Welding seam tracking and deviation rectifying system and method for narrow-gap automatic welding

Abstract

The invention relates to the technical field of welding, in particular to a welding seam tracking and deviation correcting system and method for narrow-gap automatic welding, and solves the problems that a traditional welding quality monitoring and deviation correcting method mainly depends on manual operation, the monitoring efficiency is low, missing detection is prone to occurring, alarming is inaccurate, and representativeness is not achieved. The welding seam tracking and deviation rectifying system comprises the following modules: an image acquisition module, an image analysis module, a data analysis module, a tracking and deviation rectifying platform and a deviation rectifying alarm module, according to the welding seam tracking and deviation rectifying system, automatic and efficient monitoring is achieved, accurate tracking and timely deviation rectifying of the welding seam are achieved, the welding effect is optimized, the welding quality is improved, the problems of a traditional manual monitoring method are effectively solved, the monitoring accuracy and efficiency are improved, in addition, the system further has a real-time alarm function, and the system is suitable for popularization and application. The accuracy of the alarm function is high, problems can be found and processed in time, and the safety of the pipeline is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of welding, and in particular to a weld tracking and deviation correction system and method for narrow gap automatic welding. Background Art

[0002] P92 is a high-alloy steel with high high-temperature creep strength and fatigue resistance. It is commonly used in the manufacture of high-temperature, high-pressure pipes and components. P92 steel pipes can be used in high-temperature, high-pressure piping systems, such as high-temperature steam pipelines and high-temperature, high-pressure vessels in the petrochemical, energy, and power industries. The quality of steel pipe welds directly impacts the safe operation of pipelines. With the advancement of industrial technology, welding processes are becoming increasingly demanding. This is particularly true in narrow-gap automatic welding, where the narrow weld gap places extremely high demands on the precision and stability of welding equipment. During the welding process, weld deviations may occur, and if not corrected promptly, they can seriously impact weld quality. Therefore, monitoring and correcting weld deviations in steel pipe welds are crucial. However, traditional methods for monitoring and correcting weld deviations rely primarily on manual labor, resulting in low monitoring efficiency, frequent missed detections, inaccurate alarms, and a lack of representativeness. Therefore, developing a weld tracking and correction system and method for narrow-gap automatic welding that can automatically and efficiently correct weld deviations and provide accurate alarms is of great practical significance. Summary of the Invention

[0003] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a weld tracking and correction system and method for narrow gap automatic welding: the welding object is photographed by an image acquisition module to obtain images of the pre-weld gap and the monitoring weld, and the welding deviation parameters are obtained according to the images of the pre-weld gap and the monitoring weld by the image analysis module. The welding deviation parameters include the missing weld surface value, the hole seam value and the width difference. The welding deviation coefficient is obtained according to the welding deviation parameters by the data analysis module, and a correction alarm instruction is generated according to the welding deviation coefficient by the tracking and correction platform, and the correction alarm instruction is sent to the correction alarm module. After the correction alarm instruction is received by the correction alarm module, the correction alarm bell sounds, which solves the problem that the traditional welding quality monitoring and deviation correction method mainly relies on manual operation, has low monitoring efficiency, is prone to missed detection, and the alarm is inaccurate and non-representative.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A weld tracking and correction system for narrow gap automatic welding, comprising: An image acquisition module is used to capture images of the pre-weld gap and the monitoring weld, and send the images of the pre-weld gap and the monitoring weld to the image analysis module; An image analysis module is used to obtain welding deviation parameters based on images of the pre-weld gap and the monitored weld, and send the welding deviation parameters to the data analysis module; wherein the welding deviation parameters include the missing weld surface value HM, the hole seam value DF, and the width difference value KC; A data analysis module is used to obtain a welding deviation coefficient HP according to the welding deviation parameters, and send the welding deviation coefficient HP to the tracking and correction platform; The tracking and correction platform is used to generate correction alarm instructions according to the welding deviation coefficient HP and send the correction alarm instructions to the correction alarm module; The deviation correction alarm module is used to sound the deviation correction alarm bell after receiving the deviation correction alarm instruction.

