A method and system for detecting steel pipe production data

Abstract

This application proposes a method and system for detecting steel pipe production data, belonging to the field of image processing technology. First, a first thermal infrared image of the steel pipe to be tested is acquired at a first moment based on a thermal infrared sensor. Then, a second thermal infrared image of the steel pipe to be tested is acquired at a second moment based on the same 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 tested is analyzed based on the first and second thermal infrared images, and the quality of the steel pipe is determined according to the analysis results. This method and system for detecting steel pipe production data, by observing the temperature changes during natural cooling of the steel pipe and whether the cooling process is uniform under the influence of no external environment, determines whether the wall thickness of the steel pipe is uniform enough. This can be directly detected using a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or acoustic detection methods.

Description

Technical Field

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

[0002] In the steel pipe manufacturing industry, uneven wall thickness (eccentricity) is one of the core defects that endangers the structural safety of products. Traditional inspection methods mainly rely on ultrasonic thickness measurement or manual sampling. However, contact point measurement requires scanning point by point, and a full inspection of a single steel pipe takes as long as 10-15 minutes, which is difficult to match the pace of high-speed production lines. At the same time, X-ray or ultrasonic thickness measurement requires the product to be cooled down, which is slow and costly. Summary of the Invention

[0003] This application provides a method and system for detecting steel pipe production data to improve the above-mentioned problems.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] Firstly, this application proposes a method for detecting steel pipe production data. 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:

[0006] The first thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at the first moment. When the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested in the first thermal infrared image corresponds to at least half of the unfolded outer surface image of the steel pipe to be tested.

[0007] A second thermal infrared image of the steel pipe to be tested is obtained based on a thermal infrared sensor at a second moment, and the second thermal infrared image corresponds to the first thermal infrared image.

[0008] The wall thickness distribution of the steel pipe to be tested is analyzed based on the first and second thermal infrared images, and the quality of the steel pipe is determined according to the analysis results.

[0009] In conjunction with the first aspect, in some implementations, 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 the qualification of the steel pipe to be inspected is determined according to the analysis results, including:

[0010] The first thermal infrared image is divided into grids to form multiple first sub-images, and the multiple first sub-images are divided into multiple first image groups based on different color gradients;

[0011] Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and the first target region is determined from the multiple temperature gradient regions.

[0012] The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the multiple second sub-images are grouped into multiple second image groups based on different color gradients;

[0013] Based on the distribution of the second sub-images in each second image group, the second thermal infrared image is divided into multiple temperature gradient regions, and the second target region corresponding to the first target region is determined from the multiple temperature gradient regions.

[0014] The wall thickness of the steel pipe to be tested is analyzed based on the boundary changes between the first target region and the second target region.

[0015] In conjunction with the first aspect, in some embodiments, based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and a first target region is determined from the multiple temperature gradient regions, including:

[0016] Obtain the corresponding temperature for each temperature gradient region, and obtain the first preset temperature threshold and the second preset temperature threshold;

[0017] The combination of temperature gradient regions with temperatures greater than a first preset temperature threshold and temperature gradient regions with temperatures less than a second preset temperature threshold is identified as the first target region.

[0018] In conjunction with the first aspect, in some implementation methods, wall thickness analysis of the steel pipe to be tested is performed based on the boundary changes between the first target region and the second target region, including:

[0019] Obtain the first boundary corresponding to the first target region and the second boundary corresponding to the second target region, and map the second boundary onto the first thermal infrared image;

[0020] Based on the correspondence between the first target region and the second target region, the correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of first and second boundaries;

[0021] Multiple first marker points are generated on a first boundary, and a perpendicular line to the first boundary is formed from the first marker points. The intersection of the perpendicular line with the second boundary corresponding to the first boundary is the second marker point.

[0022] Obtain distance data between multiple first marker points and second marker points, and obtain discrete representation parameters for multiple distance data;

[0023] Multiple discrete characterization parameters of the first and second boundaries are obtained, and the thickness of the steel pipe to be tested is determined to be uniform based on the multiple discrete characterization parameters.

