Steel pipe production data detection method and system

Abstract

The invention provides a steel pipe production data detection method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a first thermal infrared image of a to-be-detected steel pipe at a first moment based on a thermal infrared sensor, and obtaining a second thermal infrared image of the to-be-detected steel pipe at a second moment based on the thermal infrared sensor; the second thermal infrared image corresponds to the first thermal infrared image, finally, analyzing the wall thickness distribution of the to-be-detected steel pipe based on the first thermal infrared image and the second thermal infrared image, and determining whether the to-be-detected steel pipe is qualified or not according to an analysis result. According to the steel pipe production data detection method and system, whether the wall thickness of the steel pipe is uniform or not is determined according to the temperature change condition during natural cooling of the steel pipe and whether the cooling process of the steel pipe is uniform or not under the condition that the steel pipe is not affected by the external environment, direct detection can be conducted through the long-wave thermal infrared imager, and the detection accuracy is high. And compared with the traditional X-ray or sound wave detection, the detection is quicker, and the cost is also saved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method and system for detecting steel pipe production data. Background Art

[0002] In steel pipe manufacturing, uneven wall thickness (eccentricity) is a core defect that threatens product structural safety. Traditional inspection methods rely primarily on ultrasonic thickness measurement or manual spot checks. However, contact point measurement requires point-by-point scanning, and a full inspection of a single steel pipe can take up to 10-15 minutes, making it difficult to adapt to the pace of high-speed production lines. Furthermore, X-ray or ultrasonic thickness measurement requires the product to cool, which is slow and costly. Summary of the Invention

[0003] The present application provides a steel pipe production data detection method and system to improve the above-mentioned problems.

[0004] To achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application proposes a method for detecting steel pipe production data. The method is applicable to a steel pipe detection system. The steel pipe detection system includes a thermal infrared sensor and a controller. The method is applicable to the controller and includes: Acquire, using the thermal infrared sensor, a first thermal infrared image of the steel pipe to be inspected at a first moment, wherein, when the outer surface of the steel pipe to be inspected is fully unfolded, an area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected; Acquire a second thermal infrared image of the steel pipe to be inspected at a second moment based on the thermal infrared sensor, where the second thermal infrared image corresponds to the first thermal infrared image; The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis result.

[0005] In conjunction with the first aspect, in some embodiments, analyzing the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determining whether the steel pipe to be inspected is qualified according to the analysis results, includes: Performing grid division on the first thermal infrared image to form a plurality of first sub-images, and dividing the plurality of first sub-images into a plurality of first image groups based on different color gradients; Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into a plurality of temperature gradient regions, and a first target region is determined from the plurality of temperature gradient regions; gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, and grouping the plurality of second sub-images into a plurality of second image groups based on different color gradients; dividing the second thermal infrared image into a plurality of temperature gradient regions based on the distribution of the second sub-images in each second image group, and determining a second target region corresponding to the first target region from the plurality of temperature gradient regions; The wall thickness of the steel pipe to be inspected is analyzed based on the boundary changes between the first target area and the second target area.

[0006] In conjunction with the first aspect, in some embodiments, dividing the first thermal infrared image into multiple temperature gradient regions based on the distribution of the first sub-images in each first image group, and determining the first target region from the multiple temperature gradient regions includes: Obtaining the corresponding temperature of each temperature gradient region, and obtaining a first preset temperature threshold and a second preset temperature threshold; A combination of a temperature gradient region whose corresponding temperature is greater than a first preset temperature threshold and a temperature gradient region whose corresponding temperature is less than a second preset temperature threshold among all the temperature gradient regions is determined as a first target region.

[0007] In conjunction with the first aspect, in some embodiments, performing wall thickness analysis on the steel pipe to be inspected based on boundary changes between the first target area and the second target area includes: Acquire a first boundary corresponding to the first target area and a second boundary corresponding to the second target area, and map the second boundary into the first thermal infrared image; Based on the correspondence between the first target area and the second target area, determining the correspondence between the first boundary and the second boundary, thereby determining each corresponding pair of the first boundary and the second boundary; Generate a plurality of first marking points on a first boundary, and form a perpendicular line to the first boundary from the first marking points, wherein the intersection of the perpendicular line and the second boundary corresponding to the first boundary is a second marking point; Acquire distance data between a plurality of first marking points and a second marking point, and acquire discrete representation parameters of the plurality of distance data; A plurality of discrete characterization parameters of the first boundary and the second boundary are obtained, and it is determined whether the thickness of the steel pipe to be inspected is uniformly distributed according to the plurality of discrete characterization parameters.