[0005] As a further solution of the present invention, the specific process of the image acquisition module capturing images of the pre-weld gap and the monitoring weld is as follows: The welding objects to be welded are abutted against each other, a gap formed by the abutment of the welding objects is marked as a pre-weld gap, a weld formed by welding using welding equipment is marked as a monitoring weld, and images of the pre-weld gap and the monitoring weld are captured using a high-definition camera, and the images of the pre-weld gap and the monitoring weld are sent to an image analysis module; the welding object is a steel pipe, the steel pipe is a P92 steel pipe, and the specification of the P92 steel pipe is φ550×94mm.

[0006] As a further solution of the present invention: the specific process of the image analysis module obtaining the welding deviation parameters is as follows: Obtain the edge contour of the image of the pre-weld gap, obtain the position of the image of the pre-weld gap according to the coordinates of each point on the edge contour, and mark it as the gap area; obtain the edge contour of the image of the monitoring weld, obtain the position of the image of the monitoring weld according to the coordinates of each point on the edge contour, and mark it as the weld area; obtain the overlapping area of ​​the gap area and the weld area and the total area of ​​the gap area, obtain the area difference between the two, and mark it as the missing weld area value HM; Obtain the number of holes and cracks on the image of the monitored weld, and mark them as hole value DS and seam value FS respectively. Quantify the hole value DS and seam value FS, extract the values ​​of the hole value DS and seam value FS, and substitute them into the formula for calculation. Get the hole and seam value DF, where s1 and s2 are the preset proportional coefficients corresponding to the set hole value DS and seam value FS respectively. s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and take s1=0.38 and s2=0.62; Obtain the maximum width and minimum width of the monitored weld image, obtain the width difference between the two, and mark it as the width difference KC; The missing weld surface value HM, the hole seam value DF and the width difference KC are sent to the data analysis module.

[0007] As a further solution of the present invention: the specific process of the data analysis module obtaining the welding deviation coefficient HP is as follows: The missing weld surface value HM, hole seam value DF and width difference KC are quantified, the values ​​of the missing weld surface value HM, hole seam value DF and width difference KC are extracted, and substituted into the formula for calculation. According to the formula Obtain the welding deviation coefficient HP, where e and π are mathematical constants, and p1, p2, and p3 are the preset weight factors corresponding to the set weld surface value HM, hole value DF, and width difference KC, respectively. p1, p2, and p3 satisfy p1>p2>p3>1.213, and p1=1.82, p2=1.65, and p3=1.44 are taken. The welding deviation coefficient HP is sent to the tracking and correction platform.

[0008] As a further solution of the present invention, the specific process of the tracking and correction platform generating the correction alarm instruction is as follows: Compare the welding deviation coefficient HP with the preset welding deviation threshold HPy: If the welding deviation coefficient HP ≥ the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as a welding unqualified object; If the welding deviation coefficient HP is less than the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as a qualified welding object; Mark the first unqualified welding object as a correction object, and record the types of a preset number of welding objects starting with the correction object; Obtain the total number of welding failure objects and mark them as unqualified values ​​BS; Obtain the number of times that adjacent welding objects are both unqualified objects, and mark it as a consecutive difference value LC; Quantify the unmatched value BS and the continuous difference value LC, extract the hole value DS and the seam value FS, and substitute them into the formula to calculate. Get the anomaly coefficient YC, where π is a mathematical constant, c1 and c2 are the preset proportional coefficients corresponding to the set non-conforming value BS and the continuous non-conforming value LC, respectively. c1 and c2 satisfy c1+c2=1, 0<c1<c2<1, and c1=0.41 and c2=0.59; Compare the abnormal coefficient YC with the preset abnormal threshold YCy: If the abnormal coefficient YC≥the abnormal threshold YCy, a deviation correction alarm instruction is generated and sent to the deviation correction alarm module.