[0024] In conjunction with the first aspect, in some implementations, multiple pairs of discrete characterization parameters corresponding to the first boundary and the second boundary are obtained, and the uniformity of the thickness distribution of the steel pipe to be tested is determined based on the multiple discrete characterization parameters, including:

[0025] The average value of multiple discrete characterization parameters is obtained and compared with the 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.

[0026] In conjunction with the first aspect, in some implementations, 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 the qualification of the steel pipe to be inspected is determined according to the analysis results, including:

[0027] The first thermal infrared image is divided into grids to form multiple first sub-images, and the first temperature parameter corresponding to each first sub-image is obtained. Multiple first reference points are determined based on the multiple first sub-images.

[0028] The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the second temperature parameter corresponding to each second sub-image is obtained. Multiple second reference points are determined based on the multiple second sub-images.

[0029] The correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. The wall thickness distribution of the steel pipe to be tested is analyzed based on the offset distance.

[0030] In conjunction with the first aspect, in some implementations, the correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto a first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0031] 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.

[0032] 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.

[0033] In conjunction with the first aspect, in some implementations, the correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto a first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0034] Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, the correspondence between multiple first extreme points and multiple second extreme points is determined.

[0035] In conjunction with the first aspect, in some implementations, the correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto a first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0036] Obtain the interval distance between each pair of corresponding first and second extreme points, and obtain the average distance based on multiple interval distances;

[0037] If the average distance is greater than the preset distance, it is determined that the wall thickness distribution of the steel pipe to be tested is uneven.

[0038] Secondly, this application proposes a steel pipe production data detection system, including a thermal infrared sensor and a controller, the system being configured as follows:

[0039] The first thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at the first moment. When the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested in the first thermal infrared image corresponds to at least half of the unfolded outer surface image of the steel pipe to be tested.

[0040] A second thermal infrared image of the steel pipe to be tested is obtained based on a thermal infrared sensor at a second moment, and the second thermal infrared image corresponds to the first thermal infrared image.

[0041] The wall thickness distribution of the steel pipe to be tested is analyzed based on the first and second thermal infrared images, and the quality of the steel pipe is determined according to the analysis results.

[0042] In conjunction with the second aspect, in some implementations, 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 the qualification of the steel pipe to be inspected is determined according to the analysis results, including:

[0043] The first thermal infrared image is divided into grids to form multiple first sub-images, and the multiple first sub-images are divided into multiple first image groups based on different color gradients;

[0044] Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and the first target region is determined from the multiple temperature gradient regions.

[0045] The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the multiple second sub-images are grouped into multiple second image groups based on different color gradients;

[0046] Based on the distribution of the second sub-images in each second image group, the second thermal infrared image is divided into multiple temperature gradient regions, and the second target region corresponding to the first target region is determined from the multiple temperature gradient regions.

[0047] The wall thickness of the steel pipe to be tested is analyzed based on the boundary changes between the first target region and the second target region.

[0048] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0049] Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and the first target region is determined from these multiple temperature gradient regions, including:

[0050] Obtain the corresponding temperature for each temperature gradient region, and obtain the first preset temperature threshold and the second preset temperature threshold;

[0051] The combination of temperature gradient regions with temperatures greater than a first preset temperature threshold and temperature gradient regions with temperatures less than a second preset temperature threshold is identified as the first target region.

[0052] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0053] The wall thickness of the steel pipe to be tested is analyzed based on the boundary changes between the first and second target regions, including:

[0054] Obtain the first boundary corresponding to the first target region and the second boundary corresponding to the second target region, and map the second boundary onto the first thermal infrared image;

[0055] Based on the correspondence between the first target region and the second target region, the correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of first and second boundaries;

[0056] Multiple first marker points are generated on a first boundary, and a perpendicular line to the first boundary is formed from the first marker points. The intersection of the perpendicular line with the second boundary corresponding to the first boundary is the second marker point.