[0008] In conjunction with the first aspect, in some embodiments, obtaining a plurality of discrete characterization parameters corresponding to the first boundary and the second boundary, and determining whether the thickness of the steel pipe to be inspected is uniformly distributed based on the plurality of discrete characterization parameters includes: The average value of multiple discrete characterization parameters is obtained and compared with a preset value. If the average value is greater than the preset value, it is determined that the thickness distribution of the steel pipe to be tested is uneven.

[0009] In conjunction with the first aspect, in some embodiments, analyzing the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determining whether the steel pipe to be inspected is qualified according to the analysis results, includes: Gridding the first thermal infrared image to form a plurality of first sub-images, obtaining a first temperature parameter corresponding to each first sub-image, and determining a plurality of first reference points based on the plurality of first sub-images; Gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, obtaining a second temperature parameter corresponding to each second sub-image, and determining a plurality of second reference points based on the plurality of second sub-images; Determine the correspondence between multiple first reference points and second reference points, map the multiple second reference points to the first thermal infrared image, obtain the offset distances between the multiple corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances.

[0010] In conjunction with the first aspect, in some embodiments, determining a correspondence between a plurality of first reference points and a second reference point, mapping the plurality of second reference points to the first thermal infrared image, and obtaining offset distances between the plurality of corresponding first reference points and the second reference points, and analyzing the wall thickness distribution of the steel pipe to be inspected based on the offset distances, includes: Determine a plurality of first temperature gradient regions based on a first temperature parameter corresponding to each first sub-image, and determine, in each first temperature gradient region, the location of an extreme value point as a first reference point; Based on the second temperature parameter corresponding to each second sub-image, a plurality of second temperature gradient regions are determined, and the location of the extreme value point in each second temperature gradient region is determined as the second reference point.

[0011] In conjunction with the first aspect, in some embodiments, determining a correspondence between a plurality of first reference points and a second reference point, mapping the plurality of second reference points to the first thermal infrared image, and obtaining offset distances between the plurality of corresponding first reference points and the second reference points, and analyzing the wall thickness distribution of the steel pipe to be inspected based on the offset distances, includes: Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, a correspondence between the plurality of first extreme value points and the plurality of second extreme value points is determined.

[0012] In conjunction with the first aspect, in some embodiments, determining a correspondence between a plurality of first reference points and a second reference point, mapping the plurality of second reference points to the first thermal infrared image, and obtaining offset distances between the plurality of corresponding first reference points and the second reference points, and analyzing the wall thickness distribution of the steel pipe to be inspected based on the offset distances, includes: Obtaining the interval distance between each pair of corresponding first extreme value points and second extreme value points, and obtaining an average distance based on the multiple interval distances; If the average distance is greater than the preset distance, it is determined that the wall thickness of the steel pipe to be inspected is unevenly distributed.

[0013] In a second aspect, the present application proposes a steel pipe production data detection system, including a thermal infrared sensor and a controller, wherein the system is configured as follows: Acquire, using the thermal infrared sensor, a first thermal infrared image of the steel pipe to be inspected at a first moment, wherein, when the outer surface of the steel pipe to be inspected is fully unfolded, an area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected; Acquire a second thermal infrared image of the steel pipe to be inspected at a second moment based on the thermal infrared sensor, where the second thermal infrared image corresponds to the first thermal infrared image; The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis result.

[0014] In conjunction with the second aspect, in some embodiments, analyzing the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determining whether the steel pipe to be inspected is qualified according to the analysis results, includes: Performing grid division on the first thermal infrared image to form a plurality of first sub-images, and dividing the plurality of first sub-images into a plurality of first image groups based on different color gradients; Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into a plurality of temperature gradient regions, and a first target region is determined from the plurality of temperature gradient regions; gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, and grouping the plurality of second sub-images into a plurality of second image groups based on different color gradients; dividing the second thermal infrared image into a plurality of temperature gradient regions based on the distribution of the second sub-images in each second image group, and determining a second target region corresponding to the first target region from the plurality of temperature gradient regions; The wall thickness of the steel pipe to be inspected is analyzed based on the boundary changes between the first target area and the second target area.

[0015] In conjunction with the second aspect, in some embodiments, the system is configured to: Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into a plurality of temperature gradient regions, and a first target region is determined from the plurality of temperature gradient regions, including: Obtaining the corresponding temperature of each temperature gradient region, and obtaining a first preset temperature threshold and a second preset temperature threshold; A combination of a temperature gradient region whose corresponding temperature is greater than a first preset temperature threshold and a temperature gradient region whose corresponding temperature is less than a second preset temperature threshold among all the temperature gradient regions is determined as a first target region.

