Welding process control system and method based on high-temperature high-pressure large-diameter thick-walled pipe

Abstract

The present application relates to the technical field of welding, in particular to a welding process control system and method based on high-temperature high-pressure large-diameter thick-walled pipeline, which is used to solve the problem that the existing welding process control system and method cannot intelligently select appropriate welding parameters according to different pipelines, cannot real-time monitor the pipeline welding equipment, cannot accurately control the welding process, and affects the welding efficiency and weld quality; the welding process control system comprises the following modules: a pipeline identification module, a parameter setting module, a parameter analysis module, an equipment monitoring module, an equipment analysis module, a process control platform and an abnormal alarm module; the welding process control system can self-adaptively select the best welding parameters, can real-time monitor the abnormal conditions of the pipeline welding equipment, can accurately control the welding process, can improve the welding efficiency, can ensure the stable and reliable weld quality, and has important practical value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of welding, in particular to a welding process control system and method based on high-temperature high-pressure large-diameter thick-walled pipes. BACKGROUND

[0002] High-temperature high-pressure large-diameter thick-walled pipes have a wide range of applications in industrial production, such as petroleum chemical industry, natural gas transportation, etc. Due to the complex working environment, the welding quality of the pipes is extremely high. Patent No. CN201510290483.8 discloses a welding process control system and method, which includes a welding procedure selection unit, a welding current fine adjustment unit, a welding voltage fine adjustment unit, a storage unit, and an operation control unit. By adjusting the welding procedure selection unit to select a welding channel, the welding process parameters corresponding to the welding channel are obtained. The welding current fine adjustment unit obtains the upper limit value and the lower limit value of the welding current adjustment, adjusts the welding current fine adjustment unit, and calculates the current welding current preset value. The welding voltage fine adjustment unit obtains the upper limit value and the lower limit value of the welding voltage adjustment, adjusts the welding voltage fine adjustment unit, and calculates the current welding voltage preset value. The actual welding setting parameters can be within the specification permission range by using the invention, which ensures the effective implementation of the welding process procedure and further ensures the welding quality. However, there are still the following shortcomings: it cannot intelligently select appropriate welding parameters according to different pipes, and it cannot monitor the pipe welding equipment in real time, which leads to inaccurate control of the welding process and affects the welding efficiency and weld quality. SUMMARY

[0003] In order to overcome the above technical problems, the purpose of the present application is to provide a welding process control system and method based on high-temperature high-pressure large-diameter thick-walled pipes: a pipe diameter value is obtained by a pipe identification module, preselected parameters of a pipe welding equipment are obtained by a parameter setting module according to the pipe diameter value, and parameter optimization information of the preselected parameters is obtained, a parameter optimization coefficient is obtained by a parameter analysis module according to the parameter optimization information, a selected parameter is marked by the parameter setting module according to the preselected parameter corresponding to the parameter optimization coefficient, and the welding parameters of the pipe welding equipment are adjusted according to the selected parameter, equipment abnormal information is obtained by a device monitoring module, a device abnormality coefficient is obtained by a device analysis module according to the equipment abnormal information, an abnormal alarm instruction is generated by a process control platform according to the device abnormality coefficient, and an abnormal alarm is sounded by an abnormal alarm module after receiving the abnormal alarm instruction. The problems of the existing welding process control system and method, which cannot intelligently select appropriate welding parameters according to different pipes, cannot monitor the pipe welding equipment in real time, leads to inaccurate control of the welding process, and affects the welding efficiency and weld quality, are solved.

[0004] The purpose of the present application can be achieved by the following technical solutions:

[0005] The welding process control system based on high-temperature high-pressure large-diameter thick-wall pipeline comprises:

[0006] A pipeline identification module is configured to acquire the cross-sectional diameter of the high-temperature high-pressure large-diameter thick-wall pipeline and mark it as a pipe diameter value GJ, and send the pipe diameter value GJ to a parameter setting module.

[0007] The parameter setting module is configured to acquire preselected parameters i of the pipeline welding equipment according to the pipe diameter value GJ, acquire parameter optimization information of the preselected parameters i, and send the parameter optimization information to a parameter analysis module; wherein the parameter optimization information comprises a use frequency value YC, a use time value YS and a failure value SW; and the parameter setting module is further configured to mark the preselected parameters i corresponding to the parameter optimization coefficient CY as selected parameters, adjust the welding parameters of the pipeline welding equipment according to the selected parameters, generate a device monitoring instruction after the adjustment is completed, and send the device monitoring instruction to a device monitoring module.

[0008] The parameter analysis module is configured to acquire the parameter optimization coefficient CY according to the parameter optimization information and send the parameter optimization coefficient CY to the parameter setting module.

[0009] The device monitoring module is configured to acquire device abnormal information of the pipeline welding equipment after receiving the device monitoring instruction, and send the device abnormal information to a device analysis module; wherein the device abnormal information comprises an electrical stability value DW, a vibration value ZD and a use value SY.