[0057] Obtain distance data between multiple first marker points and second marker points, and obtain discrete representation parameters for multiple distance data;

[0058] Multiple discrete characterization parameters of the first and second boundaries are obtained, and the thickness of the steel pipe to be tested is determined to be uniform based on the multiple discrete characterization parameters.

[0059] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0060] Obtain multiple pairs of discrete characterization parameters corresponding to the first and second boundaries, and determine whether the thickness distribution of the steel pipe to be tested is uniform based on these discrete characterization parameters, including:

[0061] The average value of multiple discrete characterization parameters is obtained and compared with the 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.

[0062] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0063] The wall thickness distribution of the steel pipe under test is analyzed based on the first and second thermal infrared images. The results of the analysis determine whether the steel pipe is qualified, including:

[0064] The first thermal infrared image is divided into grids to form multiple first sub-images, and the first temperature parameter corresponding to each first sub-image is obtained. Multiple first reference points are determined based on the multiple first sub-images.

[0065] The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the second temperature parameter corresponding to each second sub-image is obtained. Multiple second reference points are determined based on the multiple second sub-images.

[0066] The correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. The wall thickness distribution of the steel pipe to be tested is analyzed based on the offset distance.

[0067] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0068] The correspondence between multiple first reference points and second reference points is determined, the multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0069] 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.

[0070] 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.

[0071] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0072] The correspondence between multiple first reference points and second reference points is determined, the multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0073] Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, the correspondence between multiple first extreme points and multiple second extreme points is determined.

[0074] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0075] The correspondence between multiple first reference points and second reference points is determined, the multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0076] Obtain the interval distance between each pair of corresponding first and second extreme points, and obtain the average distance based on multiple interval distances;

[0077] If the average distance is greater than the preset distance, it is determined that the wall thickness distribution of the steel pipe to be tested is uneven.

[0078] A third aspect of this invention provides an electronic device, which includes:

[0079] 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 to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0080] A fourth aspect 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 as described in the first aspect of the present invention.

[0081] In summary, the above methods and systems have the following technical effects:

[0082] This application proposes a method and system for detecting steel pipe production data. First, a first thermal infrared image of the steel pipe to be tested is acquired at a first moment using a thermal infrared sensor. Then, a second thermal infrared image of the steel pipe to be tested is acquired at a second moment using the same 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 tested is analyzed based on the first and second thermal infrared images. The analysis results determine whether the steel pipe is qualified. This method and system also utilizes the temperature changes during natural cooling of the steel pipe and the uniformity of the cooling process under conditions without external environmental influences to determine the wall thickness uniformity. This can be directly detected using a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or acoustic detection methods. Attached Figure Description

[0083] Figure 1 This is a schematic diagram of a process for detecting steel pipe production data according to an embodiment of this application. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] In the steel pipe manufacturing process, uneven wall thickness (eccentricity) refers to a geometric defect in which the wall thickness distribution of the steel pipe is asymmetrical on the same cross-section, or the wall thickness fluctuates beyond the allowable tolerance along the length direction. Since the eccentricity problem is inside the steel pipe, it is generally impossible to observe it directly radially, and axial inspection is not obvious when the steel pipe is long.

[0086] To address the aforementioned problems, this application proposes a method for detecting steel pipe production data. 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. The steel pipe can be placed statically during detection. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0087] S101: Based on the thermal infrared sensor, acquire the first thermal infrared image of the steel pipe to be tested at the first moment, wherein when the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested 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 tested.

[0088] A thermal infrared sensor is a device that measures the surface temperature of an object or generates a thermal image by detecting the infrared radiation (heat) emitted by the object. They operate based on the principle of infrared thermal radiation: all objects with a temperature above absolute zero emit infrared radiation, the intensity of which is positively correlated with the object's temperature.

[0089] 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. Alternatively, it can be fixed, but it must at least ensure that half or more of the area can be detected. That is, when the outer surface of the steel pipe is fully unfolded, the area corresponding to the outer surface of the steel pipe in the first thermal infrared image should correspond to at least half of the unfolded outer surface image of the steel pipe. In this embodiment, the thermal infrared sensor can be rotated around the central axis of the steel pipe so that the acquired first thermal infrared image completely corresponds to the unfolded image of the steel pipe.

