipment and a medium. Firstly, the material type, size parameters and environment temperature and humidity data of a to-be-welded pipeline are obtained, the optimal temperature curve and the welding ending moment in the welding process are predicted, then image information of a welding part is collected through a camera, the welding depth and welding width parameters are recognized, meanwhile, real-time temperature data are collected through a temperature sensor, and the welding precision is improved. Whether the welding temperature needs to be adjusted or not is judged by combining the predicted optimal temperature curve and the recognized fusion depth and fusion width parameters, if the welding temperature needs to be adjusted, a temperature adjustment value and the control power of the heating device are calculated, a corresponding temperature control instruction is generated and sent to the heating device to be executed, and automatic control and quality monitoring of the welding process are achieved. The dependence on artificial experience is reduced, and the welding efficiency and the success rate are improved.

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 are needed 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, 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 the device is used to weld and fix the pipe, the pipe needs to be inserted into a 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 the existing pipe welding device cannot monitor the welding quality in real time, and the welding quality and success rate depend on manual experience, the present application provides a pipe welding construction control method, system, equipment and medium, which adopts the following technical solutions: In a first aspect, the present application provides a pipeline welding construction control method, which is applied to a pipeline welding device, wherein the pipeline 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: Obtain the material type, size parameters and ambient temperature and humidity data of the pipe to be welded, predict the optimal temperature curve and the predicted welding end time during the welding process, wherein the optimal temperature curve includes the welding start temperature, the end temperature and the temperature change rate; obtain the 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; Acquire 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; 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; 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.

[0005] By adopting the above technical scheme, in order to solve the problem that the existing pipeline welding device cannot monitor the welding quality in real time, and the welding quality and success rate rely on manual experience, the present application first obtains the material type, size parameters and environmental temperature and humidity data of the pipe to be welded, predicts the optimal temperature curve and welding termination time in the welding process, and then collects the image information of the welding part through the camera, identifies the melting depth and melting width parameters, and at the same time, collects real-time temperature data through the temperature sensor, combines the predicted optimal temperature curve and the identified melting depth and melting width parameters, and judges 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 instruction is generated and sent to the heating device for execution; first, the temperature sensor and the camera are introduced to monitor the welding process in real time, which overcomes the limitations of manual monitoring; secondly, by predicting the optimal temperature curve and identifying the melting depth and melting width parameters, the quantitative evaluation and control of the welding quality are realized; thirdly, according to the real-time monitoring data and the prediction results, the heating power is dynamically adjusted to ensure the optimal control of the welding temperature; the whole control process has a high degree of automation, reduces the dependence on manual experience, and improves the welding efficiency and success rate.

[0006] Optionally, 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 termination time during the welding process are predicted, which specifically includes the following steps: 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; The optimum temperature curve and the welding termination time are calculated according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0007] By adopting the above technical solution, in order to further improve the accuracy and reliability of the pipeline welding temperature prediction, this application obtains the material type, dimensional parameters and environmental temperature and humidity data of the pipeline to be welded, queries the material property database, calculates the surface area and volume of the pipeline, and comprehensively considers environmental factors to predict the optimal temperature curve and welding termination time during the welding process. It can effectively improve the accuracy and reliability of the pipeline welding temperature prediction and provide a quantitative basis and indicator for subsequent welding temperature control.

[0008] Optionally, acquiring image information of the welding part collected by the camera, and identifying the welding depth parameter and the welding width parameter of the welding part based on the image information specifically comprises the following steps: Obtaining the front and side images of the welded part collected 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 melting area, the number of pixels in the deep melting area is determined, and the deep melting parameter is obtained by conversion according to the preset unit pixel size; Based on the binary image of the melt width area, the straight lines at the edge of the melt width are detected, and the distance between the straight lines is calculated to obtain the melt width parameter; the melt depth parameter and the melt width parameter of multiple consecutive frames of images are obtained, and the standard deviation of the melt depth parameter and the melt width parameter is calculated. When the standard deviation is less than a preset threshold, the current melt depth parameter and the melt width parameter are determined to be stable values, and the melt depth parameter and the melt width parameter are obtained.

[0009] By adopting the above-mentioned technical scheme, 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.