[0010] The device analysis module is configured to acquire a device abnormality coefficient SX according to the device abnormal information and send the device abnormality coefficient SX to a process control platform.

[0011] The process control platform is configured to generate an abnormal alarm instruction according to the device abnormality coefficient SX and send the abnormal alarm instruction to an abnormal alarm module.

[0012] The abnormal alarm module is configured to sound an abnormal alarm after receiving the abnormal alarm instruction.

[0013] As a further scheme of the present application, the specific process in which the parameter setting module acquires the parameter optimization information is as follows:

[0014] Acquire the welding parameters of the pipeline welding equipment at the same pipe diameter value GJ in the historical data, and mark them as preselected parameters i in sequence, i=1, …, n, n being a natural number; wherein the welding parameters comprise a welding temperature, a welding wire conveying speed and a welding time.

[0015] Acquire the number of times that the preselected parameters i are selected for use, and mark it as a use frequency value YC.

[0016] Obtaining the time difference between the time when the preselected parameter i is last selected and the current time, and marking it as the use time value YS;

[0017] Obtaining the number of welding failures and the average interval time of welding failures in the process of selecting the preselected parameter i, and marking them as the failure number value WC and the failure time value WS, respectively, quantitatively processing the failure number value WC and the failure time value WS, extracting the numerical values of the failure number value WC and the failure time value WS, and substituting them into the formula to calculate, according to the formula Obtaining the failure value SW, wherein π is a mathematical constant, w1 and w2 are preset proportion coefficients corresponding to the failure number value WC and the failure time value WS, respectively, w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, w1=0.63, and w2=0.37;

[0018] Sending the use number value YC, the use time value YS, and the failure value SW to the parameter analysis module.

[0019] As a further scheme of the application, the specific process of obtaining the parameter optimization coefficient CY by the parameter analysis module is as follows:

[0020] Quantitatively processing the use number value YC, the use time value YS, and the failure value SW, extracting the numerical values of the use number value YC, the use time value YS, and the failure value SW, and substituting them into the formula to calculate, according to the formula Obtaining the parameter optimization coefficient CY, wherein β is an error adjustment factor, β=1.107, e is a mathematical constant, c1, c2, and c3 are preset weight factors corresponding to the use number value YC, the use time value YS, and the failure value SW, respectively, c1, c2, and c3 satisfy c3>c1>c2>2.151, c1=2.69, c2=2.31, and c3=2.95;

[0021] Sending the parameter optimization coefficient CY to the parameter setting module.

[0022] As a further scheme of the application, the specific process of obtaining the device abnormality information by the device monitoring module is as follows:

[0023] After receiving the device monitoring instruction, obtaining the welding current of the pipeline welding device per unit time, obtaining the current difference value between the maximum welding current and the minimum welding current, and marking it as the current value DL, obtaining the welding voltage of the pipeline welding device per unit time, obtaining the voltage difference value between the maximum welding voltage and the minimum welding voltage, and marking it as the voltage value DY, quantitatively processing the current value DL and the voltage value DY, extracting the numerical values of the current value DL and the voltage value DY, and substituting them into the formula to calculate, according to the formula Get the electrical stability value DW, wherein d1, d2 are respectively the preset proportion coefficient corresponding to the set current value DL and voltage value DY, d1, d2 satisfy d1+d2=1, 0

[0024] The vibration frequency and the maximum vibration displacement of the pipeline welding equipment in a unit time are obtained, and are marked as vibration value ZS and vibration displacement value ZY respectively, the vibration value ZS and the vibration displacement value ZY are quantitatively processed, the vibration value ZS and the vibration displacement value ZY are extracted, and are substituted into the formula to calculate, according to the formula Get the vibration value ZD, wherein z1, z2 are respectively the preset proportion coefficient corresponding to the set vibration value ZS and vibration displacement value ZY, z1, z2 satisfy z1+z2=1, 0

[0025] The total welding times and the total maintenance times in the pipeline welding equipment historical data are obtained, and are marked as welding times value HC and maintenance times value XC respectively, the welding times value HC and the maintenance times value XC are quantitatively processed, the welding times value HC and the maintenance times value XC are extracted, and are substituted into the formula to calculate, according to the formula Get the use value SY, wherein s1, s2 are respectively the preset proportion coefficient corresponding to the set welding times value HC and maintenance times value XC, s1, s2 satisfy s1+s2=1, 0

[0026] The electrical stability value DW, the vibration value ZD and the use value SY are sent to the equipment analysis module.