[0090] S102: Based on the thermal infrared sensor, acquire the second thermal infrared image of the steel pipe to be tested at the second moment. The second thermal infrared image corresponds to the first thermal infrared image.

[0091] Understandably, the second moment is the moment following the first moment, that is, the image returned by the thermal infrared sensor after the steel pipe has been 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 and second thermal infrared images correspond completely.

[0092] S103: Analyze the wall thickness distribution of the steel pipe to be tested based on the first thermal infrared image and the second thermal infrared image, and determine whether the steel pipe to be tested is qualified based on the analysis results.

[0093] In this embodiment, since the first thermal infrared image and the second thermal infrared image correspond perfectly to the unfolded image of the steel pipe, they can also be understood as thermal infrared images of the outer surface temperature of the steel pipe at different times. It is understandable that, in an ideal state, i.e., when the temperature of the steel pipe is completely uniform, uneven thickness of the steel pipe will cause the temperature to drop more slowly at thicker areas than at symmetrical positions on the steel pipe, which 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 uniform. Therefore, the temperature change at different locations in the first thermal infrared image will also be inconsistent over time. Thus, the influence of the initial temperature needs to be considered when combining the second thermal infrared image.

[0094] Specifically, in this embodiment, the image analysis process may include the following steps:

[0095] S1031: Divide the first thermal infrared image into a grid to form multiple first sub-images, and divide the multiple first sub-images into multiple first image groups based on different color gradients.

[0096] Understandably, in this embodiment, the process of dividing the first thermal infrared image can be based on the sensing accuracy of the infrared sensor, or it can be a fixed size; no limitation is made here. In the first thermal infrared image, different temperatures correspond to different colors; that is, each first image group corresponds to a different temperature.

[0097] S1032: Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and the first target region is determined from the multiple temperature gradient regions.

[0098] Understandably, according to basic physical laws, in a group of images belonging to the same temperature gradient, each first sub-image must be nearly continuous. Therefore, based on the distribution of multiple first sub-images, all the first sub-images corresponding to each first image can be divided into a region, and multiple regions together constitute the first thermal infrared image.

[0099] In this embodiment, the corresponding temperature of each temperature gradient region can be obtained, and a first preset temperature threshold and a second preset temperature threshold can be obtained. Then, the temperature gradient region with a temperature greater than the first preset temperature threshold and the temperature gradient region with a temperature less than the second preset temperature threshold are determined to be the first target region. The first target region is also the high temperature region and the low temperature region in the actual unfolded image.

[0100] S1033: Divide the second thermal infrared image into a grid to form multiple second sub-images corresponding to the first sub-image, and group the multiple second sub-images into multiple second image groups based on different color gradients.

[0101] S1034: Based on the distribution of the second sub-images in each second image group, the second thermal infrared image is divided into multiple temperature gradient regions, and the second target region corresponding to the first target region is determined from the multiple temperature gradient regions.

[0102] Understandably, in this embodiment, since the position of the steel pipe to be detected does not change, the actual position of each point in the first thermal infrared image is consistent with its 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. Then, based on the positional relationship with the first thermal infrared image, the relatively high-temperature region and low-temperature region, i.e., the second target region in this application, can be directly determined in the second thermal infrared image.

[0103] S1035: Analyze the wall thickness of the steel pipe to be tested based on the boundary changes between the first target area and the second target area.

[0104] Understandably, during the natural cooling process of a steel pipe, except for the ends, the remaining parts, if of uniform thickness, exhibit uniform temperature changes during cooling. In this embodiment, for ease of understanding, a roughly circular high-temperature or low-temperature region is used as an example; however, in other embodiments, it can be any actual shape. For a circular high-temperature region, during natural cooling, the region's change will necessarily be a uniform reduction from the edge of the circle inwards. Ideally, the first target region and the corresponding second target region should be nested in a concentric circle manner, meaning the contraction distance is equidistant. However, when the wall thickness is uneven, thicker areas will cool slower, while thinner areas will cool faster. Therefore, uneven wall thickness will cause the boundary contraction rate to increase or decrease. Thus, the uniformity of the wall thickness can be determined by whether the boundary change is uniform. For the low-temperature region, the same principle applies, resulting in a uniform increase in temperature.