[0009] As a further solution of the present invention: a weld tracking and correction method for narrow gap automatic welding, comprising the following steps: Step 1: The image acquisition module photographs the welding object, obtains images of the pre-weld gap and the monitoring weld, and sends the images of the pre-weld gap and the monitoring weld to the image analysis module; The specific process is as follows: the welding objects to be welded are brought into contact with each other, the gap formed by the welding objects being abutted is marked as a pre-weld gap, and the weld formed by welding using the welding equipment is marked as a monitoring weld. The image acquisition module uses a high-definition camera to capture images of the pre-weld gap and the monitoring weld, and the images of the pre-weld gap and the monitoring weld are sent to the image analysis module; Step 2: The image analysis module obtains welding deviation parameters based on the images of the pre-weld gap and the monitored weld. The welding deviation parameters include the missing weld surface value HM, the hole seam value DF, and the width difference KC, and sends the welding deviation parameters to the data analysis module; The specific process is as follows: the image analysis module obtains the edge contour of the image of the pre-weld gap, obtains the position of the image of the pre-weld gap according to the coordinates of each point on the edge contour, and marks it as the gap area; obtains the edge contour of the image of the monitoring weld, obtains the position of the image of the monitoring weld according to the coordinates of each point on the edge contour, and marks it as the weld area; obtains the overlapping area of ​​the gap area and the weld area and the total area of ​​the gap area, obtains the area difference between the two, and marks it as the missing weld area value HM; The image analysis module obtains the number of holes and cracks on the image of the monitored weld, and marks them as hole value DS and seam value FS respectively. It quantifies the hole value DS and seam value FS, extracts the values ​​of the hole value DS and seam value FS, and substitutes them into the formula for calculation. Get the hole and seam value DF, where s1 and s2 are the preset proportional coefficients corresponding to the set hole value DS and seam value FS respectively. s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and take s1=0.38 and s2=0.62; The image analysis module obtains the maximum width and the minimum width of the monitored weld image, obtains the width difference between the two, and marks it as the width difference KC; The image analysis module sends the weld missing surface value HM, hole seam value DF and width difference KC to the data analysis module; Step 3: The data analysis module obtains the welding deviation coefficient HP according to the welding deviation parameters, and sends the welding deviation coefficient HP to the tracking and correction platform; The specific process is: The data analysis module quantifies the missing weld surface value HM, the hole seam value DF and the width difference KC, extracts the values ​​of the missing weld surface value HM, the hole seam value DF and the width difference KC, and substitutes them into the formula for calculation. Obtain the welding deviation coefficient HP, where e and π are mathematical constants, and p1, p2, and p3 are the preset weight factors corresponding to the set weld surface value HM, hole value DF, and width difference KC, respectively. p1, p2, and p3 satisfy p1>p2>p3>1.213, and p1=1.82, p2=1.65, and p3=1.44 are taken. The data analysis module sends the welding deviation coefficient HP to the tracking and correction platform; Step 4: The tracking and correction platform generates a correction alarm instruction according to the welding deviation coefficient HP, and sends the correction alarm instruction to the correction alarm module; The specific process is: The tracking and correction platform compares the welding deviation coefficient HP with the preset welding deviation threshold HPy: If the welding deviation coefficient HP ≥ the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as an unqualified welding object; If the welding deviation coefficient HP is less than the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as a qualified welding object; The tracking and correction platform marks the first unqualified welding object as the correction object, and records the types of a preset number of welding objects starting with the correction object; The tracking and correction platform obtains the total number of unqualified welding objects and marks them as unqualified values ​​BS; The tracking and correction platform obtains the number of times that adjacent welding objects are unqualified objects and marks it as the consecutive difference value LC; The tracking and correction platform quantifies the inconsistency value BS and the continuous difference value LC, extracts the hole value DS and the seam value FS, and substitutes them into the formula for calculation. Get the anomaly coefficient YC, where π is a mathematical constant, c1 and c2 are the preset proportional coefficients corresponding to the set non-conforming value BS and the continuous non-conforming value LC, respectively. c1 and c2 satisfy c1+c2=1, 0<c1<c2<1, and c1=0.41 and c2=0.59; The tracking and correction platform compares the anomaly coefficient YC with the preset anomaly threshold YCy: If the abnormal coefficient YC ≥ abnormal threshold YCy, a deviation correction alarm instruction is generated and sent to the deviation correction alarm module; Step 5: After receiving the deviation correction alarm instruction, the deviation correction alarm module sounds the deviation correction alarm bell.