[0010] Optionally, obtaining 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 welding depth parameter and the welding width parameter, specifically comprises the following steps: Based on the optimal temperature curve, a target temperature value at the current moment is calculated, and the target temperature value is compared with the temperature data to obtain a temperature deviation value; Based on the 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, determining a temperature adjustment threshold corresponding to the current penetration depth parameter and the weld width parameter; Whether the welding temperature needs to be adjusted is determined according to the temperature deviation value and the temperature adjustment threshold.

[0011] By adopting the above technical scheme, 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 melting depth parameter and melting width parameter 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.

[0012] 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 termination moment, the material type and the temperature adjustment value, which specifically includes the following steps: Calculate the temperature adjustment value according to the direction and size of the temperature adjustment value, combined with the preset temperature adjustment coefficient and the upper limit of the adjustment; 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; 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.

[0013] By adopting the above-mentioned technical scheme, the present application determines the temperature adjustment value according to the temperature deviation, and combines the heating power-time curve corresponding to the remaining welding time and the 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.

[0014] Optionally, the optimal temperature curve and the welding termination time are calculated according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, 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 mis the melting point temperature, T0 is the ambient temperature, τ is the time constant, ΔT is the difference between the target termination temperature and the ambient temperature, c is the specific heat capacity, ρ is the pipe material density, V is the volume, h is the convective heat transfer coefficient determined according to the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

[0015] By adopting the above-mentioned technical scheme, the present application obtains the expression of the optimal temperature curve and the termination time of welding through mathematical modeling, comprehensively considers the influence of factors such as material thermal properties, geometric parameters and environmental conditions, and improves the accuracy and applicability of the prediction results; introduces the time constant and convection heat transfer coefficient, reflects the dynamic characteristics of the heat conduction process and the influence of environmental humidity, and makes the prediction closer to reality.

[0016] In a second aspect, the present application provides a pipe welding construction control system, 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 system includes: A prediction module is used to obtain the material type, size parameters and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve in the welding process and the predicted welding end time. The optimal temperature curve includes the welding start temperature, end temperature and temperature change rate; A welding parameter acquisition module, used to acquire 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; A judgment module, used for obtaining the 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 welding depth parameter and the welding width parameter; A control power calculation module, 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 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.

[0017] Optionally, the prediction module includes: A material parameter acquisition unit, used to acquire 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 pipeline parameter acquisition unit, used to acquire the outer diameter, wall thickness and length parameters of the pipeline to be welded, and calculate the surface area and volume of the pipeline to be welded; An environmental parameter acquisition unit, used to acquire environmental temperature and relative humidity data; 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.

[0018] In a third aspect, the present application provides an electronic device, including 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.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned pipeline welding construction control method are implemented.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application first obtains the material type, size parameters and environmental temperature and humidity data of the pipe to be welded, predicts the optimal temperature curve and welding termination time during the welding process, and then collects image information of the welding part through a camera to identify the welding depth and welding width parameters. At the same time, real-time temperature data is collected through a temperature sensor, and combined with the predicted optimal temperature curve and the identified welding depth and welding 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 instructions are generated and sent to the heating device for execution, so as to realize the automatic control and quality monitoring of the welding process, reduce the dependence on manual experience, and improve the welding efficiency and success rate; 2. This application obtains the material type, size parameters and environmental 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. It can effectively improve the accuracy and reliability of the pipeline welding temperature prediction and provide a quantitative basis and index for the subsequent welding temperature control; 3. This 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a pipeline welding construction control method according to an embodiment of the present application; Figure 2 It is a flow chart of step S100 in a pipeline welding construction control method according to an embodiment of the present application; Figure 3 It is a flow chart of step S200 in a pipeline welding construction control method according to an embodiment of the present application; Figure 4 It is a flow chart of step S300 in a pipeline welding construction control method according to an embodiment of the present application; Figure 5 It is a flow chart of step S400 in a pipeline welding construction control method according to an embodiment of the present application; Figure 6 This is a module schematic diagram of a pipeline welding construction control system according to an embodiment of the present application; Figure 7 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0024] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0025] In the first aspect, the present application provides a pipeline welding construction control method, which is applied to a pipeline welding device, wherein the pipeline 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: S100, obtaining the material type, size parameters and ambient temperature and humidity data of the pipe to be welded, and predicting the optimal temperature curve and the predicted welding termination time during the welding process.