[0027] As a further scheme of the application, the specific process that the equipment analysis module obtains the equipment abnormality coefficient SX is as follows:

[0028] The electrical stability value DW, the vibration value ZD and the use value SY are quantitatively processed, the electrical stability value DW, the vibration value ZD and the use value SY are extracted, and are substituted into the formula to calculate, according to the formula Get the equipment abnormality coefficient SX, wherein η is an error adjustment factor, β=0.991, e is a mathematical constant, y1, y2 and y3 are respectively the preset weight factor corresponding to the set electrical stability value DW, vibration value ZD and use value SY, y1, y2 and y3 satisfy y2>y1>y3>0.826, y1=1.27, y2=1.64, y3=0.97;

[0029] The equipment abnormality coefficient SX is sent to the process control platform.

[0030] As a further scheme of the application, the specific process that the process control platform generates an abnormal alarm instruction is as follows:

[0031] The device anomaly coefficient SX is compared with a preset device anomaly threshold SXmax:

[0032] If the device anomaly coefficient SX> device anomaly threshold SXmax, an anomaly alarm instruction is generated, and the anomaly alarm instruction is sent to the anomaly alarm module.

[0033] As a further scheme of the present application: a high-temperature high-pressure large-diameter thick-walled pipeline welding process control method, comprising the following steps:

[0034] Step one: the pipeline identification module obtains the cross-sectional diameter of the high-temperature high-pressure large-diameter thick-walled pipeline, and marks it as the pipe diameter value GJ, and sends the pipe diameter value GJ to the parameter setting module; the specific process is as follows:

[0035] The pipeline identification module captures the video of the cross section of the high-temperature high-pressure large-diameter thick-walled pipeline to be welded by the high-definition camera, obtains the cross-sectional diameter of the high-temperature high-pressure large-diameter thick-walled pipeline, and marks it as the pipe diameter value GJ;

[0036] The pipeline identification module sends the pipe diameter value GJ to the parameter setting module;

[0037] Step two: the parameter setting module obtains the preselected parameter i of the pipeline welding equipment according to the pipe diameter value GJ, and obtains the parameter optimization information of the preselected parameter i, the parameter optimization information including the use frequency value YC, the use time value YS and the error value SW, and sends the parameter optimization information to the parameter analysis module; the specific process is as follows:

[0038] The parameter setting module obtains the welding parameters of the pipeline welding equipment when the same pipe diameter value GJ in the historical data, and marks them as preselected parameters i in turn, i=1, …, n, n is a natural number; wherein, the welding parameters include welding temperature, welding wire conveying speed and welding time;

[0039] The parameter setting module obtains the number of times the preselected parameter i is selected for use, and marks it as the use frequency value YC;

[0040] The parameter setting module obtains the time difference between the time when the preselected parameter i was last selected for use and the current time, and marks it as the use time value YS;

[0041] The parameter setting module obtains the number of times of welding errors and the average interval time of welding errors in the process of selecting the preselected parameter i for use, and marks them as the error frequency value WC and the error time value WS respectively, and quantitatively processes the error frequency value WC and the error time value WS, extracts the numerical values of the error frequency value WC and the error time value WS, and substitutes them into the formula to calculate, according to the formula Get the error value SW, wherein π is a mathematical constant, w1, w2 are the preset proportion coefficients corresponding to the error value WC and the error time value WS, respectively, w1, w2 satisfy w1+w2=1, 0<w2<w1<1, w1=0.63, w2=0.37;

[0042] The parameter setting module sends the use value YC, the use time value YS and the error value SW to the parameter analysis module;

[0043] Step three: the parameter analysis module obtains the parameter optimization coefficient CY according to the parameter optimization information, and sends the parameter optimization coefficient CY to the parameter setting module; the specific process is as follows:

[0044] The parameter analysis module quantitatively processes the use value YC, the use time value YS and the error value SW, extracts the numerical values of the use value YC, the use time value YS and the error value SW, and substitutes them into the formula to calculate, according to the formula Get the parameter optimization coefficient CY, wherein β is an error adjustment factor, β=1.107, e is a mathematical constant, c1, c2 and c3 are preset weight factors corresponding to the use value YC, the use time value YS and the error value SW, respectively, c1, c2 and c3 satisfy c3>c1>c2>2.151, c1=2.69, c2=2.31, c3=2.95;

[0045] The parameter analysis module sends the parameter optimization coefficient CY to the parameter setting module;

[0046] Step four: the parameter setting module marks the preselected parameter i corresponding to the parameter optimization coefficient CY as the selected parameter, and adjusts the welding parameters of the pipeline welding equipment according to the selected parameter, generates the equipment monitoring instruction after the adjustment is completed, and sends the equipment monitoring instruction to the equipment monitoring module; the specific process is as follows:

[0047] The parameter setting module marks the preselected parameter i corresponding to the maximum parameter optimization coefficient CY as the selected parameter;

[0048] The parameter setting module adjusts the welding parameters of the pipeline welding equipment according to the selected parameter, generates the equipment monitoring instruction after the adjustment is completed, and sends the equipment monitoring instruction to the equipment monitoring module;