[0016] In conjunction with the second aspect, in some embodiments, the system is configured to: Based on the boundary changes between the first target area and the second target area, the wall thickness analysis of the steel pipe to be inspected is performed, including: Acquire a first boundary corresponding to the first target area and a second boundary corresponding to the second target area, and map the second boundary into the first thermal infrared image; Based on the correspondence between the first target area and the second target area, determining the correspondence between the first boundary and the second boundary, thereby determining each corresponding pair of the first boundary and the second boundary; Generate a plurality of first marking points on a first boundary, and form a perpendicular line to the first boundary from the first marking points, wherein the intersection of the perpendicular line and the second boundary corresponding to the first boundary is a second marking point; Acquire distance data between a plurality of first marking points and a second marking point, and acquire discrete representation parameters of the plurality of distance data; A plurality of discrete characterization parameters of the first boundary and the second boundary are obtained, and it is determined whether the thickness of the steel pipe to be inspected is uniformly distributed according to the plurality of discrete characterization parameters.

[0017] In conjunction with the second aspect, in some embodiments, the system is configured to: Obtaining a plurality of discrete characterization parameters corresponding to the first boundary and the second boundary, and determining whether the thickness of the steel pipe to be inspected is uniformly distributed according to the plurality of discrete characterization parameters, including: The average value of multiple discrete characterization parameters is obtained and compared with a preset value. If the average value is greater than the preset value, it is determined that the thickness distribution of the steel pipe to be tested is uneven.

[0018] In conjunction with the second aspect, in some embodiments, the system is configured to: The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis results, including: Gridding the first thermal infrared image to form a plurality of first sub-images, obtaining a first temperature parameter corresponding to each first sub-image, and determining a plurality of first reference points based on the plurality of first sub-images; Gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, obtaining a second temperature parameter corresponding to each second sub-image, and determining a plurality of second reference points based on the plurality of second sub-images; Determine the correspondence between multiple first reference points and second reference points, map the multiple second reference points to the first thermal infrared image, obtain the offset distances between the multiple corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances.

[0019] In conjunction with the second aspect, in some embodiments, the system is configured to: Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Determine a plurality of first temperature gradient regions based on a first temperature parameter corresponding to each first sub-image, and determine, in each first temperature gradient region, the location of an extreme value point as a first reference point; Based on the second temperature parameter corresponding to each second sub-image, a plurality of second temperature gradient regions are determined, and the location of the extreme value point in each second temperature gradient region is determined as the second reference point.

[0020] In conjunction with the second aspect, in some embodiments, the system is configured to: Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, a correspondence between the plurality of first extreme value points and the plurality of second extreme value points is determined.

[0021] In conjunction with the second aspect, in some embodiments, the system is configured to: Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Obtaining the interval distance between each pair of corresponding first extreme value points and second extreme value points, and obtaining an average distance based on the multiple interval distances; If the average distance is greater than the preset distance, it is determined that the wall thickness of the steel pipe to be inspected is unevenly distributed.

[0022] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method proposed in the first aspect of the embodiment of the present invention.

[0023] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0024] In summary, the above method and system have the following technical effects: The present application proposes a method and system for detecting steel pipe production data. First, a first thermal infrared image of the steel pipe to be detected at the first moment is obtained based on a thermal infrared sensor. Then, a second thermal infrared image of the steel pipe to be detected at the second moment is obtained based on the thermal infrared sensor. The second thermal infrared image corresponds to the first thermal infrared image. Finally, the wall thickness distribution of the steel pipe to be detected is analyzed based on the first thermal infrared image and the second thermal infrared image. Whether the steel pipe to be detected is qualified is determined based on the analysis results. The present application proposes a method and system for detecting steel pipe production data. The method determines whether the wall thickness of the steel pipe is sufficiently uniform by observing the temperature change during the natural cooling of the steel pipe and whether the cooling process of the steel pipe is uniform without the influence of the external environment. The method can be directly detected by a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or sound wave detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of a process for detecting steel pipe production data proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] During steel pipe production, uneven wall thickness (eccentricity) refers to a geometric defect where the wall thickness distribution across a cross section is asymmetrical, or where the wall thickness fluctuates beyond the allowable tolerance along the length. Because eccentricity occurs within the pipe, it is generally not directly observable radially. Furthermore, axial inspection may not be readily apparent when the pipe is long.