[0026] The optimal temperature curve includes the welding start temperature, end temperature and temperature change rate.

[0027] In this embodiment, the material type of the pipe to be welded, such as stainless steel, carbon steel, etc., and the size parameters such as the outer diameter and wall thickness of the pipe are obtained by manual input or automatic recognition. At the same time, the temperature and relative humidity of the welding environment are measured using a temperature and humidity sensor.

[0028] Specifically, according to the acquired material type, the corresponding melting point temperature, thermal conductivity, specific heat capacity and other thermal physical parameters are queried from the preset material property database. Then, combined with the pipeline size parameters and the ambient temperature and humidity data, the optimal temperature curve expression of the welding process is derived according to the preset formula, and the starting temperature, end temperature and temperature change rate are fitted. In addition, the predicted welding end time is calculated according to the preset formula.

[0029] S200, acquiring image information of the welding part collected by a camera, and identifying a welding depth parameter and a welding width parameter of the welding part based on the image information.

[0030] In this embodiment, a camera is used to perform real-time imaging of the welded part, and the image data is transmitted to a computer for processing via an image acquisition card. The shooting angle and position of the camera can be adjusted according to actual needs to obtain a clear and comprehensive image of the welded part.

[0031] Specifically, the collected images are preprocessed, such as grayscale, filtering, and enhancement, to improve image quality. Then, image processing algorithms such as edge detection and threshold segmentation are used to extract feature information of the deep melting area and the wide melting area. For the deep melting area, the deep melting parameters are obtained through pixel statistics and scale conversion; for the wide melting area, the wide melting parameters are obtained by fitting a straight line of the wide melting edge and calculating the distance.

[0032] S300, obtaining temperature data collected by a temperature sensor, and judging 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.

[0033] In this embodiment, a temperature sensor such as a thermocouple or a thermal resistor is selected and fixed near the welding part through a sensor mounting base to collect temperature data of the welding process in real time. The arrangement position and quantity of the sensor can be optimized according to the pipe size and temperature measurement requirements.

[0034] Specifically, the real-time temperature data collected by the temperature sensor is compared with the predicted optimal temperature curve to calculate the temperature deviation value. At the same time, according to the identified melting depth parameters and melting width parameters, the preset melting depth-temperature mapping table and melting width-temperature mapping table are used to determine the temperature adjustment threshold required for the current welding quality; comprehensively consider the temperature deviation and adjustment threshold, and when the deviation exceeds the threshold range, it is determined that the welding temperature needs to be adjusted; otherwise, the current temperature is maintained and welding continues.

[0035] 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 time and the predicted welding end time, the material type and the temperature adjustment value.

[0036] In this embodiment, the temperature adjustment value is calculated according to the size and direction of the temperature deviation, combined with the preset adjustment coefficient and the upper limit of the adjustment. At the same time, the remaining time between the current moment and the predicted welding end moment is obtained as the time window for temperature adjustment.

[0037] Specifically, based on the heating power-time curve corresponding to the material type, the heating power change per unit time is determined by interpolation or fitting. Then, the temperature adjustment value is 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 a new control power to ensure that the target temperature is reached within the remaining time.

[0038] 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.

[0039] 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 termination time during the welding process are predicted, which specifically includes the following steps: S110, obtaining the material type of the pipe to be welded, and querying the melting point temperature, thermal conductivity and specific heat capacity corresponding to the material type from a preset material database.

[0040] 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, the pre-established material attribute database is accessed, and the corresponding thermophysical property parameters are searched according to the index of the material grade.

[0041] S120: 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.

[0042] In this embodiment, a measuring tool such as a vernier caliper or a laser rangefinder is used to measure geometric dimension parameters such as the outer diameter, wall thickness and length of the pipeline, and then the parameters are imported into a computer for processing through a digital interface or manual entry.

[0043] Specifically, the outer surface area of ​​the pipe is calculated based on the outer diameter and length of the pipe using the surface area formula of a cylinder. At the same time, the solid volume of the pipe is calculated based on the outer diameter, wall thickness and length using the volume formula of a cylinder. Finally, the surface area and volume parameters are passed to the heat conduction calculation module for the derivation of the optimal temperature curve.

[0044] S130: Acquire ambient temperature and relative humidity data.