[0105] Specifically, in this embodiment, the degree of change can be measured in the following ways:

[0106] The first boundary corresponding to the first target region and the second boundary corresponding to the second target region are obtained, and the second boundary is mapped onto the first thermal infrared image. Then, based on the correspondence between the first target region and the second target region, the correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of first and second boundaries. Then, multiple first marker points are generated on a first boundary, and a perpendicular line is formed from the first marker point to the first boundary. The intersection of the perpendicular line and the second boundary corresponding to the first boundary is the second marker point. Then, the distance data between the multiple first marker points and the second marker points are obtained, and the discrete representation parameters of the multiple distance data are obtained.

[0107] Understandably, the discrete characterization parameters can be variance or other parameters that can characterize the degree of dispersion, and are not limited in this embodiment. Multiple discrete characterization parameters are used for the first and second boundaries, and the uniformity of the thickness distribution of the steel pipe to be tested is determined based on these parameters. For example, the larger the multiple discrete characterization parameters, the greater the uniformity of boundary contraction or expansion. Therefore, in this embodiment, the average value is taken for overall characterization, 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. The preset value can be obtained based on actual production data, historical experience, or other methods, and the specific value is not limited in this application.

[0108] Understandably, if the thickness distribution of the steel pipe to be tested is uneven, then the steel pipe can be determined to be a substandard product.

[0109] Optionally, thermal infrared sensors can directly acquire temperature data. Therefore, in some other embodiments, the uniformity of the wall thickness can be determined directly by the degree of deviation of the extreme points. It is understandable that when the wall thickness distribution is relatively uniform, the position of the extreme points, i.e., the highest or lowest temperature points, will not change significantly due to uniform heat diffusion.

[0110] 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 a 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 a second reference point.

[0111] Then, based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, the correspondence between multiple first extreme points and multiple second extreme points is determined. Understandably, as a method, the actual position of a high-temperature or low-temperature region will not change significantly after cooling; therefore, the extreme points of that region can be determined by direct comparison to see if they have shifted.

[0112] Specifically, the distance between each pair of corresponding first and second extreme points can be obtained, and the average distance can be obtained based on multiple 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 tested is uneven.

[0113] This application proposes a method for inspecting steel pipe production data. First, a first thermal infrared image of the steel pipe to be inspected is acquired at a first moment using a thermal infrared sensor. Then, a second thermal infrared image of the steel pipe to be inspected is acquired at a second moment using the same 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 inspected is analyzed based on the first and second thermal infrared images. The result of the analysis determines whether the steel pipe is qualified. This method also determines the uniformity of the steel pipe's wall thickness by observing the temperature changes during natural cooling and assessing the uniformity of the cooling process under conditions without external environmental influences. This can be directly detected using a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or acoustic detection methods.

[0114] Based on the same inventive concept, this application also proposes a steel pipe production data detection system, including a thermal infrared sensor and a controller, wherein the system is configured as follows:

[0115] The first thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at the first moment. When the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested in the first thermal infrared image corresponds to at least half of the unfolded outer surface image of the steel pipe to be tested.

[0116] A second thermal infrared image of the steel pipe to be tested is obtained based on a thermal infrared sensor at a second moment, and the second thermal infrared image corresponds to the first thermal infrared image.

[0117] The wall thickness distribution of the steel pipe to be tested is analyzed based on the first and second thermal infrared images, and the quality of the steel pipe is determined according to the analysis results.

[0118] In some implementations, the wall thickness distribution of the steel pipe to be inspected is analyzed based on a first thermal infrared image and a second thermal infrared image, and the qualification of the steel pipe to be inspected is determined based on the analysis results, including:

[0119] The first thermal infrared image is divided into grids to form multiple first sub-images, and the multiple first sub-images are divided into multiple first image groups based on different color gradients;

[0120] Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and the first target region is determined from the multiple temperature gradient regions.