[0049] Step five: the equipment monitoring module obtains the equipment abnormal information after receiving the equipment monitoring instruction, the equipment abnormal information includes the electric stability value DW, the vibration value ZD and the use value SY, and sends the equipment abnormal information to the equipment analysis module; the specific process is as follows:

[0050] The equipment monitoring module receives the equipment monitoring instruction, acquires the welding current of the pipeline welding equipment in a unit time, acquires the current difference between the maximum welding current and the minimum welding current, and marks it as a current value DL, acquires the welding voltage of the pipeline welding equipment in a unit time, acquires the voltage difference between the maximum welding voltage and the minimum welding voltage, and marks it as a voltage value DY, quantitatively processes the current value DL and the voltage value DY, extracts the numerical values of the current value DL and the voltage value DY, and substitutes them into the formula to calculate, according to the formula obtain an electrical stability value DW, wherein d1 and d2 are respectively preset proportionality coefficients corresponding to the current value DL and the voltage value DY, d1 and d2 satisfy d1+d2=1, 0<d1<d2<1, d1=0.46, and d2=0.54.

[0051] The equipment monitoring module acquires the vibration frequency and the maximum vibration displacement of the pipeline welding equipment in a unit time, and marks them as a vibration number value ZS and a vibration displacement value ZY, respectively. The vibration number value ZS and the vibration displacement value ZY are quantitatively processed, the numerical values of the vibration number value ZS and the vibration displacement value ZY are extracted, and they are substituted into the formula to calculate, according to the formula obtain a vibration value ZD, wherein z1 and z2 are respectively preset proportionality coefficients corresponding to the vibration number value ZS and the vibration displacement value ZY, z1 and z2 satisfy z1+z2=1, 0<z2<z1<1, z1=0.58, and z2=0.42.

[0052] The equipment monitoring module acquires the total welding times and the total repair times in the historical data of the pipeline welding equipment, and marks them as a welding times value HC and a repair times value XC, respectively. The welding times value HC and the repair times value XC are quantitatively processed, the numerical values of the welding times value HC and the repair times value XC are extracted, and they are substituted into the formula to calculate, according to the formula obtain a use value SY, wherein s1 and s2 are respectively preset proportionality coefficients corresponding to the welding times value HC and the repair times value XC, s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, s1=0.26, and s2=0.74.

[0053] The equipment monitoring module sends the electrical stability value DW, the vibration value ZD, and the use value SY to the equipment analysis module. Step six: The equipment analysis module obtains an equipment abnormality coefficient SX according to the equipment abnormality information, and sends the equipment abnormality coefficient SX to the process control platform. The specific process is as follows:

[0054] The equipment analysis module quantitatively processes the electrical stability value DW, the vibration value ZD, and the use value SY, extracts the numerical values of the electrical stability value DW, the vibration value ZD, and the use value SY, and substitutes them into the formula to calculate, according to the formula An equipment abnormality coefficient SX is obtained, wherein, η is an error adjustment factor, η=0.991 is taken, e is a mathematical constant, y1, y2 and y3 are preset weight factors corresponding to the set electric steady value DW, the vibration value ZD and the use value SY respectively, y1, y2 and y3 satisfy y2>y1>y3>0.826, y1=1.27 is taken, y2=1.64 is taken, and y3=0.97 is taken;

[0055] The equipment analysis module sends the equipment abnormality coefficient SX to the process control platform.

[0056] Step seven: The process control platform generates an abnormality alarm instruction according to the equipment abnormality coefficient SX, and sends the abnormality alarm instruction to the abnormality alarm module; the specific process is as follows:

[0057] The process control platform compares the equipment abnormality coefficient SX with a preset equipment abnormality threshold SXmax:

[0058] If the equipment abnormality coefficient SX>the equipment abnormality threshold SXmax, an abnormality alarm instruction is generated, and the abnormality alarm instruction is sent to the abnormality alarm module;

[0059] Step eight: After receiving the abnormality alarm instruction, the abnormality alarm module rings an abnormality alarm.

[0060] The beneficial effects of the present application are as follows:

[0061] The welding process control system and method based on high-temperature high-pressure large-diameter thick-walled pipeline of the application, through the pipeline identification module, the pipe diameter value is obtained, through the parameter setting module, the preselected parameters of the pipeline welding equipment are obtained according to the pipe diameter value, and the parameter optimization information of the preselected parameters is obtained, through the parameter analysis module, the parameter optimization coefficient is obtained according to the parameter optimization information, through the parameter setting module, the preselected parameters corresponding to the parameter optimization coefficient are marked as selected parameters, and the welding parameters of the pipeline welding equipment are adjusted according to the selected parameters, through the equipment monitoring module, the equipment abnormal information is obtained, through the equipment analysis module, the equipment abnormal coefficient is obtained according to the equipment abnormal information, through the process control platform, the abnormal alarm instruction is generated according to the equipment abnormal coefficient, and the abnormal alarm is sounded after the abnormal alarm instruction is received by the abnormal alarm module; the welding process control system firstly selects the relatively appropriate welding parameters through the pipe diameter value, then data collection is performed on the welding parameters, the parameter optimization information is obtained, the parameter optimization coefficient obtained according to the parameter optimization information can comprehensively measure the priority selection degree of the welding parameters, and the larger the parameter optimization coefficient is, the higher the priority selection degree is, which indicates that the welding effect is good and the error is small when the corresponding welding parameters are used to weld the pipeline, then data collection is performed on the pipeline welding equipment, the equipment abnormal information is obtained, the equipment abnormal coefficient obtained according to the equipment abnormal information can comprehensively measure the abnormal degree of the pipeline welding equipment, and the larger the equipment abnormal coefficient is, the higher the abnormal degree is, which indicates that the running state of the pipeline welding equipment is not good at this time, and welding failure is prone to occur when the pipeline is welded, so the pipeline welding equipment needs to be checked and repaired; the welding process control system can adaptively select the best welding parameters, and can also monitor the abnormal conditions of the pipeline welding equipment in real time, so as to realize accurate control of the welding process, improve the welding efficiency, ensure the stable and reliable weld quality, and have important practical value. BRIEF DESCRIPTION OF DRAWINGS

[0062] The application will be further described below in combination with the drawings.

[0063] Figure 1 It is the principle diagram of the welding process control system based on high-temperature high-pressure large-diameter thick-walled pipeline in the application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the application will be clearly and completely described below in combination with the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0065] Embodiment 1

[0066] Please refer to Figure 1As shown, the embodiment is a welding process control system based on high-temperature high-pressure large-diameter thick-walled pipeline, which comprises the following modules: pipeline identification module, parameter setting module, parameter analysis module, equipment monitoring module, equipment analysis module, process control platform and abnormal alarm module.

[0067] The pipeline identification module is used to obtain the cross-sectional diameter of the high-temperature high-pressure large-diameter thick-walled pipeline and mark it as the pipe diameter value GJ, and send the pipe diameter value GJ to the parameter setting module.

[0068] The parameter setting module is used to obtain the preselected parameter i of the pipeline welding equipment according to the pipe diameter value GJ, obtain the parameter optimization information of the preselected parameter i, and send the parameter optimization information to the parameter analysis module; wherein the parameter optimization information includes the use value YC, the use time value YS and the error value SW; and according to the preselected parameter i corresponding to the parameter optimization coefficient CY, the selected parameter is marked, and the welding parameters of the pipeline welding equipment are adjusted according to the selected parameter, and after the adjustment is completed, the equipment monitoring instruction is generated and sent to the equipment monitoring module.

[0069] The parameter analysis module is used to obtain the parameter optimization coefficient CY according to the parameter optimization information, and send the parameter optimization coefficient CY to the parameter setting module.

[0070] The equipment monitoring module is used to obtain the equipment abnormal information of the pipeline welding equipment after receiving the equipment monitoring instruction, and send the equipment abnormal information to the equipment analysis module; wherein the equipment abnormal information includes the electric stability value DW, the vibration value ZD and the use value SY.

[0071] The equipment analysis module is used to obtain the equipment abnormal coefficient SX according to the equipment abnormal information, and send the equipment abnormal coefficient SX to the process control platform.

[0072] The process control platform is used to generate an abnormal alarm instruction according to the equipment abnormal coefficient SX, and send the abnormal alarm instruction to the abnormal alarm module.

[0073] The abnormal alarm module is used to sound an abnormal alarm after receiving the abnormal alarm instruction.

[0074] Embodiment 2:

[0075] The embodiment is a welding process control method based on high-temperature high-pressure large-diameter thick-walled pipeline, which comprises the following steps:

[0076] Step one: the pipeline identification module obtains the cross-sectional diameter of the high-temperature high-pressure large-diameter thick-walled pipeline, and marks it as the pipe diameter value GJ, and sends the pipe diameter value GJ to the parameter setting module; the specific process is as follows:

[0077] The pipeline identification module shoots a video of the cross section of the high-temperature high-pressure large-diameter thick-walled pipeline to be welded through a high-definition camera, obtains the cross-sectional diameter of the high-temperature high-pressure large-diameter thick-walled pipeline, and marks it as a pipe diameter value GJ;

[0078] The pipeline identification module sends the pipe diameter value GJ to the parameter setting module;

[0079] Step two: The parameter setting module obtains preselected parameters i of the pipeline welding equipment according to the pipe diameter value GJ, and obtains parameter optimization information of the preselected parameters i, the parameter optimization information including use frequency value YC, use time value YS and failure value SW, and sends the parameter optimization information to the parameter analysis module; the specific process is as follows:

[0080] The parameter setting module obtains the welding parameters of the pipeline welding equipment when the same pipe diameter value GJ in the historical data, and marks them as preselected parameters i in turn, i = 1, …, n, n is a natural number; wherein, the welding parameters include welding temperature, welding wire conveying speed and welding time;

[0081] The parameter setting module obtains the number of times the preselected parameter i is selected and used, and marks it as the use frequency value YC;

[0082] The parameter setting module obtains the time difference between the time when the preselected parameter i is selected and used last time and the current time, and marks it as the use time value YS;

[0083] The parameter setting module obtains the number of times of welding failure and the average interval time of welding failure in the process of selecting and using the preselected parameter i, and marks them as the failure frequency value WC and the failure time value WS respectively, quantitatively processes the failure frequency value WC and the failure time value WS, extracts the numerical values of the failure frequency value WC and the failure time value WS, and substitutes them into the formula to calculate, according to the formula obtains the failure value SW, wherein π is a mathematical constant, w1 and w2 are respectively preset proportion coefficients corresponding to the failure frequency value WC and the failure time value WS, w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, w1=0.63 and w2=0.37;

[0084] The parameter setting module sends the use frequency value YC, the use time value YS and the failure value SW to the parameter analysis module;

[0085] Step three: The parameter analysis module obtains a parameter optimization coefficient CY according to the parameter optimization information, and sends the parameter optimization coefficient CY to the parameter setting module; the specific process is as follows:

[0086] The parameter analysis module quantitatively processes the use frequency value YC, the use time value YS and the failure value SW, extracts the numerical values of the use frequency value YC, the use time value YS and the failure value SW, and substitutes them into the formula to calculate, according to the formula A parameter optimization coefficient CY is obtained, wherein β is an error adjustment factor, β = 1.107, e is a mathematical constant, c1, c2 and c3 are preset weight factors corresponding to the use value YC, the time value YS and the error value SW respectively, c1, c2 and c3 satisfy c3 > c1 > c2 > 2.151, c1 = 2.69, c2 = 2.31 and c3 = 2.95;

[0087] The parameter analysis module sends the parameter optimization coefficient CY to the parameter setting module.

[0088] Step four: The parameter setting module marks the preselected parameter i corresponding to the parameter optimization coefficient CY as a selected parameter, and adjusts the welding parameters of the pipeline welding equipment according to the selected parameter, generates a device monitoring instruction after the adjustment is completed, and sends the device monitoring instruction to the device monitoring module; the specific process is as follows:

[0089] The parameter setting module marks the preselected parameter i corresponding to the maximum parameter optimization coefficient CY as a selected parameter.

[0090] The parameter setting module adjusts the welding parameters of the pipeline welding equipment according to the selected parameter, generates a device monitoring instruction after the adjustment is completed, and sends the device monitoring instruction to the device monitoring module.

[0091] Step five: The device monitoring module obtains device abnormal information after receiving the device monitoring instruction, the device abnormal information includes the electric stability value DW, the vibration value ZD and the use value SY, and sends the device abnormal information to the device analysis module; the specific process is as follows:

[0092] The device monitoring module obtains the welding current of the pipeline welding equipment per unit time after receiving the device monitoring instruction, obtains the current difference between the maximum welding current and the minimum welding current, and marks it as the current value DL, obtains the welding voltage of the pipeline welding equipment per unit time, obtains the voltage difference between the maximum welding voltage and the minimum welding voltage, and marks it as the voltage value DY, quantitatively processes the current value DL and the voltage value DY, extracts the numerical value of the current value DL and the voltage value DY, and substitutes it into the formula to calculate, according to the formula An electric stability value DW is obtained, wherein d1 and d2 are preset proportion coefficients corresponding to the current value DL and the voltage value DY respectively, d1 + d2 = 1, 0 < d1 < d2 < 1, d1 = 0.46 and d2 = 0.54.

[0093] The device monitoring module obtains the vibration frequency and the maximum vibration displacement of the pipeline welding device in a unit time, and marks them as vibration value ZS and vibration displacement value ZY respectively, quantitatively processes the vibration value ZS and the vibration displacement value ZY, extracts the values of the vibration value ZS and the vibration displacement value ZY, and substitutes them into the formula to calculate the vibration value ZD according to the formula wherein z1 and z2 are preset proportionality coefficients corresponding to the vibration value ZS and the vibration displacement value ZY respectively, z1 and z2 satisfy z1+z2=1, 0<z2<z1<1, z1=0.58 and z2=0.42 are taken;

[0094] The device monitoring module obtains the total welding times and the total maintenance times in the historical data of the pipeline welding device, and marks them as welding times value HC and maintenance times value XC respectively, quantitatively processes the welding times value HC and the maintenance times value XC, extracts the values of the welding times value HC and the maintenance times value XC, and substitutes them into the formula to calculate the use value SY according to the formula wherein s1 and s2 are preset proportionality coefficients corresponding to the welding times value HC and the maintenance times value XC respectively, s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, s1=0.26 and s2=0.74 are taken;