[0045] S140, calculating the optimal temperature curve and welding termination time according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

[0046] Specifically, 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 termination temperature and the ambient temperature, c is the specific heat capacity, ρ is the pipe material density, V is the volume, h is the convective heat transfer coefficient determined according to the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

[0047] It should be noted that in actual applications, due to differences in pipe shape, material, environment and other conditions, the calculation results of the optimal temperature curve and the end time may have certain deviations. In order to improve the prediction accuracy, the empirical parameters in the calculation model can be corrected through experimental testing and data fitting, such as the relationship between the convective heat transfer coefficient h and humidity, the value range of the time constant τ, etc.

[0048] In one embodiment, referring to Figure 3 In step S200, image information of the welded part collected by the camera is obtained, and the weld depth parameter and the weld width parameter of the welded part are identified based on the image information, which specifically includes the following steps: S210, obtaining the front and side images of the welding part collected by the camera, and extracting the binary images of the welding depth area and the welding width area according to the grayscale distribution characteristics of the images.

[0049] In this embodiment, an industrial camera is used to photograph the welding position from the front and side to obtain a digital image of the molten pool morphology.

[0050] Specifically, the grayscale difference between the molten pool area and the base material area is analyzed using the grayscale histogram of the image, and a suitable threshold is selected for binarization. For the front image, the molten pool area presents a higher grayscale value, corresponding to the melt width parameter; for the side image, the molten pool area presents a gradient grayscale distribution along the depth direction, corresponding to the melt depth parameter.

[0051] S220 , determining the number of pixels in the deep penetration area based on the binary image of the deep penetration area, and obtaining a deep penetration parameter by converting it according to a preset unit pixel size.

[0052] In this embodiment, pixel statistics are performed on the extracted binary image of the deep melting 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.

[0053] Specifically, the number of pixels in the deep melting area is multiplied by the unit pixel size to obtain the actual deep melting parameter, which represents the maximum size of the molten pool in the depth direction. In practical applications, it is necessary to consider factors such as the camera installation angle and distortion, perform geometric correction on the measurement results, and improve the calculation accuracy of the deep melting parameter.

[0054] S230, based on the binary image of the weld width area, detecting straight lines at the edge of the weld width, and calculating the distance between the straight lines to obtain weld width parameters.

[0055] Specifically, on the extracted edge straight lines, two straight lines perpendicular to the welding direction are selected, corresponding to the left and right boundaries of the molten pool. Then, the distance between the two straight lines in the horizontal direction is calculated, which is the molten width parameter, indicating the maximum size of the molten pool in the width direction. It should be noted that the molten pool boundary may be irregular or discontinuous, and curve fitting, endpoint connection and other processing are required at this time.

[0056] S240, obtaining the melting depth parameter and the melting width parameter of the continuous multiple frame images, calculating the standard deviation of the melting depth parameter and the melting width parameter, and when the standard deviation is less than a preset threshold, determining that the current melting depth parameter and the melting width parameter are stable values, and obtaining the melting depth parameter and the melting width parameter.

[0057] Since the molten pool morphology will change with the fluctuation of welding conditions, the measurement results of a single frame image may have accidental errors. By calculating the arithmetic mean and standard deviation of the melting depth parameters and the melting width parameters of multiple consecutive frames, such as 20 frames, when the standard deviation is less than the preset threshold, it is considered that the molten pool morphology has reached a steady state, and the melting depth parameters and the melting width parameters at this time are the final melting depth parameters and the melting width parameters.

[0058] 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 melting depth parameter and the melting width parameter, it is determined whether the welding temperature needs to be adjusted, which specifically includes the following steps: S310. Based on the optimal temperature curve, calculate the target temperature value at the current moment, compare the target temperature value with the temperature data, and obtain a temperature deviation value.

[0059] Among them, this embodiment calculates the target temperature value at the current moment in real time according to the pre-established optimal temperature curve model. Then, the target value is subjected to difference calculation with the actual temperature data collected by the temperature sensor to obtain a temperature deviation value. A positive value indicates that the actual temperature is higher than the target temperature, and a negative value indicates that the actual temperature is lower than the target temperature. The size of the temperature deviation value reflects the quality of temperature control. The smaller the deviation, the closer the temperature is to the optimal state.

[0060] 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 weld width-temperature mapping table.