[0028] In response to the above problems, the present invention proposes a method for detecting steel pipe production data. The method is applicable to a steel pipe detection system. The steel pipe detection system includes a thermal infrared sensor and a controller. The method is applicable to the controller. The steel pipe can be placed still during detection. Figure 1 , the method comprises the following steps: S101: Acquire a first thermal infrared image of the steel pipe to be inspected at a first moment based on a thermal infrared sensor, wherein, when the outer surface of the steel pipe to be inspected is fully unfolded, an area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected.

[0029] A thermal infrared sensor is a device that measures the surface temperature or generates thermal images by detecting infrared radiation (heat) emitted by an object. They operate based on the principle of infrared thermal radiation: all objects with a temperature above absolute zero emit infrared radiation, and its intensity is positively correlated with the object's temperature.

[0030] In this embodiment, the thermal infrared sensor can observe the radial direction of the steel pipe. To improve accuracy, the thermal infrared sensor can be rotated around the steel pipe to a certain extent to ensure that the entire outer surface of the steel pipe is observed as much as possible. Of course, it can also be fixed, but it must be ensured that at least half of the area can be detected. That is, when the outer surface of the steel pipe to be inspected is fully unfolded, the area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected. In this embodiment, the thermal infrared image can be rotated around the central axis of the steel pipe so that the first thermal infrared image obtained fully corresponds to the unfolded image of the steel pipe.

[0031] S102: Acquire a second thermal infrared image of the steel pipe to be inspected at a second moment based on the thermal infrared sensor, where the second thermal infrared image corresponds to the first thermal infrared image.

[0032] It is understood that the second moment is the moment after the first moment, that is, the image returned by the thermal infrared sensor after the steel pipe has passively or actively cooled for a period of time. In this embodiment, the steel pipe should maintain the same position at the first and second moments to ensure that the first thermal infrared image and the second thermal infrared image fully correspond.

[0033] S103: Analyze the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determine whether the steel pipe to be inspected is qualified according to the analysis result.

[0034] In this embodiment, since the first thermal infrared image and the second thermal infrared image completely correspond to the expanded image of the steel pipe, it can also be understood that the first thermal infrared image and the second thermal infrared image are thermal infrared images of the outer surface temperature of the steel pipe at different times. It is understandable that in an ideal state, that is, when the temperature of the steel pipe is completely consistent, the uneven thickness of the steel pipe will cause the temperature of the thicker part to drop more slowly than that of the symmetrical position on the steel pipe. Therefore, it can be directly reflected in the infrared image. However, in reality, the temperature distribution of the steel pipe at the first moment may not be completely consistent. Therefore, when the first thermal infrared image changes over time, the temperature changes at various locations are also inconsistent. Therefore, the influence of the initial temperature needs to be considered when combining the second thermal infrared image.

[0035] Specifically, in this embodiment, the image analysis process may include the following steps: S1031: Perform grid division on the first thermal infrared image to form a plurality of first sub-images, and divide the plurality of first sub-images into a plurality of first image groups based on different color gradients.

[0036] It will be appreciated that in this embodiment, the first thermal infrared image is segmented using a scale that can be based on the sensing accuracy of the infrared sensor or a fixed scale, and this is not limited here. In the first thermal infrared image, different temperatures correspond to different colors, meaning that each first image group corresponds to a different temperature.

[0037] S1032: Based on the distribution of the first sub-images in each first image group, divide the first thermal infrared image into a plurality of temperature gradient regions, and determine a first target region from the plurality of temperature gradient regions.

[0038] It can be understood that according to basic physical laws, in a group of images belonging to the same temperature gradient, each first sub-image must be approximately continuous. Therefore, according to the distribution of multiple first sub-images, all first sub-images corresponding to each first image can be divided into an area, and multiple areas together constitute the first thermal infrared image.

[0039] In this embodiment, the corresponding temperature of each temperature gradient area can be obtained, and the first preset temperature threshold and the second preset temperature threshold can be obtained. Then, among all the temperature gradient areas, the combination of the temperature gradient area whose corresponding temperature is greater than the first preset temperature threshold and the temperature gradient area whose corresponding temperature is less than the second preset temperature threshold is determined to be the first target area. The first target area is also the high temperature area and the low temperature area in the actual expanded image.

[0040] S1033: Divide the second thermal infrared image into grids to form a plurality of second sub-images corresponding to the first sub-image, and group the plurality of second sub-images into a plurality of second image groups based on different color gradients.

[0041] S1034: Based on the distribution of the second sub-images in each second image group, the second thermal infrared image is divided into a plurality of temperature gradient regions, and a second target region corresponding to the first target region is determined from the plurality of temperature gradient regions.