[0010] Beneficial effects of the present invention: The present invention relates to a weld seam tracking and correction system and method for automatic welding of narrow gaps. The system uses an image acquisition module to photograph a welding object to obtain images of a pre-weld seam gap and a monitored weld seam. The image analysis module uses the images of the pre-weld seam gap and the monitored weld seam to obtain welding deviation parameters. The welding deviation parameters include a missing weld surface value, a hole seam value, and a width difference. The data analysis module uses the welding deviation parameters to obtain a welding deviation coefficient. The tracking and correction platform uses the welding deviation coefficient to generate a correction alarm instruction, which is sent to the correction alarm module. The correction alarm ring sounds after the correction alarm instruction is received by the correction alarm module. The weld seam tracking and correction system obtains welding deviation parameters by analyzing the pre-weld seam gap and the monitored weld seam. The welding deviation coefficient obtained according to the welding deviation parameters can comprehensively measure the degree of deviation in welding the pre-weld seam, and a larger welding deviation coefficient indicates a greater degree of deviation. The higher the degree, the more unreasonable the monitored weld is, and the subsequent welding work needs to be tracked and the deviation corrected. If an unqualified weld appears, tracking will begin to obtain the abnormality coefficient, which can measure whether the weld deviation is accidental or common. If the abnormality coefficient is small, it means that the weld deviation is accidental, and it will be automatically corrected later, and the welding process will return to normal. If the abnormality coefficient is large, it means that the weld deviation is common, and weld deviations will still often occur later, requiring alarm maintenance. The weld tracking and correction system realizes automated and efficient monitoring, achieves accurate tracking and timely correction of welds, optimizes welding effects, improves welding quality, effectively solves the problems of traditional manual monitoring methods, and improves the accuracy and efficiency of monitoring. In addition, the system also has a real-time alarm function with high accuracy, which can detect and handle problems in a timely manner, ensuring the safety of the pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present invention will be further described below with reference to the accompanying drawings.

[0012] Figure 1 This is a principle block diagram of a weld tracking and deviation correction system for narrow gap automatic welding in the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0014] Example 1: See also Figure 1As shown, this embodiment is a weld tracking and correction system for narrow gap automatic welding, which includes the following modules: image acquisition module, image analysis module, data analysis module, tracking and correction platform and correction alarm module; The image acquisition module is used to capture images of the pre-weld gap and the monitoring weld, and send the images of the pre-weld gap and the monitoring weld to the image analysis module; The image analysis module is used to obtain welding deviation parameters based on the images of the pre-weld gap and the monitored weld, and send the welding deviation parameters to the data analysis module; wherein the welding deviation parameters include the missing weld surface value HM, the hole seam value DF, and the width difference KC; The data analysis module is used to obtain a welding deviation coefficient HP according to the welding deviation parameter, and send the welding deviation coefficient HP to the tracking and correction platform; The tracking and correction platform is used to generate a correction alarm instruction according to the welding deviation coefficient HP, and send the correction alarm instruction to the correction alarm module; The deviation correction alarm module is configured to sound a deviation correction alarm bell after receiving a deviation correction alarm instruction.