[0061] Among them, this application pre-constructs a mapping relationship table between the depth of penetration, the width of the weld and the temperature, revealing the sensitivity of the geometric parameters of the weld pool to temperature changes. The temperature adjustment threshold reflects the degree to which the temperature is allowed to deviate from the optimal value under the current depth of penetration / width of the weld. The smaller the threshold, the lower the tolerance to temperature fluctuations.

[0062] S330: Determine whether the welding temperature needs to be adjusted according to the temperature deviation value and the temperature adjustment threshold.

[0063] Among them, this embodiment determines the necessity of temperature control by comparing the temperature deviation value and the adjustment threshold. If the absolute value of the temperature deviation value is less than the threshold, it means that the current temperature state is acceptable and no adjustment is required temporarily; on the contrary, if the deviation value exceeds the threshold, it is necessary to take timely measures to bring the temperature back to a reasonable range.

[0064] It is worth noting that the temperature adjustment amplitude of the present application is in a certain proportion to the magnitude of the deviation value, thereby avoiding overshoot or oscillation.

[0065] 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 termination moment, the material type and the temperature adjustment value, which specifically includes the following steps: S410: Calculate the temperature adjustment value according to the direction and size of the temperature adjustment value in combination with a preset temperature adjustment coefficient and an adjustment upper limit.

[0066] 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.

[0067] S420, obtaining the time difference between the current moment and the predicted welding termination moment, and determining the heating power change per unit time based on the time difference and the heating power-time curve corresponding to the material type.

[0068] In this embodiment, based on the remaining time from the current moment to the end of welding and the dynamic relationship between material properties and power response, the rate of change of power per unit time during the temperature adjustment process is determined. The longer the remaining time is, the slower the power adjustment strategy can be adopted; the shorter the remaining time is, the more aggressive the power adjustment strategy needs to be adopted.

[0069] 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.

[0070] 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.

[0071] S440: Add the current control power of the heating device to the heating power adjustment value to obtain a new control power.

[0072] 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 the present application.

[0073] 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.

[0074] Reference Figure 6 A pipeline welding construction control system is applied to a pipeline welding device. The pipeline 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. The camera is used to take an image of the welding part. The system includes: The prediction module is used to obtain the material type, size parameters and environmental 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 is used to acquire 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; A judgment module is used to obtain temperature data collected by a temperature sensor, and judge whether the welding temperature needs to be adjusted based on the temperature data, the optimal temperature curve, the melting depth parameter and the melting 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; 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.

[0075] In one embodiment, the prediction module includes: 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 a preset material database; A pipeline parameter acquisition unit is used to acquire the outer diameter, wall thickness and length parameters of the pipeline to be welded, and calculate the surface area and volume of the pipeline to be welded; An environmental parameter acquisition unit, used to acquire environmental temperature and relative humidity data; 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.

[0076] In one embodiment, the welding parameter acquisition module includes: An image acquisition unit is used to acquire the front and side images of the welding part collected by the camera, and extract the binary images of the welding depth area and the welding width area according to the gray distribution characteristics of the image; 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; 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; The parameter determination unit is used to obtain the melting depth parameter and the melting width parameter of multiple consecutive frames of images, calculate the standard deviation of the melting depth parameter and the melting width parameter, and when the standard deviation is less than a preset threshold, determine that the current melting depth parameter and the melting width parameter are stable values, and obtain the melting depth parameter and the melting width parameter.

[0077] In one embodiment, the determination module includes: A comparison unit, 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; An adjustment threshold determination unit is used to determine the temperature adjustment threshold corresponding to the current melt depth parameter and the melt width parameter by searching a preset melt depth-temperature mapping table and a melt width-temperature mapping table based on the melt depth parameter and the melt width parameter; The judging unit is used to judge whether the welding temperature needs to be adjusted according to the temperature deviation value and the temperature adjustment threshold.

[0078] In one embodiment, the control power calculation module includes: A temperature adjustment value calculation unit, used to calculate the temperature adjustment value according to the direction and size of the temperature adjustment value, combined with a preset temperature adjustment coefficient and an adjustment upper limit; A power variation determination unit is used to obtain the time difference between the current moment and the predicted welding termination moment, and determine the heating power variation per unit time based on the time difference and the heating power-time curve corresponding to the material type; A power adjustment value calculation unit, used to 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; 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.