[0042] As will be appreciated, in this embodiment, since the position of the steel pipe to be inspected has not changed, the actual position of each point in the first thermal infrared image also remains consistent with the position in the second thermal infrared image. The second thermal infrared image is divided into multiple temperature gradient regions using the same method as in step S1023. Subsequently, based on the positional relationship between the second thermal infrared image and the first thermal infrared image, relatively high-temperature and low-temperature regions, referred to as the second target region in this application, can be directly determined in the second thermal infrared image.

[0043] S1035: Performing wall thickness analysis on the steel pipe to be inspected based on changes in the boundary between the first target area and the second target area.

[0044] It is understandable that during the natural cooling process of the steel pipe, except for the ends of the steel pipe, the rest of the pipe is in a uniform thickness, and its temperature change during cooling is also uniform. In this embodiment, for ease of understanding, a high-temperature area or a low-temperature area that is roughly circular is taken as an example. Of course, in other embodiments, it can be any actual shape. For a high-temperature area of ​​a circle, during the natural cooling process, the change of the area must be uniformly reduced from the edge of the circle to the inside, that is, under ideal conditions, the first target area and the corresponding second target area should be nested in the form of concentric circles, that is, the shrinkage distance is equidistant. However, when the wall thickness is uneven, it will inevitably lead to slow cooling in thick areas and fast cooling in thin areas. Therefore, the uneven wall thickness will cause the speed of boundary shrinkage to increase or decrease. Therefore, whether the wall thickness is uniform can be determined by whether the boundary change is uniform. For the low-temperature area, it is also uniformly enlarged based on the same principle.

[0045] Specifically, in this embodiment, in order to measure the degree of change, the following methods can be used: A first boundary corresponding to the first target area and a second boundary corresponding to the second target area are obtained, and the second boundary is mapped into the first thermal infrared image. Then, based on the correspondence between the first target area and the second target area, a correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of first and second boundaries. Then, a plurality of first marking points are generated on the first boundary, and a perpendicular line to the first boundary is formed from the first marking point. The intersection of the perpendicular line and the second boundary corresponding to the first boundary is a second marking point. Then, distance data between the plurality of first marking points and the second marking points is obtained, and discrete characterization parameters of the plurality of distance data are obtained.

[0046] It can be understood that the discrete characterization parameter can be a variance or other parameters that can characterize the degree of discreteness, which is not specifically limited in this embodiment. Multiple pairs of discrete characterization parameters of the first boundary and the second boundary are obtained, and whether the thickness of the steel pipe to be tested is evenly distributed is determined based on the multiple discrete characterization parameters. For example, the larger the multiple discrete characterization parameters are, the greater the uniformity of the boundary contraction or expansion. Therefore, in this embodiment, the average value is taken for overall characterization, and it is compared with the preset value. If the average value is greater than the preset value, it is determined that the thickness of the steel pipe to be tested is unevenly distributed. The preset value can be obtained based on actual production data, through historical experience or other means, and the specific value is not limited in this application.

[0047] It can be understood that if the thickness distribution of the steel pipe to be tested is uneven, it can be determined that the steel pipe is a substandard product.

[0048] Alternatively, the thermal infrared sensor can directly acquire temperature data. Therefore, in some other embodiments, the degree of extreme point offset can be used to determine whether the wall thickness is uniform. It is understood that when the wall thickness distribution is relatively uniform, the location of the extreme points (i.e., the highest or lowest temperature points) will not change significantly due to uniform heat diffusion.

[0049] Specifically, in this embodiment, based on the first temperature parameter corresponding to each first sub-image, multiple first temperature gradient regions are determined, and the location of the extreme point in each first temperature gradient region is determined as the first reference point. Based on the second temperature parameter corresponding to each second sub-image, multiple second temperature gradient regions are determined, and the location of the extreme point in each second temperature gradient region is determined as the second reference point.

[0050] Then, based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, the correspondence between the plurality of first extreme points and the plurality of second extreme points is determined. It is understood that, as a method, the actual position of a high temperature region or a low temperature region will not change significantly after cooling. Therefore, direct comparison can be used to determine whether the extreme points of the region have shifted.

[0051] Specifically, the interval distance between each pair of corresponding first extreme points and second extreme points can be obtained, and the average distance can be obtained based on multiple interval distances; if the average distance is greater than the preset distance, it is determined that the wall thickness distribution of the steel pipe to be inspected is uneven.