[0121] The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the multiple second sub-images are grouped into multiple second image groups based on different color gradients;

[0122] Based on the distribution of the second sub-images in each second image group, the second thermal infrared image is divided into multiple temperature gradient regions, and the second target region corresponding to the first target region is determined from the multiple temperature gradient regions.

[0123] The wall thickness of the steel pipe to be tested is analyzed based on the boundary changes between the first target region and the second target region.

[0124] In some implementations, the system is configured as follows:

[0125] Based on the distribution of the first sub-images in each first image group, the first thermal infrared image is divided into multiple temperature gradient regions, and the first target region is determined from these multiple temperature gradient regions, including:

[0126] Obtain the corresponding temperature for each temperature gradient region, and obtain the first preset temperature threshold and the second preset temperature threshold;

[0127] The combination of temperature gradient regions with temperatures greater than a first preset temperature threshold and temperature gradient regions with temperatures less than a second preset temperature threshold is identified as the first target region.

[0128] In some implementations, the system is configured as follows:

[0129] The wall thickness of the steel pipe to be tested is analyzed based on the boundary changes between the first and second target regions, including:

[0130] Obtain the first boundary corresponding to the first target region and the second boundary corresponding to the second target region, and map the second boundary onto the first thermal infrared image;

[0131] Based on the correspondence between the first target region and the second target region, the correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of first and second boundaries;

[0132] Multiple first marker points are generated on a first boundary, and a perpendicular line to the first boundary is formed from the first marker points. The intersection of the perpendicular line with the second boundary corresponding to the first boundary is the second marker point.

[0133] Obtain distance data between multiple first marker points and second marker points, and obtain discrete representation parameters for multiple distance data;

[0134] Multiple discrete characterization parameters of the first and second boundaries are obtained, and the thickness of the steel pipe to be tested is determined to be uniform based on the multiple discrete characterization parameters.

[0135] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0136] Obtain multiple pairs of discrete characterization parameters corresponding to the first and second boundaries, and determine whether the thickness distribution of the steel pipe to be tested is uniform based on these discrete characterization parameters, including:

[0137] The average value of multiple discrete characterization parameters is obtained and compared with the 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.

[0138] In some implementations, the system is configured as follows:

[0139] The wall thickness distribution of the steel pipe under test is analyzed based on the first and second thermal infrared images. The results of the analysis determine whether the steel pipe is qualified, including:

[0140] The first thermal infrared image is divided into grids to form multiple first sub-images, and the first temperature parameter corresponding to each first sub-image is obtained. Multiple first reference points are determined based on the multiple first sub-images.

[0141] The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the second temperature parameter corresponding to each second sub-image is obtained. Multiple second reference points are determined based on the multiple second sub-images.

[0142] The correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. The wall thickness distribution of the steel pipe to be tested is analyzed based on the offset distance.

[0143] In some implementations, the system is configured as follows:

[0144] The correspondence between multiple first reference points and second reference points is determined, the multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0145] 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.

[0146] 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.

[0147] In some implementations, the system is configured as follows:

[0148] The correspondence between multiple first reference points and second reference points is determined, the multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0149] Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, the correspondence between multiple first extreme points and multiple second extreme points is determined.

[0150] In some implementations, the system is configured as follows:

[0151] The correspondence between multiple first reference points and second reference points is determined, the multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. Based on the offset distance, the wall thickness distribution of the steel pipe to be inspected is analyzed, including:

[0152] Obtain the interval distance between each pair of corresponding first and second extreme points, and obtain the average distance based on multiple interval distances;

[0153] If the average distance is greater than the preset distance, it is determined that the wall thickness distribution of the steel pipe to be tested is uneven.