[0095] The device monitoring module sends the electrical stability value DW, the vibration value ZD and the use value SY to the device analysis module;

[0096] Step six: The device analysis module obtains the device abnormality coefficient SX according to the device abnormality information, and sends the device abnormality coefficient SX to the process control platform; the specific process is as follows:

[0097] The device analysis module quantitatively processes the electrical stability value DW, the vibration value ZD and the use value SY, extracts the values of the electrical stability value DW, the vibration value ZD and the use value SY, and substitutes them into the formula to calculate the device abnormality coefficient SX according to the formula wherein η is an error adjustment factor, η=0.991 is taken, e is a mathematical constant, y1, y2 and y3 are preset weight factors corresponding to the electrical stability value DW, the vibration value ZD and the use value SY respectively, y1, y2 and y3 satisfy y2>y1>y3>0.826, y1=1.27, y2=1.64 and y3=0.97 are taken;

[0098] The device analysis module sends the device abnormality coefficient SX to the process control platform;

[0099] Step seven: The process control platform generates an abnormal alarm instruction according to the device abnormality coefficient SX, and sends the abnormal alarm instruction to the abnormal alarm module; the specific process is as follows:

[0100] The process control platform compares the equipment abnormality coefficient SX with a preset equipment abnormality threshold SXmax:

[0101] If the equipment abnormality coefficient SX>the equipment abnormality threshold SXmax, an abnormality alarm instruction is generated and sent to the abnormality alarm module;

[0102] Step eight: the abnormality alarm module rings an abnormality alarm after receiving the abnormality alarm instruction.

[0103] In the description of the present specification, the description referring to the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0104] The above is only an example and description of the present application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or replace them with similar ways, as long as they do not deviate from the invention or exceed the scope defined by the present claims, which shall belong to the protection scope of the present application.

Claims

1. A welding process control system for high-temperature, high-pressure, large-diameter, thick-walled pipelines, characterized in that: include: The pipe identification module is used to obtain the cross-sectional diameter of high-temperature and high-pressure large-diameter thick-walled pipes, mark it as the pipe diameter value GJ, and send the pipe diameter value GJ to the parameter setting module; The parameter setting module is used to obtain the pre-selected parameter i of the pipeline welding equipment based on the pipe diameter value GJ, and to obtain the parameter optimization information of the pre-selected parameter i, and send the parameter optimization information to the parameter analysis module. The parameter optimization information includes the usage value YC, the usage time value YS, and the error value SW. It is also used to mark the pre-selected parameter i corresponding to the parameter optimization coefficient CY as the selected parameter, and to adjust the welding parameters of the pipeline welding equipment according to the selected parameter. After the adjustment is completed, it generates the equipment monitoring command and sends the equipment monitoring command to the equipment monitoring module. The specific process by which the parameter setting module obtains the parameter optimization information is as follows: Obtain the welding parameters of the pipe welding equipment for the same pipe diameter value GJ in historical data, and mark them sequentially as pre-selected parameters i, i=1, ..., n, where n is a natural number; among them, the welding parameters include welding temperature, welding wire feed speed and welding time; Get the number of times the preselected parameter i is selected and mark it as the number of times YC is used; Get the time when the preselected parameter i was last selected and the current time, get the time difference between the two, and mark it as the usage time value YS; Obtain the number of welding errors and the average interval between welding errors during the process of selecting the pre-selected parameter i, and label them as error frequency value WC and error time value WS, respectively. Quantify the error frequency value WC and error time value WS, extract their values, and substitute them into the formula for calculation. The error value SW is obtained, where π is a mathematical constant, w1 and w2 are the preset proportional coefficients corresponding to the set error frequency value WC and error time value WS, respectively, and w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, and w1=0.63 and w2=0.37 are taken. The parameter analysis module is used to obtain the parameter optimization coefficient CY based on the parameter optimization information and send the parameter optimization coefficient CY to the parameter setting module. The specific process by which the parameter analysis module obtains the parameter optimization coefficient CY is as follows: The frequency (YC), time (YS), and error (SW) are quantized, and their values ​​are extracted and substituted into the formula for calculation. The parameter optimization coefficient CY is obtained, where β is the error adjustment factor, β=1.107, e is a mathematical constant, c1, c2 and c3 are the preset weight factors corresponding to the set times value YC, time value YS and error value SW, respectively, and c1, c2 and c3 satisfy c3>c1>c2>2.151, c1=2.69, c2=2.31 and c3=2.95; The equipment monitoring module is used to obtain equipment abnormality information of the pipeline welding equipment after receiving equipment monitoring instructions, and send the equipment abnormality information to the equipment analysis module; the equipment abnormality information includes electrical stability value DW, vibration value ZD, and usage value SY; The specific process by which the equipment monitoring module obtains equipment anomaly information is as follows: Upon receiving the equipment monitoring command, the welding current of the pipeline welding equipment per unit time is acquired, and the difference between the maximum and minimum welding currents is obtained and marked as the current value DL. The welding voltage of the pipeline welding equipment per unit time is acquired, and the difference between the maximum and minimum welding voltages is obtained and marked as the voltage value DY. The current value DL and the voltage value DY are quantified, their values ​​are extracted, and then substituted into the formula for calculation. The electrical stability value DW is obtained, where d1 and d2 are the preset proportional coefficients corresponding to the set current value DL and voltage value DY, respectively. d1 and d2 satisfy d1+d2=1, 0<d1<d2<1, and we take d1=0.46 and d2=0.