[0052] The present application proposes a method for detecting steel pipe production data. First, a first thermal infrared image of the steel pipe to be detected at the first moment is obtained based on a thermal infrared sensor. Then, a second thermal infrared image of the steel pipe to be detected at the second moment is obtained based on the thermal infrared sensor. The second thermal infrared image corresponds to the first thermal infrared image. Finally, the wall thickness distribution of the steel pipe to be detected is analyzed based on the first thermal infrared image and the second thermal infrared image. Whether the steel pipe to be detected is qualified is determined based on the analysis results. The present application proposes a method for detecting steel pipe production data. The method determines whether the wall thickness of the steel pipe is sufficiently uniform by observing the temperature change during natural cooling of the steel pipe and whether the cooling process of the steel pipe is uniform without the influence of the external environment. The method can be directly detected by a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or sound wave detection.

[0053] Based on the same inventive concept, the present application also proposes a steel pipe production data detection system, including a thermal infrared sensor and a controller. The system is configured as follows: Acquire, using the thermal infrared sensor, a first thermal infrared image of the steel pipe to be inspected at a first moment, wherein, when the outer surface of the steel pipe to be inspected is fully unfolded, an area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected; Acquire a second thermal infrared image of the steel pipe to be inspected at a second moment based on the thermal infrared sensor, where the second thermal infrared image corresponds to the first thermal infrared image; The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis result.

[0054] In some embodiments, analyzing the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determining whether the steel pipe to be inspected is qualified according to the analysis results, includes: Performing grid division on the first thermal infrared image to form a plurality of first sub-images, and dividing the plurality of first sub-images into a plurality of first image groups based on different color gradients; Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into a plurality of temperature gradient regions, and a first target region is determined from the plurality of temperature gradient regions; gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, and grouping the plurality of second sub-images into a plurality of second image groups based on different color gradients; dividing the second thermal infrared image into a plurality of temperature gradient regions based on the distribution of the second sub-images in each second image group, and determining a second target region corresponding to the first target region from the plurality of temperature gradient regions; The wall thickness of the steel pipe to be inspected is analyzed based on the boundary changes between the first target area and the second target area.

[0055] In some embodiments, the system is configured to: Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into a plurality of temperature gradient regions, and a first target region is determined from the plurality of temperature gradient regions, including: Obtaining the corresponding temperature of each temperature gradient region, and obtaining a first preset temperature threshold and a second preset temperature threshold; A combination of a temperature gradient region whose corresponding temperature is greater than a first preset temperature threshold and a temperature gradient region whose corresponding temperature is less than a second preset temperature threshold among all the temperature gradient regions is determined as a first target region.

[0056] In some embodiments, the system is configured to: Based on the boundary changes between the first target area and the second target area, the wall thickness analysis of the steel pipe to be inspected is performed, including: Acquire a first boundary corresponding to the first target area and a second boundary corresponding to the second target area, and map the second boundary into the first thermal infrared image; Based on the correspondence between the first target area and the second target area, determining the correspondence between the first boundary and the second boundary, thereby determining each corresponding pair of the first boundary and the second boundary; Generate a plurality of first marking points on a first boundary, and form a perpendicular line to the first boundary from the first marking points, wherein the intersection of the perpendicular line and the second boundary corresponding to the first boundary is a second marking point; Acquire distance data between a plurality of first marking points and a second marking point, and acquire discrete representation parameters of the plurality of distance data; A plurality of discrete characterization parameters of the first boundary and the second boundary are obtained, and it is determined whether the thickness of the steel pipe to be inspected is uniformly distributed according to the plurality of discrete characterization parameters.

[0057] In conjunction with the second aspect, in some embodiments, the system is configured to: Obtaining a plurality of discrete characterization parameters corresponding to the first boundary and the second boundary, and determining whether the thickness of the steel pipe to be inspected is uniformly distributed according to the plurality of discrete characterization parameters, including: The average value of multiple discrete characterization parameters is obtained and compared with a preset value. If the average value is greater than the preset value, it is determined that the thickness distribution of the steel pipe to be tested is uneven.

[0058] In some embodiments, the system is configured to: The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis results, including: Gridding the first thermal infrared image to form a plurality of first sub-images, obtaining a first temperature parameter corresponding to each first sub-image, and determining a plurality of first reference points based on the plurality of first sub-images; Gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, obtaining a second temperature parameter corresponding to each second sub-image, and determining a plurality of second reference points based on the plurality of second sub-images; Determine the correspondence between multiple first reference points and second reference points, map the multiple second reference points to the first thermal infrared image, obtain the offset distances between the multiple corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances.

[0059] In some embodiments, the system is configured to: Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Determine a plurality of first temperature gradient regions based on a first temperature parameter corresponding to each first sub-image, and determine, in each first temperature gradient region, the location of an extreme value point as a first reference point; Based on the second temperature parameter corresponding to each second sub-image, a plurality of second temperature gradient regions are determined, and the location of the extreme value point in each second temperature gradient region is determined as the second reference point.