[0154] This application proposes a steel pipe production data inspection system. First, a first thermal infrared image of the steel pipe to be inspected is acquired using a thermal infrared sensor at a first moment. Then, a second thermal infrared image of the steel pipe to be inspected is acquired using the same sensor at a second moment. The second thermal infrared image corresponds to the first thermal infrared image. Finally, the wall thickness distribution of the steel pipe to be inspected is analyzed based on the first and second thermal infrared images. The system determines whether the steel pipe is qualified based on the analysis results. This steel pipe production data inspection system also determines the uniformity of the steel pipe's wall thickness by observing the temperature changes during natural cooling and whether the cooling process is uniform under conditions without external environmental influences. This can be directly detected using a long-wave infrared thermal imager, which is faster and more cost-effective than traditional X-ray or acoustic detection methods.

[0155] Based on the same inventive concept, embodiments of this application also propose an electronic device, which includes:

[0156] 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the steel pipe production data detection method of the present application embodiments.

[0157] Furthermore, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steel pipe production data detection method of embodiments of this application.

[0158] The following is a detailed introduction to the various components of the electronic device:

[0159] In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a 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).

[0160] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0161] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0162] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, 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 not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device; the embodiments of the present invention do not specifically limit this.

[0163] A transceiver is used to communicate with network devices or with terminal devices.

[0164] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0165] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0166] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method in the above method embodiments, and will not be repeated here.

[0167] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0168] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can 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 can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0169] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is 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 (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of 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.

[0170] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0171] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0172] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply 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.

[0173] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A method for detecting steel pipe production data, characterized in that, The method is applicable to a steel pipe inspection system, the steel pipe inspection system including a thermal infrared sensor and a controller, and the method is applicable to the controller, including: The first thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at a first moment. When the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested 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 tested. A second thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at a second time, and the second thermal infrared image corresponds to the first thermal infrared image; The first thermal infrared image is divided into grids to form multiple first sub-images, and the multiple first sub-images are divided into multiple 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 multiple temperature gradient regions, and a first target region is determined from the multiple temperature gradient regions. The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the multiple second sub-images are grouped into multiple second image groups based on different color gradients; Based on the distribution of the second sub-images in each of the second image groups, the second thermal infrared image is divided into multiple temperature gradient regions, and a second target region corresponding to the first target region is determined from the multiple temperature gradient regions. The wall thickness analysis of the steel pipe to be tested, based on the boundary changes between the first target region and the second target region, includes: Obtain the first boundary corresponding to the first target region and the second boundary corresponding to the second target region, and map the second boundary onto the first thermal infrared image; Based on the correspondence between the first target region and the second target region, the correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of the first boundary and the second boundary; Multiple first marker points are generated on a first boundary, and a perpendicular line is formed from the first marker points to the first boundary. The intersection of the perpendicular line with the second boundary corresponding to the first boundary is the second marker point. Obtain distance data between multiple first marker points and second marker points, and obtain discrete representation parameters of multiple distance data; The discrete characterization parameters of multiple pairs of the first boundary and the second boundary are obtained, and the thickness of the steel pipe to be detected is determined to be uniformly distributed based on the multiple discrete characterization parameters.

2. The method for detecting steel pipe production data according to claim 1, characterized in that, Based on the distribution of the first sub-images in each of the first image groups, the first thermal infrared image is divided into multiple temperature gradient regions, and a first target region is determined from the multiple temperature gradient regions, including: Obtain the corresponding temperature for each of the temperature gradient regions, and obtain a first preset temperature threshold and a second preset temperature threshold; The temperature gradient regions with temperatures greater than the first preset temperature threshold and the temperature gradient regions with temperatures less than the second preset temperature threshold are identified as the first target region.

3. The method for detecting steel pipe production data according to claim 2, characterized in that, Acquiring multiple pairs of discrete characterization parameters corresponding to the first boundary and the second boundary, and determining whether the thickness of the steel pipe to be detected is uniformly distributed based on the multiple discrete characterization parameters, including: The average value of multiple 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 tested is uneven.