54. The vibration frequency and maximum vibration displacement of the pipeline welding equipment per unit time are obtained and labeled as vibration value ZS and vibration displacement value ZY, respectively. The vibration value ZS and vibration displacement value ZY are quantified, their numerical values ​​are extracted, and then substituted into the formula for calculation. The vibration value ZD is obtained, where z1 and z2 are the preset proportional coefficients corresponding to the set vibration value ZS and vibration displacement value ZY, respectively. z1 and z2 satisfy z1+z2=1, 0<z2<z1<1, and z1=0.58 and z2=0.42 are taken. Obtain the total number of welds and total number of repairs from the historical data of the pipeline welding equipment, and label them as weld count (HC) and repair count (XC), respectively. Quantify the weld count (HC) and repair count (XC), extract their numerical values, and substitute them into the formula for calculation. The usage value SY is obtained, where s1 and s2 are the preset proportional coefficients corresponding to the set welding cycle value HC and repair cycle value XC, respectively. s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and take s1=0.26 and s2=0.

74. The equipment analysis module is used to obtain the equipment anomaly coefficient SX based on equipment anomaly information and send the equipment anomaly coefficient SX to the process control platform; The specific process by which the equipment analysis module obtains the equipment anomaly coefficient SX is as follows: The electrical stability value DW, vibration value ZD, and usage value SY are quantified, and their numerical values ​​are extracted. These values ​​are then substituted into the formula for calculation. The equipment anomaly coefficient SX is obtained, where η is the error adjustment factor, β=0.991, e is a mathematical constant, and y1, y2 and y3 are the preset weighting factors corresponding to the set electrical stability value DW, vibration value ZD and usage value SY, respectively. y1, y2 and y3 satisfy y2>y1>y3>0.826, and y1=1.27, y2=1.64 and y3=0.97 are taken. The process control platform is used to generate abnormal alarm commands based on the equipment abnormality coefficient SX, and send the abnormal alarm commands to the abnormal alarm module.

2. The welding process control system for high-temperature, high-pressure, large-diameter, thick-walled pipelines according to claim 1, characterized in that, The specific process by which the process control platform generates abnormal alarm commands is as follows: Compare the equipment anomaly coefficient SX with the preset equipment anomaly threshold SXmax: If the equipment anomaly coefficient SX > the equipment anomaly threshold SXmax, an anomaly alarm command is generated and sent to the anomaly alarm module.

3. The welding process control method for a welding process control system based on high-temperature and high-pressure large-diameter thick-walled pipes according to claim 1, characterized in that, Includes the following steps: Step 1: The pipe identification module obtains the cross-sectional diameter of the high-temperature, high-pressure, large-diameter, thick-walled pipe and marks it as the pipe diameter value GJ, and sends the pipe diameter value GJ to the parameter setting module; Step 2: The parameter setting module obtains the pre-selected parameters i of the pipe welding equipment based on the pipe diameter value GJ, and obtains the parameter optimization information of the pre-selected parameters i. The parameter optimization information includes the usage value YC, the usage time value YS, and the error value SW, and sends the parameter optimization information to the parameter analysis module. Step 3: The parameter analysis module obtains the parameter optimization coefficient CY based on the parameter optimization information and sends the parameter optimization coefficient CY to the parameter setting module; Step 4: The parameter setting module marks the pre-selected parameter i corresponding to the parameter optimization coefficient CY as the selected parameter, and adjusts the welding parameters of the pipeline welding equipment according to the selected parameter. After the adjustment is completed, the equipment monitoring command is generated and sent to the equipment monitoring module. Step 5: After receiving the equipment monitoring command, the equipment monitoring module obtains the equipment abnormality information, including the electrical stability value DW, vibration value ZD, and usage value SY, and sends the equipment abnormality information to the equipment analysis module. Step Six: The equipment analysis module obtains the equipment anomaly coefficient SX based on the equipment anomaly information and sends the equipment anomaly coefficient SX to the process control platform; Step 7: The process control platform generates an anomaly alarm command based on the equipment anomaly coefficient SX and sends the anomaly alarm command to the anomaly alarm module; Step 8: After receiving the abnormal alarm command, the abnormal alarm module sounds an abnormal alarm.

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