[0060] In some embodiments, the system is configured to: Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, a correspondence between the plurality of first extreme value points and the plurality of second extreme value points is determined.

[0061] In some embodiments, the system is configured to: Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Obtaining the interval distance between each pair of corresponding first extreme value points and second extreme value points, and obtaining an average distance based on the multiple interval distances; If the average distance is greater than the preset distance, it is determined that the wall thickness of the steel pipe to be inspected is unevenly distributed.

[0062] The present application proposes a steel pipe production data detection system, which first obtains a first thermal infrared image of the steel pipe to be detected at the first moment based on a thermal infrared sensor, and obtains a second thermal infrared image of the steel pipe to be detected at the second moment based on the thermal infrared sensor, the second thermal infrared image corresponding to the first thermal infrared image, and finally, analyzes the wall thickness distribution of the steel pipe to be detected based on the first thermal infrared image and the second thermal infrared image, and determines whether the steel pipe to be detected is qualified based on the analysis results. The present application proposes a steel pipe production data detection system, which determines whether the wall thickness of the steel pipe is sufficiently uniform by observing the temperature change during natural cooling of the steel pipe and whether the cooling process of the steel pipe is uniform without the influence of the external environment. It can be directly detected by a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or sound wave detection.

[0063] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, the electronic device comprising: At least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steel pipe production data detection method of the embodiment of the present application.

[0064] In addition, to achieve the above-mentioned purpose, an embodiment of the present application further proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steel pipe production data detection method of the embodiment of the present application.

[0065] The following is a detailed introduction to the various components of electronic equipment: The term "processor" refers to the control center of an electronic device and can be a single processor or a collective term for multiple processing elements. For example, the processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0066] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0067] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0068] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processor via an interface circuit of the electronic device, and this is not specifically limited in the embodiments of the present invention.

[0069] A transceiver is used to communicate with network devices or terminal devices.

[0070] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0071] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0072] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method in the above method embodiment, and will not be repeated here.

[0073] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0074] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0075] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0076] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0077] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0078] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes 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 invention.

[0079] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. A method for detecting steel pipe production data, characterized in that: The method is applicable to a steel pipe detection system, which includes a thermal infrared sensor and a controller. The method is applicable to the controller and includes: obtaining, using the thermal infrared sensor, a first thermal infrared image of the steel pipe to be inspected at a first moment, wherein, when the outer surface of the steel pipe to be inspected is fully unfolded, an area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected; Acquire, based on the thermal infrared sensor, a second thermal infrared image of the steel pipe to be inspected at a second moment, wherein the second thermal infrared image corresponds to the first thermal infrared image; The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis result.

2. A steel pipe production data detection method according to claim 1, characterized in that: Analyzing the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determining whether the steel pipe to be inspected is qualified according to the analysis result, including: Performing grid division on the first thermal infrared image to form a plurality of first sub-images, and dividing the plurality of first sub-images into a plurality of first image groups based on different color gradients; Based on the distribution of the first sub-images in each of the first image groups, the first thermal infrared image is divided into a plurality of temperature gradient regions, and a first target region is determined from the plurality of temperature gradient regions; performing grid division on the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, and grouping the plurality of second sub-images into a plurality of second image groups based on different color gradients; dividing the second thermal infrared image into a plurality of the temperature gradient regions based on the distribution of the second sub-images in each of the second image groups, and determining a second target region corresponding to the first target region from the plurality of the temperature gradient regions; The wall thickness of the steel pipe to be inspected is analyzed based on the boundary changes between the first target area and the second target area.

3. A method for detecting steel pipe production data according to claim 2, characterized in that: Dividing the first thermal infrared image into a plurality of temperature gradient regions based on the distribution of the first sub-images in each of the first image groups, and determining a first target region from the plurality of temperature gradient regions, including: Obtaining the corresponding temperature of each of the temperature gradient regions, and obtaining a first preset temperature threshold and a second preset temperature threshold; Determine, among all the temperature gradient regions, a combination of the temperature gradient region whose corresponding temperature is greater than the first preset temperature threshold and the temperature gradient region whose corresponding temperature is less than the second preset temperature threshold as a first target region.