4. A method for detecting steel pipe production data, characterized in that, The method is applicable to a steel pipe inspection system, the steel pipe inspection system including a thermal infrared sensor and a controller, and the method is applicable to the controller, including: The first thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at a first moment. When the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested 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 tested. A second thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at a second time, and the second thermal infrared image corresponds to the first thermal infrared image; The first thermal infrared image is divided into grids to form multiple first sub-images, and the first temperature parameter corresponding to each first sub-image is obtained. Multiple first reference points are determined based on the multiple first sub-images. The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and a second temperature parameter corresponding to each second sub-image is obtained. Multiple second reference points are determined based on the multiple second sub-images. The correspondence between multiple first reference points and second reference points is determined, multiple second reference points are mapped onto the first thermal infrared image, and the offset distance between multiple corresponding first reference points and second reference points is obtained. The wall thickness distribution of the steel pipe to be tested is analyzed based on the offset distance.

5. The method for detecting steel pipe production data according to claim 4, characterized in that, Determine the correspondence between multiple first reference points and second reference points, map the multiple second reference points onto the first thermal infrared image, and obtain the offset distance between multiple corresponding first reference points and second reference points. Analyze the wall thickness distribution of the steel pipe to be tested based on the offset distance, including: 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.

6. The method for detecting steel pipe production data according to claim 5, characterized in that, Determine the correspondence between multiple first reference points and second reference points, map the multiple second reference points onto the first thermal infrared image, and obtain the offset distance between multiple corresponding first reference points and second reference points. Analyze the wall thickness distribution of the steel pipe to be tested based on the offset distance, including: Based on the positional correspondence between the first temperature gradient region and the second temperature gradient region, the correspondence between multiple first extreme points and multiple second extreme points is determined.

7. The method for detecting steel pipe production data according to claim 6, characterized in that, Determine the correspondence between multiple first reference points and second reference points, map the multiple second reference points onto the first thermal infrared image, and obtain the offset distance between multiple corresponding first reference points and second reference points. Analyze the wall thickness distribution of the steel pipe to be tested based on the offset distance, including: Obtain the interval distance between each pair of corresponding first extreme points and second extreme points, and obtain the average distance 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 tested is uneven.

8. A steel pipe production data detection system, characterized in that, A system for performing a steel pipe production data detection method as described in any one of claims 1-3, comprising a thermal infrared sensor and a controller, wherein the system is configured to: The first thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at a first moment. When the outer surface of the steel pipe to be tested is fully unfolded, the area corresponding to the outer surface of the steel pipe to be tested 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 tested. A second thermal infrared image of the steel pipe to be tested is obtained based on the thermal infrared sensor at a second time, and the second thermal infrared image corresponds to the first thermal infrared image; The first thermal infrared image is divided into grids to form multiple first sub-images, and the multiple first sub-images are divided into multiple 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 multiple temperature gradient regions, and a first target region is determined from the multiple temperature gradient regions. The second thermal infrared image is divided into grids to form multiple second sub-images corresponding to the first sub-image, and the multiple second sub-images are grouped into multiple second image groups based on different color gradients; Based on the distribution of the second sub-images in each of the second image groups, the second thermal infrared image is divided into multiple temperature gradient regions, and a second target region corresponding to the first target region is determined from the multiple temperature gradient regions. The wall thickness analysis of the steel pipe to be tested, based on the boundary changes between the first target region and the second target region, includes: Obtain the first boundary corresponding to the first target region and the second boundary corresponding to the second target region, and map the second boundary onto the first thermal infrared image; Based on the correspondence between the first target region and the second target region, the correspondence between the first boundary and the second boundary is determined, thereby determining each corresponding pair of the first boundary and the second boundary; Multiple first marker points are generated on a first boundary, and a perpendicular line is formed from the first marker points to the first boundary. The intersection of the perpendicular line with the second boundary corresponding to the first boundary is the second marker point. Obtain distance data between multiple first marker points and second marker points, and obtain discrete representation parameters of multiple distance data; The discrete characterization parameters of multiple pairs of the first boundary and the second boundary are obtained, and the thickness of the steel pipe to be detected is determined to be uniformly distributed based on the multiple discrete characterization parameters.

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