[0015] Example 2: This embodiment provides a method for tracking and correcting a weld seam in narrow gap automatic welding, comprising the following steps: Step 1: The image acquisition module photographs the welding object, obtains images of the pre-weld gap and the monitoring weld, and sends the images of the pre-weld gap and the monitoring weld to the image analysis module; The specific process is as follows: the welding objects to be welded are brought into contact with each other, the gap formed by the welding objects being abutted is marked as a pre-weld gap, and the weld formed by welding using the welding equipment is marked as a monitoring weld. The image acquisition module uses a high-definition camera to capture images of the pre-weld gap and the monitoring weld, and the images of the pre-weld gap and the monitoring weld are sent to the image analysis module; Step 2: The image analysis module obtains welding deviation parameters based on the images of the pre-weld gap and the monitored weld. The welding deviation parameters include the missing weld surface value HM, the hole seam value DF, and the width difference KC, and sends the welding deviation parameters to the data analysis module; The specific process is as follows: the image analysis module obtains the edge contour of the image of the pre-weld gap, obtains the position of the image of the pre-weld gap according to the coordinates of each point on the edge contour, and marks it as the gap area; obtains the edge contour of the image of the monitoring weld, obtains the position of the image of the monitoring weld according to the coordinates of each point on the edge contour, and marks it as the weld area; obtains the overlapping area of ​​the gap area and the weld area and the total area of ​​the gap area, obtains the area difference between the two, and marks it as the missing weld area value HM; The image analysis module obtains the number of holes and cracks on the image of the monitored weld, and marks them as hole value DS and seam value FS respectively. It quantifies the hole value DS and seam value FS, extracts the values ​​of the hole value DS and seam value FS, and substitutes them into the formula for calculation. Get the hole and seam value DF, where s1 and s2 are the preset proportional coefficients corresponding to the set hole value DS and seam value FS respectively. s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and take s1=0.38 and s2=0.62; The image analysis module obtains the maximum width and the minimum width of the monitored weld image, obtains the width difference between the two, and marks it as the width difference KC; The image analysis module sends the weld missing surface value HM, hole seam value DF and width difference KC to the data analysis module; Step 3: The data analysis module obtains the welding deviation coefficient HP according to the welding deviation parameters, and sends the welding deviation coefficient HP to the tracking and correction platform; The specific process is: The data analysis module quantifies the missing weld surface value HM, the hole seam value DF and the width difference KC, extracts the values ​​of the missing weld surface value HM, the hole seam value DF and the width difference KC, and substitutes them into the formula for calculation. Obtain the welding deviation coefficient HP, where e and π are mathematical constants, and p1, p2, and p3 are the preset weight factors corresponding to the set weld surface value HM, hole value DF, and width difference KC, respectively. p1, p2, and p3 satisfy p1>p2>p3>1.213, and p1=1.82, p2=1.65, and p3=1.44 are taken. The data analysis module sends the welding deviation coefficient HP to the tracking and correction platform; Step 4: The tracking and correction platform generates a correction alarm instruction according to the welding deviation coefficient HP, and sends the correction alarm instruction to the correction alarm module; The specific process is: The tracking and correction platform compares the welding deviation coefficient HP with the preset welding deviation threshold HPy: If the welding deviation coefficient HP ≥ the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as a welding unqualified object; If the welding deviation coefficient HP is less than the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as a qualified welding object; The tracking and correction platform marks the first unqualified welding object as the correction object, and records the types of a preset number of welding objects starting with the correction object; The tracking and correction platform obtains the total number of unqualified welding objects and marks them as unqualified values ​​BS; The tracking and correction platform obtains the number of times that adjacent welding objects are unqualified objects and marks it as the consecutive difference value LC; The tracking and correction platform quantifies the inconsistency value BS and the continuous difference value LC, extracts the hole value DS and the seam value FS, and substitutes them into the formula for calculation. Get the anomaly coefficient YC, where π is a mathematical constant, c1 and c2 are the preset proportional coefficients corresponding to the set non-conforming value BS and the continuous non-conforming value LC, respectively. c1 and c2 satisfy c1+c2=1, 0<c1<c2<1, and c1=0.41 and c2=0.59; The tracking and correction platform compares the anomaly coefficient YC with the preset anomaly threshold YCy: If the abnormal coefficient YC ≥ abnormal threshold YCy, a deviation correction alarm instruction is generated and sent to the deviation correction alarm module; Step 5: After receiving the deviation correction alarm instruction, the deviation correction alarm module sounds the deviation correction alarm bell.

[0016] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0017] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A weld tracking and correction system for narrow gap automatic welding, characterized in that: include: An image acquisition module is used to capture images of the pre-weld gap and the monitoring weld, and send the images of the pre-weld gap and the monitoring weld to the image analysis module; The image analysis module is used to obtain welding deviation parameters based on the images of the pre-weld gap and the monitored weld, and send the welding deviation parameters to the data analysis module. The welding deviation parameters include the missing weld surface value HM, the hole seam value DF, and the width difference value KC. The specific process of the image analysis module obtaining the welding deviation parameters is as follows: Obtain the edge contour of the image of the pre-weld gap, obtain the position of the image of the pre-weld gap according to the coordinates of each point on the edge contour, and mark it as the gap area; obtain the edge contour of the image of the monitoring weld, obtain the position of the image of the monitoring weld according to the coordinates of each point on the edge contour, and mark it as the weld area; obtain the overlapping area of ​​the gap area and the weld area and the total area of ​​the gap area, obtain the area difference between the two, and mark it as the missing weld area value HM; Obtain the number of holes and cracks on the monitored weld image, mark them as hole value DS and crack value FS respectively, quantify the hole value DS and crack value FS to obtain the hole and crack value DF; Obtain the maximum width and minimum width of the monitored weld image, obtain the width difference between the two, and mark it as the width difference KC; Send the missing weld surface value HM, hole seam value DF and width difference KC to the data analysis module; The data analysis module is used to obtain the welding deviation coefficient HP according to the welding deviation parameters, and send the welding deviation coefficient HP to the tracking and correction platform.