4. A method for detecting steel pipe production data according to claim 2, characterized in that: Performing a wall thickness analysis on the steel pipe to be inspected based on a boundary change between the first target area and the second target area includes: Acquire a first boundary corresponding to the first target area and a second boundary corresponding to the second target area, and map the second boundary into the first thermal infrared image; Determining a correspondence between the first boundary and the second boundary based on the correspondence between the first target area and the second target area, thereby determining each corresponding pair of the first boundary and the second boundary; generating a plurality of first marking points on one of the first boundaries, and forming a perpendicular line to the first boundary from the first marking points, wherein the intersection of the perpendicular line and the second boundary corresponding to the first boundary is a second marking point; Acquire a plurality of distance data between the first marking points and the second marking points, and acquire a plurality of discrete characterization parameters of the distance data; A plurality of pairs of discrete characterization parameters of the first boundary and the second boundary are obtained, and whether the thickness of the steel pipe to be inspected is uniformly distributed is determined based on the plurality of discrete characterization parameters.

5. A steel pipe production data detection method according to claim 4, characterized in that: Acquiring a plurality of pairs of the discrete characterization parameters corresponding to the first boundary and the second boundary, and determining whether the thickness of the steel pipe to be inspected is uniformly distributed according to the plurality of discrete characterization parameters, including: An average value of a plurality of the discrete characterization parameters is obtained, and the average value is compared with a preset value. If the average value is greater than the preset value, it is determined that the thickness distribution of the steel pipe to be detected is uneven.

6. A steel pipe production data detection method according to claim 1, characterized in that: Analyzing the wall thickness distribution of the steel pipe to be inspected based on the first thermal infrared image and the second thermal infrared image, and determining whether the steel pipe to be inspected is qualified according to the analysis result, including: Gridding the first thermal infrared image to form a plurality of first sub-images, obtaining a first temperature parameter corresponding to each of the first sub-images, and determining a plurality of first reference points based on the plurality of first sub-images; Gridding the second thermal infrared image to form a plurality of second sub-images corresponding to the first sub-image, obtaining a second temperature parameter corresponding to each of the second sub-images, and determining a plurality of second reference points based on the plurality of second sub-images; Determine the correspondence between the plurality of first reference points and the second reference points, map the plurality of second reference points to the first thermal infrared image, obtain the offset distances between the plurality of corresponding first reference points and the second reference points, and analyze the wall thickness distribution of the steel pipe to be inspected based on the offset distances.

7. A method for detecting steel pipe production data according to claim 6, characterized in that: Determining a correspondence between a plurality of first reference points and a second reference point, mapping the plurality of second reference points to the first thermal infrared image, obtaining a plurality of offset distances between the corresponding first reference points and the second reference points, and analyzing the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: determining a plurality of first temperature gradient regions based on the first temperature parameter corresponding to each of the first sub-images, and determining in each of the first temperature gradient regions the location of an extreme value point as the first reference point; Based on the second temperature parameter corresponding to each second sub-image, a plurality of second temperature gradient regions are determined, and the location of the extreme point in each second temperature gradient region is determined as the second reference point.

8. A method for detecting steel pipe production data according to claim 7, characterized in that: Determining a correspondence between a plurality of first reference points and a second reference point, mapping the plurality of second reference points to the first thermal infrared image, obtaining a plurality of offset distances between the corresponding first reference points and the second reference points, and analyzing the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, a correspondence between a plurality of first extreme value points and a plurality of second extreme value points is determined.

9. A method for detecting steel pipe production data according to claim 7, characterized in that: Determining a correspondence between a plurality of first reference points and a second reference point, mapping the plurality of second reference points to the first thermal infrared image, obtaining a plurality of offset distances between the corresponding first reference points and the second reference points, and analyzing the wall thickness distribution of the steel pipe to be inspected based on the offset distances, including: Obtaining the interval distance between each pair of corresponding first extreme value points and second extreme value points, and obtaining an average distance based on a plurality of the interval distances; If the average distance is greater than the preset distance, it is determined that the wall thickness of the steel pipe to be inspected is unevenly distributed.

10. A steel pipe production data detection system, characterized in that: Comprising a thermal infrared sensor and a controller, the system is configured to: obtaining, using the thermal infrared sensor, a first thermal infrared image of the steel pipe to be inspected at a first moment, wherein, when the outer surface of the steel pipe to be inspected is fully unfolded, an area corresponding to the outer surface of the steel pipe to be inspected in the first thermal infrared image corresponds to at least half of the unfolded image of the outer surface of the steel pipe to be inspected; Acquire, based on the thermal infrared sensor, a second thermal infrared image of the steel pipe to be inspected at a second moment, wherein the second thermal infrared image corresponds to the first thermal infrared image; The wall thickness distribution of the steel pipe to be inspected is analyzed based on the first thermal infrared image and the second thermal infrared image, and whether the steel pipe to be inspected is qualified is determined according to the analysis result.

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