[0079] In one embodiment, the optimal temperature curve and the welding termination time are calculated according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, 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 termination temperature and the ambient temperature, c is the specific heat capacity, ρ is the pipe material density, V is the volume, h is the convective heat transfer coefficient determined according to the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

[0080] 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 through a system bus. Among them, 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 the 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 through a network connection. When the computer program is executed by the processor, a pipeline welding construction control method is implemented.

[0081] Those skilled in the art will understand that Figure 7The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme 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 arrangement of components.

[0082] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0083] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can 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).

[0084] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A pipeline welding construction control method, 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 take an image of the welding part. The method includes the following steps: Obtain the material type, size parameters and ambient temperature and humidity data of the pipe to be welded, predict the optimal temperature curve and the predicted welding end time during the welding process, wherein the optimal temperature curve includes the welding start temperature, the end temperature and the temperature change rate; obtain the 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; Acquire 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; 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; 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.

2. The pipeline welding construction control method according to claim 1 is characterized in that: Obtain the material type, size parameters and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve and the predicted welding termination time during the welding process, which specifically includes the following steps: 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; The optimum temperature curve and the welding termination time are calculated according to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data.

3. The pipeline welding construction control method according to claim 1 is characterized in that: Acquiring image information of the welding part collected by the camera, and identifying the welding depth parameter and the welding width parameter of the welding part based on the image information, specifically includes the following steps: Obtaining the front and side images of the welded part collected 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 melting area, the number of pixels in the deep melting area is determined, and the deep melting 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 edge of the weld width are detected, and the distance between the straight lines is calculated to obtain the weld width parameter; The melting depth parameter and the melting width parameter of the continuous multiple-frame images are obtained, and the standard deviation of the melting depth parameter and the melting width parameter is calculated. When the standard deviation is less than a preset threshold, the current melting depth parameter and the melting width parameter are determined to be stable values, and the melting depth parameter and the melting width parameter are obtained.

4. The pipeline welding construction control method according to claim 1 is 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 welding depth parameter and the welding width parameter, specifically includes the following steps: Based on the optimal temperature curve, a target temperature value at the current moment is calculated, and the target temperature value is compared with the temperature data to obtain a temperature deviation value; Based on the 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, determining a temperature adjustment threshold corresponding to the current penetration depth parameter and the weld width parameter; Whether the welding temperature needs to be adjusted is determined according to the temperature deviation value and the temperature adjustment threshold.

5. The pipeline welding construction control method according to claim 1 is 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 termination moment, the material type and the temperature adjustment value, which specifically includes the following steps: Calculate the temperature adjustment value according to the direction and size of the temperature adjustment value, combined with the preset temperature adjustment coefficient and the upper limit of the adjustment; 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; 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.

6. The pipeline welding construction control method according to claim 2 is characterized in that: According to the melting point temperature, thermal conductivity, specific heat capacity, surface area, volume, ambient temperature and relative humidity data, the optimal temperature curve and the welding termination time are calculated. 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 termination temperature and the ambient temperature, c is the specific heat capacity, ρ is the pipe material density, V is the volume, h is the convective heat transfer coefficient determined according to the relative humidity, S is the surface area, λ is the thermal conductivity, and δ is the wall thickness.

7. A pipeline 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 take an image of the welding part. The system includes: A prediction module is used to obtain the material type, size parameters and ambient temperature and humidity data of the pipe to be welded, and predict the optimal temperature curve in the welding process and the predicted welding end time. The optimal temperature curve includes the welding start temperature, end temperature and temperature change rate; A welding parameter acquisition module, used to acquire 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; A judgment module, used for obtaining the 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 welding depth parameter and the welding width parameter; A control power calculation module, 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 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.

8. The pipeline welding construction control system according to claim 7 is characterized in that: The prediction module comprises: A material parameter acquisition unit, used to acquire 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 pipeline parameter acquisition unit, used to acquire the outer diameter, wall thickness and length parameters of the pipeline to be welded, and calculate the surface area and volume of the pipeline to be welded; An environmental parameter acquisition unit, used to acquire environmental temperature and relative humidity data; 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.

9. An electronic device, characterized in that: The method 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 6 are implemented.

10. 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 described in any one of claims 1 to 6 are implemented.

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