2. The weld seam tracking and correction system for narrow gap automatic welding according to claim 1, characterized in that: Also includes: The tracking and correction platform is used to generate a correction alarm instruction according to the welding deviation coefficient HP and send the correction alarm instruction to the correction alarm module.

3. The weld seam tracking and correction system for narrow gap automatic welding according to claim 1, characterized in that: Also includes: The deviation correction alarm module is used to sound the deviation correction alarm bell after receiving the deviation correction alarm instruction.

4. The weld seam tracking and correction system for narrow gap automatic welding according to claim 1, characterized in that: The specific process of the image acquisition module taking images of the pre-weld gap and the monitoring weld is as follows: The welding objects to be welded are brought into contact with each other, the gap formed by the welding objects being contacted is marked as a pre-weld gap, the weld formed by welding using welding equipment is marked as a monitoring weld, and images of the pre-weld gap and the monitoring weld are captured using a high-definition camera, and the images of the pre-weld gap and the monitoring weld are sent to an image analysis module.

5. The weld seam tracking and correction system for narrow gap automatic welding according to claim 4, characterized in that: The welding object is a steel pipe, the steel pipe is a P92 steel pipe, and the specification of the P92 steel pipe is φ550×94mm.

6. The weld seam tracking and correction system for narrow gap automatic welding according to claim 1, characterized in that: The specific process of the data analysis module obtaining the welding deviation coefficient HP is as follows: The weld surface value HM, the hole value DF and the width difference KC are quantified to obtain the welding deviation coefficient HP; The welding deviation coefficient HP is sent to the tracking and correction platform.

7. The weld seam tracking and correction system for narrow gap automatic welding according to claim 1, characterized in that: The specific process of the tracking and correction platform generating the correction alarm instruction is as follows: Compare the welding deviation coefficient HP with the preset welding deviation threshold HPy: If the welding deviation coefficient HP ≥ the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as an unqualified welding object; Mark the first unqualified welding object as a correction object, and record the types of a preset number of welding objects starting with the correction object; Get the total number of welding failure objects and mark them as unqualified values ​​BS; Obtain the number of times that adjacent welding objects are both unqualified objects, and mark it as the consecutive difference value LC; Quantify the non-conforming value BS and the consecutive non-conforming value LC to obtain the abnormal coefficient YC; Compare the abnormal coefficient YC with the preset abnormal threshold YCy: If the abnormal coefficient YC ≥ the abnormal threshold YCy, a deviation correction alarm instruction is generated and sent to the deviation correction alarm module.

8. The weld seam tracking and correction system for narrow gap automatic welding according to claim 7, characterized in that: If the welding deviation coefficient HP is less than the welding deviation threshold HPy, the type of the welding object corresponding to the welding deviation coefficient HP is marked as a welding qualified object.

9. A method for tracking and correcting the weld seam in narrow gap automatic welding, characterized in that: The following steps are involved: Step 1: The image acquisition module photographs the welding object, obtains images of the pre-weld gap and the monitoring weld, and sends the images of the pre-weld gap and the monitoring weld to the image analysis module; Step 2: The image analysis module obtains welding deviation parameters based on the images of the pre-weld gap and the monitored weld. The welding deviation parameters include the missing weld surface value HM, the hole seam value DF, and the width difference KC, and sends the welding deviation parameters to the data analysis module; Step 3: The data analysis module obtains the welding deviation coefficient HP according to the welding deviation parameters, and sends the welding deviation coefficient HP to the tracking and correction platform.

10. The method for tracking and correcting the weld seam in narrow gap automatic welding according to claim 9, characterized in that: The following steps are also included: The tracking and correction platform generates a correction alarm instruction according to the welding deviation coefficient HP, and sends the correction alarm instruction to the correction alarm module; The deviation correction alarm module sounds a deviation correction alarm bell after receiving the deviation correction alarm instruction.

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